Now, Buck thinks he can create yeast-based vaccines for a variety of viral diseases. If his vision pans out, you might be drinking a beer or eating yeast chips to protect yourself during the next outbreak. Moreover, you may be doing so just weeks after the outbreak starts, rather than waiting months or years for drug-approval processes to play out. The regulations around food-grade genetically modified yeast are much easier to navigate than the regulations around vaccines and other drug products.

While Buck’s feat might have the appearance of a gonzo biohacking experiment, it is actually the culmination of about fifteen years of work.

What is BK polyomavirus (BKV)?

For most people, BKV isn’t thought to cause noticeable symptoms. It might cause bladder cancer in some people. Most people have been infected with the virus by age seven, and it is estimated that at least 80% of people carry either a semi-active or latent form of the virus.1

Usually the immune system keeps the virus in check so it isn’t a problem. However, the virus can wreak havoc on those who are immunocompromised, such as kidney transplant recipients, who have their immune systems artificially suppressed. (For years Buck has had people on transplant waitlists emailing him begging him for a vaccine.) Polyomavirus has also been implicated in a common condition called painful bladder syndrome, although a direct causal connection hasn’t been conclusively established.

The first BKV vaccine

Buck’s lab helped discover five of the fourteen polyomaviruses known to infect humans. They also helped show that the outer coat protein of the virus is highly immunogenic when assembled into icosahedral shells. Over the past few years they have done evaluations of an injectable polyomavirus vaccine in monkeys and yeast-based polyomavirus vaccines in mice.

The first vaccine consisted of BKV’s outer coat protein as the immunogen, which assembles into icosahedral shells that are called “virus-like particles”. When injected intramuscularly into rhesus monkeys, the vaccine caused antibody levels to shoot up to a level that is believed to confer strong protection against kidney damage in transplant recipients. Antibody levels remained high for the duration of the study, which was two years.

Unfortunately, scaling up the production and purification of viral coat proteins is complex and expensive. The vaccine technology was successfully licensed to an industry partner in 2019 but human clinical trials have not yet been announced, suggesting the development of clinical-grade material has been challenging. This motivated Buck to explore whether the particles could be produced more cheaply using genetically engineered baker’s yeast (Saccharomyces cerevisiae).

Their initial tests looked at the effects of ground-up yeast sprayed into mice’s noses and scratched into their skin. The ground-up yeast delivered via these routes worked, but not as well as injecting the purified particles.

The big surprise

Polyomaviruses are primarily found in the urinary tract, not the gut. So nobody expected an oral vaccine to work, but they decided to give it a try anyway for completeness. During their first pass they fed the mice ground-up yeast, which didn’t work at all.

They then tried feeding the mice intact live yeast, mixed in with their food. To their surprise, this method elicited an antibody response. Live yeast sprayed in the nose did not elicit a response, but those consumed did.

Once in the gut, it appears that the yeast start to degrade and release the empty viral shells to trigger an immune response.2

These initial results were so shocking, they repeated the experiment several times. Buck has homebrewed beer off and on for decades, so it seemed to him as though an obvious next step for the project would be using the VP1 yeast to brew beer and then test that on himself.

Self-experimentation isn’t allowed at NIH?!

Buck submitted an Institutional Review Board (IRB) proposal to gain permission to bring home the yeast and then use them to brew beer for his own consumption. To his surprise, an institutional bioethics committee withdrew the proposal from the submission portal before it could even be considered by the IRB. The committee argued that self-experimentation is strictly forbidden. Buck says he can find no written policies to that effect. The committee also argued that Buck could not eat the yeast strain unless it was first registered with the FDA as an Investigational New Drug.3 The committee rejected the fact that yeast - including GMO strains - qualify as a food product under federal law.

According to Buck, the NIH bioethics committee’s view that self-experimentation is forbidden is false. Long before the beer self-experiment, Buck had sought and received IRB permission to collect a drop of his own blood for use in the lab under a protocol entitled, “Investigator Self-Collection of Blood Via Finger Prick for Use in Hemagglutination Assays.” No one can find any NIH regulation that forbids self-experimentation.

The self-experiment

Rather than continuing to fight the bioethics committee, Buck went to his home kitchen instead. Buck says he was inspired by the longstanding tradition of self-experimentation in biomedicine, including the relatively recent activities of the Rapid Deployment Vaccine Collaborative (Radvac) (NOTE: I currently work for Radvac on an AI-based antivirals project).4 Radvac was launched by veteran scientists in the earliest days of the SARS-CoV-2 pandemic for the purpose of creating and self-administering vaccines using relatively simple existing technologies.

Continuing this tradition and using his personal resources in his free time, Buck set up a miniature molecular biology lab, ordered synthesis of the needed plasmid DNA, transformed it into yeast, and brewed the world’s first batch of vaccine beer. In honor of his Lithuanian collaborators who shared the BKV VP1 yeast with his lab, he chose a farmhouse strain from Pakruojis, Lithuania.

He began consuming his batch as soon as he saw green fluorescence in the suspended yeast cells (this was an indicator for the production of the BKV VP1 immunogen). Buck says that the beer was among the most delicious homebrews he’s ever made.

The above figure shows the result of Buck’s experiment. Prior to consuming the beer, Buck had antibodies for BKV type I but not Type II or IV. The vaccine contained shell protein from BKV Type IV, but resulted in the production of antibodies that neutralized both type II and IV.

Previous oral vaccines

Oral vaccines are not new. A letter from 1768 describes how people living in or near modern-day India sometimes employed an oral method of smallpox variolation as a less painful alternative to scratching dried smallpox scabs into the skin. While the skin-scratching method was thought to be superior, the oral method was sometimes used with pain-averse children and was found to “produce the same effect”. A small amount of dried smallpox scabs were simply mixed with sugar and swallowed by the child.

The first widely used oral vaccine appeared for poliovirus in the mid 1950s, and it consisted of a live weakened form of the virus. Polio naturally infects the lining of the intestines, so an oral vaccine made good sense. The FDA has also approved vaccines for typhoid, cholera, rotavirus, and adenovirus types 4 and 7.5

Oral tolerance and other challenges

Despite the notable successes covered in the last section, oral vaccines face big challenges - they must survive the harsh environment of the stomach and then trigger an immune response from within the gut. The immune system has evolved not to respond strongly to novel substances found in food - otherwise you’d get a massive amount of inflammation every time you tried a new food. When a novel substance is consumed, the immune system looks for danger signals like inflammation. If none are detected, regulatory T cells may be created that actually dampen any immune response to that substance.

The current thinking in immunology is that oral vaccines must trigger an initial inflammatory response, otherwise they risk triggering tolerance to viral antigens - the exact opposite of what they are intended to do. They also must do this while keeping side effects within a manageable range. Current oral vaccines are known for side effects like abdominal pain, nausea, and diarrhea.

Given all this, one may wonder why the yeast vaccine stimulated an immune response and didn’t induce tolerance. While baker’s yeast is harmless, polysaccharides on the cell wall of yeast can stimulate an immune response in some situations, and that might be what’s happening here. Buck has a hypothesis that the VP1 immunogen binds to immune presentation cells in the small intestine called M cells, which trigger an immune cascade leading to antibody production. Both the cholera toxin subunit used in the Dukoral oral cholera vaccine and BKV VP1 particles bind to gangliosides which are known to be present on the surface of intestinal M cells.

Implications for medical research

Yeast-based vaccines open up interesting avenues for research. For instance, painful bladder syndrome, which affects about 0.5% of people in the United States, is associated with high-level shedding of BKV in the urine (or its close relative JC polyomavirus). Unfortunately, the association doesn’t conclusively prove that polyomaviruses cause painful bladder syndrome, and pharmaceutical companies are unlikely to try to develop a vaccine to prevent or cure painful bladder syndrome unless a causal link has been demonstrated. In lieu of that, people with painful bladder syndrome could consume yeast-based products designed to enhance their immune system’s response against BKV and then see if the bladder pain goes away.

In a recent video, Buck mentions that polyomavirus has been found in the brain in rare cases, which he argues makes it a possible suspect in the etiology of Alzheimer’s disease. With nearly everyone already infected with polyomavirus, establishing a link between the virus and Alzheimer’s or bladder cancer is very challenging. Now, people can consume a polyomavirus vaccine early in life, and long-term follow-up can look to see if there is any reduction in Alzheimer’s or bladder cancer relative to a matched control group that didn’t consume that vaccine.

Yeast-based vaccines should reduce vaccine hesitancy

There is a strong theoretical case that yeast-based vaccines should reduce vaccine hesitancy. For one thing, lipid nanoparticles, mRNA, and viral vectors sound scary and exotic, while yeast is a familiar constituent of beer and sourdough bread.

As discussed above, there has not been a need so far to add adjuvants, which are compounds that are often added to vaccines to stimulate an immune response. Adjuvant compounds like aluminum salts and thimerosal have previously raised the hackles of vaccine skeptics.

Most notably, between 20-30% of adults have a strong fear of needles, and a systematic review estimated that around 20% of people avoid getting the flu vaccine simply due to needle fear. Needle fear is tied into a universal human cognitive bias called the horn effect. Simply put, the horn effect principle says that when a scary thing regularly co-occurs with a harmless thing, the harmless thing becomes scary. One may argue that needles, as sharp objects, are inherently scary, creating an obvious horn effect around vaccination. But there is also a horn effect around needles themselves. Alex Tabarrok points out that needles often appear in the context of serious illness inside hospitals, and thus are associated with “serious medicine” – scary stuff. Thus, it’s not surprising that psychologists think that much of vaccine hesitancy is driven by needle fear. It’s interesting that pills are readily consumed under a doctor’s recommendation, even though the substances they contain are often far more dangerous than vaccines.

Naturally, we can expect many people will be cautious when it comes to consuming a “beer vaccine”, especially if it hasn’t gone through large scale clinical trials. However, as a growing number of people create and consume yeast-based vaccines, confidence in them will grow.

While large, well-conducted randomized Phase III trials are essential for accurately quantifying effectiveness, the FDA’s requirements for lengthy Phase III trials before people are allowed to take a vaccine makes vaccines look scary. As Buck told Futurism news: “Our response for the past half century has been to imagine that we can rebuild public trust in vaccines with displays of increasingly stringent FDA approval standards. This approach backfired. Imagine if I set out to do safety testing on a banana, and I dressed up in a hazmat suit and handled the banana with tongs… you’d think: ‘wow, it looks like bananas might be about as safe as nuclear waste’.”

Implications for pandemic preparedness

Buck believes it is likely possible to create yeast-based vaccines for bird flu or SARS-CoV-2. This is a somewhat bold claim, but it’s well worth considering the implications for biosecurity.

Current SARS-CoV-2 vaccines help prevent hospitalization and death, but are highly suboptimal as they do a very poor job of preventing infection. This is because they mainly induce systemic immunity (IgG antibodies) but do a poorer job of inducing mucosal immunity (IgA antibodies). IgA antibodies secreted in mucus can provide a critical “first line of defense” against a virus right where it is most likely to enter the body - the nasal passageway.

A strategy that could be explored with yeast-based vaccines is called “prime and spike”. This consists of using an adjuvanted injectable vaccine followed by an unadjuvanted nasal vaccine. Roughly speaking, the injectable vaccine stimulates systemic immunity while the nasal vaccine stimulates mucosal immunity. A yeast-based SARS-CoV-2 vaccine sprayed in the nose of someone who has already taken an injectable SARS-CoV-2 vaccine could induce robust mucosal immunity throughout the nasal passage, potentially reducing infection risk dramatically.

During a pandemic, yeast could be distributed rapidly in a decentralized manner via two complementary form factors - dried yeast chips and beer.6 The dried yeast could be eaten either as chips or crackers or packed into (possibly enteric) capsules.

An advantage of beer is that every microbrewery in the country is equipped to produce it in bulk. Buck has already spoken with breweries that are interested in brewing vaccine beers, and there are many kidney transplant patients interested in consuming them. Given the positive response Buck has gotten already to his vaccine beer for the relatively obscure polyomavirus, during a pandemic it’s easy to imagine many microbreweries would want to make vaccine beer available in their community, and that many people would be interested in consuming them.

Although most standard beers are fermented for at a week to allow the flavor to mature, Buck says that Scandinavian and Baltic farmhouse styles are designed to be consumed within a few days of brewing. The production of dried yeast “chips” is faster than beer production, but not radically so. Starting from a small amount of yeast, growing enough yeast to create a batch of chips would likely take a few days. Next, the yeast needs to be air-dried or loaded into a food dehydrator at low temperature. This typically takes another day or so. Research shows that yeast-based vaccines can remain shelf stable for a year or longer.

Animals can be vaccinated easily en masse — farmers can respond to bird flu simply by sprinkling the yeast in feedstock.7 Recently, the USDA invested 2.5M in yeast-based vaccines that display the hemagglutinin protein found on three H5N1 strains of the avian flu.

Obviously the biggest win here is the ability to produce vaccines in a decentralized manner and start distributing them to people in weeks rather than waiting for EUAs from the FDA, which we saw were very slow or never happened during the pandemic. Clinical trials to study safety and effectiveness would be held in parallel. Selling GMO yeast is legal in the US as long as the products are not labeled or promoted as treating any particular disease. Yeast vaccines could be sold for “boosting the immune system”, a general-purpose claim already found on many products. For more on how to sell GMO yeast legally as a Generally Recognized as Safe (GRAS) food product, see this post.

A notable precedent is Lumina Probiotic, a GMO bacteria that does not produce lactic acid, which is responsible for tooth decay, cavities, and bad breath. It is completely legal to sell as long as it is not sold for preventing cavities. Another precedent is the company Evolv, which sells a supplement containing GMO yeast that expresses a GLP-1-like peptide. I can’t help but also mention ZBiotics, which sells a GMO probiotic bacteria that’s been modified to produce an enzyme similar to the one your liver uses to process ethanol.

What’s next?

Currently Buck is planning Phase I human trials of the BKV vaccine, and starting research into yeast-based vaccines for the flu, COVID-19, and adenovirus.

Chris’s brother has started Remy LLC, a company chartered to sell food-grade GMO yeast products.

Radvac, where I work on AI for antiviral repurposing, has just started a new research program focused on yeast-based vaccines.

Thank you to Chris Buck for providing extensive comments on an early draft of this piece, and to Preston Estep for proofreading.

Further reading:

• Preprint: “An Edible Polyomavirus Vaccine”

• Preprint: “Vaccine Beer: A Personal Healthcare Report”

• Chris Buck’s Substack

• 80 minute interview with Chris Buck on food-based vaccines and related topics

• 21 minute roundtable discussion: “Food-based vaccine revolution”

• Remy - high tech food - buy yeast expressing BKV VP1

• “Binghamton University researcher to lead $2.5 million project to create better avian flu vaccine”

• Preprint: “A Secreted Subunit SARS-CoV-2 RBD-CpE Yeast Oral Vaccine Induces an Adaptive Immune Response in BALB/c Mice”

• 2021 Scientific Reports paper: “High immune efficacy against different avian influenza H5N1 viruses due to oral administration of a Saccharomyces cerevisiae-based vaccine in chickens”

• 2024 Frontiers in Immunology paper: “Saccharomyces cerevisiae oral immunization in mice using multi-antigen of the African swine fever virus elicits a robust immune response”

• 2025 Microbial Cell Factories paper: “Cell surface display of VP1 of foot-and-mouth disease virus on Saccharomyces cerevisiae”

Footnotes

We’ve already made great strides in lowering rates of CVD — in the 50 year period from 1950 to 1999 age-adjusted death rates from CVD fell by 56% according to the CDC.

The vast majority of CVD is caused by a very well-understood mechanism - atherosclerosis. While we have no good tools to reverse its progression,2 we do have several tools for preventing it. I won’t go into the mechanisms of atherosclerosis here, for that I recommend reading Outlive by Peter Attia.3

The landmark Framingham Heart Study showed that the absence of established modifiable CVD risk factors at an age of 50 years is associated with a 90% lower lifetime risk for men and a 79% lower risk for women.

So, it’s not crazy to claim that by getting people to stop smoking and implementing proven interventions like statins early enough to enough individuals, we can prevent 90% of CVD.

Step 1: Make statins OTC

Recently Alex Kesin presented a series of powerful arguments as to why statins should be available OTC (over-the-counter).

Statins are extremely safe - many drugs that are already OTC cause serious adverse events at 10-100x higher rates:4

There are also OTC drugs that are unsafe to take long term, most notably OTC “sleep aids” (Benadryl and Unisom).5 We also shouldn’t forget the litany of unsafe substances that are freely available “OTC” — things like cigarettes, vapes, alcohol, marijuana, and soda pop.

Statins are extremely safe, extremely effective, and extremely cheap. Alex Kesin estimated that the cost per quality-adjusted life year saved is “less than a fast food meal.” In 2018, Sniderman et al. estimated that for every seven people who take statins for thirty years, one life is saved.

The benefit of statins accrues at least linearly, and perhaps even super-linearly, with every year they circulate in the bloodstream.

Only 35% of people currently eligible under current guidelines are taking statins, according to research published in Annals of Internal Medicine.

The FDA has rejected applications to make statins OTC four times previously. None of those denials had anything to do with the safety or efficacy of the drug. Instead, the FDA feared that people would not be smart enough to know if they might benefit from taking a statin.

The FDA's assumptions about consumer intelligence and behavior have no basis in science, yet they've held those positions firmly.

One path forward here might be to meet the FDA “in the middle” on the issue. We could require pharmacies to administer a short survey to people before selling them statins OTC, similar to what is done with vaccines or pseudoephedrine. Indeed, a survey is exactly the sort of thing FDA now invites under their new "additional condition for nonprescription use" rule.

A 2021 study showed that when people took a survey on a simple smartphone app there was 96% agreement between the app’s output and the judgement of a physician for rosuvastatin. People used the app at home, and 20% of the people in the study had “limited literacy.”

Making statins OTC will help, but it would be a mistake to think that’s all we need to do.

Step 2: Combat statin hesitancy

There is a widespread belief that statins are likely to cause muscle pain, and surveys show that 10-20% of people report experiencing muscle pain while on them.

However, in the original clinical trials on statins, less than 5% of people reported muscle pain as a side effect. What’s going on here?

Two very interesting studies investigated statin use and muscle pain using a “within-subject” study design. In these studies, each participant was randomly alternated each month between taking a statin, taking a placebo, or taking neither. The first study had 60 participants and was published in two journal articles in 2020 and 2021. The second study had 200 participants and was published in 2021. Remarkably, both studies reached the exact same conclusion -- about 90% of “statin-induced muscle pain” is due to the nocebo effect.

What this means is that people report muscle pain while taking statins because they have a high expectation of experiencing it. This is consistent with a growing literature on predictive processing and psychosomatic pain, a topic I’ve touched on before.

Now, one thing that is important to mention about the two aforementioned studies is that they preselected people on statins who had complained about muscle pain to their doctor. If we look at some recent clinical trials, the side effect rate for muscle soreness is often around 10% in the treatment arm and 5% in the placebo arm, suggesting lower 50% nocebo effect. Altogether, the results suggest that statins actually do cause slight muscle pain in a small fraction of individuals (1-5%), but rarely ever cause more serious pain, except through the nocebo effect.

According to Matthews et al. (2016), the widespread belief that statins cause muscle pain can be at least partially traced back to a 2013 article published in the British Medical Journal. Unfortunately, the article mistakenly stated that muscle pain occurs in 18-22% of people. Six months later an errata was issued correcting the mistake, but the damage had already been done, as many fear-mongering articles had appeared in the media as a result (reporting on erratas, alas, does not sell newspapers).

Few things grab more clicks online than fear-mongering about a widely prescribed medication. Researchers have detected transient decreases in statin prescriptions following fear-mongering events in the media.

Step 3: Deploy gene editing

For one thing, it’s not clear how many people would actually start taking a statin regularly if they became OTC. In 2004 a statin called simvastatin became OTC in the UK, under the brand name Heart-Pro. By most accounts the uptake was quite low, and the manufacturer stopped selling it OTC in 2010. There were multiple reasons it was a dud. It had a high cost of about $400 annually, could only be shelved behind-the-counter, and could only be sold after a screening survey was administered by a pharmacist. Alex Kesin suggests that “faint-hearted marketing” might also have contributed to the lackluster uptake.

Even with cheap OTC that were marketed aggressively, it’s not clear if many at-risk people would rush out to start taking one.

Furthermore, the statistics on long-term adherence to statin therapy are not great, with adherence rates at one year around 40-61% across different populations. It’s hard to get people to stick to taking any drug consistently, especially drugs that aren’t treating an active condition. The worst adherence rates are found among the elderly and disabled, for whom making regular trips to the pharmacy can be a challenge.

If only there were a treatment that could be administered just once but would confer a lifetime’s worth of protection…

That’s where gene therapies come in.

There are a couple specific genetic mutations which greatly reduce CVD risk. A single mutation in the PCSK9 gene, which regulates low-density lipoprotein, confers a 50-88% lower risk of CVD with no known deleterious effects. Similarly, there are loss-of-function mutations in APOC3 or ANGPLT3 that result in 40-50% lower risk. These mutations are currently quite rare, with prevalences between 0.5 - 1%.

A company called Verve Therapeutics has been developing a gene therapy that edits the PCSK9 gene. They were recently acquired by Eli Lilly for $1.3 billion. The nice thing about targeting PCSK9 is you only have to edit the genes in the liver. The liver is one of the easiest tissue types to target in the body since all blood circulates through the liver’s dense network of capillaries.

Verve Therapeutics’ treatment has showed promising results in their Phase I trial and the company will be initiating Phase II trials later in 2025.

Step 4: Screen and intervene much earlier

Autopsy studies on teenagers show that about 10-15% have fatty streaks in their arteries. Fatty streaks are the earliest phase of atherosclerosis. Over the course of decades, fatty streaks turn into fatty plaques, which turn into fibrous plaques, which can then turn into blockages or calcified plaques.

The fact that atherosclerosis often starts in childhood has been known for a long time — the following figure is from a paper published in 1958:

Back then, autopsies showed that nearly all children had fatty plaque streaks, and many young adults had fibrous plaques. During the Korean war (1950 - 1953), 77% of soldiers who died (average age 22 years) were characterized as having “advanced atherosclerosis”. A lot of those GIs were probably smoking.

Today the situation has improved, due to a large decline in smoking, dietary changes, and other factors.

A large autopsy study conducted more recently found that more than half of people aged 15-37 had fatty streaks but only 10-20% had fibrotic plaques.

Why do some people have fibrotic plaques or even calcifications in their 20s while others have nothing? The fact is that it’s very hard to disentangle all the causative factors - it’s a complex mix of genetics, diet, and other exposures.

The good news is we don’t have to know exactly why someone is developing atherosclerosis at an accelerated rate to be able to intervene to slow or even stop its progression.

As early as 1961, doctors had recognized that high-risk individuals must be caught as early as possible, ideally during childhood years:

“I do think that pediatricians have definite responsibilities if we are ever to make progress in stopping the ravages of this disease.” - Russell L. Holman, M.D., 1961.

Today, high risk individuals are found using either the Framingham Risk Score or the Pooled Cohort Equations risk score. Individuals who score high may be offered statins. For those with moderate risk, additional testing is often run, like a coronary artery calcification (CAC) scan or blood testing for C-reactive protein.

This is all well and good, but there are several other types of screening we should be doing a lot more of:

The first is a simple ultrasound measurement of the thickness of the carotid artery wall. The test only takes about ten minutes to complete.

You might be wondering about CT and MRI scans. Screening using a CT scan (like a CAC scan of the heart) is a bit controversial due to the non-negligible radiation exposure, but (assuming the CT is done correctly), the risk/reward is generally favorable. MRI could in theory be used for screening, but it’s currently way too expensive.

However, we can utilize existing medical images. For instance, small vascular calcifications are often observed on mammograms:

Atherosclerotic plaques in the heart, aorta, and major arteries can be observed in most non-contrast CT scans and in some MRI scans.6 Plaques in the aorta can be observed on DEXA scans, which are typically only used to measure bone mineral density.

If calcifications or plaques are present on a scan, they might be noticed. If they are noticed, they might be mentioned in the radiology report. If that happens, then the report might be read in full by a doctor. After that, the doctor might tell the patient they have plaques. Finally, the patient might act on that information. You’ll notice there were a lot of “mights” there.

In actuality the doctor is very unlikely to tell the patient about the calcifications or plaques unless they are deemed an imminent threat, due to a widespread dislike in the medical establishment towards screening and “incidental findings.”7

That’s what needs to change.

To wrap up this section, I want to discuss genetic testing. There are a handful of genetic variants which dramatically increase CVD risk, but they are pretty rare. A dramatic example is familial hypercholesterolemia, which occurs in about 1 in 250 people. There is a strong case for testing for rare genetic diseases during IVF so parents can avoid creating children with such conditions.

CVD has been estimated to be 40-60% heritable. In theory, polygenic risk models should have a lot of utility. Currently, however, their ability to stratify individuals by risk is somewhat limited:

Thus, the incremental value of using a polygenic risk score on top of standard screening methods (like the pooled cohort equation) is low at the moment (see this paper).

Conclusion

“… most physicians and cardiology experts would still insist that one’s thirties are too young to begin to focus on primary prevention of cardiac disease…

… Once you understand that apoB particles—LDL, VLDL, Lp(a)—are causally linked to CVD, the game completely changes. The only way to stop the disease is to remove the cause, and the best time to do that is now.

Still struggling with this idea? Consider the following example. We know that smoking is causally linked to lung cancer. Should we tell someone to stop smoking only after their ten-year risk of lung cancer reaches a certain threshold? That is, do we think it’s okay for people to keep smoking until they are sixty-five and then quit? Or should we do everything we can to help young people, who have maybe just picked up the habit, quit altogether? When viewed this way, the answer is unambiguous. The sooner you cut the head off the snake, the lower the risk that it will bite you.” - Peter Attia

The path to reducing cardiovascular disease is clear. It starts by getting more people on statins earlier. For the rare patients who can’t tolerate statins, there are a number of other options.8 In the future, gene therapies will allow a one-shot treatment, ameliorating the issue of statin non-compliance.

When I said in the subtitle that “cardiovascular disease is a policy choice”, I was thinking not just about government policy but also healthcare policy writ large.

The astute reader may have noticed some gaps in the steps I outlined. I don’t know what the best ways are to combat misinformation about statins, and I don’t know the best ways to get doctors to screen earlier and prescribe statins earlier. If you have ideas, please leave them in the comments!

The paper came up organically in a conversation I had a few days ago. The next day I learned the paper is likely 100% fake. A press release from MIT states that they could not ascertain the provenance for any of the data presented. The graduate student responsible for the paper has been expelled, and the paper has been withdrawn by arXiv for violating arXiv’s code of conduct.

This news is a month old, but somehow I missed it.

I’m afraid the news hasn’t percolated as far as it should. That’s why I want to discuss what happened here, even though this story has already been covered by the Wall Street Journal, Stuart Buck, and Ben Shindel.

What happened?

The paper purported to present findings from a large randomized controlled trial of N=1,018 materials science researchers (!) all located at a single R&D lab at a “US-based firm”.

The top-line finding was that researchers who were given access to an “AI tool” discovered 44% more materials, resulting in a 39% increase in patent filings, and a 17% increase in new prototype materials.

Simply stating these results doesn’t do the paper full justice though — it is 78 pages long and contains a variety of very sophisticated sub-analyses and additional findings. For instance, the paper also claimed that a subset of less experienced scientists didn’t benefit from using the tool, while more experienced scientists were able to capitalize on using the tool.

Toner-Rodgers presented the paper at a National Bureau of Economic Research conference just two days after it was published on arXiv. The paper was covered by the Wall Street Journal, The Atlantic, Nature, and numerous other outlets. Since its publication, the paper has been cited by the European Central Bank and in Congress, and it has been cited dozens of times.

The findings confirmed a widely held belief that AI is the answer to ending scientific and technological stagnation. Additionally, the paper acknowledges help from Daron Acemoglu and David Autor, two big names in economics. In the Wall Street Journal Acemoglu praised the paper, saying it was “fantastic”. Acemoglu’s praise of the paper was particularly meaningful given his broader skepticism about AI’s economic impacts. It’s interesting that both of these researchers, who apparently saw the paper in advance, were not surprised that a first-year graduate student was able to do so much data analysis work on their own (more on that in a bit).

It’s all fake?!

According to the Wall Street Journal, the investigation at MIT began when a “computer scientist with expertise in materials science” started emailing people in the MIT Economics Department and asking questions. Nobody could find answers. Eventually someone asked MIT administrators to investigate.

Here’s what MIT administrators said in their statement:

“While student privacy laws and MIT policy prohibit the disclosure of the outcome of this review, we are writing to inform you that MIT has no confidence in the provenance, reliability or validity of the data and has no confidence in the veracity of the research contained in the paper…”

That’s a pretty strong statement! Daron Acemoglu and David Autor also issued a statement, which reads in part:

“… we want to be clear that we have no confidence in the provenance, reliability or validity of the data and in the veracity of the research.”

Again, wow. I think it’s pretty safe to say the paper is completely fake.

