If you have questions, you can email me at eryneym@gmail.com, DM me on Twitter or Substack. Or put them in the comments below!

Also, none of the below constitutes medical advice. (Seriously. This is not medical advice - Ed.)

Enjoy.

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Algobaker @algobaker

How will the economics of capsid designs end up working out? Will there be libraries of patented designs, and any new group designing a new payload will have to choose between paying the tax to use an existing capsid they see can already do the job, vs paying to do the experiments necessary to patent-bust it?

Great question. Obligatory mention that I’m a former employee and current shareholder of Dyno Therapeutics, which (I personally think) is setting the frontier of capsid design.

Now that I’m done talking my book, let’s turn to your question. Engineering genetic delivery vectors today is mostly about picking your priorities. Generally what people try to select for is a virus that can go to specific organs / regions of the body, but there’s also considerations like avoiding the immune system, avoidance of the liver (we call this detargeting) or to a lesser extent, production efficiency. An entire field exists around trying to manipulate these viruses using AI models and fancy protein engineering, but it’s not really something that can be done without intense experimentation with very long feedback loops that take a lot of time.

How can we shave time? Well, AGI of course! This is because it’s probably the only real avenue towards avoiding the requirement of testing hypotheses in the lab. If you can avoid the experiments, you save both time and money, and thus open up a ton of options for yourself.

Until we have superhuman intelligence my sense is your need to pay the tax to the current incumbents for existing capsid IP comes down to how much you’re feeling the AGI, and secondarily, how organ-specific you need a capsid to be. Though the ideal world is one where you have a 1000-fold better brain delivery capsid than, say, AAV9 (the best “free” natural variant for the brain), you need to ask yourself whether you can get away with 5x? What about 2x? In that case it’s possible you can design your own capsid with a combination of off the shelf ML models, a good cloning and production pipeline and some non-human primates. I don’t know what it would cost you (at least $250k if I had to guess) but it would definitely take you time.

It’s worth mentioning here that when I say AGI I mean legitimately AGI, not models that are 10% better at writing code. I personally think the only real way it becomes cost efficient to engineer your own capsids instead of paying for it is that the field gets access to protein ML models that can tell you with very high confidence how a protein will behave in a zero-shot manner. Despite what you read online, we are not there yet, and we won’t be for many years.

The downstream consequences of this timeline is an exercise I will leave to the reader.

David Dales @d2dev_

Now AI is out and public figures are telling me more hospitals are hiring more doctors to use the AI - can you confirm or deny with data? I heard x-rays and MRI scans largely use AI to detect issues these days. I think it was in the last 5 minutes of the recent Lex Friedman/Jensen Huang podcast.

Source

I’m going to take your question to refer to radiology, as that is what people generally mean by AI replacing doctors. Radiologists are actually in such high demand that they could easily out-earn neurosurgeons if they decided to work more than a few months per year. But how can this be true? Well, let’s start by first addressing the misconception that AI is replacing radiologists.

NVIDIA CEO Jensen Huang, and also Anthropic CEO Dario Amodei, are both incredibly smart guys who have gotten this one wrong completely. I won’t comment on how that happens — keep in mind they both have reason to push the narrative that AI can do complex jobs easily today — but I will say that this is a pervasive myth in the tech community. I will put it very plainly here: no hospital is replacing radiologists with AI today. While the range of what AI models can do is growing (see here this study on a new neuroradiology model from Michigan), the skillset is still incomplete, and thus hasn’t changed the job landscape at all.

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The hiring effects you see with radiologists are actually the result of something entirely unrelated to AI, and that is the rise of advanced imaging in medicine. Previously (and by previously, I mean like 50 years ago), doctors put a lot of weight in their clinical intuition and the art of the physical exam. Sadly, that is a fading skillset, but we can directly tie it to the rise of on-demand CT and MRI in healthcare. Have an ear ache? Head CT. Strange lump? Ultrasound. Think you tweaked your knee? Let’s get you an MRI. All of these things were at some point diagnosed from physical exam findings, but now get imaging. The result is that we get imaging on way more patients than we used to, and that amount is further increasing as new modalities get added.

Source

I can’t read the future, so I won’t make a prediction here about eventual AI capabilities in radiology. The point is though that I think the hiring happening in healthcare is very much real today.

Claire Goldsmith @c_goldsmith

What is going to happen with monoclonal antibodies over the next two decades? Everyone talks about costs coming down and this not being a particularly attractive part of the market long-term, but it’s doing very well for big players today (J&J etc). Other than patent cliffs, what do you think actually manifests that cost curve compression? Seems much more like a manufacturing problem than a design problem. Also, which comes first, broad use of mabs for more disease areas outside indication or major decrease in cost of manufacturing?

Are you feeling the AGI, Claire? Monoclonal design is getting better thanks to advances in AI-enabled protein design — faster than most proteins right now, I’d say — but honestly, target selection seems like more of the rate limiter here. Everyone seems to be tackling the same exact ideas. Structure models like AlphaFold or RosettaFold or Chai seem poised to help the design of drugs that act on established targets with improved efficacy / potency (referred to as me-betters) but they don’t really help you pick out novel idea space. In theory, that’s where AGI helps. I am not convinced we have it yet, though.

There’s another element to the economics here which is that the arrival of biosimilars (“generics” for antibodies) drives the price of monoclonal antibodies down significantly, upwards of 80%. When this happens doctors become more willing to prescribe a particular biologic for off-label use. I expect that to happen more and more, especially now that the FDA has said their goal is to make biosimilars easier to get through the pipeline.

