The Bitter Lesson for Biology — Adam Green on Virtual Cells and Scaling Laws

Markov Biosciences, a startup in San Francisco, is betting that virtual cells will soon have their GPT moment.Jun 12, 2026Markov Biosciences, a startup in San Francisco, is betting that biology is about to have its GPT moment. In this episode, founder Adam Green explains the "bitter lesson" for biology, the idea borrowed from Richard Sutton that large unbiased datasets and the right training objective tend to outcompete models with hard-coded rules and human priors. Adam thinks, in particular, that the virtual cell field overinvested in collecting expensive perturbation data. Green’s counterargument is that the data needed to train useful virtual cells is not limiting, but rather compute (and the loss function) are. By treating single-cell RNA-seq as a ranking problem rather than raw counts (a century-old idea traceable to a 1927 psychophysics paper), they found that virtual cells pre-trained on plain observational data show clean scaling laws, getting monotonically better at predicting unseen perturbations as the models grow, and beating a state-of-the-art model built specifically for that task.If you’d like to sponsor a future episode, please email nsmccarty3@gmail.com. To listen to this episode, search for “The New Biology” on your favorite podcast app.00:00 — Cold open 01:58 — First prospective clinical predictions from a virtual cell model 05:38 — What is a "virtual cell"? 08:01 — The problems with single-cell RNA-seq 11:31 — The urns analogy 19:54 — Why RNA, and observational vs. perturbational data 23:29 — The bitter lesson for biology 29:06 — Generative ranking and geometric Plackett-Luce 38:27 — Ablations and loss function 47:23 — Cells as specimens 59:26 — The Antibody-Drug conjugate case study1:11:16 — Will we ever understand biology?Adam Green (00:00) Yeah, I think we’re talking past each other.Niko McCarty (00:02) Like what Markus Covert is doing—saying, I want to simulate a cell using mathematical equations.

Is that kind of thing useful?Adam Green (00:11) I think it’s a fun abstraction. The sort of unsupervised pre-training and scaling we saw in text, images, protein sequence modeling is not going to work in the same way when we bring it to single-cell biology, and therefore we need a new approach. If you said that in 2018, it’s insane. When I say something like that now in 2026 about biological world models, people think you’re insane. As you scale the model up and it saw more and more observational data—then you fine-tune a tiny bit of perturbational data, and then you evaluate it on perturbations it has not seen. It gets monotonically better at that task. So much so that it beats the current state-of-the-art model that was pre-trained on perturbational data with multiple injected knowledge sources, specifically for the task of perturbation prediction. Yeah, I think long term the ambition is solved biology.Niko McCarty (01:03) Today’s guest is Adam Green. He’s the founder of Markov Biosciences, a company building a virtual cell for biology. And he has some viewpoints about how to train these models that differ from the mainstream. One of the things I really want to get at in this conversation is this idea of: will we ever develop a complete understanding of the cell? And if so, how will we do that? Will we do it using black-box models with sparse autoencoders, where we can interpret the outputs of that model? Or will we ever be able to build a bottom-up mechanistic understanding of the cell? But before we go there, I want to ask Adam about a recent paper that Markov put out, where they made very specific predictions about a class of drugs known as antibody-drug conjugates in cancer. So Adam, welcome.Adam Green (01:58) Yeah, good to be here. So we put out a paper—if you could call it that, a Twitter article currently—on a particular antibody-drug conjugate. So antibody-drug conjugates are the hottest modality in oncology right now. There are hundreds of clinical trials ongoing. The basic concept is: small molecules are promiscuous, they’re hard to target any particular cell type with.