After the news broke, Will Wang on X discovered that Corning has filed a complaint against the author of the paper (Aidan Toner-Rodgers) for registering the domain corningresearch.com. It appears the author may have been interested in creating a fake website to put fake data on.

The skeptics

You can find me expressing a bit of skepticism about the paper the day it was announced:

However, I never imagined the paper was fake. My skepticism was about the generalizability of the findings and implications for AI-driven productivity gains in general. In 2016-2018 I did research on using machine learning to predict the properties of molecular crystals and on using generative models to generate new molecular structures. I’m pretty far behind on the state of the art, but I’ve loosely followed the field over the years.

When I saw the MIT paper, two similar papers came to my mind, both of which garnered significant hype but were actually “nothing burgers”. You might remember these papers blowing up on social media:

The first is a 2019 paper from the startup Insilico Medicine which appeared in Nature Biotechnology. The researchers announced they had used generative AI to discover a new drug molecule, which they then synthesized and tested successfully in mice, all in 46 days. The only issue is that the drug was nearly identical to existing drugs - drugs that had been in the model’s training data. That fact was only revealed later in a couple blog posts that relatively few people read. Experts who looked the new drug molecule said it was utterly uninteresting. In other words, the model was no better than a pharmaceutical chemist sketching candidate molecular structures on a dinner napkin.

The second paper, also from 2019, was a Nature paper that claimed to have invented an “AI” that could “predict in advance the discovery of future materials”. The “AI” (actually just a Word2Vec embedding model) was trained on the literature only up until a particular cutoff year. The embeddings were then used to predict future materials that appeared in the literature after that. However, the materials it “predicted in advance” were actually already listed in the literature as candidate materials. Furthermore, the model was developed and tested on just one narrow set of of materials - thermoelectrics. Thermoelectrics were chosen because they are exceptionally easy — they are described with a standard nomenclature (unlike other domains) and there are a relatively small number of candidate elements one might consider. Many critics pointed out that the methodology was unlikely to translate easily to other material types.

Now I want to talk about two people who voiced skepticism of the MIT paper - Robert Palgrave and Stuart Buck.

Robert Palgrave is a Professor of Inorganic and Materials Chemistry at the University College London. In a Twitter thread written less than a week after the paper came out, he voices a number of concerns. Essentially, he’s pointing out that the findings of the paper were out of line with previous literature on the subject. However, the discussion gets quite technical, and I’m afraid he was largely ignored. Palgrave reminds me of the experts on superconductors who expressed skepticism about LK-99 but were also largely ignored during the LK-99 mania.

Stuart Buck is founder of the Good Science Project, and he ends up looking really good here. Here’s what he said back in December:

After the news broke about the paper, Stuart shared this:

Hindsight is 20-20.

It’s interesting how many red flags there were in this paper that were not commented on right away. This paper got a lot of eyeballs, so if there was ever a chance for internet peer review to prove itself, this was it!

To being with, there’s an obvious question here - why would a company fund a very large trial on AI, get amazingly positive results, and then give all their data to a random first year Ph.D. student at MIT? On top of that, why would they choose to remain anonymous? Why would they not trumpet these findings at every possible venue? Most companies are quick to brag about cool ways they are using AI, to help attract investors and talent. Including big materials companies like Corning:

The next red flag was the sheer variety of materials that the AI tool could be applied to:

The structures of these materials at the atomic level are vastly different, and how they are represented is also different. From the literature I have seen, machine learning based approaches developed for materials generation for metals will not be applicable biomaterials without considerable modification. The idea that one monolithic AI tool could work across all these domains is certainly not unimaginable, but it is out of line with the existing literature.

Additionally, Ben Shindel points out that the author uses a sophisticated technique for measuring material similarity. This is surprising for two reasons - first, it’s surprising that a first year economics student would be able to implement it, and secondly, because the method is likely to run into issues with polymers and biomaterials (ChatGPT confirms this). These are the sort of technical issues that only experts (like peer reviewers) might notice (Ben Shindel did his PhD in materials science).

Ben points out some other red flags - check out his article for detail. Stuart Buck provides this observation:

Pointed questions are no longer welcome in science

Stuart Buck’s questioning was not received well:

“I heard from other people last year that it was a breach of etiquette for me even to ask questions about the AI paper, because the undertone or implication might be that the paper could conceivably, possibly, maybe be fraudulent, and that was the worst possible thing you could ever even remotely hint at.

By analogy: when you get a mortgage to buy a house, the bank typically wants documentation about where the down payment came from. They're not accusing *you* of malfeasance per se, but the source of the down payment (e.g., your own savings versus a gift from parents) affects what the bank thinks you can actually repay. If $100,000 showed up in your account last month with no plausible explanation, the bank will want to know more.

That said, I don't want any new bureaucratic requirements for research akin to IRBs. Absolutely not. That would only slow everything down, and probably wouldn't deter actual fraudsters very much anyway.

But cultural change would be nice. It should be much more normalized and routine to ask probing questions about the provenance of data, about how scholars had time to do a particularly demanding project, and so forth.” - Stuart Buck

This is something I’ve heard several times over the years - that asking pointed questions is increasingly not welcome, and that the culture of science is shifting to become less critical. A culture of criticism is foundational to scientific progress, and if we lose that, scientific progress will stagnate, no matter how much funding is provided.

Earlier this year I got an email from RECOVER commemorating the third anniversary of the program. Here’s how they characterized the impact so far:

Notice what’s missing? Treatments.

Why are we making so little progress? Well, a big part of the reason is that biomedical trials are very expensive and take a really long time to conduct. On the Institute for Progress website, Willy Chertman and Ruxandra Tesloianu published a list of possible reforms to address that problem.

There are other reasons though for the lack of progress, having to do with the complex nature of Long COVID:

Long COVID is defined in a really broad way

The standard definition for Long COVID boils down to “excess symptoms three months after a COVID-19 infection that are not attributable to any other cause.”

The SARS-CoV-2 virus can damage many parts of the body, leading to many possible mechanisms and symptoms.

Over 200 Long COVID symptoms have been reported, including, to name a few:

• Loss of smell

• Myocarditis

• Atrial fibrulation

• Shortness of breath

• Tinnitus

• Fatigue

• Neuropathic pain

Believe it or not, the World Health Organization’s working group on defining Long COVID came up with an even broader definition. First, they decided that a positive test for COVID-19 isn’t necessary, only a “history of COVID-19”. Next, they decided that symptoms do not have to have started during or right after the acute phase of COVID-19 — they just have to have had a minimum duration of two months. In other words, it’s OK if patient’s symptoms started a month after COVID! A detailed study on adolescents found that, using the WHO’s definition, “acute COVID-19 was not an independent risk factor for Long COVID”. Check out the paper - it’s absurd!

There have been several papers that have tried to look at electronic health records (EHRs) and apply advanced clustering algorithms to identify a “Long COVID” cluster. Relatedly, some studies use machine learning to identify Long COVID patients from EHRs or blood biomarkers. I’m not sure these techniques help reduce the problem of false positives. More intriguing is using clustering to identify subclusters. A bunch of papers do this, and it appears there are some high-level clusters one can distinguish (like neurological, cardiopulmonary, etc).

False positives

Any symptom may be accidentally associated with COVID-19 due to temporal correlation. Fatigue, the most common Long COVID symptom, is extremely common and has numerous causes (at any given time, about 25% of people report suffering from fatigue). Samples of “Long COVID” patients may contain some false positives, where their symptoms are caused by something completely unrelated to their COVID infection. This makes doing research on the condition more difficult.

False negatives

False negatives are also possible. It’s easy for people to either not notice or be in denial about Long COVID symptoms. Based on experience interviewing over 200 people, Long COVID researcher David Putrino says that 25% of people who say they have recovered from COVID-19 actually have significant Long COVID symptoms upon further investigation. This seems really high to me, but it’s plausible. When conducting trials, careful screening of the control group is important for this reason.

Complexity.

Each person with Long COVID may have some or even all of these mechanisms causing their symptoms:

• Organ damage

• Blood-brain barrier damage

• Epstein-Barr Reactivation

• Persistent virus

• Immune system dysfunction / autoimmune activation

• Mitochondrial damage

• Microclots (especially amyloid fibrin microclots)

• False fatigue alarms generated by brain (aka a “faulty central governor”) or, more generally a type of functional neurological disorder causing fatigue

• Autonomic nervous system dysfunction (POTS)

• Depression (can be triggered by inflammation)

• Anxiety (including health anxiety, somatization)

• High stress (can cause fatigue, burnout)

• Deconditioning (happens within a few weeks, much faster than people realize)

• Sleep issues (for instance, being sick leads to a lot of time in bed which can upset Circardian rhythm leading to either insomnia or hypersomnia)

• Symptoms from something else that are being mistakenly attributed to COVID-19 due to timing (false positive symptoms — this is super common)

• Iatrogenic harm (ie from taking supplements, etc — also very common)

There is at least weak evidence for all of these, which you can verify for yourself via Google Scholar. There are additional proposed mechanisms which I feel skeptical about so I left them off the list: mast cell activation, cytomegalovirus reactivation, and microbiome disruption.

The relative proportions of these are not clear. My own suspicion is they are all roughly equally likely.

The fact there are many causal mechanisms and each patient may have one, two, or a even a dozen different causes is not something you hear widely discussed. Instead, researchers like to focus on a single cause and stake a claim that the causal mechanism they are studying is really important, perhaps even “the” cause for Long COVID.

There is a also a big split between people talking about brain-based mechanisms and “organic” mechanisms.3 The brain-based people insist it’s 100% brain-based, while the “organic” people insist it’s 100% organic. As Scott Alexander said a while ago, that’s not how any of this works. At the very least, all chronic conditions have a “psychosomatic shadow”.4

There are causal interconnections between a lot of these. Here’s one attempt to map out the causal web:

There are potentially causal arrows missing here — for instance some people think that inflammatory cytokines can cause depression.

The complexity of human health is astounding. A full understanding of the causal web for a complex chronic condition like Long COVID requires acknowledging that biological, neurological, psychological, and social-environmental factors all play a role and all feedback on each other (this is the biopychosocial model in a nutshell). Our biomedical research system is not well setup to handle complex bio-psycho-social conditions.

To think about this correctly, it’s also important to distinguish predisposing, precipitating, and perpetuating factors. A predisposing factor like depression might exacerbate Long COVID and make it harder to recover. Two months of persistent virus or Epstein-Barr reactivation might lead to the brain developing false fatigue alarms which becomes a perpetuating factor even after the Epstein-Barr reactivation has ended.

Anyway, the good news is I don’t think we have to fully understand this causal web to treat Long COVID. We only have to be able to identify the root mechanisms which are perpetuating the illness.

The other good news is that most of the things I listed above have established treatments. There is room for improvement in some areas, however. For instance, it appears there’s no cure for POTS, only band aid treatments that compensate for symptoms. Likewise, there’s no established treatment approach for “immune system dysfunction / autoimmune activation,” although there are a variety of drugs one can try.

It looks to me like the main difficulty here is figuring out which causal mechanisms any given person has.

How strategic was the RECOVER program? Did anyone roadmap this out?

Recently Adam Marblestone and others have been talking about how we don’t do enough strategic roadmapping in science. By roadmapping things out, we can identify bottlenecks to progress, whether they be scientific or institutional.

The RECOVER team apparently did some roadmapping. For instance, they decided to invest in infrastructure for biospecimen processing and data centralization. They also funded the RECOVER OMICS task force and research on “multi-omics” tools for Long COVID subtyping.

Speaking somewhat naively, I think a good initial investment would be to assemble a large sample of Long COVID patients and test them thoroughly to find the relative proportions for each of the key mechanisms listed above. Most of those mechanisms I listed were pretty obviously important by mid 2021, and some of them had been known to be implicated in other post-viral syndromes.

The only mechanism for which there is not a test is “false fatigue alarms”/“functional neurological disorder” - it remains a diagnosis of exclusion. There may be some types of immune dysfunction and autoimmune activation we don’t have tests for, as well.

To summarize how I conceive of everything personally:

It’s interesting to ponder the testing costs (I got these costs from ChatGPT and my own personal experience). Remember, a Long COVID patient may have some or even all of these problems. Testing for all would cost thousands of dollars!

It seems a good use of funds might be to invest in developing better testing technologies which are cheaper and more precise.

Going on a bit of an aside here, from a first principles perspective, there is no reason that medical imaging needs to be as expensive as it is. Of course, there is an initial fixed cost for the scanner. Then money is needed to pay for electricity to run the scanner. MRI cooling systems require regular maintenance and liquid helium top-off.

However, the main reason medical imaging costs so much is that technicians and clinicians have to be involved, and they require salaries and health insurance. The reason that MRI costs a lot more than CT is primarily that MRI scans take longer to run and technicians have to stand around that whole time.

A few years ago I had the idea of creating a “self serve MRI.” This is an MRI that is available 24/7 for anyone to use. An AI agent would instruct patients how to lie in the scanner and carry out the scanning protocol. For many things it’s possible to get away with the MRI being low-field, which would make it safe to bring metal into it (low-field MRIs should be cheaper to build too). AI could analyze all the images. In theory, there does not need to be a human in the loop.

MRI could look for organ damage in patients where it is suspected. It can also be used to gauge blood-brain barrier integrity, if a constrast agent is given.

PET scans to detect persistent virus is cool but probably there are cheaper methods. Naively, I think that persistent virus should leave some trace in the blood. Microfluidics combined with advanced immunological sensors might be the way to go.

As far as the tests for mitochondrial damage and microclots, I have no idea how those tests work. I hope someone is looking into why they cost so much.

Concluding thoughts

Building off the previous section, this is how I envision a strategic approach, at a high level:

1. Use testing to determine the prevalence of different disease mechanisms in the Long COVID population.

2. Strategically fund better testing technologies where it would be high impact to do so.

3. Using the initial results from 1., conduct large clinical trials that target specific mechanisms. Route patients to trials based on the mechanisms they have active, as revealed by testing.

RECOVER seems to be following this sort of strategy to some degree.

About 65% of the initial RECOVER funds went to “Clinical Research Studies.” It seems the bulk of that went to retrospective analyses and observational cohort studies. These studies often have many biases and tend to just document symptom prevalence and correlative risk factors. Some money went to autopsy studies and pathobiology studies, which are great since they illuminate mechanisms. Side note - for unknown reasons, the pathology study grants were recently terminated, but fortunately they were reinstated after an outcry was raised.

Out of the initial funding, only 15% went to randomized clinical trials. That’s not enough. Additionally, some of the trials are just putting band aids on, not treating mechanisms, in my view.5 Advocates are generally not happy with the selection of trials and some people think more experts on post-viral syndromes should have been consulted beforehand.

Globally, there are at least 100 active clinical trials studying Long COVID (see this table), and an additional 23 that have completed. About 9,000 articles have been published on Long COVID so far, and this number may double in the next year. I am currently working on having an LLM read as many of these as I can download. The LLM will be instructed to report back on the topics studied in each paper, what type of study it is, and the primary outcome of the study. With the help of the LLM, I’ll be able to create a comprehensive map of Long COVID research. Doing so should shed a lot more clarity on the overall situation.

More generally, critiquing “breakthroughs” like this is in line with two core focus areas of this blog/newsletter — scitech progress and metascience.

There’s an interesting saga here. Claims similar Microsoft’s have been made before. Those claims were published in major journals like Science and Nature but later retracted. There was evidence of sloppy data handling, confirmation bias, and selective reporting of data. While no explicit fraud has been proven, the saga has resonances with previous articles I have done on pathological water science, the history of fraud in Alzheimers’ research, the drug Cerebrolysin, and LK-99.

What did Microsoft announce?

On February 17th, Microsoft published a 23 page “roadmap to fault-tolerant quantum computation” paper on arXiv. Two days later they announced the “Majorana 1”, a “quantum chip” powered by a “Topological Core.” The chip is very impressive looking:

The announcement was carefully orchestrated for maximum effect. The media blitz consisted of a Nature paper, a press release, a high production value video, and a laudatory New York Times article by Cade Metz, all of which dropped within hours of each other.

The second sentence of their press release contains the core claim regarding the chip:

“It leverages the world’s first topoconductor, a breakthrough type of material which can observe and control Majorana particles to produce more reliable and scalable qubits, which are the building blocks for quantum computers.”

The press release claims each chip has eight topological qubits:

“Today, the company has placed eight topological qubits on a chip designed to scale to one million.”

I think many have assumed that the Nature paper gives evidence for these topological qubits. Nope! It only presents evidence for Majorana particles. Furthermore, many physicists are skeptical of the paper’s claim to have created Majorana particles. There has been a history of similar claims before that haven’t panned out, including several retracted papers.

Note - in the next two sections I try to explain some of the physics, because I find it interesting… but this stuff is quite tricky to understand! If you’re more interested in the backstory and metascience, skip ahead.

What are Majorana particles?

One of the key things I learned from reading Richard Feynman during my physics days is there are profound benefits to trying to understand first principles yourself. So, I’ll try to explain some of the first principles here as well. Unfortunately, it’s difficult, but even a rough picture is better than none.

Majorana particles have been one of the most elusive in all of physics. In the 1930s physicists realized that particles in our universe are one of two types — fermions or bosons. Fermions have half-integer spin, while bosons have integer spin (“spin” refers to the particle’s intrinsic angular momentum). For instance, the humble electron is a fermion with a spin of 1/2, while the photon is a boson with a spin of 1. Quantum field theory predicts that fermions have anti-particles while bosons do not. The theory further predicts that two fermions cannot inhabit the same quantum state (the “Pauli exclusion principle”), but two bosons can.

In 1937 Ettore Majorana hypothesized that there might exist a third class of particle that are their own antiparticle. It was a purely theoretical idea.

In 1982 Frank Wilczek showed that the mathematics of quantum field theory indicate that if the universe was two dimensional, the distinction between fermions and bosons would break down. In two dimensions, when particles are swapped they exhibit any phase change, resulting in special statistical properties. For this reason, he called such particles “anyons.” They can also have any spin, whereas in 3D particles are forced to have either integer or half-integer spin.

Majorana particles are a special type of theoretical anyon with spin 1/2 and “non-Abelian” character, a mathematical property. Some physicists think that neutrinos might be Majorana particles living in 3D, but this is a minority view.1

Wilczek also proposed that there might be quasiparticles in condensed matter systems (materials) that are anyons. Just as a particle is a quantum excitation of a field, a quasiparticle is a quantum excitation of a material. Quasiparticles can sometimes be conceptualized as localized vibrational modes in a material with quantum properties (discrete energy and angular momentum). Other times, however, they are nonlocal - spread out over multiple locations at once. Majorana quasiparticles are predicted to have this nonlocal character.

In 2012, it was claimed that a Majorana quasiparticle was created in a piece of semiconducting wire in a magnetic field. The wire spans a normal conductor and a superconductor:

This quasiparticle is claimed to be nonlocal - inhabiting two parts of the wire at once.

Furthermore, this quasiparticle is predicted to have a spin of 1/2, but with zero charge, zero mass, zero non-spin angular momentum, and zero energy. So, you might wonder — how do we even know they are there? Well, physicists argue that their presence affects the relation between current and voltage through the device. Since the presence of these “ghost” quasiparticles is being inferred indirectly, it’s possible the observed data could be due to other phenomena happening in the device. Several times physicists have presented evidence for Majorana particles, but then other physicists have discovered other “look alike” phenomena that can explain the same data.

What is a topological qubit?

In this section, I’ll attempt to explain what a topological qubit is and how it is constructed from Majorana quasiparticles. I have not seen any recent article make any serious attempt at explaining this stuff for a lay audience. There’s a good reason for this — understanding Majorana quasiparticles requires understanding three areas of physics at an advanced level - condensed matter theory, quantum field theory, and relativistic electromagnetism. When I took a class in quantum computation in 2012, there were a couple lectures on topological qubits, but I couldn’t fully grok the material. I didn’t understand things completely then, and certainly don’t understand things now.

Still, I think I can paint a rough impression of what’s going on — or rather, what is supposed to be going on.

Electrons play a big role here. Electrons can be conceptualized as tiny spinning charges. That spin means the electron creates its own magnetic field. When an external magnetic field is applied to a system of moving electrons, the electrons start to move in circular paths. When an electron moves in a circular path, it starts to interact with its own spin due to relativistic effects. When very high fields are applied, the spins of the electrons get “pinned” in particular directions. This creates what physicists call “spin texture” in the material. This pinning phenomenon helps make the Majorana quasiparticles more robust, and it sets the scene for a phenomena physicists call “braiding.”

If one Majorana quasiparticle are revolved around another in a very high magnetic field, what happens is conceptually similar to rotating one rope around another, or equivalently, twisting two ropes around each other:

The twisting of two ropes can be done two distinct ways - clockwise or counterclockwise. Once two ropes are twisted, they can be hard to untwist. If the ropes are twisted and the ends fixed, then they are impossible to untwist. The number of twists and the directions of those twists is said to be “topologically robust.” If you can do quantum computations by twisting “Majorana ropes”, the process should be very robust to noise.

Here’s one way of twisting three ropes:

Here are what some actual quantum computation gate operations would look like, using either four or six topological qubits:

Russian-American physicist Alexei Kitaev proposed this sort of method for robust quantum computation in 1997. Since then, research groups around the world have been attempting to realize it.

What is the history here?

Microsoft founded “Station Q” in 2006. From day one, they have focused largely on topological quantum computation. Michael Freedman, a Fields Medealist, was a founding faculty. In the early days, the research at Station Q was purely mathematical and theoretical in nature. Some physicists believed Majorana quasiparticles could exist, but it was not at all clear. It’s interesting that Microsoft funded this research given there was really no clear prospect for commercialization at the time.

In 2012, a Delft University team studied a nanowire similar to the one pictured above. They found some unexpected peaks in the conductance data which seemed to indicate Majorana quasiparticles. Microsoft got really interested, and started investing money in to the Delft University group and other experimental groups at the Niels Bohr Institute and the University of Sydney.

In 2014 a Princeton University team presented evidence for “Majorana states” around iron atoms in a superconductor. Around this time, physicists found other explanations for the data presented by the Delft and Princeton teams. I think it’s fair to say most physicists did not believe the claims.

Retraction #1
A 2017 Science paper by researchers from Tsinghua University reported “chiral” Majorana quasiparticles in a superconducting quantum Hall device. Researchers at Princeton and Purdue tried to reproduce their claims, but were not able to. Frustrated physicists started to scrutinize the data in the original paper, and they started finding irregularities. Under pressure, the Tsinghua University researchers conducted an internal review of their data. In 2022, five years after the original paper, the Tsinghua researchers requested their paper be retracted and Science quickly obliged their request. The authors admitted that in their rush to publish, mistakes had been made. It turns out their devices were extremely sensitive to external noise, leading to extreme irregularities in their data. The researchers hastily jumped to a conclusion without considering other explanations. The saga is said to have hurt the reputation of Tsinghua University.

Retraction #2
In 2018, a Microsoft-supported group at Delft University published a paper in Nature, which again claimed to have created Majorana quasiparticles in a nanowire. This time, the data was more convincing, and it was quickly hailed as a breakthrough. Over time, however, physicists started to notice inconsistencies in the data. Serious concerns came to light in 2020. Those concerns ultimately caused Nature to retract the paper in 2021. As a result, Delft University commissioned a team of independent experts to determine if any wrongdoing took place. The team found that the researchers had omitted from publication data which contradicted their results — a serious failing of scientific rigor. It is unfortunately not an uncommon issue. The investigators could not find any evidence that the researchers intended to mislead, however. Instead, they attributed the failing to poor data handling practices and confirmation bias (humans’ innate bias towards focus on confirming data while disregarding non-confirming data).

Also in 2018, a team at the Chinese Academy of Sciences published a paper in Science which presented evidence for Majorana quasiparticles in an iron-based superconductor. In 2019, another Chinese team reported similar findings in Nature Physics. In 2021, an alternate explanation for the 2018 and 2019 data was found.

Expression of concern
In 2020 a team centered at the Niels Bohr Institute in Copenhagen published evidence for Majorana quasiparticles in Science. As concerns were raised, the journal requested the authors to release more of their data. Shortly after the scientists released more data, the journal slapped an “Expression of Concern” on the paper. The notice states that the “tunneling spectroscopy data published in the original paper are not representative of the entirety of the data released in association with this project.” This was yet another case of scientists selectively reporting data. The authors came out strongly against the expression of concern and stood by every claim they made in the paper. The journal requested that the Niels Bohr Institute conduct an investigation. The investigation found no evidence for wrongdoing but acknowledged “shortcomings.” Some scientists have argued that even the additional data was selectively released. In other words, the full data generated by the experiment has still not been released. If that is the case, it suggests intentional misconduct.

Did Microsoft lie about creating a topological qubit?

The Nature paper comes with this revealing comment:

“The editorial team wishes to point out that the results in this manuscript do not represent evidence for the presence of Majorana zero modes in the reported devices. The work is published for introducing a device architecture that might enable fusion experiments using future Majorana zero modes.”

No conclusive evidence of a Majorana quasiparticle is presented in the paper, only partial, controversial evidence.2 Without those particles, topological qubits cannot be formed. Yet, the press release says the chip contains eight topological qubits. What’s going on? If Microsoft has data proving the existence of even a single topological qubit, why haven’t they published it?

On Scott Aaronson’s blog, Microsoft technical fellow Chetan Nayak has given an explanation in the comments. He says that the original Nature paper was submitted almost a year ago on March 5, 2024. In the intervening time, he says that Microsoft has made significant progress. Some of that progress has been reported in a technical seminar Microsoft recently held, and more will be reported at the upcoming “March Meeting” of the America Physical Society.

I have no doubt Microsoft has made some progress in the last year. The question still remains — where’s the data?

If a topological qubit has been created, does that put Microsoft in the lead?

Nobody really knows. Quantum computing expert Scott Aaronson says he doesn’t know. It took ~20-30 years for other approaches to get from 1-5 qubits to 10-50. Nobody knows if faster progress will be made with topological qubits and if the topological qubit approach can “leapfrog” other approaches. Microsoft is the only commercial company investing substantial money into this approach. Apparently, others are not willing to bet on a “leapfrog.”

Metascience notes

This above saga highlights some ways we could do science better:

• There should be a norm that announcements should be accompanied with raw data that backs up the announcement’s claims. An even stronger norm would be that groups should wait until peer reviewed publication before announcing a major breakthrough. Microsoft did neither of these things.

• Journals should require that authors publish all of the data collected during their experiments, and this data should be viewable by peer reviewers during review. This will help with the problems around selective reporting that currently plague science, although it doesn’t completely solve the issue. Note that simply uploading the raw data is easy - just upload the files! What is much more time-consuming is if additional requirements are tacked on to make the data more findable, accessible, interoperatable, and reusable (“FAIR”). Simply uploading the raw data as-is is a very low bar!3

• Requiring both pre-registration of experiment plans and publication of all data can go further to help with the issue of selective reporting. I don’t think pre-registration of experimental plans is done at all in physics. It probably should be, especially for areas of physics with a history of retractions and selective reporting.

• Science journalism should be more skeptical and critical. Instead of merely parroting claims from press releases like Cade Metz did in the New York Times, science journalists should be critical and should explain reasons to be skeptical (like historical context, etc).

I asked Claude if it had further suggestions. Here are two suggestions it made, which I think were rather creative (and more aggressive than what I had been contemplating):

• Establish cooling-off periods between journal acceptance and press releases. The current rush to publicize findings often leads to overhyped claims. A mandatory waiting period would allow for more measured communication.

• Journals should adopt stronger policies against serial offenders. Fields or research groups with patterns of retracted papers should face heightened scrutiny and more rigorous review standards.

One final point:

• The ~20 year quest to find Majorana quasiparticles and realize a topological qubit has an air of pathological science. In particular, the data has always been at the edge of believably, and the experiments have been plagued with confounds. The situation bares resemblance to other areas of pathological science where tremendous claims are made on the basis of noisy data lying at the edge of significance (examples: cold fusion, polywater, exclusion zone water). I think there is a better than not chance that Microsoft has made Majorana quasiparticles. However, it’s also possible they haven’t and that in 50 years this will be viewed as an episode of pathological science (I’d put a ~20% chance on that).

I want to thank ChatGPT and Claude for extensive copyediting, discussion, and fact-checking.

As part of the research I did for my article on peer review, I read some of a book by Ziman called Public Knowledge: The Social Dimension of Science, which was published in 1968. The book is notable for containing a strong defense of peer review. So far I’ve read about half of it, and one of the most interesting parts has been his description of German science prior to Word War II. That’s what I want to dive into here, but first, let me give some context on the book:

Background on Public Knowledge: The Social Dimension of Science

In the first chapter of the book, Ziman considers several approaches towards defining what science is that have been proposed by philosophers. Ziman points out that philosophers thus far have generally framed science in terms of an abstract method. If someone is following that method, they are doing science.

The central thesis of Ziman’s book is that science is not a solitary activity. This is because the end goal of science is not just knowledge but public knowledge. Public knowledge consists of truths that have been agreed upon through a process of consensus building among many scientists.