If your question on manufacturing is one of cost, I would argue that’s not really the problem. Right now most monoclonals can be made around $100/g. If we look at the cancer buster Keytruda, which is dosed at ~2mg/kg every few weeks, you’re looking at a max of around $500 in terms of production costs. The gap between that number and the $150,000 price tag is owed to amortization of R&D and clinical trials.

Claire Goldsmith @c_goldsmith

Growing organs…. Is this working? Will we be able to do it? What problems does it actually solve? Transplant success rates after the 1-year mark are not improving, and I don’t think organ supply is the problem.

If you’re referring to growing whole organs, we’re quite far off, so I think it would be unwise to be all-in on this. There are other options though, like xenotransplantation – taking organs from other organisms (namely pigs) – which are kind of getting there. NYU has a trial for kidney transplants from pigs and as of today, the longest survival time is 9 months. Again, not bad, but as you point out, not enough. This is a little above my pay grade, but my understanding is that the main way we humanize pig organs is to eliminate endogenous retroviruses within the animal that immediately activate our immune systems if they get transplanted. Unfortunately, there seems to be some antigen that has yet to reveal itself, which ends up the same way as many human-to-human transplants — rejection.

Whole organs are a difficult business, but patches seem viable. Lots of companies are working on this, new and old. I wrote about one last year, Polyphron, but there are plenty of others. The goal of these approaches is to swap out broken bits of an organ. Also not there yet.

You didn’t ask, but I think it’s kind of cool that since our last edition, the Pope issued an official decree that Catholics are able to accept pig organs according to the Written Word. America’s Pope supports American biotech. Annuit cœptis.

waitingonyou @Imyouropnow

Why are there more investments in AI innovation at the bench rather than the beside? Do you think it might be possible to infer immunotherapeutic effects (w/o drug perturbation experiments) through, for example, cytokine-symptom effects at the bedside?

Your question reminds me of a fantastic book I read a few months before the COVID-19 pandemic, The Great Influenza. It’s about the Spanish Flu (or Kansas Flu, IYKYK). The flu accelerated medical science, but split the process of discovery between the bench and the bedside. There became a specialist class of researchers whose whole thing became studying biological phenomena independent of the treatment of patients. This is great, but it did have cultural consequences. Doctors don’t really do the physician-scientist thing like they used to. They rely on the biological sciences to be the engine of discovery and while some patient-facing physicians try things in the clinic, our healthcare system works well by having doctors mostly implement things once they’ve been established as safe and effective in small numbers via trials.

So, why is there no AI innovation at the bedside? Well, it’s not immediately useful. AI-powered tools like OpenEvidence are great for distilling dense medical literature to a specific question, but that’s not really the same thing as innovating, is it?

Now, I do think there is a lot of useful biological data to be gathered from the clinic for those with the stomach to figure out how to get it. A large part of the limitations are the fact that we have very poor measurement tools, which is why I spent some time working on this at Caltech. But the tools largely still need to be built. There probably are insights that can still be gathered, though, so if you’re an engineer or scientist who wants clinical data, try connecting with a clinician.

Towards your specific comment about cytokine-symptom effects, I expect it’s mostly just that doctors are waiting for the science to clarify what they should do. AI is best served doing that, because as things go today, that’s not really the job of doctors. An interesting question is whether AI will enable that to happen, though. That’s one whose answer has yet to reveal itself to me.

Ashlee Vance @ashleevance

If I’ve already had shingles, should I take the vaccine anyway, too?

The simple answer is yes. The longer answer is definitely yes.

Shingles is the result of varicella zoster — the virus that causes chickenpox — staying dormant in your nerves after you clear the initial infection. Because our immune systems wane in efficacy over our life, the virus generally gets reactivated resulting in what I’ve heard described as the worst pain imaginable. If you’re unlucky, you can actually get it again at some later point — having shingles once doesn’t mean you’re set for life. It’s recommended that anyone over 50 gets it. As a Sensitive Young Man of 29 Years Age, I naturally haven’t gotten the shingles shot, but I’ve heard it’s quite painful. Still, less painful than shingles!

I’ll add that a study in December 2025 in Wales shows that, while the shingles vaccine doesn’t stop dementia once it gets going, it can slow its progression down and even prevent new cases in the vaccinated. They use two different cohorts to demonstrate that the effect is real and is not the result of weird selection effects. Interestingly, they show that this effect is stronger in women than in men.

So, Miss Ashlee, I’d get your vaccine.

Happy Friday.

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Today, Franceschi is the co-founder and CEO of Brex, a financial technology company that was just acquired by Capital One for $5.15 billion. Franceschi is all of 29 years old now, so he’s done alright.

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Brex led a new wave of companies that brought more modern financial tools first to start-ups and then to businesses of all sizes. Over the years, it’s had some ups and downs, and Franceschi has been remarkably open about Brex’s stumbles, his mental health struggles and about the areas where he thinks Brex got things very right.

Franceschi remains a hacker at heart and has been experimenting away with AI agents. He, in fact, says he’s running Brex – and his life – with a team of AI agents that read his e-mails and Slack messages, perform job recruiting tasks and schedule his day-to-day activities.

We get into all of this on the episode, charting Franceschi’s rise from hacking phenom to running a multi-billion-dollar company and discussing where he thinks AI and money are heading.

Do we have journalistic conflicts with this episode? Yes, we do. Brex has been the top sponsor of our podcast and video series. You can learn more about the depths of our relationship and what Brex can do for your business right here.

The podcast is also made possible by E1 Ventures, which backs the most ambitious founders and start-ups.

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About 25 years ago, Musk sold his finance tech company PayPal and left dot-com life to get into rockets with the founding of SpaceX. Hardly anyone considered this a rational choice on Musk’s part. Space, after all, was where rich people went to blow their fortunes and fail.