But antibodies are quite specific. And so, what if you were to conjugate an antibody with a small molecule or some kind of payload, and use it as a kind of precision-guided payload delivery system to a cancer cell? People have been trying this for, I guess, two-plus decades. And we looked at one of the most popular targets for these ADCs, which is TROP2. TROP2 shows up on many of these epithelial tumors—lung, breast, bladder. And the surprising thing: we found no one really knew how the complex of the antibody bound to the receptor internalizes into the cell to deliver the payload. It’s something you’d think would obviously be known, given that thousands of patients have been dosed. There are already approvals for these ADCs. And so we took a virtual cell and we queried it, and we said: what is providing the ride for this receptor across the membrane, and then after that, how does it traffic inside the cell to reach its destination? I think our model came up with a pretty clear prediction. It’s falsifiable. It seems to converge with other lines of evidence from clinical pharmacokinetics, tumor expression. And what makes it interesting is, I think it is the first prospective prediction from a virtual cell with real clinical stakes and large sums of pharma revenue on the line. It could pan out, it might not pan out, but as a class of thing that can be done with virtual cells, I think it is unique and the first of its kind.Niko McCarty (04:03) What were the actual predictions, and do you know of anybody testing these? Are you planning to fund experimental studies to do this, or are you just kind of hoping that the big pharma companies will test your predictions?Adam Green (04:18) We’ve scoped experimental packages with CROs to test the predictions, all the way from the initial mechanism—which we believe is the co-localization of this receptor with a particular tetraspanin, which is a special type of protein that we think organizes the trafficking, or the internalization, of this complex into the cell. And so we have a bunch of experiments looking at two different drugs: Datroway, which is AstraZeneca’s drug, and Gilead’s drug, Trodelvy. And what we think explains the difference in their two pharmacokinetics, their clinical outcomes.

Niko McCarty (05:03) And just to clarify: you made these predictions using a virtual cell model that Markov trained, and you did so without any underlying biological knowledge about ADCs. What I want to ask, basically, is: okay, ninety percent of drugs fail, and everybody talks about Eroom’s law. My question is, in which ways would virtual cell models, like the kind that Markov is training, actually increase that efficacy? Because we haven’t really seen evidence yet that virtual cell models actually improve clinical success rates of drugs.Adam Green (05:38) Yeah, it’s a big question. Maybe it’d be useful to step back and define this term “virtual cell,” because it’s pretty nebulous. Personally, I’m not in favor of the term. I think it’s been so debased as to be beyond use. But generally, what people are gesturing at with this term is a machine learning model trained on some sort of biological data that does something. That’s not a very useful definition, but it’s such a big umbrella that that’s basically what it means. Two axes that you could parse this at are: the scope of the system you’re concerned with—so maybe it is actually at the level of the cell, as the name might imply; maybe it’s at the level of a spatial tumor biopsy; maybe it’s even at the level of clinical response. That’s one axis. And then the other axis is: how do we relate to these things as scientific objects? What do we expect of them? The distinction I like here is between simulators and specimens. A simulator, I think, is the dominant view of what a virtual cell is. It is a stand-in for experiments we’d otherwise have to run in the lab. And the thinking goes something like: experiments are really important for biology, for whatever reason. They’re slow, they’re noisy, they’re often costly. If you had a computational stand-in for these experiments that are costly, take a long time, and are really noisy, and you could run it at basically zero cost, it would somehow accelerate biomedical progress. The alternative view, and the one I think we subscribe to at Markov, is that virtual cells are going to be more useful as specimens.

By this I mean: if you train a machine learning model on biological data the right way, it should learn—in making the loss go down—something about the nature of the underlying biological system you’re trying to model. The tough part is, how do you actually extract that understanding from the model and make it useful?Niko McCarty (08:01) I want to understand how Markov trains a virtual cell. Obviously you have some belief that single-cell RNA-seq is the right sort of data to collect, but you feel that there are serious flaws with that data. So I want to go through all that, and then along the way kind of understand what other people are doing and how Markov differs. So let’s just start with a discussion of how people capture single-cell RNA-seq data, and what are the sources of bias in that data collection when somebody measures the transcripts within a cell?Adam Green (08:38) So when you do this process of encapsulating a cell in a droplet and lysing its contents, there are a few sources of technical noise that can emerge. In the case of these polyadenylated capture methods like 10x 3’, one step is just: how many of the transcripts do you capture? You can imagine a cell is quite literally a bag of molecules. It’s a bunch of RNAs, proteins, et cetera, floating in solution. And if you want to get an accurate representation of that cell, the thing you’d want to do is capture all of its contents. But due to quirks of the library chemistry, you usually only capture a subset. Initially this is quite low, on the order of sub-ten percent. With modern library prep techniques, you’re getting thirty-plus percent. So it’s better.Niko McCarty (09:31) So how many transcripts does a typical human cell have floating around?Adam Green (09:37) Yeah, it depends on the cell type—is it highly differentiated or not. But say, on the order of a hundred thousand is a rough estimate.Niko McCarty (09:47) So you might capture twenty thousand of those transcripts.Adam Green (09:50) The big question is, how many of the transcripts do you capture? And then downstream, what proportion of them get sequenced and show up?