Thus, a person stranded on a desert island cannot do science:

“Technology, Art, and Religion are perhaps possible for Robinson Crusoe, but Law and Science are not.” - pg 10

A scientist’s allegiance should be toward the creation of a consensus:

“What I have tried to show, in Chapter 3, is that the criteria of proof in Science are public, and not private; that the allegiance of the scientist is towards the creation of a consensus.” - pg 78

For Ziman, a full account of what science is must include methodological, psychological, and sociological elements. Ziman’s thinking was highly influenced by his contemporary Robert K. Merton, one of the founders of the field of sociology of science. Indeed, he cites several times Merton’s 1942 paper on “scientific norms”.

Ziman describes the peer reviewer as a “linchpin” in the process of consensus building. This is in part because peer reviewers help ensure that scientific arguments conform to high standards of rigor and objectivity, rather than drifting into sloppy reasoning and polemics. For Ziman, the scientific literature is an important resource which must be protected. It is also the stage where debates play out, and peer reviewers (“referees”) help make sure those debates play out in a civil way. Ultimately, the consensus is what appears in textbooks and is taught in university classrooms.

I say all this just to provide an overview of what this book is about. What I wish to focus on in this post is Ziman’s description of German science prior to World War II, which appears in Chapter Five.

German scientific paternalism

Chapter Five is entitled “The Individual Scientist,” but it’s really about the processes by which scientists are created.

In the very old days, scientists were oddballs, willing to withdraw themselves from normal society. Many found themselves in tension with existing orthodoxies and establishments. They were supported by their own funds or wealthy patrons. This system limited the number of scientists in the world. Even if there had been lots of patrons willing to fund science, people are not naturally prone to becoming scientists. Becoming a good scientist requires walking a “straight and narrow path”. In the old days, some managed to walk that path, but many brilliant individuals fell off into quackery, religion, or the occult instead.2

The development of academic institutions for scientific education and research greatly expanded the number of scientists in the world. Now people could be trained to walk the scientific path. The new institutions captured people who would have otherwise gone into other fields like law or business. They were then trained in the methods and norms of science and provided jobs in scientific research.

According to Ziman, these institutions first arose in Germany. Here’s the key passage (trimmed down):

“The German universities, which seem to have been the first large-scale, self-consciously professional research organizations to offer a career of Science and Scholarship to anyone with the talents for it, depended upon a system of deliberate apprenticeship. This system, being in fact a State Civil Service, was very formal and rigidly hierarchical…

… During his long apprenticeship, the German academic internalized the conventions and criteria of the scientific life. From bitter experience he learnt not to get caught in personal controversy, nor to speculate on the basis of inadequate evidence. Under the harsh eye of his own professor he acquired the habit of checking and re-checking his observations, of writing accurately and impersonally, and of being the foremost critic of his own work. The carrot of a juicy Chair drew forth his utmost of energy and imagination — yet always within the constraints of the discipline of his seniors.

In its day, the German academic system was the admiration of the world, and extraordinarily successful as a medium in which scientific research flourished …

… There was a price to pay. The individual scientist was no longer working solely for his own amusement, nor for the abstract advancement of learning, or for posthumous glory; he was also seeking personal promotion. As we are now fully aware, this is the catalyst for the release of vast floods of tedious, prolix minutiae, impressive for quantity if seldom for quality; 'publish or perish' is not a new cry…

The mechanisms of character formation which drove the German academic system were paternalistic-often patriarchal. The power of the Professor Ordinarius3 over his students and assistants was almost that of a Roman father over his family. Upon his recommendation depended all hope of promotion …

… The German system emphasized the social character of the Wissenschaft by making sure that every new discovery or theory was thoroughly examined and exhaustively tested before admission into the consensus. The virtues that it most strongly encouraged were those of painstaking care, loving attention to detail, precision of language and argument. No side turning was to be left unexplored, no gaps were to be tolerated in the logic. The great era of German Science (which died in 1930 and has not been revived) was not only an age of experimental, observational and textual discoveries; it was the age when the foundations of Pure Mathematics were drilled deeper, to reach the firmer bedrock of formal logic; it was an age of the treatise and Handbuch, and of scientific and technological education.

It is the argument of this essay that such attention to observational accuracy, logical rigour and encyclopedic detail is quite as essential to Science as imagination and inspiration. Without these 'Germanic' virtues, Science would disintegrate into schools and sects, prophets and their coteries of disciples.

But the enforcement of high critical standards by distant, anonymous authorities and institutions, such as those of editors, referees and review writers, is psychologically impracticable. The public peace is not preserved by the abstract power of the impersonal judge and vigilant policemen; it depends in detail upon the conscience of the individual citizen, moulded in childhood by the direct and personal influence of stern but loving parents. High critical standards must become part of the intellectual conscience of the scholar; he must acquire the psychological strength to withstand the temptation of shoddy thinking and tawdry brilliance. The paternalistic upbringing of the German academic of the old school gave him this moral stiffening in its most puritan mood.”

Ziman goes on to contrast the German system with the systems that evolved in the UK and America. I’ve scanned and uploaded the entire chapter to my website, if anyone wishes to read it.

The historical record

The historical record somewhat supports the notion of a “golden era of German science” spanning the fifty year period between 1880 and 1930. During this period the German language was dominant in many fields of science. As late as the 1960s many important scientific texts could still only be found in German.

The foundations of quantum mechanics (“Quantenmechaniks”) were chiefly developed in Germany during that period, as well as in nearby Copenhagan. The quantum revolution got kicked off with Max Planck’s quantization of electromagnetic waves in 1900. No single individual invented the theory of quantum mechanics — it was the result of an intense period of collaboration between many theorists and experimentalists. Some Germans who were involved in this were Max Planck (1858–1947), James Franck (1882 - 1964), Gustav Ludwig Hertz (1887–1975), Werner Heisenberg (1901–1976), Max Born (1882–1970), Wolfgang Pauli (1900–1958), Pascual Jordan (1902–1980), Arnold Sommerfeld (1868–1951), Otto Stern (1888–1969), and Albert Einstein (1879 - 1955).

Moving beyond quantum mechanics, Wilhelm Röntgen (1845–1923) discovered X-rays in 1895. X-ray crystallography was invented by Max von Laue in 1912 and further pioneered by Paul Peter Ewald (1888–1985). Otto Hahn and Lise Meitner did pioneering work together on radioactivity between 1907 and 1938. In chemistry, Haber and Bosch invented the process for creating ammonia in 1909. Walther Nernst (1864–1941) pushed forward the theory of thermodynamics, developing the third law of thermodynamics between 1906 and 1912.

There were other important scientists who came from elsewhere to work at German universities, such as Erwin Schrödinger, who was Austrian but did all of his work at German universities. Felix Bloch (1905 – 1983) did his Ph.D. under Heisenberg, as did German-born Rudolf Peierls and many others. Many of the most famous Hungarian “Martians” from that period went to Germany to study or work there. John von Neumann (1903 - 1957) studied under David Hilbert at the University of Göttingen and later worked at the University of Berlin. Hungarian Theodor von Kármán (1881–1963) was director of the Aeronautical Institute at Aachen University in Germany between 1908 - 1930, where he made seminal contributions to aerodynamics. Hungarian polymath Michael Polanyi (1891–1976) worked at the University of Karlsruhe and the Kaiser Wilhelm Institute in Berlin. The Hungarians Edward Teller (1908–2003), Leó Szilárd (1898–1964), and Eugene Wigner (1902–1995) all worked for many years in Germany until they were forced to emigrate to America during the Nazi’s great purge of 1933 (all of them came in helpful during the Manhattan Project).

In biology, Robert Koch (1843–1910) did pioneering work on bacteriology, discovering the bacteria for tuberculosis, cholera, anthrax, and other diseases. Max Weber (1864–1920) made contributions to sociology. In psychology, the Germans Max Wertheimer (1880 – 1943), Kurt Koffka (1886 – 1941), and Wolfgang Köhler (1887 – 1967) invented the field of gestalt psychology and pioneered the science of perception. In geology, Alfred Wegener (1880-1930) came up with the continental drift theory. Mathematical advances were made by David Hilbert (1862 - 1943), Edumund Landau (1877 - 1938), Georg Cantor (1845 – 1918), Felix Klein (1849–1925), Hermann Minkowski (1864–1909) (who was also Polish/Russian), Emmy Noether (1882–1935), Erhard Schmidt (1876–1959), Hermann Weyl (1885–1955), and others.

Delicate balancing acts

Creating social institutions that consistently produce high quality science is very challenging, involving many delicate balancing acts:

Maintaining high standards without choking science with bureaucracy

The rigid hierarchy of the German academic system allowed those at the top to enforce very high standards of rigor. As just outlined, the historical record shows this system worked well. There were downsides, however. Some scientists became overly focused on climbing to the next level in the hierarchy, resorting to whatever means they could use to do so. Often this boiled down to publishing incremental or highly derivative work at a high rate to impress superiors. Such work may have been rigorous, but it was also boring, resulting in little advancement for science. More generally, these sort of systems can be prone to bureaucratic sluggishness — people end up spending too much time “checking with higher-ups” and ensuring compliance with rules. Rigidly hierarchical systems can also be slower to change, since the people at the top often resist change. People at the top are usually older and often have the most to lose from any dramatic advances. In his book, Ziman talks about how the less-hierarchical American academic system in the 1960s was faster at absorbing and teaching new ideas, relative to the systems in the UK and Germany. However, Ziman claims that the nimbleness of the American universities was not without cost. He says that American researchers would more easily get caught up in passing fads and fashions, and that this came at the expense of their mastery of more timeless fundamentals. According to Ziman, American Ph.D.s also were lacking in “critical attitude,” although they tended to be more proficient with the latest techniques.

Teaching respect for the consensus without slipping into dogmatism

The current consensus demands respect, especially in more developed fields like physics. At the same time, the scientist must be trained to remain open to change, even revolutionary change. Achieving the right balance here is not easy. Most science takes place within the confines of existing consensus knowledge and some overarching theoretical paradigm. Even during periods of revolution, existing consensus knowledge must be explained by the new paradigm. So, understanding the existing consensus is important, as is refining the ways in which the knowledge in the existing consensus is ordered and formalized. Furthermore, most scientific advances take place within a paradigm — this is what Kuhn called “normal” science. If too many people work on trying to develop a new paradigm, this would come at great expense to “normal science.” At the same time, the current paradigm and existing consensus should not be revered as an orthodoxy that is beyond questioning.

Regulating scientific communication without squashing bold new ideas and eccentricity

Ziman is adamant that scientific communication should take place within the regulated confines of peer-reviewed journals. He views the integrity of the scientific literature as sacrosanct. In his view, each peer reviewer (referee) acts “like a traffic policeman on point duty, keeping the traffic moving smoothly by imposing an orderly succession and conformity to the general rules.” One important function of referees and editors, Ziman notes, is to enforce standards of communication, something I talk about in my article on peer review. Of course, the potential downsides to gate-keeping by peer reviewers and editors have been much discussed — bold new ideas may have a harder time getting published. Ziman acknowledges and discusses this trade-off.

Establishing science as a career for the masses, without it becoming just a career

Ziman discusses the “mass production” of scientists that kicked off after World War II. He talks about how the “American graduate school” had replaced the “German Institut.” Relative to European schools, the American schools had larger class sizes, more coursework, and more focus on exams. He sees both pros and cons to this. According to Ziman:

“The strength of the old German system was the way in which the spirit of enquiry was passed on as an oral tradition.”

According to Ziman, it is harder to pass on this “spirit” through “mass methods.” Students become focused on getting good grades and impressing their professors. When they start research, their focus is on publishing papers and getting citations. They hope to achieve a “breakthrough” to garner plaudits from those around them and broader fame from society at large. When the focus is on such parochial things, the deeper and broader picture of what science is about can be lost:

“He does not see himself as engaged in a larger struggle with ignorance and error, as a member of a great movement, as a contributor to man’s understanding of nature. The graduate school, by its mechanization of learning, has thrown philosophy out of the window.”

Conclusion

Even if one is able to create a scientific system that balances all of these things correctly, it is yet another thing to keep it going — the processes and norms of the system must be faithfully inculcated into each new generation. The cataclysmic disruptions caused by the Nazis quickly drew the curtains on the golden age of German science. Rebuilding German science to where it is today took decades to achieve.

There are dozens of quantum computing startups,1 but only three that are publicly traded - IonQ, Rigetti Computing, and Quantum Computing Inc. In this post, I analyze the engineering challenges each company faces. These challenges stem from well-understood physics. Four approaches to building a quantum computer are currently dominant,2 and it seems likely one will ultimately win out. Using physics and engineering knowledge we may be able to gain some insight into the commercial viability of each of these approaches.

However, a full analysis of these companies for investment purposes would require looking at their financials, marketing/brand success, current contracts, and the quality of their human capital. I stick to the physics here because it is what I find most interesting and am most knowledgeable about. Nothing in this post is intended as investment advice.

Background info (skip if familiar with qubits, decoherence, gates, etc)

Quantum computers leverage the principles of quantum mechanics to perform computations using qubits, the quantum equivalent of classical bits. Unlike classical bits, which can be either 0 or 1, qubits can exist in a quantum superposition of states.

However, qubits are highly sensitive to their environment, making them prone to decoherence. Decoherence occurs when qubits interact with their environment, altering the qubit's quantum state in a stochastic manner.

Qubits can’t do much on their own. They need to be entangled. Entanglement is a phenomenon where qubits become interconnected, such that the state of one qubit is directly related to the state of another, even over long distances. When entangled, qubits can form gates, similar to the AND, OR, and NOR gates found in classical computers. Some gates are unique to quantum computers, for instance the controlled not (CNOT) gate. A key metric is gate fidelity, which should be above 99.9%. Since qubits are a scarce resource, qubits often need to be re-used during the course of a computation. So one qubit might be used as part of an AND gate and then later used to implement an OR gate. This requires manipulating the qubits with external inputs.

So, the core challenge of building a quantum computer revolves around isolating the qubits from the environment while still being able to manipulate those qubits to 1. load initial data into some qubits, 2. apply initial gates, 3. re-use qubits during the computation by applying different gates as needed, and 4. read the answer out.

IonQ - trapped ion qubits

I think IonQ may be the most promising among the three publicly traded quantum computing startups. The company was formed out of Chris Monroe’s group at the University of Maryland’s Joint Quantum Institute in 2015. They recently announced support from Maryland’s state government to build a “quantum intelligence campus” near the University of Maryland (UMD). They hope to raise one billion to make this campus happen. It’s not entirely clear where that money will come from.

IonQ's quantum computer consists of a linear chain of positively charged ions, held in place by electric and magnetic fields. Historically, these were ytterbium ions (atomic #70), but around 2023 they transitioned to lighter barium ions (atomic #56).

Let’s look at how many ions they have managed to trap as a function of time:

2016: 5
2017: 5-10
2018: 10-11 (?)
2019: 13-20
2020: 32
2021: 32
2022: 32
2023: 32-35 (IonQ Aria)
2024: 40 (IonQ Forte)

Their next target is 64-65 ions, in their Tempo system. However, it’s doubtful the number of ions can keep increasing like this for much longer — we’ll get to why a bit later. First, I want to explain that the number of ions is not the same as the number of usable qubits in each system. Some qubits are needed for error correction, which yields a smaller number of algorithmic qubits (AQs). Additionally, in IonQ’s Forte system, one of the ions is used for optical alignment purposes rather than computation. One has to read IonQ’s marketing materials closely. IonQ Forte, which has 40 ions, was said to have a “capacity of up to 35 qubits.” This doesn’t necessarily mean that they have actually realized 35 AQs yet; it’s just a theoretical maximum which may or may not have been realized. The actual number of AQs depends on system noise. IonQ’s 32-35 ion Aria system achieved 20 AQs in 2022. Currently they can get 25 AQs with their Aria system, and this is what they make available in their cloud service. Some of IonQ’s current marketing materials say that Forte, with 40 ions, yields 36 AQs, one higher than the capacity they stated in January 2024. According to Quantinuum, another privately held company that builds trapped ion quantum computers, the number of AQs IonQ had achieved with Forte in March 2024 was only around nine, despite them advertising it has having 32 at the time. Another more recent article I saw suggested that Forte only has twelve AQs. If the number of AQs achieved so far with Forte’s longer ion chain is currently less than the number achieved with Aria’s smaller chain, that is a red flag in my book (if you know more about this, comment below).

As you may have guessed by this point, creating these traps is not easy. To be more precise, trapping the ions is pretty easy, but getting them to entangle and do quantum computation is not. Lasers need to be used to cool each ion to millikelvin temperatures. Then, precisely manipulated EM fields need to be used to move the ions close to each other and entangle them. Laser pulses have to be aimed at each ion to manipulate their energy levels. The magnetic field in the trap has to be kept extremely uniform, as any slight deviation can ruin the quantum state. Vibrational states of the ion chain are utilized in quantum computation, so naturally these systems are very sensitive to vibration. I have heard that a truck rumbling by on the street can disturb these systems. Setting up and calibrating a single ion trap chip can take six months. Still, it has been done, and since only the ions need to be kept cold, no large-scale cooling apparatus is needed (the rest of the chip can operate at room temperature). As we mentioned, noise is a problem here. Some ion trap devices that have been built are referred to as "noisy intermediate-scale quantum" (NISQ) devices. The number of algorithms that can be run on NISQ devices is greatly reduced. Still, the noise issues here arguably are not as great as other approaches. Ion trap quantum computers can exhibit extremely long coherence times, reaching as high as seconds or minutes.

The downside of ion traps, however, is slow gate operation. Roughly speaking, the "clockrate" of these quantum computers is greatly reduced, because some gate operations require that ions be physically moved around. On a hypothetical 20-million-qubit superconducting quantum computer, it was estimated in 2019 that factoring a 2048-bit integer to break RSA encryption would take about eight hours. On an ion trap quantum computer, by contrast, this would be 1,000 times slower — it would take 8,000 hours, or 333 days. Currently, superconducting approaches remain competitive with ion traps, so this may concern investors.

All quantum computing approaches have serious issues when it comes to scaling to the millions of qubits that are needed for any practical application. For decades, physicists have debated the scaling prospects of ion traps vs. superconducting qubits vs. more exotic approaches. When I was in the field in 2012, physicists were much more pessimistic about the scaling prospects of ion traps. I’m not really aware of the current state of the discourse, but the scaling challenges of ion traps appear to remain very substantial.

As the chain of ions gets longer, it becomes harder and harder to control. The number of quantum vibrational modes, called phonons, increases with the number of ions. As more ions are added, limiting unwanted phonons becomes harder, because the lowest energy required needed to excite a mode decreases. This is very fundamental to the physics involved, and there is no way to get around it. Recall that the longest chain IonQ has created has 40 ions. IonQ’s competitor, Quantinuum, which is ahead of IonQ in many respects, has a device with 56 qubits, which requires at least 56 ions. Fortunately, the number of control electrodes needed grows only linearly with the number of ions. There is no upper bound for how long of a chain can be created, but overall the engineering challenges grow non-linearly as the size of the chain grows.

Since these ions are positively charged, they want to fly apart. As more and more ions are added onto the ends of a 1D chain, they have to be spaced further and further apart to hold them in place. If you look at pictures in Rigetti’s marketing materials (like the picture above), you’ll see that the ions near the center are spaced closer together, while the ions near the edge of the chain have to be spaced further apart. Roughly speaking, the required length of an ion chain scales as N^(4/3). Researchers are also looking at if it is possible to trap the ions in a 2D grid, rather than a 1D chain. The area required for a 2D grid scales as N^(2/3), rather than N^(1/2). It should be clarified here that 2D grids remain a complete fantasy — they have not been demonstrated yet.

According to both Claude and GPT-4o, it is unlikely we will ever have 1D ion traps with more than 100 ions, or 2D grids with more than 10,000. To get around these limitations, IonQ has proposed that two qubits living in separate traps may be entangled via a quantum photonic interconnect. As of October 2024, this has not yet been demonstrated. IonQ’s roadmap says they will achieve this in 2025. As to how feasible this is, the details here are quite complex, and I don’t have time to fully dig into this for this cursory review, so I can’t really say. However, my intuition tells me this will be extremely challenging for them. As I discuss later, quantum-grade photonic devices haven’t been miniaturized yet. Furthermore, my guess is that linking one qubit from one chip to another will not be enough - you’ll have to link multiple qubits, or even the majority of qubits. However, a robust link is achieved, that could make ion traps a clear front-runner vs. other approaches.

Rigetti Computing - superconducting qubits

Rigetti computing was formed in 2013 by IBM physicist Chad Rigetti and is headquartered in sunny Berkeley, California. Like IBM, Rigetti is pursuing a superconducting approach. Superconducting qubits come in many forms, but researchers have settled on “transmon qubits” as the most promising approach, and that is what both IBM and Rigetti use now. In superconductors, electrons bunch up into Cooper pairs and this enables them to travel through a material’s crystal lattice without encountering any resistance. The movement of electrons (current flow) through a superconductor can be described by Schrödinger’s equation. So, a superconductor is characterized by a well-defined macroscopic wavefunction, something which is rare in physics and requires very cold temperatures.

If two superconductors are separated by a thin insulating barrier, the Cooper pairs can tunnel through the barrier, even though classically they would not have enough energy to do so. Now, the phase of the macroscopic wavefunctions in the two different superconductors is going to be different. As a result, the current of Cooper pairs crossing the barrier oscillates as a function of time, even if the electric potential applied to the system is static and not changing. This phenomenon is known as the Josephson effect and the system as a whole is called a Josephson junction. Remarkably, the oscillating current created by the Josephson effect does not dissipate energy. Now, if a “shunting” capacitor is wired to either side of the junction, you end up with a quantum mechanical circuit which is known as a “transmission-line shunted plasma oscillation” or transmon. Since it is quantum mechanical, the oscillations in this circuit have discrete energy levels. If one applies microwave frequency AC pulses, the energy of the transmon’s oscillation can be increased. Due to the nonlinear properties of the Josephson effect, the transmon’s energy levels are unevenly spaced, which is a very useful property for control purposes. The uneven spacing of the transmon’s energy levels means it is hard to accidentally excite the transmon to a much higher energy level than the target energy level. In quantum computing, each of these transmon circuits constitutes one qubit. Transmon qubits are entangled using either capacitive coupling, microwave resonators, or a variety of other approaches.

Two different superconducting materials are used in these quantum computers — the qubits are made with aluminum wires, and the superconducting wiring connecting them is made with niobium. Aluminum needs to be cooled to 1.2 Kelvin to become superconducting, while niobium requires a temperature of 9.2 Kelvin. However, to reduce thermal energy below the transmon’s level spacing and to avoid rapid decoherence, these circuits must be cooled much colder, to 0.02 Kelvin (2 milliKelvin). This requirement of ultra-cold temperatures, which is strictly required by the physics involved, is one of the main challenges for this approach. Large and complex cooling systems known as dilution refrigerators are required. Oxford Instruments’ Nanoscience division manufactures these refrigerators for Rigetti.

Similarly to the ion traps we discussed in the previous section, superconducting quantum systems are very delicate and are sensitive to vibrations and stray EM fields. (Both can cause problems, but the main issue here is stray EM fields, not vibration.) So, the dilution refrigerator must be suspended from a cage and isolated from the external environment:

Rigetti’s latest “Ankaa” system has 84 qubits and has gate times between 56 and 72 nanoseconds, with gate fidelities between 99.0% and 99.5%. Rigetti’s gates are significantly faster than IBM’s, but they have lower fidelity. IBM’s systems also have more qubits. This graphic compiled in March 2023 by Olivier Ezratty summarizes how things stood back then. At that time, both IBM’s and Google’s systems were clearly superior. According to commentaries online, this is still the case.

Quantum Computing, Inc. - photonic qubits

Quantum Computing Inc. has a rather amusing history. For brevity, I’ll refer to them by their stock ticker, QUBT. QUBT was initially incorporated in Nevada on July 25, 2001, as Ticketcart, Inc., a company focusing on online ink-jet cartridge sales. In 2007, they acquired Innovative Beverage Group, Inc., and subsequently they changed their name to Innovative Beverage Group Holdings, Inc (IBGH). This company went public in 2008. IBGH ceased operations and went into receivership in 2017 - 2018. In February 2018 it became QUBT. This transformation involved the shell of a failed company being used to create a publicly traded quantum computing company without having to go through the traditional Initial Public Offering (IPO) procedures. This process looks very suspicious, but is actually not that unusual nowadays, and so is not a major cause for concern in itself. Before getting to the physics, there are a few points about this company which I feel I must mention, because they should raise alarm bells for any investor. Firstly, their recent NASA contract was only worth $26,000. Secondly, the company claims to have a “fabrication foundry” in Arizona for building “nonlinear optical devices based on periodically poled lithium niobate.” An investigation by Iceberg Research has cast a lot of doubts on legitimacy of this claim.

Anyway, QUBT has a completely different approach compared to the other two companies discussed so far. Their approach is based on the control and manipulation of photons, a field called photonics. The precise details on how their system works are murky, and the materials they have published are hard to understand, but I can talk in general terms about what they are trying to do.

Qubits can be realized with photons in a couple different ways. Recall that a qubit is a two state quantum-mechanical system. In a “polarization qubit,” a photon is put into a superposition of horizontal or vertical polarization. In a “photonic path qubit,” a photon is put into a superposition over two different paths. There are also some more esoteric approaches. It’s not clear which of these approaches QUBT is using. I asked GPT-4o for help on this question, and after searching the web it said that the precise nature of their qubits has not been disclosed.

Photonic quantum computers can run at room temperature. They may also suffer less from decoherence. Two photons flying through empty space can pass right through each other without interacting (except at extremely high energies). This sounds wonderful. However, difficulties are encountered when it comes to trying to control the state of these photons and entangle them together using optical devices. The interaction of the photons with optical devices (like crystals, glass, etc) can cause their quantum state to decohere.

In quantum mechanics, measurement is usually defined as a process which completely collapses a wavefunction, destroying any quantum superposition. So normally in quantum computation, measurement is only performed at the very end of the computation, to extract the result. However, it is possible to do weak measurements which only partially collapse a wavefunction or barely perturb it at all.

By doing repeated weak measurements, a wavefunction can be “frozen” and prevented from evolving in unwanted directions. This is called the quantum Zeno effect. QUBT claims to be using this effect to counter-balance decoherence. Because decoherence is usually a highly random process, I’m a little skeptical about this claim.

It’s worth mentioning in passing that, while weak measurement is an actual thing, it is poorly understood. I did some research on this topic as part of a class project in 2011. At that time, the subject was controversial, and some physicists I talked to argued that the concept itself was confused and/or that the purported phenomena were not real. It looks like the phenomena of weak measurement and the quantum Zeno effect are both more established and accepted now, but I think there is still ongoing controversy over how they should be understood.

Even if decoherence is brought under control in a photonic quantum computer, photons may be absorbed when moving through optical devices. Obviously this is not good. Random loss of photons in these quantum computers is practically unavoidable, but it can be dealt with. To do so you need an enormous overhead of error correcting qubits.

Finally, let’s discuss the scaling prospects for photonic quantum computers. Here’s what they look like currently:

What you are at looking here is numerous lasers, beam splitters, polarizers, mirrors, and other optical devices sitting on an optical table, which takes up most of a room. As with other quantum computers, photonic quantum computers are extremely sensitive to vibrations, so the optical table floats on compressed air to dampen vibrations. They don’t say how many qubits the system in the picture has, but my guess is it’s likely twenty qubits at most, yielding just a handful of AQs. However, let’s be generous and assume you could 100 physical qubits from an optical table like this. Recall that for a useful quantum computer you need at least 1,000,000 AQs. Let’s assume the ratio of error correcting qubits to AQs is 100,000 to 1 (it could be as high as 250,000:1, but we’ll be generous here). Then you would need 1,000,000*100,000/100 = 1,000,000,000 optical tables. That’s a lot! However, the situation is actually worse than this. We assumed the number of optical devices required grows linearly with the number of algorithmic qubits. However, because you need a high density of entanglement between qubits for them to be useful, the number of devices required grows super-linearly. According to GPT-4o, the number of optical devices required for a useful photonic quantum computer grows quadratically with the number of qubits (roughly as N^2). So, you would need something like 1,000,000,000^2 or 1,000,000,000,000,000,000 optical tables worth of optical devices to get 1,000,000 AQs. Of course, further advances in quantum error correction could bring this number down somewhat, but even if we were able to get rid of quantum error correction completely, we’d still be looking at (1,000,000/100)^2 = 100,000,000 tables of equipment. (GPT-4o thinks this analysis is right, however, if you see a mistake please comment below).

“OK,” you might say, “but surely all this will be miniaturized, just like we learned how to miniaturize circuits, radios, lasers, etc.” Consider this DGX superpod, which contains 256 H100 GPUs and can fit in a small datacenter:

Each GPU has 80 billion transistors. So there are at least 20.48 trillion transistors here (20,480,000,000,000). Which is indeed a very large number of electrical devices in a small space.