For his part, Bhatt co-founded the investing service Robinhood in 2013 and has now decided to get into the space business as well via a start-up called Aetherflux. The company aims to build a network of solar panel-packed satellites that suck up sunshine and then beam it down to Earth via infrared lasers. Yes. Actual space lasers. What could go wrong?

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The lasers would feed antennas and ground stations on Earth with energy. In theory, you could then direct power just about anywhere without needing to build a ton of infrastructure on the ground. Army convoys, data centers, etc. could just have electricity sent to them in remote areas.

Bhatt explains all of this in the episode and gets deep into his personal story. He also recounts starting and running Robinhood through its ups and downs, including being both beloved and despised.

Will the space lasers work? I dunno. It’s a lot. But we are fully in the era of trying new, bold ideas in Low Earth Orbit, and, well, I wrote a book predicting this very thing, and so am very much here for it.

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The Core Memory podcast is on all major platforms and on our YouTube channel over here. If you enjoy the show, please leave a review and tell your friends.

This podcast is sponsored by Brex, the intelligent finance platform built to help companies spend smarter and move faster.

We run on Brex and so should you. Learn more about Brex right here.

The podcast is also made possible by E1 Ventures, which backs the most ambitious founders and start-ups.

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The drone whirred above us. The loud hum combined with some precious Seattle sunshine made it easy to follow the four-armed machine as it zoomed into the distance. The drone’s creator, Blake Resnick, watched with a big grin. “This is always our most dramatic demo,” he told me, as he unfolded a clear pair of safety glasses.

We stood in the parking lot of a drone company called Brinc, founded by Resnick in 2017. They produce extra fancy hardware, like the Lemur 2 that zipped over our heads, for tactical responders in the public safety field. Firefighters can use its glass breaker to bust into a building and scope out an inferno. Police officers can flick on its thermal vision to find criminals in hiding. A SWAT team, per Brinc’s website, can prompt it to “deliver a pack of cigarettes during a negotiation.”

“We’ve seen those drones get shot, seen it stabbed, we’ve seen them flown into ceiling fans,” Resnick told me. “They go through some stuff.”

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Our drone had a far more important mission on this day: to impress a journalist. Its teleoperator was Dmitry Tarasov, an operations manager at Brinc and one of Resnick’s closest colleagues. He flipped through features as Resnick said them aloud. Show her night vision, now flip on the police lights and sirens. They played a pre-programmed message, alerting the neighborhood about a fictitious missing child. He called the drone to demonstrate its two-way communication capability. “One, two, three, four, five,” Resnick’s voice boomed through the drone.

The drone descended, navigated itself underneath some of the parked cars before Tarasov piloted it directly into a concrete wall — on purpose. Resnick rattled off words to describe each component: LiDAR, tungsten carbide, 20,000 RPM. He seemed like a proud dad listing little league trophies.

Finally, the drone approached a sheet of glass propped up nearby. With a little tap — tink, pop! — it turned to dust. “This was the first drone in the world with the glass breaching capability,” Resnick said, never losing his grin.

IF YOU weren’t already aware, I’ll tell you now — there’s a lot of drama in the drone world.

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This is the first edition of what I hope will become a staple for Core Memory. Though I’d love to comment on politics, religion, culture and dating, I think my experience is best directed toward addressing questions about biology, medicine and healthtech. The vision here is to get you up to speed quickly on disparate bio and health topics through our regular installments.

For readers new to my stuff, I’m a 4th year medical student at the University of Vermont with prior experience in biological engineering of mosquitoes at George Church’s lab at Harvard, AAV (Adeno-associated virus) at Dyno Therapeutics and wearable monitoring devices at Caltech.

If you have questions, you can email me at eryneym@gmail.com, DM me on Twitter or Substack. Or put them in the comments below!

Also, none of the below constitutes medical advice. (Seriously. This is not medical advice - Ed.)

Enjoy.

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Effie Klimi @effiebio

What do you advise people who have leaned onto the peptide craze? What will medicine look like if this trend keeps increasing in intensity?

Peptides, peptides, peptides . . .

I have written about these a bit with regards to the inflammation/pain claims that exist around BPC-157. My full thoughts on peptides are complicated, but can be summarized this way: buyer beware.

I think it is valid to point out that on-demand intelligence with AI or even just the internet has meant people feel like they can start taking control over their own health. You can really feel it, especially in tech communities as Jasmine Sun highlighted in NYT late last year. The problem is that humans are credulous, and hucksters know it. There is some balance to be had between four phases of trials, which include thousands of patients over 15 years, and me tweeting that Chemical X discovered by an Uzbek scientist in 1844 cured my face blindness. Unfortunately, right now things look too much like the latter than the former.

There is a very real, competent minority of well-resourced people who are looking to take healthcare into their own hands. Peptides are an example of that, and self-designing mRNA vaccines is another. In both instances, it is possibly the case that many N-of-1 treatments exist that could work for the individual, but there isn’t really any rigor that exists that can help generalize these results. That’s really the problem. Trial abundance isn’t my wheelhouse, but Rux Teslo has written extensively about clinical trial abundance, and I recommend you check that out.

Just so no one can say I am being completely unreasonable, I think that some of the peptides that people are buying actually do work. Specifically, I’m referring to retatrutide, Eli Lilly’s new triple hormone agonist that has shown better safety and efficacy than current GLP1s for weight loss. People are buying that one because they have (probably correctly) ascertained that the FDA’s stamp of approval is as good as guaranteed in a year or so. I just hope they reward Eli Lilly for their labor and actually get on that one when it comes out, instead of buying from a random compounder that is stealing EL’s IP.