With silicon technology, the density of components has been increasing exponentially over time — that’s Moore’s law in action. However, comparing these optical benches to silicon-based electronics is like comparing apples to oranges. The harsh reality is that we have no idea how to miniaturize most of the optical devices that are needed. Photonic quantum computers require ultra-high end devices, at the very edge of our engineering abilities. These devices come from a specialized vendors that have already spent decades refining their design. Each laser need to be super high precision, with zero frequency or mode drift, and capable of yielding individual photons on demand with near-perfect reliability. The mirrors have to be perfectly planar with near-zero absorption. The electro-optical modulators, optical fibers, and beam splitters also need to have extremely low absorption and scattering cross sections. Then, all of the devices have to be placed and aligned with exquisite precision. As any graduate student in photonic quantum computing can attest, aligning and calibrating one of these tables can take weeks or months. So, while in theory a lot of this could be miniaturized, it seems to me we are very far from that. Maybe superintelligent AI could teach us how to do it.

Comparing metrics for ion traps, superconducting, and photonic

Ratio of error correcting qubits to algorithmic qubits

This ratio is not fixed with the number of AQs — the number of error-correcting qubits needed scales super-linearly. The precise ratio needed also varies depending on the algorithm being run. I did a bunch of Google searching and talked with GPT-4o about this for a while, and here are some rough ranges for the ratio that would likely be needed for a useful quantum computer:

Trapped-ion quantum computer: 10 - 1,000
Superconducting quantum computer: 400 - 10,000
Photonic quantum computer: 1,000 - 500,000

Best two-qubit gate fidelities so far

Scott Aaronson says this is the most important metric. Scott says that 99.9% is the lower threshold to get to a useful machine. Part of the excitement around quantum is that several companies are hovering right around this threshold, or have surpassed it in some experiments. All of these numbers are from Wikipedia, which seems to be one of the most up-to-date references online.

Trapped-ion quantum computer: 99.87 (Quantinuum), 99.3 (IonQ)
Superconducting quantum computer: 99.897 (IBM), 99.67 (Google), 94.7 (Rigetti)
Photonic quantum computer: 93.8 (Quandela)

Best quantum volume so far

Quantum volume is a far superior metric compared to “number of qubits”, because it takes into account the degree of entanglement of the qubits.

Trapped-ion quantum computer: ~2^20 (Quantinuum)
Superconducting quantum computer: 2^9 (IBM)
Photonic quantum computer: ? (probably < 2^9)

Isn’t there a “Moore’s law” for quantum computers?

A lot of people think there is a "Moore's law" for quantum computing. For instance, some people think the number of qubits is increasing exponentially over time and will continue to do so. However, for Moore's law to be realized for CPUs, exponentially increasing levels of investment were required to maintain progress (I wrote about this in 2015). That only happened because CPUs are general purpose, with new applications opening up over time. Because new applications become available with more and more compute, the demand for general purpose compute is essentially infinite. Quantum computers are not general purpose — they only have one major proven application (decryption), plus a few highly questionable niche applications. Over the last decade, large governments and industry have invested millions into quantum computing. If there is little or no fruit soon, that funding could easily dry up, similar to how it did during the "AI winter" in the 1980s.

But what about D-Wave?

D-Wave Quantum Systems, Inc. was formed in 1999. It is named after d-wave superconductors. While they call themselves “The Quantum Computing Company” (TM), they don’t actually make quantum computers. They make devices called quantum annealers, which they claim can help speed up optimization problems like optimizing neural nets, etc. There is no proof of any benefit from their devices vs. traditional computing hardware. Despite their devices having no practical utility, they are extremely adept at marketing and tricking people into buying their devices. This marketing is misleading at best and fraudulent at worst. A decade or so ago, D-Wave marketed their machines as having hundreds or thousands of qubits. While this was technically true, their qubits were extremely noisy and completely unusable. Recently, D-Wave’s CEO went on CNBC and made a number of completely false statements about how customers were using their machines. Over the years, many organizations have buy devices from them, including NASA, the US Air Force, Lockheed Martin, and Google. Organizations buy from D-Wave out of fear of “falling behind the curve,” not because their devices have any practical utility. While there is much more I could say about D-Wave, I wanted to limit this article to actual quantum computing companies.

Thank you to ChatGPT, Claude, and Quillbot for helping me research and write this, and to Greg Fitzgerald and Ben Ballweg for providing feedback on an earlier draft of this post.

Notice this was not just from Google Quantum, it was from Google Quantum AI. Google was not satisfied with limiting themselves to just one buzzword.

This was a marketing coup. The Google team announced that their chip, named Willow, solved a problem in just five minutes that would take a supercomputer 10,000,000,000,000,000,000,000,000 years. This accomplishment made headlines around the world:

But what was this problem that they solved? It’s a problem that Google invented several years ago called “quantum circuit sampling.” This is not the first time they’ve used this problem to compare their quantum machines to classical computers. Here’s how quantum physicist John Preskill describes it:

“The problem their machine solved with astounding speed was carefully chosen just for the purpose of demonstrating the quantum computer’s superiority. It is not otherwise a problem of much practical interest."

Quantum circuit sampling involves creating a random quantum circuit and then sampling from the output. This is very difficult for classical computers to do, because the number of possible states you have to keep track of to simulate the evolution of a quantum circuit grows exponentially with the number of qubits. It is a problem with zero practical utility. Google’s Willow chip has 105 qubits, which is far beyond what classical computers can simulate. So this accomplishment is not surprising and is not a sign of any major progress.

There was a second aspect to Google’s announcement that had to do with quantum error correction. This advancement is much harder to understand, and received much less attention, although it is more substantial. Quantum circuits are highly prone to noise, and require lots of qubits to be dedicated to error correction. The qubits available for computation are called “algorithmic qubits,” while the rest are error correcting qubits. These error correcting qubits can also have errors themselves. Google showed that the number of error correcting qubits they require does not increase uncontrollably as the system is scaled up in size. This was described as a critical milestone towards building a practical, large-scale quantum computer. That said, the extent to which this development represents a true breakthrough milestone is unclear. Certainly, it appears an important threshold has been passed. To me, what they accomplished is a bit like a getting a car’s engine running. Up until now, the car was being pushed by hand. Now they started the car, but the car is still moving very slow, and it still has to travel many miles.

As a result of the announcement, quantum computing stocks skyrocketed:

That figure is from December 25th. The stock bubble continued an explosive run-up until January 7th, when NVIDIA CEO Jensen Huang said that quantum computers were “15 to 30 years away.” The quantum bubble popped and the stocks crashed almost 50% in less than a day:

It seems unlikely these stocks are going to recover anytime soon, although another run-up in price is certainly possible in the future.

What are quantum computers?

I’m going go assume readers are already familiar with the basics of quantum computing. However, I’ll review a few basic points. Classical computers are built around manipulating bits, which can only take on two states, “0” or “1”. Quantum computers are based around qubits. A qubit is a two state quantum system, and thus can be in any superposition of two states. Thus, it can be 0, 1, or “any combination” of the two. If we consider a group of qubits, the number of possible states grows as 2^N, that is, exponentially.

With a quantum computer, many computations can run in parallel on these qubits, all in superposition. Some people on social media have been saying that these different computations run in parallel universes, but that is based on a misunderstanding of the many-worlds interpretation of quantum mechanics. If the many-worlds interpretation is true (about 50% of quantum foundations experts subscribe to it), then the process of branching into different universes (“worlds”) occurs when a quantum system interacts with its outside environment through a process called decoherence. For instance, decoherence can occur during interaction with a macroscopic measurement device. Anyway, the point is that when the circuits in the quantum computer are measured (‘looked at’), the superposition is lost. If you are very clever, then you may be able to figure out a way to collapse that superposition so that the desired answer to your computation pops out at the end.

This is where most elementary explanations stop. I’d like to go a bit further, and explain that a qubit can also be in something called a mixture of quantum states. Long story short, the result is that the state of a qubit can be visualized as living within a sphere.

The states on the surface are called “pure states” while the states within the sphere are called “mixed states.” Understanding this isn’t actually important for anything that follows, but this provides an example of how these things are actually more complicated than how they are typically described in popular science articles.

Quantum computers have very few applications

Given the progress that has been made in the last few decades, it seems likely we will get to useful quantum computers some day, barring the world ending or some other catastrophe. However, that doesn’t mean that everyone will have a quantum computer on their desk, or that AI will be run on quantum computers. The idea that quantum computers will replace classical computers is probably the most common misunderstanding about this subject right now. There is a big narrative among amateur investors that quantum computing is an existential threat to companies like NVIDIA. That’s just not the case.

Putting aside optimization algorithms and quantum simulation for a second, there are only three really useful quantum algorithms known:

• Shor’s algorithm for factoring (used to break encryption) — invented by Peter Shor at MIT in 1994.

• Grover’s algorithm for search — invented by Lov Grover at Bell Labs in 1996.

• The quantum Fourier transform — invented by Don Coppersmith at IBM in 1994.

No major algorithms of any note with a proven quantum advantage have been discovered since 1996. This is despite hundreds of physicists and mathematicians trying to discover new algorithms. Algorithmic progress in quantum computing is super hard. Many experts in the field believe the number of computational problems where you can get a quantum advantage is very limited.

When it comes to optimization, the benefits of a quantum computer are hotly contested. Anyone who says that quantum computers are proven better than classical ones when it comes to optimization is either misinformed, confused, or lying. The field has been yo-yoing around on this question for over a decade. There is a lot confusion about the benefit of quantum optimization algorithms, even among experts, and my read of the situation is that the issue is far from settled. Scott Aaronson has been writing about this for a long time, on his blog Shtetl-Optimized. His voice has generally been drowned out by many spouting boosterism and misleading information on the subject.

When Richard Feynman first proposed quantum computers in 1991, he thought their main application would be to simulate quantum systems. The biggest application here will be to help physicists with certain niche simulation problems in physics and materials science that classical computers can’t solve because the systems being simulated involve an enormous number of quantum interactions. For instance, high temperature superconductors are hard to simulate with classical hardware. So, quantum computers could help us find better superconductors. There are also problems in high energy particle physics where a quantum computer could come in helpful. Commercially, quantum simulation might useful for materials discovery, maybe. It is also possible that quantum simulation could be useful for drug discovery. It may be possible to simulate a small drug molecule in isolation with a million qubits. However, to simulate a drug-protein interaction a vastly larger number will be needed (lying somewhere between 100 million to 5 billion qubits total according to an analysis by GPT-4o). Another point is that for drug discovery, perfect quantum simulations are usually not needed or that useful. In fact, right now most drug companies don’t even do classical molecular dynamics simulation, and the few that do use a lot of approximations in their simulations. For quantum simulation (whether for drugs or materials), approximate methods like density functional theory or quantum Monte Carlo are usually good enough. Classical algorithms for approximate quantum simulation have improved dramatically over the past few decades. Recently machine learning has been used to accelerate those algorithms, dramatically lowering computational requirements. Thus, it is highly questionable whether or not future quantum computers will be able to achieve a definitive advantage compared to classical computers for commercial applications requiring simulation.

Long story short, the only major proven use-case for quantum computers is decryption. The two other algorithms listed above have a lot of caveats — the devil is in the details. For instance, with search and the quantum Fourier transform, there is substantial overhead involved in loading the data into the quantum computer and reading the result out. This overhead, which is rarely discussed outside of expert-circles, seriously limits the utility of both algorithms. The development of quantum RAM could help, but that requires its own set of major breakthroughs.

Investing in quantum computing is very risky

• In the next post in this two-part series, I review three publicly traded quantum computing companies, and each one is pursuing a different approach. There are three dominant approaches being explored by commercial players - ion traps, superconducting, and optical/photonic. There are also a few less common approaches, like neutral atom quantum computing. There are pros and cons to each approach and no clear winner — that’s why they all exist and are all getting funding. At some point, one of these approaches will likely win out. Right now, nobody knows which one will win, so that is one reason investing in quantum computing companies is risky. Of course, one way reducing this risk in your portfolio is to invest in companies pursuing a variety of approaches and hope that the losses you incur on the losers is compensated by the winner(s).

• Right now, the only major application of quantum computers is decryption. Of course, more applications may be developed, but algorithmic progress has been extremely slow. Quantum-resistant encryption algorithms exist today and will be used more and more going forward. By the time quantum computers are able to decrypt stuff, the world may have moved to quantum-resistant encryption years or decades earlier.

• According to analyst Martin Shkreli, the NSA’s entire budget is around 11 billion, and the entire decryption business at NSA is probably worth about 1 billion annually. When you run the numbers, that means these stocks are still overvalued, even if any single company could capture a sizable part of NSA’s budget.

• All approaches to quantum computing have scaling challenges. There is no clear timeline as to when the >1 million qubits needed for a useful machine will be reached. Of course, these companies all have scaling timelines, but those timelines are pure speculation, based on pure trend-line extrapolation at best.

• Let me do the most optimistic trend-line extrapolation I could find. For a quantum computer for decryption, you’d need about 1 million qubits (about 4,000 AQs, with the rest for error correction), and they also need to be significantly entangled. This means you’re going to need a quantum volume between 2^30 to 2^40, according to GPT-4o. According to this source, quantum volumes have been increasing exponentially over time, doubling about every 4 months, roughly. If this was to continue, the target of 2^30 - 2^40 will be reached in 5 - 8 years. That is pretty good! However, I am extremely skeptical about this extrapolation, since the vast majority of tech trends plateau. Drawing an analogy to Moore’s law here is not a good argument. Also, Moore’s law is largely predicated on exponentially increasing investment over time. This requires that the return on investment stays fixed during scaling. There is no good argument that will be the case here, since the number of applications and possible customers is strictly limited, unlike general-purpose computation, for which there is near-infinite market demand.

• Recall that the demand for quantum decryption is expected to decrease over time. Right now governments are supporting the development of quantum computers, but at some point they may decide it’s not worth continued investment, and the field could enter a “quantum winter.” That would almost certainly kill all of these startups.

• When it comes to startups in particular, they are competing against big players like Google and IBM. Right now, they are ahead. IBM is beating Rigetti on two-qubit gate fidelity and number of qubits. IonQ is competing with Quantinuum, a spinoff from Honeywell, and they are leading in quantum volume (one of the most important metrics) by a large margin. Most startups go bust. In the end there are usually a few big winners. I see no reason why that won’t also be the case here.

Thank you to ChatGPT , Claude, and Quillbot for helping me research and write this, and to Greg Fitzgerald and Ben Ballweg for providing feedback on an earlier draft of this post. I also want to acknowledge Martin Shkreli’s YouTube streams, from which I got a number of insights.

Part 2

Read Part 2 here (published 1/18/25)

In the second part of this two-part series, I analyze three publicly-traded quantum computing startups - IonQ, Rigetti, and Quantum Computing, Inc.. These three companies are taking different approaches, and I explore the physics of each approach.

External links to learn more

Videos:

• “Quantum Hype Goes Crazy. But Why?” - physicist Sabine Hossenfelder (Jan 2025)

• “Revealing the Truth About Quantum Computing with Scott Aaronson” (Oct 2023)

Articles:

• “Above my pay grade: Jensen Huang and the quantum computing stock market crash” - by quantum computing algorithms Professor Scott Aaronson. (2025)

• "Quantum computing has a hype problem" - by Sankar Das Sarma, chaired faculty at UMD's prestigious Joint Quantum Institute. (2022)

• “Why I Coined the Term ‘Quantum Supremacy’” - by well-known quantum computing expert Prof. John Preskill. (2019)

Scientific articles:

• Abbas, Amira, et al. “Challenges and Opportunities in Quantum Optimization.” Nature Reviews Physics, 6 (12), Dec. 2024, pp. 718–35.

“This is a remarkable age. It is an age of skyscrapers, submarines, airplanes, dirigibles, phonographs, safety razors, radios, electric lights, steam and hot-water heat, gas stoves, vacuum cleaners, and other wonderful inventions. Many diseases have been conquered, and life has been lengthened. Life has been made more comfortable and in most ways safer. Such progress has been made that today the ordinary American workman enjoys more comforts and opportunities that were denied kings and queens a few centuries ago.”

So reads the end of page four of Science and the Way to Health, a textbook for middle schoolers written in 1929, long before Steven Pinker’s Enlightenment Now and the Progress Studies movement. The authors go on:

“Science is the wonder worker. What has brought about this remarkable change in the world? There is but one answer, — science. This is a word from the Latin, meaning ‘to know’…”

Two pages later, the book starts dropping graphs and discussions reminiscent of Our World in Data:

Remember typhoid from the game Oregon Trail?

Typhoid was largely eliminated in the United States due to insights from the germ theory of disease and better sanitation technology. In the 35 years between 1891 and 1926, the rate of typhoid deaths in Chicago dropped from ~170/100,000 to only ~1/100,000.

Scientifically advanced vs. scientifically primitive peoples

To show the power of science, the book contrasts modern man with scientifically primitive cultures that do not or did not have science. The book gives several examples of scientifically primitive people, along with data and anecdotes on how they are or were less healthy. The groups discussed are the native peoples of Panama, the Zulu, and Europeans in the Middle Ages. Wrong ideas are specifically described. For instance, the book discusses the Zulu’s belief in witch doctors, and also their belief that by inducing sneezes using “snuff” they can cure disease. With regards to primitive Europeans, the book discusses the medieval practice of bloodletting, noting that the practice is still sometimes still used as a “last resort” (in the 1920s).

Now, imagine the uproar that might ensue from some corners today if a textbook made this sort of direct comparison between two groups, with one group happening to be white and the other black. Some social justice warriors would no doubt raise an outcry. The book would be branded as Eurocentric, racist, and white supremacist, since it doesn’t give equal space to describing the intellectual achievements of the Zulu or to recognizing the validity of indigenous ways of knowing. The picture of the African witch doctor might be described as a “culturally insensitive caricature”. The book would also be criticized for not describing ways in which Zulu society is more healthy than contemporary American society.1

Yet, the book’s explanations are anti-racist.

The book posits that differential rates of progress are due to one group having access to and using ideas derived from the scientific method, which itself is an idea. The other groups, by contrast, ended up with ideas that were not scientifically derived and were in error. This explanation directly competes with any racist explanation that tries to explain differences as stemming from intrinsic biological differences (ie in intelligence or temperament). An assumption of biological difference may be natural for school children — after all, the Zulu look different on the outside due to genetics, so it is easy to imagine they may be different on the inside due to genetics. It also feels good to think that one is intrinsically superior. Of course, the full explanation for differential rates of progress around the world is much more complicated than merely what is described in the book, 2 but the role of the scientific method and the importance of good explanations (ideas) scarcely can be denied.

Valiant health knights

Chapter three is titled “Valiant Health Knights”. Here’s how it starts:

“Since the beginning of history illness and death have been a challenge to man. They have inspired his fear, resourcefulness, and courage.
At the beginning all was mysterious, but primitive man craved some explanation for sickness. Since science had not begun, he resorted to imagination and superstition.
…
Beginning with the seventeenth century there was light. Now enter the lists a long line of heroes who often lost their lives in the battle for humanity. The story of this conquest of disease — a conquest which is still going on — is one of the most fascinating stories of civilization.”

Over the course of 43 pages, the stories of the following “health knights” are told:

What stands out here is the praise lavished on these scientists. They are described as “heroes”, “knights”, and “martyrs”. Lister is described as “the prince of surgeons,” and it is said that “every modern hospital today is in a sense a monument to the intelligence and skill of Joseph Lister.” Words like “distinguished,” “beloved,” and “great” pepper the prose. Edward Jenner is called a “friend of mankind” who “made possible the conquest of smallpox.”

It is interesting how smallpox is described:

“In intelligent communities today smallpox is so rare that some physicians have never seen a case in their practice. About one hundred and fifty years ago it was so common that ten out of every hundred deaths were caused by smallpox. It was found in the hovel and in the palace… In the early eighteenth century it was unusual not to meet anybody on the streets of London whose face was not scarred by smallpox.”

America had a particular problem with anti-vaxxers then, just as it does today:

“Because of considerable opposition to vaccination, the United States unfortunately exceeds most of the civilized nations of the world in the annual number of smallpox cases.”

Heroic yellow fever “challenge trial” participants

The section on Walter Reed is quite interesting because it describes something like a challenge trial, with one of the two participants described as “one of the world’s great heroes.” In 1900, an epidemic of yellow fever broke out amongst the American troops stationed in Cuba. An army board led by Walter Reed was set up to figure out the cause of the disease. Reed suspected the mosquito was responsible. Two members of the commission allowed themselves to be bitten by mosquitoes while they were treating people with yellow fever. Both came down with the fever. This suggested, but did not prove, that the mosquito was to blame. To prove that the mosquito was the carrier, a quarantined area was set up and two “challenge trial” participants were recruited, a private named John R. Kissinger and another named Moran. These brave participants knew that they would likely have to suffer from the disease. Kissinger said “I volunteer solely for the cause of humanity and the interests of science.” His one condition for taking part in the experiment was that he was to receive no compensation. After living in the quarantined area for a period, both men were exposed to mosquitos that had fed on yellow fever patients. Both quickly came down with the fever. The rest, as they say, is history. The book describes how the eradication of mosquitoes from Panama made the construction of the Panama Canal possible and has allowed safe travel and trade to occur throughout the canal zone.

The rest of the book — pretty normal

The rest of the book is pretty much a normal health textbook, reflecting the state of scientific knowledge at the time. There are chapters on vaccination and public health, cellular biology, the human circulatory system, human skeletal and muscular anatomy, the human digestive system, nutrition, endocrinology, the human nervous system, mental health, and the dangers of alcohol and drugs. Notably absent is any discussion of the reproductive system or human reproduction.

Most of the information is still accurate, but there are some glaring and amusing anachronisms. For instance, the book says there are “about ninety” chemical elements, and it describes elevated body temperature during a cold as being due to “immune cells expending a lot of energy.”

There is a chapter on “dress for health, comfort, beauty, and economy” that posits that loose-fitting clothes are healthier than tight-fitting ones. It also says that high heels are bad for posture. (I have no idea if either of these are true but they both seem plausible.) Modern clothing designs, textiles, and fashions are described as superior to clothes from centuries ago, echoing a common theme of progress which permeates the book.

The book anticipates Slate Star Codex by 89 years by explicitly calling out the dangers of CO2 build-up in school rooms and bedrooms. The book recommends keeping windows cracked open:

There is an entire chapter on “safe milk and water supplies,” things we take for granted today. The processes of pasteurization and water filtration are described in great detail and in glowing terms — at the time, such things were exciting “high tech” stuff! Techniques are described for designing wells to prevent contamination. The book goes into great detail about how to safely milk a cow, and how to safely store and transport milk. As an example of the level of detail, the book discusses the development of the milk can:

The book calls the milk can on the far right a “hooded milk pail.” The hood prevents dirt from falling off the body of the cow into the milk during milking.

Conclusion

In conclusion, the first three chapters of this book are truly remarkable. The first chapter acknowledges progress as real and desirable. The authors present graphs and data to prove that human health is getting better. The book explains how progress has been unlocked by the scientific method, and it contrasts the health of groups of people who have science with those who don’t.

The second chapter is entitled “How Science May Advance Healthful Living”. It is all about how science can inform how we should live our lives, and gives many examples of daily practices for healthy living.

Chapter three highlights many heroes of science, providing role models for school children. They are framed as “valiant health knights.” They are described as “courageous” several times, which is interesting because the virtue of courage is hardly ever mentioned today.

Some of the later chapters are examples of what Jason Crawford calls “industrial literacy,” since they explain all of the remarkable advancements and technologies that undergird things that might otherwise be taken for granted. In particular, the chapter on “safe milk and water supplies” stood out in this respect. To a lesser extent, the chapter on “healthful air” and how modern buildings are ventilated also fits the mold.

It would be interesting to contrast this textbook with middle school health textbooks today, to the extent that health is still taught at all in middle school. I recall taking two health classes in middle school, but I don’t remember anything remotely like what is in this book being taught.

Joseph Gulfo, MD

My #1 pick is Dr. Joseph Gulfo, MD, MBA. He has 25 years of experience in the pharmaceutical and medical device industries. As President and COO of Anthra Pharmaceuticals, he was instrumental in the 1998 approval of Valstar, a treatment for bladder cancer. Then, at Cytogen Corporation he contributed to the development of the monoclonal antibody ProstaScint. Finally, and most famously, as President and CEO of MELA Sciences he fought the FDA to get their MelaFind diagnostic device approved. Gulfo’s battle with the FDA led to a congressional hearing and is documented in his book Innovation Breakdown, which I reviewed previously. Gulfo has worked closely with all three major branches of the FDA - the Center for Drug Evaluation and Research (CDER), the Center for Biologics Evaluation and Research (CBER), and the Center for Devices and Radiological Health (CDRH).

Gulfo’s battle with the FDA while at the helm of MELA Sciences took a drastic toll on his health. Thus, after that ordeal he decided to retire from industry and focus full time on writing and research. He has written detailed policy documents on how to improve the FDA in conjunction with the Niskansen center and Mercatus center. He is currently a senior fellow at the Progressive Policy Institute.

According to Dr. Gulfo, the FDA must return to its original mandate, which emphasized promptly and efficiently taking action in a timely manner. As I’ve written about previously, the FDA currently moves way too slowly, which costs lives. Gulfo also points out that the original mandate states the FDA should promote the public health. Today, the FDA always describes itself as protecting the public health. This linguistic distinction may seem like nit-picking, but it reflects a deep change in philosophy that has taken hold over the years at the FDA. An over-focus on safety and protection comes at the cost of innovation and means many valuable drugs and treatments never make it to market.

Gulfo also points out that the FDA's legal mandate is to focus solely on safety and effectiveness. Over time the FDA has moved far beyond this, as officials have sought to increase the power of the FDA to micromanage healthcare in the US. Today, the FDA often insists that new drugs and devices must be better and cheaper than existing alternatives. While this sounds good, there are several major problems with this:

First, such requirements rule out personalized medicine in advance. Clinical trials only measure how good a drug works on average among a chosen group of trial participants. However, a drug may be worse on average than existing drugs but still be better for certain individuals. In some cases, the drug may be better for entire subpopulations that were not included in the original trial. By setting and requiring a “one size fits all” standard for what constitutes “better”, the FDA greatly limits what drugs can get to market. In the process, the number of choices doctors have is greatly reduced, and along with it their ability to personalize care.

Another fact we need to consider is that clinical trials are never perfect or complete sources of information. A larger trial may actually determine that a given drug is actually better than existing standard of care, on average. Sometimes rare side effects and interactions are only discovered post-approval. If a patient is experiencing a rare side effect or interaction, then it can be helpful to have an alternative available, even if that alternative is not better on average.

Finally, demanding that drugs must be better than existing alternatives rules out combination therapies. A drug may be "worse" on its own, but useful in combination with existing drugs. Currently, combination therapies are generally not studied in FDA-approved trials. The discovery of combination therapies is almost entirely done post-market. This is especially important when tackling complex diseases like cancer.

The FDA also bars drugs and therapies that it thinks would be too expensive. However, considering cost is not in their mandate. Trying to predict prices in advance is, to borrow a phrase from Hayek, a “fatal conceit.” We simply do not have the ability to do so. In the end, drug prices can only be sorted out by the market.

According to Gulfo, the only criteria should be whether a drug, device, or therapy is safe and effective in some manner. After that, post-market surveillance, additional studies, and consensus practices among doctors will figure out which drugs have the most clinical utility and under what specific circumstances.

Gulfo is also mad at the FDA for overstepping their authority by banning perfectly valid tests over concerns about whether the information the tests provide is “clinically useful”. If a test gives accurate information, it should be approved. Doctors know best if and how to use any information a test provides. As an example, the FDA shut down 23andMe’s genetic testing program over concerns over how the results might be used. However, how to structure care based on test results is best decided by doctors working in conjunction with their patients, not bureaucrats in Washington, DC. Barring companies from sharing valid test results also violates the First Amendment!

Finally, I want to highlight Gulfo’s plan for clearer standards around what “effectiveness” means. Right now, the FDA sets the bar for “effectiveness” in a very haphazard way. The FDA’s ever-changing and often capricious definitions of effectiveness make it hard for companies to plan their R&D. Clearer standards around effectiveness would also increase venture capitalists’ ability to invest in drug and medical device startups.

Gulfo has a great plan here. He has carefully delineated several tiers or types of effectiveness:

Each type is color-coded, so doctors can easily understand which type of measure was used to approve a drug. Importantly, Gulfo strongly supports biomarker-based measures of effectiveness, which have been controversial at the FDA over the years. Enabling approval based on biomarker change is critical for enabling the approval of drugs and therapies that slow or reverse aging.

Balaji Srinivasan

Let’s now talk about Balaji Srinivasan. Always "the smartest person in the room", he is a polymathic thinker. Over the years he has worn many hats. In 2007 he co-founded the genetic testing company Counsyl. Later he served as CTO of Coinbase and as a General Partner at investment firm Andreessen Horowitz. Recently he wrote a book called The Network State, which envisions a future where technology enables communities to form “self-sovereign” states connected by shared values.

Balaji spoke brilliantly about the FDA for over an hour on Lex Fridman's podcast. More recently he spoke on the podcast of Vivek Ramaswamy, a close ally of Donald Trump who will soon be co-running the Department of Government Efficiency (DOGE).

Balaji understands the root cause of most problems at the FDA. Citing the book Reputation and Power, Balaji talks about the FDA's incentive structure which leads it to focus on protecting its reputation and increasing its power. Whenever a drug is approved that causes issues post approval, FDA officials are hauled in front of Congress and grilled. However, whenever a drug is not approved that should have been, nothing happens. People may die, but their deaths are “invisible” (the “invisible graveyard”). As a result of this, FDA officials focus more on protecting their reputation and minimizing risk rather than doing rational risk-benefit analysis.

Balaji sums up what the FDA currently does as “minimizing side effects rather than maximizing effect sizes”. Approval thresholds need to be set in a rational way, weighing potential benefits and harms. Due to perverse incentives, the FDA focuses too much on reducing false positives rather than reducing false negatives.

Empirical work backs up these claims. In a 2017 paper in the Journal of Economics by renowned MIT economist Andrew W. Lo and others, it was found via a Bayesian analysis that the FDA's thresholds for approval of cancer drugs are ten times higher than they should be.

In 2016, Balaji was seriously considered by Trump’s team to be FDA Commissioner (alongside Gulfo and O’Neill). At the time, Balaji was branded by mainstream media outlets as a "radical libertarian.” While Balaji certainly has many libertarian views, when it comes to the FDA this is simply not true. In fact, Balaji has said he would not fire anyone from the FDA and he supports higher salaries at the FDA. Higher salaries are necessary for the FDA to be able to attract and keep the top talent they desperately need. Of course, this cannot be done by the Commissioner unilaterally and requires Congress to act.

Furthermore, Balaji wants to create new centers of excellence for regulation, co-located at leading universities and hospitals. These centers could develop new pathways for approval. This system would allow the FDA to scientifically study innovative new ways of doing regulation. Over time, trials could be done comparing the effectiveness of different approval pathways. This is similar to the system used in the EU, where different "Notified Bodies" are authorized to assess and approve medical devices. This allows a greater diversity of approval mechanisms and creates a more competitive system for regulation.

Balaji also has the endorsement of renowned geroscientist Aubrey de Grey, who points out that he will instill much-needed FOMO (“fear of missing out”) at the FDA. Balaji has spoken about how more drugs and therapies will be developed and marketed elsewhere if the FDA continues to lag far behind the cutting edge, like they do now. Having a strong, smart, and capable FDA is critical for our national competitiveness.

Unfortunately, one potential issue Balaji has is that he has lived in Singapore the past four years. He has also talked about renouncing his citizenship, so he may be perceived as disloyal to the USA. From what I can tell he is still a U.S. Citizen, however (if anyone has further information, please comment below).

Jim O'Neill

Finally we have Jim O’Neill. Unlike the other two candidates, Jim has experience working in the federal government, first as a speechwriter for the HHS Secretary and later as Principal Associate Deputy Secretary at HHS, between 2007 and 2008. While the precise extent of his accomplishments at HHS are unclear, this is certainly an asset he brings to the table. Later, O’Neill was a Managing Director at Peter Thiel's Mithril Capital. He later co-ran the Thiel Fellowship program. To quote the Foresight Institute, “Thiel Fellows have created more than $90 billion in equity value by founding companies including Freenome, Luminar, Figma, Upstart, OYO, Ethereum, Workflow, Fossa, and the Longevity Fund.” O'Neill was CEO of SENS Research Foundation until his abrupt departure in July 2021.

While I agree with most of O’Neill’s views on the FDA, life extension, and other matters, I think he is the weakest out of these three candidates. While he occasionally posts on Twitter, O’Neill has not been writing and speaking about FDA policy as much as the other two candidates. Additionally, I have heard troubling things about his character and things he has done in the past. I won’t repeat what I’ve heard here, since I don’t like sharing gossip when it might be wrong or I might be missing context. Regardless of the validity of the gossip around O’Neill, while O’Neill has held leadership positions, I am much more impressed by the leadership accomplishments of Gulfo and Srinivasan.

(post publication addendum) - In 2014 O’Neill supported rolling back the FDA to only doing efficacy testing. This is not something I’m comfortable with, since I don’t think post-market observational trials can do a good job of determining efficacy — only RCTs can do that. (And, post-market, it would be hard to convince people to sign up for an RCT where they may not get the drug.)

As a final note, there have been 19 FDA commissioners since the founding of the modern FDA in 1962. 17 out of those 19 have held medical degrees. The remaining two held Ph.D.s in the biomedical sciences. Breaking from this tradition may cause more pushback and resistance from the FDA’s establishment. This is one reason to lean towards Gulfo, who has an MD.

If you liked this post, consider sharing this related thread on X (also on Mastodon).

Context: almost two years ago Ben Recht asked on Twitter if there were any pieces that articulated the value of peer review, since every piece he had seen was critical of it. Nobody could reference an article. Despite peer review having widespread support among scientists, it seems nobody has bothered to write a comprehensive defense of it, laying out all the arguments. Since then, it’s been on my mind to write a “steelman” of peer review, meaning a good faith defense (the opposite of a “strawman”.)

Well, over the past few months I finally managed to do that. It turned out to be a ridiculous amount of work — by far the hardest thing I’ve written. Part of the reason is that many of the critiques of peer review are valid. Additionally, the topic is quite complex and woefully understudied. (What studies exist are mainly on the question of single vs double blinding or on small modifications to peer review, not how well it does its job.)

Asimov Press

A Defense of Peer Review

“I think peer review is, like democracy, bad, but better than anything else…

Read more

a year ago · 10 likes · Asimov Press and Dan Elton

About a year ago I wrote a post trying to gauge how much of the peer-reviewed literature in non-predatory peer-reviewed journals has deliberate fraud. A number that often comes up is 2%. However, according to forensic metascience expert James Heathers, that number is flawed and out of date. According to a recent review article by Heathers, the true number is likely around 14%.

I find many people are either not aware of this issue, or they acknowledge the issue but wave it away as not important.

An example is someone who I met recently who works at the National Science Foundation. From their vantage point, fraud is mostly being committed in China, not in the USA, which they described as having far superior systems for grant dispersal and oversight. This is largely true, but it doesn’t mean fraud from the US is not an important issue.1

Another example is Adam Mastroianni, who has a popular article here on Substack called “Science is a strong-link problem”. Mastroianni conceptualizes scientific knowledge as a series of links. Strong links are added to this chain, while weak links are quickly disregarded. According to Mastroianni, making strong links is the most important thing, much more important than reducing the number of weak links. This is a hopelessly simplistic way of conceptualizing scientific knowledge, but that’s a different story for a different time. I want to focus on the implication of his post for how we should think about fraud. While the post doesn’t mention fraud explicitly, his framework implies we shouldn’t worry much about it. (Also, one section is literally titled, in all caps, “CHEATERS SOMETIMES WIN AND THAT’S OKAY”).

According to Mastroianni, “strong link problems” like science don’t need much oversight or regulation. Mastroianni claims that oversight and regulation can stifle the creative freedom needed to create strong links. As an example of a “strong link problem” he points to music. It doesn’t make sense to crack down on bad music. As an example of a “weak link problem” he points to food safety.2 One lapse in food safety can cause big problems.

More generally, Mastroianni and others (like Donald Braben) have a strong bent against oversight on scientific research (ie in grant funding, peer review) due to a concern that oversight stifles bold thinking and scientific freedom.

However, when it comes to fraud this is not an “either/or” situation. We can fund smart, bold, innovative thinkers and give them a lot of freedom while also having strong, muscular oversight against fraud.

I believe that:

1. Ongoing high-level fraud indicates something is rotten in scientific culture.
2. Fraud often is incredibly destructive.
3. We aren’t doing nearly enough about it.

We need new institutions for oversight that are capable of quickly rooting out fraudulent articles from the scientific literature and leveling harsh penalties on those implicated in the production of fraud.

There are several obvious problems caused by fraud:

1. Wasted resources on the fraud itself.
2. Wasted resources doing replications, follow-up work, etc.
2. Weakened trust in scientific institutions.
4. Poisoning of the scientific literature.

I want to linger on that last point a bit. There are not always “gold standard” textbooks that can be turned to for answers, and even if there are, many do not have them readily available. Increasingly, people are relying on scientific literature published online for answers about important questions. As an example, consider that there are about 20,000 genes, each of which can be mutated many different ways. If someone has a rare genetic disorder, then doctors need to consult the literature to figure out what the effects might be. A similar story exists for the 13,155 drugs listed on DrugBank, and for the ~300,000 known materials. More generally, “reality has a surprising amount of detail”.

It becomes a lot harder to research niche topics when the literature is contaminated with fraud.

Now let’s turn to the main topic of this post:

Recent high-level fraud in Alzheimer’s

It’s easy to get lost in the fascinating details of each case and miss the forest for the trees. So, I’m going to review each case briefly.

Berislav Zlokovic

• Number of papers found with suspicious images - “dozens”

• Influential papers with fraud - Nature Medicine (2004), Stroke (2013), The Journal of Neuroscience (2013), Frontiers in Neuroscience (2022)

• Taxpayer money lost as a result - at least 30 million

• Pharma company impacts - ZZ Biotech received 30 million from the NIH to explore drug candidate 3K3A-APC for stroke recovery based on Zlokovic’s work. The drug didn’t work and there were six deaths in the active group compared to one in the placebo group. Suspect papers have been cited in 49 patents by 30 companies, universities, and foundations.

Notes: As explained in Science’s expose, Zlokovic is the top-ranked researcher in the world when it comes to Alzheimer’s and the blood-brain barrier, both in terms of number of papers (25) and number of citations (14,341). Suspect papers affect both the Alzheimer’s and stroke research fields. Two former lab members say that Zlokovic instructed them to make sure their lab notebooks were “clean”, meaning making sure they did not have data and results that challenged their paper’s conclusions.

Eliezer Masliah

• Number of papers found with suspicious images - 132

• Influential papers with fraud - BMC Neuroscience (2014)

• Taxpayer money lost as a result - at least millions (?)

• Pharma company impacts - The drug prasinezumab is being developed by Roche Pharmaceuticals largely off of Masliah’s work. So far a Phase 2 trial found no effect. Development of the Parkinson’s drug Minzasolmin was largely based on Masliah’s work. 238 active patents cite Masliah’s fraudulent work.

Notes: Masliah served as Director of the National Institute on Aging’s Division of Neuroscience between 2016 and 2024. NIH conducted an investigation between December 2023 and September 2024, found evidence of misconduct, and notified the HHS Office of Research Integrity of their findings. As explained in Science’s expose, Masliah is easily within the top five most cited researchers in the Alzheimer’s field and within the top ten for Parkinson’s. He is the 2nd most cited researcher for papers on Cerebrolysin for Alzheimer’s, having published 22 papers on the subject. As documented in detail by Mu Wang, research and interest in Cerebrolysin as a treatment for Alzheimer’s and other conditions exploded after Masliah’s fraudulent work, done in conjunction with Ever Pharma.

Marc Tessier-Lavigne

• Number of papers found with suspicious images - 12 total, 5 major

• Influential papers with fraud - Cell (1999, 903 citations), Science (2001, 743 citations, 2001, 322 citations), Nature (2004, 2009, 834 citations)

• Taxpayer money lost as a result - likely millions

• Pharma company impacts - Genentech built a lot of their research off of Tessier-Lavigne’s work, who had been an employee there between 2001 and 2008. They appear to have lost millions as a result. In 2012, they found they could not reproduce the results of Tessier-Lavigne’s fraudulent 2009 work.

Notes: Genentech urged Tessier-Lavigne to retract his 2009 Nature paper in 2012. It took until 2023 (14 years) before it was retracted. It took 22 years to retract two fraudulent Science papers. Concerns about some of his papers were raised several times on PubPeer starting in 2015. People didn’t start paying attention until Theo Baker published in the Stanford Daily in late 2023. While direct evidence linking Tessier-Lavigne to the creation of the doctored images remains elusive, he was forced to resign from the Presidency of Stanford in July 2023. However, he was allowed to continue leading his lab and doing research. According to Elizabeth Bik, the fraud in the Science and Nature papers affects the results, something Stanford investigators disagreed with.

Sylvain Lesné

• Number of papers found with suspicious images - 20

• Influential papers with fraud - Nature (2006, 3515 citations), The Journal of Neuroscience (2012), Brain (2013)

• Taxpayer money lost as a result - at least 10s of millions, possibly 100s of millions lost chasing Aβ*56 as a key culprit in Alzheimer’s.

• Pharma company impacts - Cassava Biosciences based their clinical trials of Simufilam partially off Lesné’s work. - UPDATE - it seems this isn’t really true, my mistake. Wang (discussed below) affected Cassava. A commenter on X says Lilly, Biogen, and Eisai were affected.

Notes: It took 16 years before the fraud was found in the 2006 Nature paper, which is said to have greatly influenced the direction of the field. The main suspect, Sylvain Lesné, remains a professor at the University of Minnesota, Twin Cities.

Hoau-Yan Wang

• Number of papers found with suspicious images - 20

• Influential papers with fraud - The Journal of Neuroscience (2012)

• Taxpayer money lost as a result - at least $16 million, probably a lot more.

• Pharma company impacts - Cassava Biosciences based their clinical trials of Simufilam off of Wang’s work. Wang served as a paid advisor to the company. Two other Cassava employees are co-authors on some of the fraudulent works.

Notes: The fraudster remains a professor at CUNY, despite CUNY doing a 50 page investigation that found “highly suggestive” evidence that he is culpable for 14/31 allegations. A June 2024 indictment by the Department of Justice states that he also committed fraud on grant applications between 2017 to 2021. The fraudulent applications led to $16 million in grant funding to Wang from the NIH.

Domenico Praticò

• Number of papers found with suspicious images/data - 36

• Influential papers with fraud - Molecular Neurobiology (2014), others

• Taxpayer money lost as a result - many millions (one grant alone was $3.8 M)

• Pharma company impacts - unknown

Notes: Praticò blames a graduate student for flaws in two of the 36 papers. Mu Wang found that some of his water maze data was too good to be true. She wrote a letter about it to the Office of Research Integrity at HHS. Wang, Elizabeth Bik, and others have been urging Temple to investigate Praticò for years, but they have refused to do so. A blog post on For Better Science from 2020 documents a failed whistleblower attempt to get Praticò investigated.

Quotes

“The immediate, obvious damage is wasted NIH funding and wasted thinking in the field because people are using these results as a starting point for their own experiments.” - Prof. Thomas Südhof, Stanford (2022)

“Journals and granting institutions don’t know how to deal with image manipulation. They’re not subjecting images to sophisticated analysis, even though those tools are very widely available. It’s not some magic skill. It’s their job to do the gatekeeping.” - Prof. John Forsayeth, UCSF (2022)

“Dozens of companies are working on anti-Aβ treatment options, all of which may have to undergo a momentous shift if authorities confirm the authors falsified their findings.” - Reed Slater (2022)

“Lesné’s work now appears suspect across his entire publication record. Aβ*56 itself does not seem to exist. Other researchers had failed to find it even in the first years after the 2006 publication, but that did not slow the beta-amyloid-oligomer field down at all. It was going to grow anyway, but it’s for sure that the Aβ*56 stuff turbocharged it, too.” - Derek Lowe (2022)

Addendum

On X, Elizabeth Bik makes an important summary observation:

Further reading

• The Academic Culture of Fraud by Ben Landau-Taylor

From my own perspective, excitement around and identification with transhumanism went into high gear after the publication of Kurzweil’s The Singularity is Near in 2005, and then peaked sometime around 2015. I can think of a few reasons for why this might be. In my last post, I discussed transhumanism’s aesthetics problem, which makes people feel that transhumanism is “icky”. In this post I’ll briefly review the conspiracy theories around transhumanism that have arisen since 2010. Like bad aesthetics, these conspiracy theories create a “stench” around transhumanism and the global transhumanist movement.

Transhumanism conspiracy theories on the right

Klaus Schwab, the founder of the World Economic Forum (WEF), is an engineer by training and a long-time futurist with transhumanist leanings. In his 2017 book “The Fourth Industrial Revolution” he proselytizes about technologies like nanomaterials, organ printing, driverless cars, virtual reality, wearable computing, internet of things, blockchain, 3D printing, robotics, and brain-computer interfaces.

At the 2017 WEF, Schwab sat down with Google co-founder Sergey Brin. Speaking with a thick German accent, Schwab said “Can you imagine that in ten years when we are sitting here, we have an implant in our brains? and I can immediately feel—because we all will have implants, and we measure your brain waves—and I can immediately tell how the people react to your answers.” After a long pause, Sergey admitted it was imaginable, but then went on to say that it’s very, very hard to predict how and when such technologies will be developed. Sergey seemed uncomfortable with Schwab’s idea. One can almost detect an undercurrent of excitement in his voice when Schwab raised the prospect of being able to read the thoughts and feelings of his audience. It’s not that far of a mental leap to conclude that we are barreling towards a future where elites will want to monitor the thoughts and feelings of the masses.

In 2018 an even more ignominious spectacle played out at the WEF, when Yuval Noah Harari gave a talk entitled “The Future of Life: Will the Future Be Human?”. The talk discussed two major themes from his 2015 book Homo Deus. Firstly, Harari contends that evolutionary forces will lead to humans being supplanted by post-human entities. Secondly, Harari sees human behavior as increasingly molded by AI algorithms, creating a growing risk of mass control by large corporations or governments. To be clear, Harari was not arguing that either of these things are good or bad, he was just dispassionately laying out his argument for what he sees as likely to happen.

The optics of this event were perfect fodder for right-wing conspiracy theorists. There was the very Jewish and openly gay Harari standing in front of a rapt audience of globalist “Davos men”, calmly explaining how the masses will be controlled by AI algorithms and how humanity will be supplanted by a new race of “posthumans”. It was also fodder for religious conservatives who think transhumanism and transgenderism are intertwined movements.

If things were not already bad enough, the death of Jeffrey Epstein in 2019 resulted in a flurry of articles about how he was a transhumanist. While Epstein was, as far as I can tell, not actually involved in the transhumanist movement, the articles are largely correct that Epstein had an interest in cryonics and funded researchers working on gene editing technology. In 2018 Epstein donated $100,000 to the transhumanist organization Humanity+ through his nonprofit, Gratitude America, Ltd (Humanity+ did not realize the money was from him at the time).

Then in 2020, the pandemic hit. In June 2020 the World Economic Forum announced a plan called “The Great Reset” and conspiracy theories about globalist plans for world domination kicked into high gear. COVID lockdowns stoked fears of government overreach, and the rise of the lab leak theory got many people thinking about the hubris of technological development.

Reporting on the “growing transhumanist threat” became a mainstay at Infowars. Sometime around 2022 right-wing agitator Steve Bannon started to pay Joe Allen to come on his show The War Room to give regular reports on transhumanism. Joe Allen’s reporting work culminated in the 2023 publication of his book Dark Aeon, for which Bannon wrote the foreword.

According to Allen, transhumanism has tie-ins with gnosticism, a heretical offshoot of early Christianity. Gnostics, like transhumanists, view the body as flawed. Just as Gnosticism was a heresy, Allen believes transhumanism is a heresy undergirded by devil spirits and agents of Satan. As evidence for a connection to Gnosticism, Allen points to Sophia the Robot, noting that “Sophia takes her name from the Gnostic goddess—or aeon—who in her confusion, abandoned the fullness of eternal light.”

It doesn’t help that Max More has a 1989 article entitled “In Praise of the Devil” and the US Transhumanist Party’s 2024 presidential candidate, Tom Ross, has a 2023 article entitled “The Synergy of Gnosticism and Transhumanism.” (Side note - Gennady Stolyarov II pointed out that a leading gnostic thinker, Miguel Conner, is a critic of transhumanism. Also, Stoylarov says that Ross’s article was mostly outreach to the Gnostic community, although he has publically said he is a Gnostic.)

Similar to Bannon and Allen in the US, in Europe far right firebrand Stefan Magnet has been taking to German and Austrian airwaves to discuss how transhumanism is a “satanic plan” being implemented by globalists who are in cahoots with big tech corporations and the LGBTQ movement. Magnet is essentially Germany’s Alex Jones — he owns a far right TV channel called AUF1. In 2022 he published a book entitled Transhumanismus: Krieg gegen die Menschheit (Transhumanism: the War Against Humanity). Drawing from the conspiracy theorist’s playbook, Magnet sees several global trends as intimately connected - vaccination efforts, transhumanism, and the LGBTQ movement. Magnet proclaims that the COVID-19 vaccination program was a globalist plan to warm up the masses for the next phase of biotechnological control. Soon, he says, vaccines mandated by globalists will contain nanobots capable of controlling thoughts. Not long after that, Magnet believes globalists will make humans infertile and babies will be bred in government-run laboratories, similar to Brave New World.

Meanwhile in Russia, right-wing philosopher Aleksandr Dugin, who has been called “Putin’s brain”, has also been railing against transhumanism, which he sees as symptomatic of the selfish decadence of western liberalism. Dugin frequently refers to western liberalism as a “cancer” and warns that “liberalism will mean precisely the human is optional: so you can choose your individual identity to be human [or] not be human. That has a name: transhumanism, post-humanism, singularity, artificial intelligence”. Of course, he is not far off the mark here, transhumanists do value freedom of bodily autonomy and some are interested in becoming “posthuman” (whatever that means) if technology allows it.

Transhumanism conspiracy theories on the left

While the right-wing conspiracy theorists fret about leftist WEF globalists pushing transhumanism, the left-wing conspiracy theorists worry right-wing billionaires pushing transhumanism. The usual suspects are Peter Thiel, Elon Musk, and Marc Andreesen. Occasionally Skype co-founder Jaan Tallinn and OpenAI co-founder Sam Altman are also thrown into the mix.

The main conspiracy theory on the left comes from Emile Torres and Timit Gebru. They propose that several future-oriented ideologies should be considered as a bundle - transhumanism, extropianism, singularitarianism, cosmism, rationalism, effective altruism, and longtermism (TESCREAL). These ideologies do share a few things in common — they are popular in the Bay Area, and they involve thinking about the effects of technology and advocating for certain forms of technological development.

A big problem here is imagining that all these movements are all acting in some concerted fashion. In fact, as many have pointed out, there are major conflicts between these movements. Most notably, many rationalists, effective altruists, and longtermists want to slow or pause AI development, while many transhumanists and extropians want to accelerate it. There are also many disparate actors within each movement. Within transhumanism, for instance, there are quite distinct strands of libertarian transhumanism, left/progressive transhumanism, and apolitical/anti-political transhumanism.

Anyway, here’s the two core claims they make:

(On TESCREAL ideologies) “all trace their lineage back to the first-wave Anglo-American eugenics tradition.” - Torres, March 2023, in a viral thread on X.

“The same discriminatory attitudes that animated first-wave eugenics are pervasive within the TESCREAL literature and community” - Gebru and Torres, 2024.

The main piece of evidence for the first claim is the fact that Julian Huxley, who is considered to have coined the term “transhumanism”,1 was a liberal eugenicist (“liberal” in the sense that he was against government coercion — he also staunchly rejected race-based eugenics throughout his life).

There is a persistent misconception that Julian Huxley is the “founder” of the transhumanist movement. While Huxley had many proto-transhumanist views, he was not at all influential in the formation of the modern transhumanist movement. Natasha Vita-More goes to pains to explain this in her 2019 article “History of Transhumanism”.

Huxley is mistakenly called the “founder” because in his 1957 book, New Bottles for New Wine, he coined the term in a chapter entitled “transhumanism.” Here’s what he said:

“The human species can, if it wishes, transcend itself —not just sporadically, an individual here in one way, an individual there in another way, but in its entirety, as humanity. We need a name for this new belief. Perhaps transhumanism will serve: man remaining man, but trans­cending himself, by realizing new possibilities of and for his human nature.” - Julian Huxley in New Bottles for New Wine.

Germline genetic engineering has certainly been discussed by some transhumanists, but it is tiny fraction of what transhumanists advocate for. Germline genetic engineering is certainly eugenics, in so far as it is done to improve the health and wellbeing of children, but it’s hard to argue it’s bad when the child clearly benefits and it is undertaken voluntarily with full information regarding risks. As shocking as it may sound, a lot of “eugenics” is actually fine and non-controversial. We just don’t call it eugenics because of the terrible connotations that word has taken on. For instance, most people think incest is wrong and it’s OK to choose not to have a child because you are worried they may inherit your mental health issues. Most people are OK with preimplantation genetic testing for Tay-Sachs disease and other genetic diseases as well.

I am not aware of any transhumanist ever advocating for coercive eugenics. In fact, the transhumanist movement tends to skew libertarian, away from government intervention. If one defines eugenics in a broad way (“advocating for the creation of better humans”) then transhumanism is very much eugenicist. However, many mundane things also fall under the category of “efforts to improve children” — parenting, better childhood nutrition, improved schools, reducing lead exposure, cleaner air. Unfortunately, many people on the left view any talk of “making children smarter” or “increasing intelligence” as “problematic”, even though the concept of general intelligence and scales for measuring it have been well established by psychological science.2 Fundamentally, people generally value intelligence, health, wisdom, and good behavior in themselves, their friends, and their children. Transhumanists are just not afraid to use technology to help with the furtherance of such goals.

Anyway, I don’t have any desire to spend time here deconstructing all of Torres & Gebru’s arguments. It would take hours to write a full rebuttal to all their points. I do want to make a general point about Torres, though, who I have been following for several years (I even met him 2 or 3 times in ~2018). He was once a transhumanist, so he knows all the figures well. In recent years Torres has become very good at a particular style of rhetoric. He obsessively scours the web for any material he can use to tarnish his adversaries. He finds loose associations that fit his narrative (like “X was on podcast Y which also hosted controversial person Z”) and cherry picks quotes in a misleading way. Finally he weaves it altogether into a narrative. Unfortunately, a long string of anecdotes and cherry-picked misleading quotes can be made into quite a forceful sounding argument.

A common thread

A while ago Zoltan Istvan noted a common thread running through conspiracy theories about transhumanism:

“Despite the obvious successes of 21st Century science and technology—all which can be considered transhumanist in design—many conspiracy theorists do not easily see the benefits. Instead, they choose to focus on the negative side of things; they choose to hate transhumanism. If you tell them the Alzheimer’s sufferer can remember more via a brain implant, they'll tell you it's also a tracking device. If you tell them artificial hearts are coming and will help eliminate heart disease, they tell you only the elite will be able to afford it. If you tell them a vaccine may thwart Ebola, they tell you it will make children who get it autistic.” - Zoltan Istvan.

This is related to the phenomena of pessimism sounding smart, but where the pessimism isn’t grounded in reality at all.

So is the term “transhumanism” perma-f*cked?

A few years ago long-time transhumanist David Wood published a book entitled Vital Foresight: The Case For Active Transhumanism. It’s interesting to hear the pushback he got when his friends found out he intended to put “the T word” on the book’s cover:

“Don’t put that word on the cover of your book!” That’s the advice I received from a number of friends when they heard what I was writing about. They urged me to avoid “the ‘T’ word” – “transhumanism”. That word has bad vibes, they said. It’s toxic. T for toxic. I understand where they’re coming from. Later in this book, I’ll dig into reasons why various people are uncomfortable with the whole concept. I’ll explain why I nevertheless see “transhumanism” as an apt term for a set of transformational ideas that will be key to our collective wellbeing in the 2020s and beyond. T for transformational. And, yes, T for timely.” - David Wood

Indeed, whether or not the term “transhumanism” should be used has been an increasingly a point of debate within the transhumanist community. The World Transhumanist Association rebranded itself as Humanity+ in 2009. A few years ago the United States Transhumanist Party hosted discussions internally about renaming the organization, with some wanting to rename it the “longevity party”. Ultimately the membership voted by a large margin not to rename the organization, but the fact there was any debate about it at all is significant.

I’m personally conflicted on whether we should try to rehabilitate the term “transhumanism” or avoid it and find a new term (or set of terms) to rally around. Maybe when I have my thoughts more ironed out I’ll write something more definitive about the issue.

To close out this piece though I’d like to share what Balaji Srinivasan had to say on this issue. Anyone who reads Balaji’s Twitter/X knows that he is very bullish on transhumanism in general, especially in places like India, where he thinks transhumanist ideas are going mainstream among India’s youth. Here’s what he said on the Tim Ferriss show in 2022 (emphasis mine, of course):

“The problem is people have pathologized what they think transhumanism is like the, I don’t know, the Klaus Schwab version from the World Economic Forum where you all become misshapen monsters and you’re edited…

We need probably a different term for it, and I’ve got some terms that I’ve been thinking about.

I like to use the term optimalism as opposed to optimism. You are optimizing yourself. You are being positive. There’s an objective function. Transhumanism, you might say it’s just a change, whether that change is positive or negative is unstated. But optimalism is, in a sense, you’re improving. You’re getting better. It is optimal physical fitness. It is optimal health. It is taking care of oneself. It is optimizing finances. It is optimizing everything.” - Balaji Srinivasan

Thank you to Ben Ballweg and Gennady Stolyarov II for proofreading and providing comments.