If you’re into peptides, power to you, but consider asking your peptide dealer if they’ve run even a small blinded trial of 25 people. Or better yet, be agentic and organize one yourself. I firmly believe there is simply too great a cost to abandoning rigor.

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Alex Kesin @alexkesin

Is the whole toxoplasmosis <> enhanced risk taking behavior thing real? Can I become a better poker player if I infect myself with it?

A myth has been created around the Toxoplasma gondii parasite. As far as I can tell, it stems mostly from work that comes from one Czech scientist Jaroslav Flegr. Throughout the 90s and 2000s, he started publishing increasingly provocative articles linking toxoplasmosis with high risk behaviors through surveys and questionnaires. According to him, those infected with the parasite are more likely to get into traffic accidents, be schizophrenic, and more recently, be entrepreneurs.

The problem is that there is a fairly iron-clad rebuttal from a group at Duke. They followed 1,000 people in New Zealand from birth until age 38, testing them for antibodies against the parasite to see who had been exposed or was currently infected, then tried to search for associations. Unfortunately for you, Alex, none were found.

Flegr himself is listed as a co-author on a study challenging the ground truth behind some of his earlier toxo<>high risk claims. In that paper, they tested for an association between toxoplasma infection and financial decision-making using real monetary incentives in a case-control design. They found no significant evidence that risk attitude or loss aversion was associated with infection.

There are lots of examples of mainstream media picking up tidbits of the most interesting, spurious findings in fields. You can find volumes of pop culture articles about how wine makes you live longer or how chocolate is good for you. Being a good citizen scientist means developing some antibodies against these sorts of studies. You’ll live longer that way.

Adith Arun @aditharun_

What does the future of being a doctor look like in the age of AI? Non-surgical only.

I wrote about five different answers to this question, so consider that my base level of confidence in the future. My current view has been shaped by discussions with Joe Janizek, a radiology resident at Stanford with a PhD in computer science.

The kinds of AI that will slowly encroach on core aspects of medical care are reliant on data for the most common presentations, and are therefore very good at managing them. Note-writing, diagnosis of simple rashes and prescription refills fall mostly under that. What happens with the most challenging stuff, though? I’m referring to those 1-2 patients you see who are just kind of confusing, where the story and medical picture just don’t make sense, and when you have a diagnosis, management is a mild nightmare. After talking to Joe, I think it is reasonable to assume the bulk of medicine will revolve around dealing with those cases exclusively. It’s not too dissimilar to what LLM-powered coding agents are doing for AI research right now, honestly.

If you are OK with having to keep your brain CPU at 100% all the time, then welcome to the future of medicine.

David Dales @d2dev_

What is the most exciting result you’ve seen happen to a patient lately (avoiding theoretical science)?

Antisense oligonucleotides are showing promise. One very recent win is Zorevunersen, a treatment for the devastating neurological disease Dravet syndrome, described in this New England Journal of Medicine article earlier this month. There is much I can say about the science, but I will instead just point you to this video. This is the type of result that makes me glad I went into biology and medicine. It’s undifferentiable from magic.

John Whittaker @johnowhitaker

Microfluidics in bio: buzzword or are there lots of genuine good uses? My mind buckets ‘microfluidics’ with ‘graphene’ as something hyped as having lots of potential solutions that are always somehow a little ways in the future...

I don’t know much about graphene, but I think it’s clear that microfluidics has already made a real impact on biology: single cell RNA sequencing. It’s advanced enough since 2013ish that 10X Genomics licensed some of the original technology out of Harvard and now anyone in the US can implement it in their workflow. With 2025 revenue of nearly $700m, we can count this as a win for microfluidics. If digital droplet PCR is included in this category of boring quantification tools, the revenue goes well past $1B annually. For reference, the graphene global market across all companies is less than the revenue of a single company using microfluidics for a single assay.

There are other areas where I think the graphene comparator is a little more true, though. Organ-on-a-chip depends on microfluidics, but the technology has yet to really break out of labs. The FDA (and the Wyss Institute) are hoping that this ends up being the thing that can move the biotech industry away from reliance on animal models, and are legislating as such. Time will tell.

In my mind something stops being a meme once it becomes so boring and integrated into a field that it no longer becomes associated with a buzzword. I don’t know many people losing their minds over droplet quantification.

Adic @adic_9

Do you think we’ll get broadly available CAR-T or similar autoimmune drug free remission meds in the next 10 years?

CAR-T is a new cell therapy modality that mostly gets paired with cancer, but there’s reason to be fairly optimistic it has potential for autoimmune disease. I’ll point specifically at lupus. A case series a couple of years back involving a cohort of severe lupus and some other patients showed a CD19-targeted CAR-T treatment resulted in 100% disease-free remission. This persisted for the duration of follow up, averaging 15 months across the patient cohort.

Open questions remain about how durable these treatments are, but, on the surface, there’s reason to be excited. As with all things frontier, cost is a pretty big burden, but the current mainstay of autoimmune diseases – monoclonal antibodies – ain’t exactly cheap either, and patients are getting those at least monthly. I’m optimistic on this one. I put my p(win) at 75% by 2035 for an SLE treatment clearing a Phase III clinical trial.

Niko McCarty @NikoMcCarty

Why is the experimental hit rate for protein binder design so low?

Deep ball knowledge from Niko here. Proteins are dynamic. Everyone knows this, but what can really be done with this information? Not much right now.

AlphaFold and the tools that followed it made it easy to model static proteins, but don’t perform so well with proteins that move. There’s a neat paper from 2018 that suggests that as much as 5% of all proteins known in the Protein Data Bank are fold-switching, meaning they have extreme conformation changes in their active vs inactive state. If you used AlphaFold, you might get one state or the other, but, with no way to see the full spectrum of conformations, it’s hard to know how some proteins engage with their target.

This is doubly true for things like antibody binders, where the interaction requires understanding how a designed binder changes between its states. Thinking about antibodies, complementarity-determining regions (CDRs), particularly the H3 loop, are among the most conformationally diverse structural elements in all of biology. They’re essentially floppy loops that sample a huge ensemble of states, and the binding-competent conformation may only be transiently populated. So you’re trying to dock two moving objects against each other, and the computational tools are handing you a single snapshot of each.

And this gets at a deeper problem with how the field currently designs binders. Most pipelines optimize for a single, static interface. They score a candidate based on how well it packs against one conformation of the target, with maybe some light sampling around the backbone. But the protein your binder actually encounters in solution, on a cell surface, or in an assay isn’t frozen. A designed interface that looks perfect against the crystal structure may be competing with a conformation that buries the epitope entirely, or that rearranges key side chains at the binding site. You don’t see that with RosettaFold or Chai or Boltz.

I don’t want to point any fingers, but I’m afraid part of why there seems to be a disconnect between the papers and the reality stems from the way binder benchmarks are highlighted in papers. State-of-the-art generative design tools typically report experimental hit rates in the single-digit percentages for novel targets because they specifically select from carefully chosen, well-behaved targets with rigid binding sites. It doesn’t work as well when you move to a GPCR, a cytokine receptor with a flexible extracellular domain, or a viral glycoprotein that samples multiple perfusion states. It’s basically guaranteed that your hit rate drops, it’s more a question of whether that’ll be 10-fold or 100-fold.

A second, more nuanced perspective is that the energy landscape of binding is shallow. The difference between a binder with nanomolar affinity and one that doesn’t bind at all can come down to one or two residue contacts worth of free energy (~1–3 kcal/mol). Current models, even good ones, don’t resolve energy differences at that scale reliably, meaning they have very poor resolution for single mutation variants. When your scoring function has noise on the order of the signal you’re trying to detect, you’re essentially gambling on which designs to take to the lab.

The solutions to these problems are pretty boring. Mostly it comes down to finding a way to unify dynamics with the current frontier models. Molecular dynamics tries to get at this, but they’re a PITA to use and have their own limitations. I’m sure someone’s working on this. Once that’s solved, I expect paper hit rates to become real.

Niko McCarty @NikoMcCarty

What is the connection between certain viruses (ie Coxsackie) and various chronic diseases? Is this understudied?

The broader virus-chronic disease link is quite well-established at this point, so it feels odd to say it’s understudied but, well, it’s understudied. A current well-described example of one such virus is Ebstein-Barr Virus (EBV) better known to the public as the cause of mono. EBV is involved in a ton of processes. It can cause nasopharyngeal carcinoma, certain lymphomas, and more recently in 2022, was shown to be a major driver of multiple sclerosis. The MS component is important because of the amount of effort required to show the dynamics.

A Harvard group used millions of patient samples to demonstrate that EBV is essentially a prerequisite to developing MS, whereas the associations I mentioned before took significantly less effort. A component of that seems to be related to the fact that the kind of data required to demonstrate causality for disease processes like MS are molecular in a way that was not needed for cancer. For instance, for lymphoma, you can actually see, visually, the cells infected with EBV change their appearance and behavior. If you look for long enough, you’ll see them turn. Maybe MS has a similar thing, but we don’t know it yet and there’s no way to sample from the brain easily, so large data cohorts are required for now.

Time course of EBV infection. Doesn’t require being a doctor to see that something is going on there.

There are plenty of other examples here, by the way. T-cell leukemia/lymphoma is caused by HTLV-1, another virus, while hepatitis C can cause liver cancer if someone is infected chronically for long enough. Just last year there was the result that the shingles vaccine seems to stave off dementia (though maybe some selection bias was involved). I don’t have any strong opinion on the chances this will happen with Coxsackie virus, but it would not surprise me if it turns out to be implicated in some autoimmune processes, especially since there’s some evidence that it is related to type 1 diabetes. It seems like the challenge is mostly about finding the thing to anchor on when looking at large cohort studies, as was the case with EBV x MS.

Jonas Kubilius @qbilius

Top-3 diseases that could be tackled if we came up with novel (epi)genome editors (not just more prime editors)

You said three diseases, so the easiest answer is all diseases of the same mechanism: Huntington’s, myotonic dystrophy, and Fragile-X syndrome. They’re all caused by trinucleotide repeats that are hard to address with existing prime editors. What you would ideally want is a way to silence expanded alleles while preserving safe copies – a task that lends itself well to epigenetics.

Imprinting disorders like Prader-Willi or Angelman seem viable here, too. They are caused by unintended silencing of either the maternal (Prader-Willi) or paternal (Angelman) copies of chromosomes. Reversing that cannot be done with gene editors, but can theoretically be done with epigenome ones. I’m less optimistic that you can undo the developmental consequences of that imprinting, though.

Jonas Kubilius @qbilius

In one of your pieces/podcasts you were skeptical of personalized genome editor costs ever going down below 100k. Why is that?

To clarify my position, I view this as more of a delivery problem than an editor one. The core issue is that even if you have a perfect genetic cargo that can solve a particular disease, we don’t have great tools that reliably get it all where you need it to. Delivery is just not a solved problem, with the exception of the liver where cheaper options like lipid nanoparticles are viable. For every other organ, the best tools we have are viruses, and until gene therapies can move beyond either natural or synthetic viruses, I just really don’t see how the cost can go below $100,000. The reagents alone, ignoring regulatory costs, for an adult human sized batch of AAV or lentivirus might cost you $50,000 minimum. It’s likely way more if avoiding immediately killing someone with endotoxin from your production process is part of your objectives.