A recent video of Bryan Johnson injecting Cerebrolysin has over a million views on X and 570,000 views on YouTube. The drug, which is advertised as a “peptide combination”, can be purchased easily online and appears to be growing in popularity among biohackers and transhumanists. The subreddit r/Cerebrolysin has 3,100 members.

TL;DR

Unfortunately, our investigation indicates that the benefits attributed to Cerebrolysin are biologically implausible and unlikely to be real. Here’s what we found:

• Cerebrolysin has been used clinically since the 1950s, and has escaped regulatory oversight due to some combination of being a “natural product” and being grandfathered in.

• Basic information that would be required for any FDA approved drug is missing, including information on the drug’s synthesis, composition, and pharmacokinetics.

• Ever Pharma’s claim that it contains neurotrophic peptides in therapeutic quantities is likely false. HPLC and other evidence show Cerebrolysin is composed of amino acids, phosphates, and salt, along with some random protein fragments.

• Ever Pharma’s marketing materials for Cerebrolysin contain numerous scientific errors.

• Many scientific papers on Cerebrolysin appear to have ties to its manufacturer, Ever Pharma, and sometimes those ties are not reported.

• Ever Pharma’s explanation of how the putative peptides in Cerebrolyin cross the blood-brain barrier does not make sense and flies in the face of scientific research which shows that most peptides do not cross the blood-brain barrier (including neurotrophic peptides like BDNF, CDNF, and GDNF).

• Since neurotrophic factors are the proposed mechanism for Cerebrolysin’s action, it is reasonable to doubt claims of Cerebrolysin’s efficacy. Unfortunately, most biomedical research papers are false. It may have a mild therapeutic effect in some contexts, but the research on this is shaky. It is likely safe to inject in small quantities, but is almost certainly a waste of money for anyone looking to improve their cognitive function.

Table of contents

1. Introduction

2. The long history of Cerebrolysin

3. Cerebrolysin.com is full of scientific errors

4. The evidence base for Cerebrolysin contains conflicts of interest and a statistically improbable rate of success

5. So WTH is Cerebrolysin?

6. We only found one study giving evidence of neurotrophic factors in Cerebrolysin, and it’s kinda sus

7. HPLC-mass spectroscopy of Cerebrolysin fails to show any neurotrophic peptides

8. Storage instructions are incongruent with peptides and there is no immune response

9. The putative active ingredients are unlikely to cross the blood-brain barrier

10. Concluding metascience thoughts

Introduction

One of us (Dan) was recently exposed to Cerebrolysin at the Manifest conference in Berkeley, where a speaker spoke very highly about it and even passed around ampoules of it for the audience to inspect.

Dan then searched for Cerebrolysin on X and found a video by Bryan Johnson from May 23 that shows him injecting Cerebrolysin. Johnson describes it as a “new longevity therapy” that “fosters neuronal growth and repair which may improve memory.”

Dan sent the video to Greg Fitzgerald, who is a 6th year neuroscience Ph.D. student at SUNY Albany. Greg is well-versed on the use of neurotrophic peptides for treating CNS disorders and was immediately skeptical and surprised he had not heard of it before. After Greg researched it, he felt a professional responsibility to write up his findings. He sent his writeup to Dan, who then extensively edited and expanded it.

Our critique covers three major topics: (1) sketchy marketing practices, (2) shoddy evidence base, and (3) implausible biological claims. But first, it’s interesting to understand the history of this strange substance.

The long history of Cerebrolysin

To our knowledge, the “secret history” of Cerebrolysin has not been illuminated anywhere to date.

Cerebrolysin was invented by the Austrian psychiatrist and neurologist Gerhart Harrer (1917 - 2011), who started using it in his practice around 1951. Between 1954 and 1990 he published at least ten articles on it. In a 1954 article, he describes Cerebrolysin as an “amino acid mixture containing all biologically important amino acids, including glutamic acid.” He describes how the aqueous injection used in his studies contains about 1 g worth of amino acids that are created by lysing pig brain tissue. In a 1959 paper, Harrer describes a paper chromatographic analysis he conducted, which showed the presence of 18 amino acids. He says that when dried, the substance is 57% amino acids by weight, with the rest being phosphates and inorganic salts. 

It seems Harrer believed that pure amino acids have considerable therapeutic potential when given intravenously, but that the relative proportions of those amino acids should be tuned to treat different tissues. Thus, to heal brain tissue he decided to derive amino acids from pig brain tissue. Pigs were a fortuitous choice due to the fact that they are especially resistant to prion disease.

Today we know this reasoning is flawed. Your cells precisely regulate the uptake of amino acids by regulating amino acid transporters. So as long as sufficient quantities of each amino acid are ingested, cells are smart enough to take in the quantities they need.

This picture of Cerebrolysin as an amino acid mixture is consistent with marketing materials from that time. For instance, in a 1959 issue of “American Professional Pharmacist” it was described as an “amino acid combination”:

From what we can tell, the drug has been used for ischemic stroke patients since the 1970s. Hundreds of scientific papers on Cerebrolysin were published between 1990 and the present day. Papers can be found arguing that Cerebrolysin is helpful for treating traumatic brain injury, stroke, diabetes, Alzheimer’s, Parkinson’s, dementia, seizures, diabetic neuropathy, PTSD, delirium, subarachnoid hemorrhage, closed head injury, nerve injury, facial paralysis, depression, multiple sclerosis, and amyotrophic lateral sclerosis.

For some reason, interest in Cerebrolysin increased dramatically after 2014. This may be due to an advertising campaign by Ever Pharma, including the creation of cerebrolysin.com in 2017.

Today, Ever Pharma has business operations in thirteen countries1 and claims that Cerebrolysin is used in over 50 countries. While this may be true, it is primarily used in Russia, China, and several post-Soviet countries.

As we mentioned, Cerebrolysin is now widely used “off-label” by biohackers and longevity enthusiasts.

Cerebrolysin.com is full of scientific errors

At first glance, the Cerebrolysin website appears very professional, with many polished diagrams showing how Cerebrolysin works. A closer look however shows scientific sloppiness that is far from professional. For example, consider this figure:

We noticed a few odd things about this figure alone. First, glucose oxidase is an intracellular protein that would never be in the bloodstream under normal circumstances. It is misleading to show Cerebrolysin as larger than glucose oxidase, since the putative peptides in Cerebrolysin are described as being around 10 kDa while glucose oxidase is 180 kDa. Closer inspection shows this is a 3D graphic where the Cerebrolysin molecule is meant to be closer to the viewer. As such, the relative size of each molecule is impossible to judge. This sort of 3D graphic is deliberately avoided in scientific papers. 

Of course, many FDA-approved drugs also have deceptive marketing. There are examples of drug companies persuading doctors to prescribe off-label, drug commercials that exaggerate expected benefits, and drug companies manipulating the FDA approval process. (At the same time, the FDA is sometimes too hesitant to authorize the sale of experimental drugs out of a misplaced concern about patients being exploited.) Despite all these problems with our medical system in the US, the level of deception used by Ever Pharma in marketing Cerebrolysin is worse than anything we’ve seen for an FDA-approved drug.

The Cerebrolysin website should be contrasted with the website of any FDA-approved drug. As an example, let's look at the website for the recently approved Alzheimer's drug, Aduhelm (aducanumab). The process of navigating pertinent information is pretty straightforward. With three clicks one can navigate to the clinical trial results that led to the drug's approval. One can find preclinical results about BBB permeability, detailed rationales for the doses chosen for the final product, and a complete description of the aducanumab molecule and the other constituents of the drug. With a little extra searching, you can even find some details about the quality assurance that goes into ensuring that every batch of drug is uniform.

Medicine websites are always partly marketing, but we feel Cerebrolysin.com is pure marketing. Regardless of how you feel about the FDA, it is good that there are laws preventing drug companies from directly advertising their products with specious claims.

To show what we mean about specious claims, let’s take a look at this chart:

These are wild claims. You will not find any FDA-approved drug with so many claims, for the simple reason that it would be near-impossible to substantiate all of them.

We encourage readers to dig into the references provided to Ever Pharma to see if they actually substantiate the claims in question — in our opinion they largely do not (we will give some examples in the next section).

An example of Ever Pharma’s sloppy citation practices can be seen in the "Mode of Action" page:

What appears to be eight citations is actually just four citations, duplicated. The citations aren't in a uniform style, suggesting they've been copied-and-pasted from other places. One of the key citations, (Frey, 2004), was never published, and isn't available anywhere online (nor could we obtain it from Dr. Frey or Ever Pharma). Furthermore, these citations are not attached to specific claims in the text. We are asked to believe that the dozens of claims made in the text are substantiated by just these four sources.

If one were to be maximally charitable, one might say that all the webpages fall under the auspices of 'marketing', and to judge them as we would a journal article amounts to a category error.

However, the Cerebrolysin website acts as if it is a repository of scientific knowledge. For instance, it contains a "monograph”, which is presented as a technical resource, and shorter documents that are said to summarize the findings of “rigorous clinical trials”.

We found that the “monograph” is little better than the other materials on the website. For instance, consider this table:

Above the chart there is a claim about "double-blind, placebo-controlled trials", but  within the chart, none of the cited studies meet this description. Nor is this problem limited to this one chart -- similar false claims about trials can be found throughout this document.

This same chart contains another deception: the study in the lowest row of the chart is actually a systematic review, not a meta-analysis as the chart description claims. The chart summarizes that article as providing evidence of "improvement of cognitive performance. Higher quality of live [sic]." Now let's compare that to the actual text of the article:

“Authors' conclusions: Cerebrolysin may have positive effects on cognitive function and global function in elderly patients with vascular dementia of mild to moderate severity, but there is still insufficient evidence to recommend Cerebrolysin as a routine treatment for vascular dementia due to the limited number of included trials, wide variety of treatment durations and short‐term follow‐up in most of the trials.”

If the writers of the Cerebrolysin monograph are so willing to blatantly misrepresent the sources they're using as evidence, it ought to make us highly skeptical of everything they have to say.

The evidence base for Cerebrolysin contains conflicts of interest and a statistically improbable rate of success

Perhaps you have read all this and say, “who cares about these issues with the website and monograph?” On some level, the contents of the website have no relation to the actual efficacy of Cerebrolysin, which Ever Pharma claims "has been proven in 87 double-blind-studies and trials with more than 17,000 patients."

Since hundreds of articles have been published on Cerebrolysin, I (Greg) decided to look at the top 10 most-cited articles on Google Scholar that contained the terms "Cerebrolysin" + "double-blind". Presumably, these 10 articles are representative of all the double-blind studies of Cerebrolysin. If anything, their quality may be better than average since they earned more citations and a higher search ranking.

The most apparent problem with these articles is the conflicts of interest. Nine of the top ten articles on the first page of results have authors who are affiliated with Ever Pharma (or its former name, EBEWE Pharma), and therefore have a financial stake in Cerebrolysin being shown to be effective.2

In one of these articles, we are provided with the following comment:

Co-authors HM and ED are employees of EBEWE Pharma. Both have no ownership in the company, and their compensation is not linked to the outcome of research projects. (Alvarez et al, 2011)

This isn’t very reassuring — working for the company that manufactures Cerebrolysin certainly seems to give them a stake in the outcome of any research project they might be involved in.

I (Greg) went through the next several pages of Google Scholar results in an attempt to find a single publication that tested Cerebrolysin that found a null result. There were none. This degree of uniform success is statistically implausible, especially given the comparatively small sample sizes used in all of those studies. If you look up clinical trials of Alzheimer's disease drugs like donepezil and galantamine, you will find some trials that failed to observe any benefit. Given random sampling and small effect sizes, the absence of any trials with null results is very suspicious.

Another concerning feature of the evidence base for Cerebrolysin is the absence of pre-registration. For those unfamiliar, when you do a federally-funded clinical trial in the US you are required to preregister on clinicaltrials.gov. You need to specify your methods, your method of recruiting patients, and other relevant information. The EU has a similar system for pre-registering clinical trials.

Despite hundreds of papers on Cerebrolysin, a search for Cerebrolysin on the EU Clinical Trials Register returns only five results.

In most published clinical trials on Cerebrolysin, basic questions like "what country were these trials conducted in?" are not addressed. It appears that a large number were done at Russian State Medical University. In the Russian clinical trial database there are only two reported trials for Cerebrolysin — in 2013 (completed) and 2015 (suspended).

Another disturbing pattern we noticed is that most of the review articles about Cerebrolysin have the same set of authors, with their accompanying conflicts of interest. One of the most-cited reviews, Plosker & Gauthier (2009) fails to disclose an important relationship between one of their authors and Ever Pharma. An article from the same year makes clear that “Gauthier is a member of the EVER scientific advisory board.”

We could find only a few articles written on Cerebrolysin that are completely independent from Ever Phara. Three of those are Cochrane reviews, from 2019, 2020, and 2023. Those reviews found there is insufficient evidence to conclude that Cerebrolysin is effective in treating either ischemic stroke or vascular dementia. Somewhat disturbingly, the 2020 review notes a higher rate of severe side effects among the patients given Cerebrolysin across several trials, although there was no difference when it came to mortality or mild side effects.

So WTH is Cerebrolysin?

Greg had a lot of questions when looking at Ever Pharma’s materials. What species of pig are used? How old are those pigs? How are the pigs sourced? How are they killed? What parts of the brain do they grind up? What are the active ingredients in Cerebrolysin? What assays have been done to measure the concentration and bioactivity of the active ingredients? Is there batch-to-batch variation? How were the recommended doses chosen for Cerebrolysin? Why is it recommended that Cerebrolysin is cycled for some diseases but not others? What ingredients penetrate the blood-brain barrier?

Across several sources, Cerebrolysin is said to be "standardized". However we could not find any description of this “standardized” procedure.

Here’s what we could find:

One source says that Cerebrolysin contains "young pig brain". We could find no information on how the pig brain matter is obtained. The most economical option would be pigs slaughtered for meat. However, proteins begin deteriorating immediately upon death, so the freshness of the samples is an important issue.

The pig brain matter is ground up and hydrolyzed with enzymes. In biochemistry, hydrolysis is a type of reaction that cleaves organic molecules into smaller components. For instance, a protein is hydrolyzed into multiple, smaller polypeptide fragments. If you allow the hydrolysis to run long enough, those polypeptides will be hydrolyzed into individual amino acids.

The enzymes used are likely peptidases, because Cerebrolysin's label mentions sodium hydroxide among its inactive ingredients. The only reason sodium hydroxide would be in the drug is as a means of halting enzymatic degradation. Peptidases only function in acidic environments, so adding a strong base like sodium hydroxide is a common way to halt the degradation procedure.

After being hydrolyzed the resulting “lysate” is centrifuged to remove all insoluble components.

According to the marketing materials, Cerbrolysin contains “neurotrophic factors”. However, as we discussed earlier, between 1950 - ~1990 Cerebrolysin was described as a mixture of amino acids with some residual peptide fragments. It’s been described a bit differently over the years:

• Trojanova et al. (1976) describe Cerebrolysin as amino acids, “aligopeptides” [sic] and nucleotides.

• Kofler et al. (1990) describe Cerebrolysin as a “hydrolysate containing free amino acids (about 85%) as well as low-molecular protein-free amino acid sequences (about 15%)."

• Hartbauer et al. (2001) describe Cerebrolysin as “approximately 25% low molecular weight peptides (10 kDA) and a mixture of approximately 75% free amino acids, based on the total nitrogen content.”3

Taken together, these descriptions indicate that the Cerebrolysin is ~75% amino acids, with the other ~25% being short polypeptide sequences that are the remnants of once-intact proteins that have been enzymatically degraded. Kofler et al. (1990) and Hartbauer et al. (2001) don’t provide any explanation for their ratios but their similarity and specificity are noteworthy. We suspect that these ratios were provided by Ever Pharma.

Based on our research, there seems to have been a transition in Cerebrolysin’s marketing from “amino acids” to “neurotrophic peptides” that occured in the 1980s. Yet, we could find nothing to suggest that the company updated their tissue preparation procedures in that time period.

While an enzymatic degradation procedure is compatible with the goal of producing amino acids or small peptide fragments, it is incompatible with the goal of producing neurotrophic proteins. This is because any peptide fragments would not retain their neurotrophic properties.4

Now, there are well-established techniques for measuring the concentration of neurotrophic factors in biological samples. There are even services that will perform the assays for you if you send them your samples. The fact there is no succinct summary of all the neurotrophic factors in Cerebrolysin and their concentrations is mind-bending.

It’s also worth taking a step back to ask if it’s reasonable to begin with that a lysate of pig brain tissue would contain an appreciable amount of neurotrophic factors. Neurotrophic proteins account for a miniscule proportion of total brain proteins, and Cerebrolysin is further diluted in water. Nowhere is any enrichment step described, and no such enrichment techniques existed when Cerebrolysin was invented in the mid 1950s.

All of our skepticism about the constituents of Cerebrolysin could all be immediately resolved if the manufacturers just performed an ELISA assay on their product, and posted the result.

Let’s drill down on Hartbauer et al.’s claim that Cerebrolysin contains 25% “~10 kDa” peptides, because it widely cited, and is cited in Ever Pharma’s marketing materials. Here are the sizes of several neurotrophic factors: NGF, 26.5 kDa; BDNF: 27.8 KDa; PDGF: 32 kDa. This would imply that whatever neurotrophic factors existed in the pig brain tissue have been reduced to fragments about half the size. In general, proteins do not retain their signaling potential after so much of their structure has been removed, although some have suggested this is the case with Cerebrolysin.

Now, we contend that there is no lysing method that would produce a solution of 75% amino acids and 25% ~10 kDa peptide fragments. Haurtbauer et al. likely meant to say “25% < 10 kDa peptides” not “25% ~ 10kDa peptides”. In fact, this is how a different paper quotes them.

Consider three distributions consisting of amino acids and < 10 kDa peptides:

Of these models, only Model 1 is consistent with lysing. Model 3 is only plausible if one mixed two different solutions, each of which underwent a different synthesis procedure.5

This discussion about the likely distribution of peptide sizes in Cerebrolysin is obviously significant, since if most peptide fragments are way smaller than 10 kDa, then the claim about it having neurotrophic properties is very unlikely.

We only found one study giving evidence of neurotrophic factors in Cerebrolysin, and it’s kinda sus

This figure from Chen et al (2007) is the only direct evidence we could find of neurotrophic factors in Cerebrolysin.

Of course, this figure is displayed prominently in Ever Pharma’s materials. The figure shows the outcome of an ELISA immunoreactivity assay for seven neurotrophic factors. We wonder why the authors did not include the two best-known neurotrophic factors, BDNF and NGF.

The y-axis (OD 405) refers to the optical density at 405 nm, and it is a proxy for concentration. The x-axis shows different dilutions of Cerebrolysin (CL), from 0.1 to 1040 nmol/µL, where the authors say that “1040 corresponds to the undiluted drug.”

At the end of the day, all this assay shows is that something in Cerebrolysin has immunoreactivity with the antibodies in the assay. This is not slam-dunk evidence that these peptides are actually present in Cerebrolysin, much less at a therapeutic concentrations.

We are both perplexed by the metric “nmol peptides” used to quantify the x-axis. Since Cerebrolysin is a mixture of many peptides, using molarity as a measurement is meaningless, unless you know the precise breakdown of the constituents. (For those who don't know, in order to measure how many moles something is, you need to know its molecular weight.) In the figure caption, the authors write: "[Cerebrolysin] was diluted from 1040 (undiluted) to 0.1 nmol peptides/50 µL." Neither of us can figure out where out where the number 1040 comes from.

HPLC-mass spectroscopy of Cerebrolysin fails to show any neurotrophic peptides

We are giving Gevaert et al. (2015) its own section. It’s an extremely important work because it’s the only paper that does HPLC-mass spectroscopy to characterize all the constituents of Cerebrolysin. It’s a true tour-de-force. Their methods are far more sophisticated than typical HPLC-mass spectroscopy study, allowing for separation of small peptide fragments. This allowed them to sequence these fragments and detect any matches with known proteins (e.g. neurotrophic factors). Regarding the observed size of the peptides:

The peptide length ranges from 6 to 15 amino acids, with an average length of 9.2 amino acids; the peptide mass ranges from 682.34 Da to 1889.05 Da, with an average mass of 1015.89 Da.

This observation is closest to Model 1 from the previous section. Of the peptide fragments that were long enough to match with an existing sequence, "no fragments of known nootropic proteins were identified".

Not surprisingly, the fragments they found come from the most abundant proteins found in the brain, such as cytoskeletal proteins (actin, tubulin) and myelin basic protein:

This is what one would predict to see from a random sampling of brain proteins. Neurotrophic factors account for only a miniscule fraction of all brain proteins, and there’s no reason to suppose they’d be especially resistant to enzymatic degradation.

Storage instructions are incongruent with peptides and there is no immune response

The storage instructions for Cerebrolysin are unrealistic for a product that is alleged to actually contain neurotrophic peptides. We are instructed to store Cerebrolysin at "room temperature not exceeding 25°C."

Greg has worked with brain lysates and dissolved preparations of neurotrophic factors in a lab. Until shown otherwise, the background assumption for peptides in aqueous solution is that they will degrade in a few days. Refrigeration can extend this to a few weeks at most. Moreover, carrier proteins are often required to prevent peptides from sticking to the glass or plastic container in which it's been stored. There is no indication that any such carrier proteins are added to Cerebrolysin.

Considering all the steps taken in biology labs to preserve the peptides in their samples, the burden is on Ever Pharma to justify their claims about having found a method to ensure the stability of dissolved peptides at room temperature.

Another piece of indirect evidence that Cerebrolysin probably contains only trace amounts of any intact proteins is the fact that injecting it doesn't provoke an immune response. Ordinarily, if you inject proteins from another species into your blood, your immune system will attack those proteins and you'll experience a systemic immune reaction. Since Cerebrolysin doesn't cause this kind of reaction, it's likely because it contains very few intact proteins. Gusev & Skvortsova (2003) discuss this, noting

Brain-specific peptides are the active fraction of the drug. Their low molecular weight avoids the possibility of anaphylaxis.

However, this explanation doesn't make sense, since the immune system is perfectly able to recognize and react to ~10 kDa proteins. Back when pig insulin (5.8 kDa) was used to treat Type 1 diabetes, a systemic immune response was a common side effect:

As they were not of human origin, bovine and porcine insulins elicited immune responses that either made their administration unpredictable or in some cases led to hypersensitivity reactions.

The putative active ingredients are unlikely to cross the blood-brain barrier

It is well-established that exogenous BDNF and CDNF cannot penetrate the blood-brain barrier. Here’s what Ever Pharma says:

“Brain-specific peptides are the active fraction of the drug. Their low molecular weight ... allows easy permeation of peptides through the BBB and their active involvement in the metabolism of brain neurons [138, 263]. (Gusev & Skvortsova 2003)”

The website cites two articles to substantiate the claim that Cerebrolysin crosses the blood-brain barrier:

Gschanes et al. (1997) doesn't come close to substantiating this claim. It is an inappropriate inference based on an observed biological effect in a rat model of ischemic stroke. There are alternative mechanisms for improved stroke recovery that don't involve blood-to-brain transport of Cerebrolysin. Somewhat disturbingly, the rats in the study who received Cerebrolysin but didn't have the experimentally-induced strokes had worse cognitive performance than the rats who received the placebo treatment.6

As far as Frey et al. (2004), we emailed both Ever Pharma and Dr. Frey yet failed to secure a copy. Dr. Frey told us that he does recall working with Ever Pharma (EBEWE) around that time, and he described how they may have done some work separating out the amino acids from the other components, however he said he was not directly working in the lab at that time.

Amusingly, Dr. Frey has argued elsewhere that blood-to-brain transport is not a feasible approach to delivering neurotrophic peptides to the brain. Here is a quote from a 2010 article co-authored by Frey : 

Part of the problem is that these large neurotrophic protein molecules to the CNS do not efficiently cross the blood–barrier into the CNS (Poduslo & Curran, 1996; Thorne & Frey, 2001). Clinical trials have demonstrated that systemic delivery at doses that are sufficiently high to result in therapeutic levels within the CNS parenchyma also result in significant systemic side-effects (Thoenen & Sendtner, 2002). These studies suggest the need for alternative methods of drug delivery to realize the clinical promise of these neuroprotective factors.

Here is a representative opinion about the role of the BBB in restricting drug delivery to the brain:

The most important factor limiting the development of new drugs for the central nervous system (CNS) is the blood–brain barrier (BBB). The BBB limits the brain penetration of most CNS drug candidates.... Radiolabeled histamine, a small molecule of just 111 Da, was injected intravenously into an adult mouse, and the animal was killed 30 mins later for whole-body autoradiography [1]. The study shows that the small molecule readily penetrates into the post-vascular space of all organs of the body, except for the brain and spinal cord. The limited penetration of drugs into the brain is the rule, not the exception. Essentially, 100% of large-molecule pharmaceutics, including peptides, recombinant proteins, monoclonal antibodies, RNA interference (RNAi)-based drugs and gene therapies, do not cross the BBB.

Just for amusement, here’s are the radioactivity levels observed in a mouse that had been injected with radiolabeled histamine:

Considering that the putative peptides comprising Cerebrolysin are ~100 times larger than histamine, the assumption ought to be that none of the components of Cerebrolysin other than free amino acids enter the brain.

The two main criteria for a molecule to cross the BBB are size and hydrophobicity, as described in this box:

The neurotrophic peptides and peptide fragments that are supposedly in Cerebrolysin fail both criteria.

Being small and hydrophobic should be considered a near-necessary condition for a molecule to cross the BBB, but is still not at all sufficient. For the vendors of Cerebrolysin to simply assert that their product contains peptides that cross the BBB reveals either profound ignorance or a willingness to lie to their customers.

While there are endogenous mechanisms for blood-to-brain transport of proteins, this only occurs for specific proteins that have a dedicated transport mechanism. For example, insulin and IGF-1 have well-characterized BBB transport mechanisms. IGF-1 (and likely IGF-2) are the only neurotrophic factors with such mechanisms, and they are likely unique in this respect.

Concluding metascience thoughts

The burden of proof is on the proponents of Cerebrolysin to demonstrate that Cerebrolysin contains neurotrophic peptides and that they cross the blood-brain barrier.

What are we to make of the hundreds of publications that show effects from Cerebrolysin in rats and humans? Well, they are likely wrong.

The replication crisis and work of Ionnidis and other metascientists leads us to believe that most scientific research is false, especially in biomedicine, and especially in lower-tier biomedical journals, where all the primary source papers on Cerebrolysin appear.

The literature on Cerebrolysin, while very extensive and impressive-looking, is plagued with problems which are widespread in science:

• Conflicts of interest biasing research, and conflicts not being reported (one study suggests that around 15% of biomedical review articles do not properly report conflict of interests).

• Publication bias (we could find zero articles reporting 100% null results).

• Shoddy techniques (using diagnostic instruments that have not been rigorously validated, using jerry-rigged home-grown lab equipment).

• No pre-registration, improper study design, small sample sizes, p-hacking, the file drawer effect.

Clearly, we’ve got a lot of work to do to clean up science.

Note (added October 10, 2024): It turns out a lot of the early papers on Cerebrolysin have strong evidence of fraud. A total of 48 papers have been flagged as containing improper image duplication/manipulation or suspicious data. See this incredibly damning investigative report by Mu Yang which was published on October 8, 2024.

5

We found a paper that measured the distribution of the lengths of peptide fragments for multiple proteins after common enzymatic degradation procedures. From Kisselev et al (1998)

The distribution of fragments was invariant across different proteins. The most common length of a peptide was ~1000 Da. Since the average size of an amino acid is 110 Da, this means the most common length of a peptide fragment was 9 amino acids. This suggests that you would need to do a very aggressive enzymatic digestion procedure --analogous to what occurs in your stomach --to yield a solution that is 75% amino acids. Anyway, Kisselev et al (1998) still supports our assertion that it would be impossible to produce a hydrolysate where 25% was ~10 kDa, and 75% ~110 Da (i.e. single amino acids).

Three heads pop up. Let’s take a closer look:

They are all pretty similar. All women, which is curious. All have shaven heads -- also curious. Finally, all have weird things sticking out of their heads — curiouser and curiouser.

This sort of imagery is likely to trigger a disgust response. Millions of years of evolution have hard wired us to find bodily mutilation disgusting on some level.

Disgust is often described as a “moral emotion”. Thus, these images could instill in people the idea that transhumanism is morally wrong. A possible exception is people who are already accustomed to seeing this imagery.

In the moral foundations framework, disgust is lumped under “degradation of the sacred”. That is another way of looking at this. Most religious traditions treat the human body as sacred and worthy of respect and awe. Young women are generally considered to be pure and beautiful. Here, we see them defiled and degraded.

The internet is flooded with imagery like this. Check out Google images:

Many of these images are some combination of creepy, disgusting, or just plain weird.

Most of these images come from news articles about transhumanism. Some of these articles are clearly against transhumanism, while others appear to be neutral or a bit sympathetic. In many cases here, editors and publishers probably realized that grotesque or weird images will catch eyes and result in more clicks.