It’s not free to synthesize the input DNA required for human-sized batches of virus, and your reagents have to be actually sterile, which comes at a pricing premium. This is a place where cost saving on reagents makes you penny-wise but pound foolish. Throw in costs associated with trials and you’re talking about several hundred thousands more to the cost of the treatment, and that’s just to break even.

I do sincerely hope other, cheaper delivery modalities can pop up that give tissue-level resolution, but that hasn’t happened so far. For now, viruses are what we have. Everything comes at a cost.

Mosasaurus @mosasaurus27

Is it accurate to view/represent the cell as a machine?

No, it is not accurate, though it is convenient to say so. Unlike machines, cells are never off unless they are dead. Processes are dynamic. Pathways do not pause, but rather just slow down. Everything is happening chaotically all the time, with inputs and outputs feeding into their own pathway and kicking off other processes, too. A neural network feels like a slightly better representation of how a cell behaves, which means that computation can serve as a tool for modeling cells. But an actual machine, no.

My old colleague Duo Peng and his team at CZI have some interesting work towards establishing a better model for how a cell behaves, but it’s not quite there yet.

Ashlee Vance @ashleevance

What’s the deal with the dog cancer thing?

Rosie and The Australian Cure is an interesting case of all discourse simultaneously being right and wrong, my own contributions included. If I had to summarize, the current stances are scientists saying the treatment doesn’t work, and the tech world saying they’re missing the point, and this is all about one man’s extreme agency being turbocharged by AI. On the surface, it’s the story of an Australian entrepreneur using his financial resources and AI to act outside of the healthcare system to treat his beloved pet of her cancer. Heartwarming. Sadly, misleading.

According the reporting we have so far, Rosie’s owner used ChatGPT to design an mRNA vaccine against her solid tissue tumor, resulting in a 50% reduction in size. The use of consumer AI to troubleshoot illness is cool in its own right, but the trouble comes when techno-optimism pushes the suggestion that the cure to cancer is at our fingertips thanks to Grok. Truthfully, there’s reason to want to pump the brakes a little bit here. Namely, we don’t even know if the guy’s treatment even did anything because he simultaneously treated his dog with a checkpoint inhibitor, which has already been established as a treatment for her cancer type. The technical details are scant as of now, but the ones we do have, especially around the use of AlphaFold in the process, also has me scratching my head a bit.

I completely understand the desire for optimism. It is empowering to believe that on-demand intelligence will allow everyday people to address the problems in their lives, but it’s just not the case that AI added much here. If you’re looking for examples of that being true, there are better stories that champion the use of AI in giving laymen the capabilities of the scientific elite – even examples in humans! Check out the story of Sid, who used his resources and AI to treat his own cancer.

Why the need to stretch the truth? I suspect a part of it is a desire to believe that we can completely discard the shackles of establishment science. Peptides have shown that people feel they are as capable as doctors and scientists when it comes to their own health. I think this is just another instance of that. But unlike the inflammation meme perpetuated by Elite Human Capital of SF, which is embarrassing but mostly harmless, creating false hope for diseases like cancer is horrible. It’s actually worse than horrible, it’s shameful, and I think caution is in order.

People with limited time left get desperate and justifiably reach out to grab everything and anything they think will help them. Abandoning mechanisms to separate the wheat from the chaff – which is what clinical trials are meant to do – will mean condemning people who picked wrong.

Let’s see how this story plays out and revisit it in a couple months. As I see it, there are enough scientific gaps that call for skepticism.

Happy Friday.

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Paul Conyngham and his dog Rosie gained worldwide attention over the past week for breaking new medical ground. Using a variety of artificial intelligence tools, Conyngham – and some doctors and scientists in Australia – managed to create a personalized (petalized?) cancer treatment for Rosie that appears to be working.

The story resonated with the public for a couple of big reasons. First off, Conyngham has no real science or biology background. He’s a longtime AI researcher who used things like ChatGPT, Gemini and Grok to give him a plan for how to attack Rosie’s untreatable cancer and then how to craft and shape a unique mRNA shot for his pup. This exercise demonstrated the powers of AI technology to aid all of us with extra knowledge and skills and just how far bio-tech has come in terms of new cancer therapies.

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Most people have had their hearts warmed by the tale of Paul and Rosie. Dude’s dog is dying. Dude goes to great lengths to try and solve the problem. Dude and his dog seem to mark a major moment for AI and medicine.

Some other people on the internet, however, are less excited by the story. They argue that the AI tools did very little here and that the science isn’t terribly conclusive or ground-breaking. Companies like Moderna and BioNTech already have personalized cancer vaccine data in trials, and it looks good. Who cares if we did the same thing for a dog? Rosie has also been treated with chemotherapy drugs, so we don’t even know if the mRNA technology is really the thing shrinking her tumors. And so on.

You can find some of the major criticisms here and here.

Some of the pushback may be valid, although Conyngham isn’t having it – as you’ll hear in the episode. It also sort of misses the point of this story.

After talking to Conyngham, it’s clear enough to me that he used AI in some profound ways here and that what was done with Rosie is symbolic of a huge shift in medicine. Regulators better get ready because the tools now exist for people to do rather daring experimentation on their pets and themselves. People in dire circumstances and with some means are going to be pushing the boundaries of what’s possible on a regular basis.

Paul and Rosie hit a nerve because their journey bundled up some massive technological and societal shifts into a tidy narrative.

Anyway, come listen to Paul and have a peek at Rosie.

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This podcast is sponsored by Brex, the intelligent finance platform built to help companies spend smarter and move faster.