Strangely, I’ve noticed that even people who are very aligned with transhumanism can fall into using this sort of imagery. For instance, there is a new biography of Zoltan Istvan coming out in July 2024. (Istvan founded the U.S. Transhumanist Party and ran for president in 2016.) Based on his tweets, the author appears to be largely sympathetic to Zoltan’s work. However, the cover makes Zoltan look creepy and nefarious.

The color scheme seems designed to cast Zoltan in a negative light. This is the same sort of color scheme used by advertisers when they show competitor’s products - dark, drab, and dull.

In recent years it’s been noted that fewer and fewer people want to publicly identify as transhumanists, despite the growing popularity of transhumanist ideas. I believe this is partially due to poor aesthetics creating a “negative halo” around the term. Another factor is the proliferation of conspiracy theories around transhumanism on both the far left and far right — something I plan to discuss in my next post (here).

Take a look at this new book by Austrian right-wing agitator Stefan Magnet:

The title translates to “Transhumanism: War Against Humanity”. Again, we see defilement. It’s interesting that the pattern on the face is similar to the iconography on the cover of the new Zoltan biography.

I’ve also noticed that most transhumanist organizations are not great at graphic design. Maybe this is because transhumanism tends to attract science and math types, not artistic types. Just for entertainment, here are two examples of bad graphic design:

Hopefully it is clear what is bad about these designs.

OK, so what might good transhumanist aesthetics look like?

Let’s consider a list of transhumanist technologies: genetic engineering, age reversal biomedicine, nanotechnology, brain computer interfaces, friendly artificial intelligence, helpful robots, spaceships, space colonies. I can think of ways to visualize these technologies that are aesthetically pleasing, with the exception of friendly AI.

Let’s consider core values more generally. According to transhumanist philosopher Nick Bostrom, the core transhumanist value is “having the opportunity to explore the transhuman and posthuman realms”. This is very hard to illustrate, because, as Bostrom admits, we don’t actually know what the posthuman realm will look like.

From this core value Bostrom derives a long list of derivative values including “caring about wellbeing of all sentience, diversity (many forms), saving lives, peace, international cooperation, pragmatism, engineering, science.” These are easier to visualize, although they are quite general and not specifically tied to transhumanism.

The art of Robert McCall

The art of Robert McCall (1919-2010) holds a special place in the hearts of many transhumanists and futurists. If I recall correctly, for several years his work "The Prologue and the Promise" was used by the Foresight Institute on the webpage of their “Existential Hope” initiative.

Here are a few other of his works:

Others random stuff I found

Much of this is not explicitly transhumanist, but is provided to be food for thought.

This is loosely analogous to the monthly “links” posts on Astral Codex Ten or Keller Scholl’s “Monthly Shorts”.

When I first posted this I said I was going to do monthly, but shortly afterwards I realized bimonthly would be nicer for my readers (less inbox clutter). I don’t know about you, but I have thousands of unread emails in my “Substack” folder!

My blog posts this past month

Published on Substack:

• New WHO report - defining "airborne" is very, very complicated

Published on Medium:

• Some criticisms I had of EA-funded AI safety efforts (mostly written in early 2021)

• Some notes on loneliness and tips for dealing with it (written 2018)

• Losing a whole year — my Long COVID story

My top LinkedIN posts this past month

• RCT shows that ECG AI triggered alerts save lives

• Demo of Krea AI shows the future of artistic creation with AI

• Evidence of dimethyl sulfide on exoplanet K2-18b found by James Web

• Filming with Briar Prestidge in Boston

• How I try to stay on top of all the latest developments in AI

• MRISegmentator-Abdomen - a new model from Dr. Summers lab at NIH

• OpenAI GPT4o - a list of all the new capabilities (Shared on Marginal Revolution by Tyler Cowen!)

• Brief summary of main announcements at Google I/O

• Top OpenAI safety researchers jump ship

• H5N1 airborne between ferrets

• New guidelines for PTSD from VA and DoD

• AI Camp Boston May meetup (pictures)

• Amazing new open source LLM for medicine - OpenBioLLM70B

• Massive study in BMJ on vaccinated vs unvaccinated cardiovascular risk

• Romain Huet OpenAI video generator demo

• H5N1 can survive in raw milk - NEJM article

• AI for the Life Sciences event at Bentley University (picture)

• CDC PPE regulations for H5N1 - downplaying airborne transmission again

My top X posts this month

• New LLM evaluation strategy - “elements of world knowledge”

• Calling out a professor criticizing golden rice (original Xeet here)

• Thoughts on the Russsia-Ukraine war

Top blog posts I read in May 2024

• Profile: The Far Out Initiative on Astral Codex Ten (May 15, 2024) (on on the movement to abolish suffering)

Top podcasts I listened to in May 2024

• Jim Rutt show interview with Trent McConagy on “bci/acc” - A Pragmatic Path to Compete with Artificial Superintelligence

• Robert Wright podcast: The Cosmic Thought of Pierre Teilhard de Cardin

• James D. Miller podcast - Malcomb Collins on grabby aliens and AI risk - quite a zany episode - many wild ideas on Fermi paradox and AI x-risk I haven’t heard before

Bonus section - blog posts I enjoyed reading in January 2024

• “Neuroscience is pre-paradigmatic. Consciousness is why” by Erik Hoel (Jan 9 2024)

• “Conditional Approval for Human Drugs” by Alex Tabarrok (Jan 29 2024)

• “Making every researcher seek grants is a broken model” by Jason Crawford (Jan 26 2024)

• “Zeroth-Principles Thinking” by Bryan Johnson (Mar 9 2021)

• “The False Claim of Cryonics as Pseudoscience” by Max More (Jan 31 2024)

• “The dead and dying at the gates of oncology clinical trials” by Jake Seliger (Jan 29 2024)

• “My idea of sacredness, divinity, and religion” by Kaj Sotala (Oct 29 2023)

• “New Trek Loves Hostile Architecture” - by Eneasz Brodski (Jul 22 2022)

Bonus section - news & articles I read in January 2024

Japan landed on the moon - there was a cool little spheroidal robot.

Big scandal at the Dana-Farber Cancer Institute in Boston - 6 papers published by their researchers are being retracted and 31 corrected after a data sleuth found tons of evidence of image manipulation. It’s mostly the photoshopping of images of western blots, and whereas some of it might be in error… lots of it is obviously fraud. There might be more to come. It is, to quote Derek Lowe, “a disgrace” - and that’s at one of the top research institutions in the US!

Large language models cannot generalize “A is B” to “B is A” (related twitter thread). The researchers christen it the “reversal curse”. As I said on Twitter, this is yet another way LLMs are weird aliens. While humans have a similar issue in memory recall, this points to a severe deficit in reasoning which I suspect will not be easily remedied just by scaling to larger models and more data.

10 emerging antidepressants to keep an eye on - very interesting to read about potential new treatments. List includes esmethadone, r-ketamine, and psilocybin. Interestingly, an ultra-short acting (2-3 hour half life) orexin antagonist has also made the list — Seltorexant. It is under development for “major depressive disorder with insomnia symptoms”.

An Enormous Gravity ‘Hum’ Moves Through the Universe - It turns out that scientists have been carefully monitoring pulsars across our galaxy for decades now, noting tiny shifts in timing over time between them. These waves have an insanely long wavelength: “Whereas LIGO’s waves might vibrate a few hundred times a second, it might take years or decades for a single one of these gravitational waves to pass by at the speed of light.” It is believed that they are due to supermassive black holes orbiting each other, however they could also be due to more exotic phenomena like cosmic strings.

Private Bard conversations accidentally leaked out by Google. Users who shared Bard conversations with friends and colleagues were taken by surprise when they realized that these interactions are now searchable via Google.

It was March 28th, 2020. COVID-19 was spreading through the United States. Trump had recently declared a national emergency. Most flights from Europe had been stopped.

Everyone was looking to public officials for advice on how to stay safe.

The World Health Organization (WHO) had one overarching job in the midst of this crisis — to educate the public, using the best available scientific knowledge, on how the virus spreads and how to avoid it.

They failed.

Even before any experiments were run and any data collected, science said that SARS-CoV-2 virus should be transmitted through the air considerable distances inside rooms and on trains, buses, and airplanes. Why? Because the COVID-19 virus is nearly identical to SARS-CoV-1, the virus that causes SARS. Just look at these two microscope images:

In 2003 about 8,400 cases of SARS were recorded. A 2004 study found evidence of airborne spread of SARS through a large housing complex. A 2005 study documented airborne spread in a hospital ward between beds. Another study from 2005 used air sampling near the bed of an infected patient to directly measure the spread of the viral RNA through the air via aerosols.

Based on this, and based on experience with other viruses like MERS, RSV, and influenza, in early 2020 most experts believed SARS-CoV-2 should also be able to be transmitted at least several meters through the air.

A research letter published in JAMA on March 4th, 2020 reported on air sampling done in the bedrooms of COVID-19 patients in a Chinese hospital. The air samples were all negative. However, they were careful to note those negative results may have been due to the rapid rate of air exchange in the hospital’s rooms coupled with insufficient air sampling time. The researchers also swabbed around the rooms, finding viral RNA almost everywhere they swabbed, including near the ventilation ducts. This led them to conclude that the virus was being spread considerable distance by respiratory droplets.

Finally, a study published on March 17th, 2020 in the New England Journal of Medicine presented direct experimental evidence that the virus can linger in the air in tiny droplets (aerosols) for hours while remaining viable. Years of research had already established that human speech generates large amounts of aerosols. It doesn’t take a Ph.D. to realize this is a recipe for long-distance spread through the air.

(Post-publication addendum): There was also a study from March 10, 2020 by researchers in Wuhan that provided additional evidence of spread via aerosols. They suggested that the redoning of masks creates aerosols laced with SARS-CoV-2.

The WHO decided not to relay any of this critical information to the public. Instead they decided to make a definitive announcement that “COVID-19 is NOT airborne”, even capitalizing the word “not” for emphasis.

To avoid COVID-19, according to the WHO, you just had to stay greater than 1 meter away from infected people and avoid contact with contaminated surfaces.

The disaster that issued from this science communication failure is hard to wrap one’s mind around.

How much time, energy, and money was lost sanitizing surfaces? How many people decided not to mask or social distance? How many hospitals and schools did not invest in air filtration? How many people died as a result?

It took until October 2020 for the WHO to acknowledge that COVID-19 can be carried through the air more than 1 meter. Even then, it took another six months to a year before they had updated all of the advice on their website to reflect the reality of airborne transmission.

The WHO’s new attempt to deflect blame

About a week and a half ago the WHO released a 52 page report entitled “Global technical consultation report on proposed terminology for pathogens that transmit through the air”. The stated purpose of the report is to define what is meant by the term “airborne”. According to the report, this is a task of “enormous complexity”:

It’s pretty clear what’s going on here. The WHO is trying to save face by making it seem like the definition of “airborne” is very complicated. They are asking us to cut them some slack for their horrible mistake. In the rest of this post I’ll explain why they don’t deserve any forgiveness or slack for their error.

Everyone knows the definition of “airborne”

As the word itself implies, "airborne" means "carried through the air". Just as how "waterborne" means "carried through water" and "bloodborne" means "carried through blood". Both the Merriam Webster and Oxford dictionaries make this clear.

There is only one major complication that needs addressing — how far does viable virus travel through the air, on average?

Larger droplets fall to the ground faster, so they don’t have as much time to travel, either by diffusion or air currents. So viruses that depend on larger droplets for transmission don’t travel as far through the air.

As you can see from the figure below, a 100 micrometer droplet takes 5 seconds on average to fall from 1.5 meters height to the ground, while a 1 micrometer droplet takes 12.2 hours to fall the same distance, on average.

The confusing and unnecessary debate about “droplet” vs “airborne” transmission

The WHO could have said something like “COVID-19 is airborne. Experience with SARS and preliminary data suggests it can be transmitted over distances of five meters or more in a room with an infected person in it”.

Apparently, however, the WHO thought this was far too complicated and nuanced for everyday peons to understand. They wanted to give a simple “YES” or “NO”. I suspect they went with “NO” because they were worried people would lose their minds and panic otherwise. If so that is very ugly elitism, but it would be consistent to similar elitism that we saw at the CDC and FDA during the pandemic.

Of course, WHO officials knew that COVID-19 had to transmitted through the air to some degree. At least 1 mm. So they decided to redefine the word “airborne”.

Following a convention that already existed in some scientific literature, they decided to invoke a distinction between "droplet transmission" and "airborne transmission".

Droplet transmission is when the virus can only travel via heavy droplets that quickly fall to the ground. However, even though the virus is still traveling through the air, droplet transmission is not “airborne transmission”. Instead, the term “airborne transmission” means “travel further distances via smaller droplets (aerosols)”.

Obviously that is very confusing. Where do you draw the line? There is no natural dividing point between the two forms of transmission, as this graphic illustrates:

New terms should invented sparingly, and only when helpful. Arbitrary distinctions should only be invoked when absolutely necessary. Ironically, in what was apparently an attempt to simplify things down to a “NO” on the question of airborne transmission they ended up making things way more confusing than they needed to be.

Whether to invoke this distinction and where to draw the line became the subject of debate among public health experts. Thus we ended up with a parade of articles along the lines of "IS COVID-19 AIRBORNE? SCIENTISTS DEBATE". The articles made it sound like scientists had no idea whether COVID-19 could travel through the air. The actual reality was that almost all disease experts worth their salt believed that COVID-19 could travel at least a few meters through the air. The WHO was singularly out of step with the vast majority of the scientific community.

Measuring how far a virus can be transmitted through the air is very easy

Most of the evidence for the airborne transmission of COVID-19 gathered early on was indirect. However, transmission through the air is very easy to directly quantify. First, have an infected person stand in a room with still air and then have them speak. Alternatively they can lay in a hospital bed. The person will start to generate droplets. Next, wait a period of time for the droplets to dissipate and reach steady state concentration in the air. Then, collect air samples at different distances away from the speaker and test them for viral RNA. You will see the concentration of viral RNA in the air decreases with distance. Finally, you also can try to culture the virus to see if it’s still viable. If you want to prove without a doubt that the virus can infect through the air, you can use animals like guinea pigs (this was done in 1959 to prove that tuberculosis is airborne).

Now, you do need to generate and sample for some time (potentially hours) to get enough copies of virus at the longer distances. On the basis of this, some people say that the experiment is “hard”. But this is still an easy experiment as far as experiments go. I bet you could do this experiment really well for under $10,000.

However, the WHO did not want to run this simple experiment before making a proclamation to the entire world about the airborne transmissibility of COVID-19.

Experiments similar to this started to appear in the literature in mid 2020. However, as far as I can tell there is still no standard experimental framework here similar to how there are standardized ways of measuring other things in medicine like children’s bone development or the amount of calcified plaque in the heart.

Conclusion - #COVIDisAirborne

Shortly after the WHO’s announcement a mountain of evidence started to emerge showing that COVID-19 can travel through the air considerable distances — at the least much more than 1 meter. An April 27, 2020 study documented the spread of COVID-19 through two Chinese hospitals, giving evidence for long-distance aerosol transmission. An August 4, 2020 study reported that air samples 5 m from a hospital bed contained large amounts of virus. By September 2020 there were at least eight studies on air sampling in hospitals and in controlled environments, including some studies that tried to culture virus to see it was viable. The results were not uniform, and one study found no evidence for airborne transmission even at very close ranges. However, when viewed in aggregate the studies pointed to significant transmission potential through the air (at least five meters), especially in rooms with poor ventilation. Today every health authority in the world acknowledges that COVID-19 can be transmitted long distances via aerosols (across rooms, though ventilation ducts, etc).

As a reminder — the next pandemic is coming, it's just a matter of time.. and it could be far more deadly.

COVID-19 was a test for humanity. We did really poorly - either a D or an F.

I hope people learn from all of the failings that happened. At the very least, can we please not try to cover things up by inventing silly excuses for past failings?

Further reading

• The 60-Year-Old Scientific Screwup That Helped Covid Kill WIRED magazine, May 2021

• 239 Experts With One Big Claim: The Coronavirus Is Airborne The New York Times, July 2020.

• Coronavirus Disease 2019 and Airborne Transmission: Science Rejected, Lives Lost. Can Society Do Better? Clinical Infectious Diseases, May 2023

• Airborne pathogens: controlling words won’t control transmission, letter to The Lancet, May 2024

• The WHO's claim that COVID wasn't airborne cost millions of lives. Now, they're changing the definition of airborne. The Gauntlet News Substack, May 2024

➡️ Getting COVID-19 now feels like getting the flu or less, for almost everyone.

A very carefully conducted study published in JAMA in 2022 compared the time course of symptoms experienced by COVID-19 patients with COVID-19 negative patients who had an upper respiratory tract infection. The two groups were statistically indistinguishable in terms of their symptom burden and time course. Interestingly, the study found both groups had 20-30% reporting symptoms after three months. (There was no ‘no-disease’ control group, so a lot of that is just a background rate.) It’s important to point out that this study did not look for differences in symptom burden lasting longer than three months, and it was likely not powered to do so. That brings me to my next point:

➡️ About 5-10% of people will get some form of Long COVID for a year or longer. About ~2% will have life-deranging illness for a year or longer.

Having a symptom longer than three months is considered the threshold for “Long COVID” by most researchers. Many studies now show that about 30% of people will have at least one persistent symptom for three months. Fatigue for six months or longer is the typical threshold used for diagnosing “chronic fatigue syndrome.” The number of people experiencing one or more symptoms for 6 months or longer is about 10%. After that there is a long tail. About 5-10% will have a persistent symptom for a year or longer. I would guess that about ~2% will have life-deranging illness for a year or longer (ie completely unable to work, greatly reduced activity levels, chronic fatigue and brain fog, etc.) Many of these people will suffer enormously, both psychologically and physically. Since COVID-19 just started in 2020, we don’t have much data on recovery rates from Long COVID. However, if we look at recovery from glandular fever (mono) or chronic fatigue syndrome more generally, what we find is that the percent recovering each year decreases over time. This decreasing recovery rate results in a long-tail of patients who are stuck with the illness for a very long time (years or decades). If Long COVID is similar to post-viral syndromes generally then we might expect the fraction recovering between years 1 and 2 to be 20%. Thus, we can infer that about 4% will have some form of Long COVID for two years, and 3% will have some form for five years or longer. The exact number is not that important. Even 1% is a big deal. (addendum: a 2023 survey by Jake Skarbinski found 0.5% of people who have had COVID have developed long term chronic fatigue which started after COVID).

➡️ Every COVID-19 infection can cause brain damage and small long-lasting or permanent drops in IQ.

What worries me here is small drops in IQ that people don’t notice. There is emerging evidence that even mild COVID-19 can have long-lasting effects on the brain - damaging the blood-brain barrier and leading to subtle deficits in memory and cognition. See the first appendix for a detailed summary of the literature.

➡️ Every COVID-19 infection may cause long-term weakening of people’s immune systems, leaving them more susceptible to getting COVID-19 and other illnesses in the future (this is speculative, but possible.)

The evidence for this right now is weak, but from what I have read, many doctors are worried about the possibility. A paper from 2020 reported that COVID-19 triggers “T cell exhaustion” in both hospitalized and non-hospitalized patients, resulting in “long-term immune dysfunction.” A March 2023 paper in Immunity found “a major reduction in both the magnitude and functionality of peak CD8⁺ T cell responses in previously infected individuals after vaccination.” The authors note that this is similar to what is seen in diseases like HIV. What they found is that people who have had COVID-19 are less able to mount a proper response to vaccination compared to COVID-19 naive controls. Another paper published in August 2023 in Cell found changes in the immune system that lasted over a year. Apparently COVID-19 can trigger epigenetic changes in the stem cells that form new immune cells. Unfortunately these epigenetic alterations are inherited by the next crop of stem cells. The result is persistently elevated production of white blood cells together with more cytokines (such as IL-6) being spewed out, causing higher systemic inflammation. While this is not a weakening of the immune system (it actually looks like a persistent strengthening), it doesn’t sound good. The bottom line is that COVID-19 appears to be causing long-lasting immune dysfunction in some individuals.

Minor update 3/19/25 — see also this paper in Scientific Reports from April 29, 2024 which found lower immune response in Long COVID patients.

COVID-19 is sticking around..

When the WHO declared "the pandemic is over" last May almost everyone took that as a sign that it was safe to return to how they lived before. (CORRECTION - technically what the WHO announced was the end of the “Public Health Emergency of International Concern”). The narrative shifted immediately overnight - COVID-19 moved from “pandemic” to “endemic.” Behavioural patterns quickly jumped back to how they were in the “before times.” Nobody masks and when people get sick they don’t quarantine for very long.

The idea that "COVID is just like the flu", which was once only popular on the right, has become mainstream. It goes without saying that this statement is simply not true. The flu does not cause “long flu” at the rate that SARS-CoV-2 causes Long COVID. SARS-CoV-2 is also far more infectious than the flu virus. Severe COVID-19 also looks a lot different than severe flu.

Many people also assume that the COVID-19 virus is getting weaker over time. From an outside perspective, we should expect SARS-CoV-2 to evolve to be weaker over time based on what has been seen with other viruses. Famously, the H1N1 virus that caused the 1918 flu evolved into weaker strains that still make up a small fraction of the seasonal flu today.

However, studies do not show the virus evolving to become weaker in the past two years or so. The average case of COVID-19 today is less severe, but that is only because of vaccination and prior immunity, not because the virus itself is less dangerous.

The stark reality we must face is that the overall burden of COVID is not decreasing. (By “burden” I mean the integration of symptoms over time and people.)

Antibodies (whether from infection or from vaccines) don’t last very long, and the virus is quickly evolving. People are not getting boosters. So it’s even plausible that the overall burden of COVID-19 may actually increase in the next few years.

Let’s look at this in more detail:

➡️ Wastewater data shows the exact same viral load patterns as last year.
We can observe this on both the CDC’s national platform and local monitoring platforms like the Boston-area one. What this indicates is that collectively the amount of SARS-CoV-2 virus that is ravaging our bodies is not decreasing. Also, the last big wave of COVID-19 this January overwhelmed the Massachusetts General Hospital as well as some other hospitals. This was barely reported in the news compared to the reporting in the previous two cycles.

➡️ Death rates from COVID-19 in 2024 are only slightly less than 2023.
See the New York Times dashboard (they are one of the few places that still have a functional dashboard.) You would think that with boosters and the miracle drug Paxlovid that death rates would be down more. In addition, 1.2 million Americans have already died (we should expect the weakest to die first), and the reporting standards for COVID-19 deaths have declined.

➡️ Rates of Long COVID are not declining and actually just ticked up slightly.
Data from the CDC shows that in mid 2022 the fraction of Americans experiencing Long COVID was around 7%. This number decreased somewhat to around 5.5% in 2023 but recently increased up to 6.8%. Studies show that an individual’s risk of getting Long COVID increases with every subsequent infection. With fewer people getting boosters and better immune evasion in the latest variants more people are experiencing reinfections. So maybe we shouldn’t be surprised that Long COVID rates just increased.

➡️ The virus doesn’t seem to be becoming weaker.
Angiotensin converting enzyme 2 (ACE-2) binding correlates with both disease severity and transmissibility. The first major variant, Alpha, had stronger ACE-2 binding according to assays. Subsequent work shows that the Delta variant had weaker ACE-2 binding compared to Alpha but still was stronger than the original strain. Understanding Omicron is complicated due to the large number of subvariants. Early data in mice suggested that the early Omicron variant was weaker than Delta. However, more recent variants of Omicron have had stronger ACE-2 binding. The Omicron BA.2.86 variant which emerged last summer has increased ACE2 binding, increased antibody evasion, and comparable lung cell entry rate compared to the earlier Omicron BA.2. Similarly, one study reports that Omicron BA.2.75 had the “strongest ACE-2 affinity of any variant to date.” A descendant of Omicron B.2.86 called JN.1 started to achieve dominance in early 2024. While early evidence suggested it was more severe, the CDC now thinks it is “not any worse” than previous strains. My overall read on all this is that 1. the virus is definitely learning how to spread faster and better evade prior immunity and 2. the severity/strength of the virus is fluctuating with no overall trend that is easily discernible. (Again, “severity” means how bad the disease is for unvaccinated individuals with no prior immunity.)

➡️ Scientists are struggling to keep up with the virus’s evolution.
Right now there is a lot of debate about why JN.1 is spreading so fast. Initially people thought it must be escaping the immune system better, but recent work indicates this not to be the case. Something is happening with JN.1 and scientists have yet to figure out what it is.

Coronaviruses have been found to exhibit viral recombination. During recombination, RNA fragments from two or more different variants combine to create a new variant. This process allows the virus to evolve faster, which is scary.

COVID-19 denialism and the future of humanity

It’s fair to say that most of the world is in a state of denial about COVID-19.

Wastewater COVID levels remain just as high as last year and 23 million Americans are experiencing Long COVID. The virus continues to evolve rapidly and is constantly finding new ways to avoid immunity.

For most people, getting COVID-19 may mean a week or two of sickness followed by a small amount of long-term damage (essentially accelerated aging.) However, a small fraction of people are having long-term effects.

I know several people who have had COVID-19 three times and one of my coworkers has had COVID-19 five times. My concern is the cumulative toll that takes on our bodies and brains.

Just a three point downward shift in IQ in the United States would increase the number of adults with an IQ less than 70 by 2.8 million. It would also dramatically lower the number of geniuses (IQ > 145), which could have an effect on progress in science and technology.

This is speculative, but there appears to be a tail risk here that involves long-term enfeeblement of our species.

What should we be doing in response to all this? Well, hardening infrastructure with air filtration and UV-C. Developing better masks. Massive investments in research on the long-term health effects of COVID-19 and how to treat them. Development of better vaccines (pan coronavirus vaccines, nasal vaccines). Probably many other things — let me know in the comments!

Appendix - the research on the cognitive effects of COVID-19

I think it’s worth discussing the plausible mechanisms first. In severe COVID-19, coagulation, vascular injury, hypoxia, and large fluctuations in blood sodium can all cause immediate and permanent brain damage. A figure from Scientific American talks about how during severe COVID-19 macrophages in blood vessels can damage the brain:

In mild or moderate COVID-19 several mechanisms have been postulated:

One widely discussed possibility is direct infiltration of the virus into the brain. While bits of SARS-CoV-2 virus have been found in the brain in autopsy studies, it seems to be quite rare. One of the ways the virus is thought to get into the brain is through the nose. Anosmia (loss of smell) indicates a high viral load in the nose and indicates a higher risk for brain infiltration. If the virus does get into the brain it’s very bad news. Research in human brain organoids shows that the virus can trigger fusion of neurons and glia cells, which disrupts brain function.

The next way brain damage may occur is through inflammatory cytokines. Neuroinflammation has long been thought to be the cause of the poorly understood symptom called “brain fog” which is one of the most commonly reported and longest persisting symptoms of COVID-19. Cytokines activate immune cells called microglia in the brain. The microglia go into “overdrive”, producing large amounts of reactive oxygen species and more pro-inflammatory cytokines. The cytokine CCL11 is of particular note because it has been observed to be greatly elevated during COVID-19. Animal studies show that CCL11 reduces neurogenesis and triggers the loss of oligodendrocytes, which form the important myelin sheaths around axons. A study that looked at mice and human autopsies found clear signs of both microglia activation and loss of oligodendrocytes after COVID-19.

Next is damage to the blood-brain barrier. A recent study in Long COVID patients shows clear evidence of blood-brain barrier damage. My understanding is that once this damage occurs it is very hard to repair. It goes without saying that a leaky blood-brain barrier means that toxins can more easily enter the brain. A leaky blood-brain barrier has been linked to multiple sclerosis, dementia, Alzheimers, psychosis, and epilepsy. Is this is sort of damage occurring in regular mild cases of COVID-19? I would guess it probably is, but to a lesser degree that what is seen in Long COVID patients.

Finally, it is widely hypothesized that the inactivation of the ACE-2 receptor by the SARS-CoV-2 virus may reduce brain-derived neurotrophic factor (BDNF) and have other negative effects on the brain. There is already some convincing-looking evidence for this effect in humans. ACE-2 blocking can also mess with serotonin synthesis and increase stress and anxiety. While the ACE-2 receptor is mainly known for being expressed in abdominal organs and the heart, it is also expressed in neurons, especially in the hippocampus. I would guess that the effects of ACE-2 blocking are temporary, and go away once the virus is cleared, but recall that the virus can persist in the body for quite some time.

So, what evidence is there that COVID-19 can have long-term consequences on the brain? It’s tough to summarize the literature in a short space. Not surprisingly I found recent media reports do a somewhat sloppy job. The first thing I look at in any study is the patient population. Some studies look at severe COVID-19 (I’ve ignored those below), while others look at elderly populations or populations with Long COVID. Only recently have there been studies that look at younger populations and/or populations who had mild COVID-19. All of the studies I looked at are listed in the Appendix, and to save space not all are discussed here.