We run on Brex and so should you. Learn more about Brex right here.

The podcast is also made possible by E1 Ventures, which backs the most ambitious founders and start-ups.

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The brain, insulated in a cooler, came in through the back door of a start-up called Bexorg located in New Haven, Connecticut. It then went into the hands of Josip Butkovic, a Croatian-born surgeon, who carried the package to a prep table in Bexorg’s basement. Next, Butkovic removed the brain and placed it in a metal bowl. Gelatinous and shiny, the brain spread out to push against the bowl’s edges.

Butkovic, dressed in scrubs, spent a half-hour inspecting the brain and repairing some of its vasculature. He also began inserting tubes and valves into the organ. Shortly thereafter, he placed the brain onto a cart and transported it upstairs into a room that looked part laboratory and part hospital. It had computers and various types of testing equipment throughout and then several large, rectangular pods that were enclosed in plexiglass and plastic to protect them from things floating in the air and that were filled with medical equipment. Butkovic brought the brain into one of these pods and started to attach it to a machine of Bexorg’s invention – a hardware system that could advance brain science and drug development in astonishing ways.

The machine is what’s known as a perfusion device, meaning that it perfuses or passes fluids and gases through an organ to keep it functional. Doctors use something similar – a heart-lung bypass machine - during open heart surgery to replicate the work of the heart and the lungs and circulate oxygenated blood through the body. No one, though, has created an equivalent machine for the brain - except for Bexorg. The start-up’s scientists, after many years of research, have developed a way to keep a human brain going outside of the body for up to a day.

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This is the part of the story where some of you will be wondering if/fearing that the brains have been reanimated and are perhaps once again alive in a thinking or feeling sense. To which, Bexorg’s co-founders - Zvonimir Vrselja and Nenad Sestan - would say, “No.” The brains come from donors and, by the time they arrive at Bexorg, they’ve been dead for several hours. They have no electrical activity. The neurons inside are not firing. What Bexorg is doing is reviving the brains on a molecular level and restoring their base, biological function like metabolizing oxygen and glucose.

The brains Bexorg receives often come from people who were afflicted with conditions like dementia and Parkinson’s and Alzheimer’s disease. Bexorg connects these brains to its perfusion system so that it can test new therapies on the organs and then measure the results. And this is a very big deal because we currently lack a good way to test drugs on the brains of humans. People, after all, don’t like their brains to be experimented on while they’re alive nor is there a convenient means of sampling from a living brain to see what’s happening inside of it.

For these reasons, most new brain therapies are tested first on rodents and then sometimes primates but with abysmal results. By the time a drug gets near a clinical trial in humans, a pharma company might have spent a decade and a $1 billion or more. And, at that point, its drug will likely still fail. Drugs aimed at central nervous system conditions have a failure rate that hovers around 95 percent, while therapies meant to slow or reverse Parkinson’s and Alzheimer’s fail about 99 percent of the time.

Bexorg’s hope, then, is to give researchers a way to test their drugs much earlier on real human brains and to see what works and what doesn’t before tons of money has been spent. “We are not doing this as a research project,” says Vrselja, Bexorg’s CEO. “I want to see therapies for Alzheimer’s and for Parkinson’s. There are people waiting for those drugs, and we want to push as quickly as possible to get those drugs and those cures to them.”

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The story of the U.S. solar industry over the past twenty years is a painful read. Technologies invented at U.S. universities and national laboratories made their way to China where they were perfect…

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It’s not just lodging, Airbnb calls it belonging. Uber doesn’t hire taxi drivers, they employ partners. That ethos — grandiose stories g…

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These cells with a clunky name hold the promise of being able to reverse the aging process across our bodies. Put rather bluntly, your old, wine-soaked liver could become like your twenty-something, Jell-O-shot-soaked liver. Your aging neurons could fire like they once did. And your tired heart could be fresh and loving again.

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Billions of dollars have been funneled toward trying to figure out how to push iPSCs into our organs safely and effectively. We have not cracked the code yet, but there are signs that scientists are getting closer.

Nabiha Saklayen, the co-founder and CEO of Cellino Bio, is an iPSC whiz and joined the podcast this week to bring us all up to speed on the technology. She covers how iPSCs work, their history and the state of iPSC treatments around the world.

Her company is trying to take iPSCs, which have largely been made by hand, and mass produce them to accelerate experimentation and hopefully therapies and to reduce costs around this fascinating technology.

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The Core Memory podcast is on all major platforms and on our YouTube channel over here. If you enjoy the show, please leave a review and tell your friends.

This podcast is sponsored by Brex, the intelligent finance platform built to help companies spend smarter and move faster.

We run on Brex and so should you. Learn more about Brex right here.

The podcast is also made possible by E1 Ventures, which backs the most ambitious founders and start-ups.

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He thinks the world will fill with poorly written code that no one understands and that software bugs will proliferate through critical systems. Your airplane that has gotten safer and safer with each passing decade will be running on code that no one has really checked all that well. Which would be bad.

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What’s more, Wilson fears that humans will lose their software writing skills over time as AI takes on more and more tasks. We’ll become dumber as a whole. Which would also be bad.

Wilson is a mathematician turned start-up founder who built the company Antithesis in a bid to modernize software testing techniques and help humans write better code.

In this episode, we get into his life story, his fears around AI software and what he thinks we should do to make massive improvements to the code that underlies everything.

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The Core Memory podcast is on all major platforms and on our YouTube channel over here. If you enjoy the show, please leave a review and tell your friends.

This podcast is sponsored by Brex, the intelligent finance platform built to help companies spend smarter and move faster.

We run on Brex and so should you. Learn more about Brex right here.