The best evidence is in studies on elderly populations. A large study on older patients in the UK Biobank showed brain alterations and evidence of cognitive decline. Data from the Veterans Administration in the US show a similar effect. A meta-analysis of 11 studies looking at older adults concluded that getting COVID-19 nearly doubles the risk of “new onset dementia” after 12 months. This is consistent with a wide body of emerging research showing that some viral infections are implicated in the pathogenesis of Alzheimer’s disease. Other culprits of note are cytomegalovirus, HIV, varicela zoster virus, Epstein–Barr virus (EBV), and the hepatitis C virus.

The next best evidence for brain alterations due to COVID-19 is in populations with Long COVID. When I first looked at the literature on Long COVID and brain damage in 2022 I found several MRI/fMRI studies with mixed results - some studies found evidence of brain damage while others found none. Now a somewhat clearer picture is emerging and overall it isn’t good news for those with Long COVID. In patients with Long COVID (illness > 3 months) the ongoing IQ loss appears to be about 6 IQ points on average.

What about healthy younger people who have mild COVID-19? This is still an emerging area of research. A very large observational study suggests small cognitive deficits that persist for a year or more and scale up with illness duration. One study of 54 healthy individuals between 18-54 who had COVID-19 found lower performance on cognitive tests. A survey of 188,137 Norwegians found a small decrease in reported memory function after COVID-19 that persisted up to 36 months.

Appendix - the studies on cognitive effects

1. Vavougios, George D., et al. “Investigating the Prevalence of Cognitive Impairment in Mild and Moderate COVID‐19 Patients Two Months Post‐discharge: Associations with Physical Fitness and Respiratory Function.” Alzheimer’s & Dementia, vol. 17, no. S6, Dec. 2021, p. e057752.

2. Zamponi, Hernan P., et al. “Olfactory Dysfunction and Chronic Cognitive Impairment Following SARS‐CoV‐2 Infection in a Sample of Older Adults from the Andes Mountains of Argentina.” Alzheimer’s & Dementia, vol. 17, no. S6, Dec. 2021, p. e057897.

3. Douaud, Gwenaëlle, et al. “SARS-CoV-2 Is Associated with Changes in Brain Structure in UK Biobank.” Nature, vol. 604, no. 7907, Apr. 2022.

   This much-publicized paper looks at structural MRI images of 785 people in the UK Biobank. They claim to find reductions in grey matter thickness in certain regions as well as a reduction in ‘global brain size’. They also found cognitive decline on something called the “Trail Making Test”, a simple test that asks participants to connect dots as quickly as possible. The study only looked at people age 51 - 81 years and the cognitive decline was almost non-existent for those around 50 and increased with age.

4. Holdsworth, David A., et al. “Comprehensive Clinical Assessment Identifies Specific Neurocognitive Deficits in Working-Age Patients with Long-COVID.” PLOS ONE, vol. 17, no. 6, June 2022, p. e0267392.

5. Fernández-Castañeda, Anthony, et al. “Mild Respiratory COVID Can Cause Multi-Lineage Neural Cell and Myelin Dysregulation.” Cell, vol. 185, no. 14, July 2022, pp. 2452-2468.e16.

   This work was a combination of a mouse study and an autopsy study. Strikingly, clear differences were seen in brain micrographs between COVID-19 patients and controls. They find evidence of increased microglial activity, and loss of myelination. As discussed above, the cytokine CCL11 is pointed to as the culprit.

6. Wang, Lindsey, et al. “Association of COVID-19 with New-Onset Alzheimer’s Disease.” Journal of Alzheimer’s Disease, vol. 89, no. 2, Sept. 2022, pp. 411–14.

   This was a simple retrospective cohort study of 6,245,282 older adults (age ≥65 years) who had medical encounters between 2/2020–5/2021. They found an increased risk for Alzheimer’s disease diagnosis within 360 days of COVID-19 diagnosis (hazard ratio = 1.69.)

7. Xu, Evan, et al. “Long-Term Neurologic Outcomes of COVID-19.” Nature Medicine, vol. 28, no. 11, Nov. 2022, pp. 2406–15.

   This study looks at Veterans Affairs data with ~150,000 COVID-19 patients and finds a somewhat increased risk for “neurologic sequela” in the first year after COVID-19 (hazard ratio 1.42.) Limitations of this study are its observational nature and limited correction for confounds.

8. De Paula, Jonas Jardim, et al. “Selective Visuoconstructional Impairment Following Mild COVID-19 with Inflammatory and Neuroimaging Correlation Findings.” Molecular Psychiatry, vol. 28, no. 2, Feb. 2023, pp. 553–63.

   This study looked at 192 people who had COVID-19 (71% female.) The sample was relatively young (average age 38 years.) Only 6% were hospitalized. 64% had anosmia, which is interesting and potentially a source of bias in this sample. About 25% of the patients had a “visuoconstructive deficit” which is a deficit in a very particular type of cognition - arranging objects spatially (“deficit” is defined as being 1.5 std below average, which we expect to normally be about 3.3%.) I haven’t done a detailed analysis of the stats but this study looks p-hacked to me.

9. Petersen, Marvin, et al. “Brain Imaging and Neuropsychological Assessment of Individuals Recovered from a Mild to Moderate SARS-CoV-2 Infection.” Proceedings of the National Academy of Sciences, vol. 120, no. 22, May 2023, p. e2217232120.

   This study was mostly negative findings, although that is not what was reported in outlets like Scientific American. They looked at 223 unvaccinated individuals who had “mild to moderate” COVID-19 and 223 matched controls. Most patients were around 40 - 60 years old. They found two statistically significant MRI biomarkers tested out a total of 20 (with corrections for p-hacking applied.) Notably, they did not find any difference in neuropsychological test scores. What they found is “subtle changes in white matter extracellular water content.” They say this “relates” to approximately 7 years of “healthy aging.” They say “microglia and astrocytes emit cytokines upon activation, inducing osmosis of water from the blood into the extracellular space.” While this work needs to be replicated, I think it is significant given the amount of time that had passed after infection in these patients (range was 163 - 318 days, mean 289 days.)

10. Martínez-Mármol, Ramón, et al. “SARS-CoV-2 Infection and Viral Fusogens Cause Neuronal and Glial Fusion That Compromises Neuronal Activity.” Science Advances, vol. 9, no. 23, June 2023, pg. 2248.
    This was a study on human and mouse brain organoids. They show that SARS-CoV-2 infection causes “fusion between nuerons and glia” which “compromises neuronal activity.”

11. Demir, Biçem, et al. “Long-Lasting Cognitive Effects of COVID-19: Is There a Role of BDNF?” European Archives of Psychiatry and Clinical Neuroscience, vol. 273, no. 6, Sept. 2023, pp. 1339–47.

    This study looked at 54 individuals between ages 18-50 who had gotten mild COVID-19 within a six month window. They found lower BDNF and lower performance on a digit span test compared to a control group. I’m surprised this one has not been more widely reported.

12. (PREPRINT) Michael, Benedict, et al. Post-COVID Cognitive Deficits at One Year Are Global and Associated with Elevated Brain Injury Markers and Grey Matter Volume Reduction: National Prospective Study. In Review, 5 Jan. 2024.

    This study looked at 351 patients who had been hospitalized with COVID-19 and compared them to 2,927 controls. Not surprisingly, there are small differences in brain structure that persist for over a year. A downside of this paper is potential p-hacking. An article in Scientific American reports the results are “equivalent to 20 years of aging.” I’ve glanced through the paper and I’m not sure how that claim was arrived at.

13. Brusaferri, Ludovica, et al. “Neuroimmune Activation and Increased Brain Aging in Chronic Pain Patients after the COVID-19 Pandemic Onset.” Brain, Behavior, and Immunity, vol. 116, Feb. 2024, pp. 259–66.

    This is an observational study so hard to draw inferences from. Depression and mental health issues are linked with brain inflammation although the direction of causality is debated.

14. Ellingjord-Dale, Merete, et al. “Prospective Memory Assessment before and after COVID-19.” New England Journal of Medicine, vol. 390, no. 9, Feb. 2024, pp. 863–65.

    In this Correspondence Letter, the researchers report results from a cohort study on 188,137 Norwegians that ran between March 27, 2020 to April 26, 2023. Patients who have tested positive had somewhat worse memory scores and the effect persisted up to 18-36 months. There is an issue with this study, though, which is response bias.

15. (PREPRINT) Shan, Dan and Wang, Congxiyu and Crawford, Trevor and Holland, Carol. “Temporal Association between COVID-19 Infection and Subsequent New-Onset Dementia in Older Adults: A Systematic Review and Meta-Analysis.”

16. Greene, Chris, et al. “Blood–Brain Barrier Disruption and Sustained Systemic Inflammation in Individuals with Long COVID-Associated Cognitive Impairment.” Nature Neuroscience, vol. 27, no. 3, Mar. 2024, pp. 421–32.

17. Partiot, Emma, et al. “Brain Exposure to SARS-CoV-2 Virions Perturbs Synaptic Homeostasis.” Nature Microbiology, Mar. 2024.

18. Safadieh, Ghida Hasan, et al. “Neuroimaging Findings in Children with COVID-19 Infection: A Systematic Review and Meta-Analysis.” Scientific Reports, vol. 14, no. 1, Feb. 2024, p. 4790.

19. Matthews, Beverly, et al. “Long-Term Cognitive Effects of COVID-19 Studied with Repeated Neuropsychological Testing.” BMJ Case Reports, vol. 17, no. 4, Apr. 2024, p. e256711

20. Hampshire, Adam, et al. “Cognition and Memory after Covid-19 in a Large Community Sample.” New England Journal of Medicine, vol. 390, no. 9, Feb. 2024, pp. 806–18

    This is the key figure: A drop in Global Cognitive Score of -0.42 for those with Long COVID (>= 12 weeks duration) is equivalent to losing 6 IQ points.

Bonus appendix - a conservative Fermi estimate of expected days lost each year

First we have to estimate the average person’s chance of getting COVID-19 each year. Since people aren’t testing for or reporting COVID-19 very much, the current data is unreliable. However, recall that the wastewater data shows a similar amount of COVID-19 virus between 3/2023 - 3/2024 as between 3/2022 - 3/2023. The number of reported COVID-19 cases between 3/2022 - 3/2023 in the US was 72 million, or about 21% of the US population.

So suppose the chance of getting COVID-19 each year is 15% and it takes two weeks to recover. Then the expected loss of time each year is two days. If we factor in a 4% chance of losing a year to Long COVID then we have another expected loss of two days, or four days in total. If we factor in a 1% chance of losing 20 years then that adds 10 days, or a total of 14 days expected loss each year. Then we have to factor in the possibility of persistent brain damage as well as all of the secondary effects, such as spreading COVID to other people, lowered economic output etc. All of this adds up to a pretty compelling case for masking (with a well-fitting N95 mask or respirator.)

Acknowledgements - Thank you to Ben Ballweg and Cheryl Elton for helping proofread this.

An American team just soft landed a craft on the moon for the first time since Apollo 17 in 1972, 52 years ago. This is incredibly exciting news for humanity. Even more exciting is the fact that this feat was largely accomplished by private companies. NASA invested $118 million in Intuitive Machines as part of their Commercial Lunar Payload Services program. NASA also built six science experiments for the mission and provided access to their deep space network. However at the end of the day it was Intuitive Machines that built and operated the lander. SpaceX provided the boost towards the moon using their Falcon 9 rocket.

In this post I’ll discuss some incredible revelations from NASA’s press conference yesterday in Houston. The mission almost failed, but luck was on their side. Two problems effectively canceled each other out in a very fortuitous way.

Context

The past decade there has been renewed interest in the moon, with several new players looking to develop lunar capabilities (China, India, and Japan) in addition to the US and Russia. Landing on the moon is not just a matter of national pride. Firstly, the moon is a great “training ground” for developing all the capabilities needed to go to Mars. Secondly, it’s much cheaper to launch the propellant needed for Mars missions from the moon than from Earth, owing to lunar gravity being only 1/6 of Earth’s. A lunar fuel manufacturing plant and depot starts to look attractive when one considers how much fuel will be needed to transport goods to Mars in order to build a self-sustaining colony.

On August 23, 2023 India’s Chandrayaan-3 successfully landed on the moon, an incredible accomplishment, especially given the tiny budget of the Chandrayaan program (US $170 million). This followed the loss of the Chandrayaan-2 probe in 2019 when it crashed into the moon.

On August 19, 2023 Russia attempted to soft-land a craft on the moon for the first time since 1976. Their 1.2 ton craft suffered an issue when an engine couldn’t be shut down. The craft crashed into the moon at high velocity, creating a crater that was visible from orbit.

On September 6, 2023 Japan landed their SLIM probe on the moon. The craft landed upside down and sustained damage to its antenna. Still, the mission was was deemed a success and the landing was within 100 m of their target, the first ever precision lunar landing.

On January 8, 2024 the startup Astrobotics launched their Peregrine lander towards the moon, which was also funded by NASA. Unfortunately, a propellant leak developed shortly after launch, leading to the decision to terminate the mission and and direct the craft back to Earth for a controlled re-entry over the Pacific Ocean.

Revelations from yesterday’s press conference

• The initial orbit of the craft wasn't right - it was too elliptical. Additional burns were needed to correct it. This was actually good because they were forced to activate their laser rangefinders to help correct their orbit.

• It turned out that the laser rangefinders didn't work - they forgot to disable a safety switch before flight! At first, they thought everything was working, but then after digging deeper the next morning they realized there were errors in the telemetry. That clued them into the fact the laser rangefinders were not working.

• Under normal operations the rangefinders would not have been given the command to turn on until five minutes before landing. That would not have been enough time to develop a fix!

• After Intuitive Machines CEO Stephen Altemus told CTO Dr. Timothy Crain they did not have rangefinders, Altemus says “his face got absolutely white, because it was like a punch in the stomach that we were going to lose the mission.”

• The failure of the rangefinders led to a "hail Mary" moment. They had to develop a makeshift laser rangefinder using an experimental NASA instrument which by a stroke of luck happened to be on board. The situation had dramatic Apollo 13 vibes but under even tighter time pressure.

• A team of over thirty engineers worked a "48 hour day" (!) to work through all the challenges.

• They had to abort their initial landing and orbit one more time to give time to update the software. Guidance and navigation had to be shut down for a short period of time in order to do the update. As a result of these changes in the mission they had to disable EagleCam, a special camera that was going to deploy to film the landing.

• They were running low on helium which was a cause for concern. (I believe this is used to pressurize tanks??)

• They hit the lunar surface at 6 mph vertically instead of 2-4 mph and with a sideways velocity of 2 mph instead of 0 mph.

• As a result the vehicle tipped over, possibly because they tripped on a rock, or because a leg got caught and broke (they don't know yet).

• They had difficulty with communications the whole time. Because they were using fixed antennae, they had to lose signal during the roll maneuver during descent. After landing, communications got even harder because one antenna is aimed at the surface of the moon. Only sufficiently large radio dishes around the world can communicate with it so they do not have 24 hour communications.

• Initially they thought they were upright, but they later realized the data they were looking at was "stale". It wasn't until several hours later that they figured out they were sideways by closely inspecting the readouts from the tank gauges.

• As a last note, I found it interesting that the mission will only last two weeks since the lithium batteries on board cannot survive the cold of lunar night.

At the end of the day, they soft landed with all instruments and payloads operational and they expect to get all of the data they wanted. This is an incredible accomplishment!

Still, the details of what transpired are a sobering reminder that space is hard and that succeeding in space requires careful and concerted effort by humanity’s best engineers.

2,977 people died on September 11th, 2001. The September 11th attacks led to the creation of the 9/11 Commission about a year later. That commission held high-profile public hearings and produced an extensive report that became widely read, selling over 800,000 copies.

The pandemic directly caused at least 1,130,000 deaths. That is the same as 379 9/11s. So where is the reckoning? Why no commission?

There was a major push for a COVID-19 commission, but ultimately it stalled on Capitol Hill. In early 2021 four foundations (Schmidt Futures, the Skoll Foundation, Stand Together, and The Rockefeller Foundation) created the COVID Commission Planning Group, led by former 9/11 commission director Philip Zelikow. Sadly, their website is now offline.

On March 15, 2022 the Senate’s health committee voted 20-to-2 in favor of the PREVENT Pandemics Act. Part of the act prescribed the establishment of “an independent task force to conduct a comprehensive review of the COVID-19 response”. The term “task force” was seen as less divisive-sounding than “commission”.1 Terminology aside, the act called for an independent committee with full subpoena powers, very similar to the 9/11 Commission.

Many in the Senate advocated for the bill, but it received no support from The White House. Many parts of the PREVENT Act were later adopted into the Consolidated Appropriations Act which was signed into law by Joe Biden on December 29, 2022. Sadly, though, the independent task force measure was dropped.

If we’re not going to have a comprehensive COVID-19 commission, I’d like to suggest something more targeted -- a commission to investigate the FDA’s response in particular.

I would hope that such a commission would be easier for Congress to pass. The FDA is pretty non-partisan by design. That should allay fears that the commission might become politicized.

Let’s review some of the major issues at the FDA during the pandemic:

The rapid test shortage

“We spent our own million dollars developing this thing, at their encouragement, and then they just treat you like a criminal.” - Chris Patterson, CEO of WHPM, Nov 2021.

The episode with rapid tests is a complex boondoggle. What follows is my attempt at an overview:

The first rapid test was developed in March 2020 by E25Bio. The inventors immediately contacted the FDA. They had a factory ready to go that could make 100,000 per week. However, the FDA didn’t put out guidance on rapid tests until July 2020. The guidance asked companies to validate their tests against PCR tests and also required the test’s sensitivity to be 90% as good as gold-standard 40x RT-PCR (later lowered to 80% in November 2021).

When the FDA finally approved the first rapid test in November 2020, it was only available by prescription. Many articles speculate that FDA officials were worried people were not smart enough to understand the risk of false negatives with such tests. This sounds right, but it’s also worth pointing out that PCR tests, which have very low negative rates, also required a doctor’s prescription initially as well.

We can look at timelines for two different tests (picked somewhat randomly) to get a sense of the delay here - Roche’s rapid test was authorized on Feb 26th, 2021 in Germany, on March 1st, 2021 in the UK, but not until December 24th, 2021 in the US. ACON’s test was authorized in Germany on April 8th, 2021, in the EU on May 14th, 2021, and in the US on October 4th, 2021. In general, dozens of rapid tests were approved in the EU in spring 2021 that were not approved in the US until winter 2021. That delay may not seem like a big deal looking back now, but at the time it was critical. When the delta variant hit in July 2021 other countries were awash in cheap rapid tests, but there were barely any available in the US. The first tests approved in the US were also about three times as expensive as tests in other countries ($15 vs ~$5).

So what happened?

It appears FDA officials fundamentally did not understand that it’s OK if rapid tests are not as sensitive as PCR tests. The chief benefit of rapid tests is that they can be administered easily at home and give immediate results. PCR tests, by contrast, require driving somewhere and then waiting 12 - 24 hours for a result. Rapid tests are actually quite sensitive during days ~2-4 of illness when patients are most infectious. The high sensitivity of PCR tests can actually be a problem, since people are flagged as “positive” far after they’ve stopped being infectious.

It appears much of went wrong is due to completely unnecessary delays by FDA officials. At least one scientist quit in protest. As Pro Publica reported:

“The FDA reviewer who quit this May described what the delays looked like from the inside. With a background in virology, he could evaluate the hundreds of pages in an application within a few days. But then, something strange happened: The applications would go nowhere for months as higher-up officials seemed paralyzed by indecision.”

I suspect that one of the culprits here is Jeffrey Shuren, who serves as the Director of the FDA’s device center. Dr. Shuren is the same character that appears responsible for screwing over MELA Sciences in 2010 (something I wrote about previously).

What should change? We need a commission to fully answer that question.

However, many changes have already been proposed. In a December 2021 Twitter thread, Michael Mina identified three changes that could speed up test approvals. First, he suggests acting immediately after EUAs happen in peer nations (reciprocity!). Second, he argues that new rapid tests should be compared to a gold standard rapid test like BinaxNOW, instead of requiring comparison with PCR. That would speed up trials. Third, he suggests the FDA should allow EUA trials to recruit people who have already tested positive. This would obviously speed things along as well.

Now, to the FDA’s credit, they did ask Booz Allen Hamilton to come in and do a postmortem on what happened with tests. Their lengthy report has a lot of granular recommendations.

Most likely larger systemic changes are necessary however, like having the emergency use pathway administered by a different agency.

Pfizer should have been authorized earlier

Maxwell Tabarrok has a fantastic article entitled “How Many People Are In The Invisible Graveyard?”. The article looks at how many people died because of delays in authorizing the Pfizer vaccine. The Pfizer vaccine is of particular interest because it had the largest trials that reported out their results first because of larger statistical power. Pfizer also had much larger manufacturing capacity than Moderna.

I think there is a strong case that the Pfizer vaccine should have been approved after their Phase II data came out on June 29th, 2020. (See footnote for details2)

According to Tabarrok’s analysis, that would have saved about 160,000 lives.

You might think that the delay was OK because Pfizer needed time to manufacture the doses. In actuality it appears Pfizer had millions of doses already manufactured in August. Furthermore, as both Tabarrok and economist Tyler Cowen have argued, if the FDA had made clear that an earlier approval was possible, then that would have led to vaccine production ramping up faster (elasticity of supply).

Even if one thinks that waiting for the Phase III trial was warranted, then in an ideal world the vaccine would have been authorized shortly after the Phase III trial’s read-out date, which was going to be in mid-October. According to Tabarrok, that would have saved 60,000 lives.

What actually happened is that on October 6th the FDA updated their guidance to require four additional weeks of follow-up in the Phase III trial, effectively pushing out any chance of an EUA until after the election.

The FDA did not approve Pfizer’s vaccine until December 11th, 2020, three weeks after Pfizer submitted their EUA application. Even if we just look at that three week delay, that is at least 10,000 lives lost.

All of Tabarrok’s estimates only consider first-order effects. They do not consider the second-order effects of slowed transmission and fewer variants of concern. A full investigation is needed to more accurately determine the full number of bodies in the invisible graveyard.

The AstraZeneca vaccine was never authorized

This, in my view, was the second or third most devastating thing the FDA did. This travesty was also wildly underreported. Part of the reason is there isn’t much to report on. All of the key decisions took place behind closed doors. There is zero transparency into the FDA’s decision making here.

Let’s briefly review what happened:

July 30, 2020: It is reported that the Trump administration was exploring a pathway by which the FDA could issue an authorization for the AstraZeneca vaccine in October. This causes pushback from Democrats.
October 9th, 2020: Out of the blue Nancy Pelosi tells reporters that the FDA should not approve the AstraZeneca vaccine on the basis of their first Phase III trial alone, which was conducted on patients from the UK, Brazil, and South Africa.
December 8th, 2020: Phase III efficacy data is published in The Lancet. (90% effectiveness)
December 30th, 2020: AstraZeneca’s vaccine is approved in the UK.

In an ideal world, AstraZeneca’s vaccine would have been approved in the US in December 2020. The vaccine was by far the cheapest ($4 a shot) and did not require ultra-cold storage like the mRNA vaccines. In late January 2021 we learned that a facility in Baltimore had millions of doses stockpiled and was manufacturing millions more. In March the New York Times estimated there were 28 million doses there.

I imagine some people may be tempted to say that everything worked out OK because AstraZeneca was later shown to be a “sup-par” vaccine and was paused in the EU due to concerns about clotting. Well, as analyses have shown, that pause was a massive mistake, costing many lives. The clotting risk is about equivalent across all the vaccines, but AstraZeneca appears to have been targeted by EU countries for political reasons. All of the vaccines have similar efficacy at preventing hospitalization and death, with some evidence showing AstraZeneca is actually superior to Pfizer in that regard.

Anyway, this brings us to our next topic:

We never donated our extra AstraZeneca vaccines

By April 2021 the US really had no need for the 28+ million AstraZeneca doses that were languishing in a Baltimore warehouse.3 Vaccine supply had caught up with demand. At that point we should have immediately donated all of them to India. A big wave was hitting India, with 300,000 cases and 2,500 reported deaths each day (the actual number was probably several times higher). Even a very conservative estimate shows that donating those doses could have saved ~5,000 lives.4

While this failure lies directly with The White House, I also blame this failure on the FDA because I suspect if AstraZeneca had been authorized then any extra doses would have been donated right away.

Addendum (added 1-29-24): someone pointed out that it later came out that Emergent, the company manufacturing AstraZeneca doses in Baltimore, had serious issues in their manufacturing plant. It’s quite possible the FDA knew about at this at the time and that is why the doses were not donated. This also may tie into why AstraZeneca was not authorized, but I still think there is no good excuse for that.

The FDA’s crackdown on laboratory developed tests

"Dr. Stephen Hahn, 60, the commissioner of the Food and Drug Administration, enforced regulations that paradoxically made it tougher for hospitals, private clinics and companies to deploy diagnostic tests in an emergency," - The New York Times on March 28, 2020.

In January 2020, many scientists wanted to start testing for COVID-19 using PCR. Many started to do so without government approval. The FDA then began to issue cease-and-desist letters. Prior to the pandemic, laboratory developed tests (LDTs) were never FDA regulated. The FDA had for a long time claimed they have the right to regulate such tests but had always chosen not to (a view that many legal scholars completely reject). That situation suddenly changed when the pandemic started and the FDA began to require approval. As Alex Tabarrok explained, the FDA “reversed the logic of emergency.”

The CDC then tried to develop a standard test and failed. Former CDC Director Tom Frieden has said that the situation with PCR tests was “a perfect storm of three separate failures,” noting the crackdown on private labs, the botched test at the CDC, and overstrict FDA rules. The delay caused by these failings is widely acknowledged to have left the US “blind” during the start of the pandemic. Many lives were undoubtedly lost as a result.

Distilleries could not produce hand sanitizer due to FDA regulations

We now know that COVID-19 isn’t transmitted much on surfaces, but it very well could have been different. We really didn’t know at the time. To their credit, very early in the pandemic (around February 2020) the FDA loosened their rules regarding hand sanitizer production. However, they still required the addition of a denaturant to the alcohol. This was a big problem for distilleries, who wanted to help meet the growing demand for hand sanitizer. Introducing a denaturant to their lines would have rendered them useless for future alcohol production barring extremely costly cleaning procedures. Reason Magazine had an article about this in April 2020.

The FDA made it hard to import masks from other countries

Unlike N95 masks for construction workers, masks for healthcare workers have to be manufactured on a production line certified by the FDA. To their credit, on March 19th, 2020 the FDA loosened their regulations to allow industrial N95 masks to be sold to healthcare workers. Unfortunately, the FDA made it nearly impossible to import masks from other countries. Chinese manufacturers had much larger capacity and could also manufacture N95s at three times lower cost. This led to “grey markets” popping up. I believe this situation was eventually improved after a few months, but a severe shortage of masks (especially N95s) persisted for quite some time.

The FDA barred their inspectors from traveling

“Grocery store workers are working, meat packers are working, hell bars and restaurants are open in many parts of the country but FDA inspectors aren’t inspecting. It boggles the mind.” - Alex Tabarok, Marginal Revolution.

The New York Times wrote about this in March 2021. FDA officials prevented many of their inspectors from traveling during the pandemic, citing guidelines from the CDC which discouraged travel. As a result the roll-out of several important anti-cancer drugs was delayed months because inspectors could not inspect their manufacturing facilities. At least 52 drug applications were delayed.

The FDA never endorsed challenge trials

"Right now, it gives people some ethical heartburn and scientifically, it's complicated.” - FDA CBER Director Peter Marks on challenge trials, July 2020.

In April 2020 35 federal lawmakers called on the FDA to consider human challenge trials. Over 1,500 volunteers had signed up with 1DaySooner at that time. According to a report, an FDA spokesman “expressed skepticism” and said that the idea “flies in the face of most federal regulations on protection of human research subjects.”

The UK approved human challenge trials in February 2021. The UK’s trial was obviously way too late, but it was a valuable proof-of-concept. 18 people were deliberately infected, 16 of whom developed symptoms. None had any long-term consequences to their health. Part of the trick was infecting people with the lowest possible dose (essentially variolation, something Robin Hanson argued for in February 2020). In many viral illnesses, symptom severity appears to be correlated with initial viral load, and COVID-19 seems to be the same way.

However, I put this last because I’m not sure how much we can blame the FDA for this failing. Bioethicists came out in droves to publish papers denouncing challenge trials. It’s also very unlikely that local IRBs would stomach challenge trials. So FDA officials may have looked at all that and decided it wasn’t going to happen regardless.

Conclusion

I hope it is clear why we need a fully-empowered independent commission to investigate the FDA’s failings and make recommendations to congress.

The good news is that all of the problems outlined here appear avoidable in future pandemics. We just need the political will to legislate the necessary changes.

A overarching theme here is that the original intent of the Emergency Use Authorization legislation was not followed by the FDA. Instead, the FDA tried to follow their normal procedures but on a compressed timeline. Read Nikki Teran’s excellent article “Taking Emergency Use Authorization Seriously” for more on that. New legislation is clearly needed.

For further reference see my vaccine approval timeline.

Thank you to Ben Ballweg for doing a very thorough proofreading of this post.