The podcast is also made possible by E1 Ventures, which backs the most ambitious founders and start-ups.

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(If you can’t tell, we’re going a little off schedule with this week’s podcast. I heard about Prouse’s story from a friend and had no choice but to have her on the show.)

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Born in Italy, Prouse is a former journalist who ended up in Iraq in 2003 and went to work trying to rebuild the country’s health infrastructure first for the Red Cross and then on behalf of the U.S. government. She lived in constant danger for many years and proved adept at moving between the U.S., Iraqi and Iranian powers because of her unique approach to problem-solving.

More recently, Prouse has worked in Silicon Valley, including a stint at Google where she found complaints from the workforce about the quality of the quinoa and sushi quite comical.

As if her career was not dramatic enough, Prouse also survived a brain tumor during what were meant to be her easier years.

We discuss all of this in the show, using Prouse’s best-selling memoir as a guide through her journey.

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The Core Memory podcast is on all major platforms and on our YouTube channel over here. If you enjoy the show, please leave a review and tell your friends.

This podcast is sponsored by Brex, the intelligent finance platform built to help companies spend smarter and move faster.

We run on Brex and so should you. Learn more about Brex right here.

The podcast is also made possible by E1 Ventures, which backs the most ambitious founders and start-ups.'

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Every decision a woman makes about when to have children is made against this clock. The clock does not pause for careers, for bad timing, for partners who aren’t ready, for cancer treatment at 30.

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It’s a bottleneck that never made sense to synthetic biologist Merrick Pierson Smela. With the help of modern tools of genetics, we’ve managed to turn skin into stem cells and edit human genomes to cure diseases. Why shouldn’t it be possible to help women make new eggs?

Last year, he founded Ovelle Bio in Boston, to manufacture human eggs from stem cells. The process is called in vitro gametogenesis, or IVG. If it works, the clock stops. Eggs become renewable. And the most fundamental constraint on female fertility disappears.

To do it, they have their eyes set on beating nature at its own game. “Our approach results in a method that’s faster and more efficient than natural biology,” Smela shares.

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We just spent ten days filming in Los Angeles, and I have come back with thoughts. Mostly that the U.S. is totally fucked, but there are signs of life and hope.

People have been writing about rise of The Gundo for a while now. This refers to El Segundo, Calif. and the collection of twenty-somethings who have formed start-ups there. Many of these young companies center on hardware and on defense and on aerospace, and then there’s also nuclear reactor action, geoengineering, mining, additive manufacturing and other hard-tech things happening.

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The Gundo has a vibe. It’s young, testosterone-rich, patriotic and somewhat Christian. Some of this feels like theater to me, and, frankly, The Gundo receives too much attention. It’s not really the thing.

The thing is Los Angeles and its mass of interconnected cities.

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A dozen or so journalists sat around a white oak table. Most of them were stiff and quiet. A few others were more active, clacking away on their keyboards in a bid to note every word tumbling out of the executives’ mouths. Perplexity powers curiosity glowed on a projector screen in a pleasant serif style.

The press wasn’t useful to Perplexity, one of the executives explained. The company doesn’t want to waste time on podcasts. They’re too focused on shipping features to engage in insular, navel-gazing AI drama. We don’t have fancy houses with wonderful dinners to impress you, he added. The chairs creaked in response.

Sometime during this executive’s spiel—maybe after his Epstein joke failed to get a reaction and he anxiously asked if it was recorded—I wrote that Perplexity seemed to have forgotten what it was trying to be.

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Boyden has helped develop new techniques for imaging the brain and the body, including optogenetics and expansion microscopy. He’s also known for nurturing all-star talent at his lab at MIT and he and his students have gone on to form numerous bio-tech start-ups. Overall, Boyden is regarded as one of the top scientific minds of this era.

It was a genuine honor to have Boyden on the show, and we’re sure you’ll enjoy this episode.

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In this episode, filmed at Boyden’s office, we discuss his background as a child prodigy, his work and what it might actually mean to engineer something like a mind or consciousness. We also get into Boyden’s skepticism around current large language models and the state of science funding in the U.S.

The Core Memory podcast is on all major platforms and on our YouTube channel over here. If you enjoy the show, please leave a review and tell your friends.

This podcast is sponsored by Brex, the intelligent finance platform built to help companies spend smarter and move faster.

We run on Brex and so should you. Learn more about Brex right here.

The podcast is also made possible by E1 Ventures, which backs the most ambitious founders and start-ups.

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Cometeer flash freezes som…

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But, for Peter Barrett, we will always make an exception. He’s a general partner at Playground Global and is one of those people who knows an awful lot about an awful lot of things. He is one of my favorite people to listen to and gets my mind racing with tons of new ideas every time we speak.

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Self-taught, Peter spent the early part of his career as a force of nature in the software industry. He was visited by the Men In Black as a teenager. He helped start Rocket Science Games, which was the hottest video game maker in town before it wasn’t. While there, Peter happened to employ a young intern named Elon Musk. . .

Later, Peter would be part of the team that created WebTV and a longtime distinguished engineer at Microsoft, working alongside Bill Gates and Steve Ballmer.

These days Peter goes deep on deep tech at Playground. As such, we talk quantum computing, the insane world of AI agents, nuclear power, data centers in space (and why they won’t work) and whether or not humans should be in total panic.

The Core Memory podcast is on all major platforms and on our YouTube channel over here. If you enjoy the show, please leave a review and tell your friends.

This podcast is sponsored by Brex, the intelligent finance platform built to help companies spend smarter and move faster.

We run on Brex and so should you. Learn more about Brex right here.

The podcast is also made possible by E1 Ventures, which backs the most ambitious founders and start-ups.

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