Dwarkesh Patel: prediction

Transcript context

…Eroom's Law is a funny portmanteau created by a friend of mine, Jack Scannell. He inverted the notion of Moore's Law, which is the doubling of compute density on silicon chips every few years. Moore's Law has graciously given us massive increases in compute performance over several decades. Eroom's Law is the inverse of that. In biopharma, what we're actually seeing is that there's a very consistent decrease in the number of new molecular entities, new medicines that we're able to invent, per billion dollars invested. This trend actually starts way back in the 1950s and persists through many different technological transitions along the way. It seems to be an incredibly consistent feature of trying to make new medicines. In a weird way, Eroom's Law is actually very similar to the scaling laws you have in ML, where you have this very consistent logarithmic relationship. You throw in more inputs and you get consistently diminishing outputs. The difference, of course, is that this trend in ML has been used to raise exponentially more investment and to drive more hype towards AI. Whereas in biotech, modulo NewLimit's new round, it has driven down valuations, driven down excitement and energy. With AI at least you can internalize the extra cost and the extra benefits because there's this general purpose model you're training. This year you spend $100 million training a model, next year $1 billion, the year after that, $10 billion. But it's one general purpose model, unlike, “We made money on this drug and now we're going to use that money to invest in 10 different drugs in 10 different bespoke ways. ” I was gearing up to ask you, what would a general purpose platform—where even if you had diminishing returns, at least you can have this less bespoke way of designing drugs—look like for biotech? I'm going to slightly dodge your question first to maybe analyze something really interesting that you highlighted. You have these two phenomena: ML scaling and then scaling in terms of the cost for new drug discovery. Why is it that the patterns of investment have been so different? There are probably two key features that might explain this difference. One is that the returns to the scaled output in the case of ML actually are expected to increase super exponentially. If you actually reach AGI, it's going to be a much larger value than just even a few logs back on the performance curve that people are following. Whereas in the life sciences thus far, each of those products we're generating further and further out on the Eroom's Law curve as time moves forward, haven't necessarily scaled in their potential revenue and their potential returns quite so much. You're seeing these increased costs not counterbalanced by increased ROI. The other piece of it that you highlighted is that it’s unlike building a general model where potentially by making larger investments, you can be able to solve a broader addressable market, moving from solving very narrow tasks to eventually replacing large fractions of white collar intelligence. In biotech, when you're traditionally able to develop a medicine in a given indication—”I was able to treat disease X”—it doesn't necessarily engender you to be able to then treat “disease Y” more readily. Typically where these biotech firms in general have been able to develop unique expertise is on making molecules to target particular genes, so “I'm really good at making a molecule that intervenes on gene X or gene Y.” It turns out that the ability to make those molecules more rapidly isn't actually reducing the largest risk in the process. This means that the ability to go from one or two outputs one year to then four the next is much more limited. This brings us then to the question of what the general model would be in biology. I think it reduces down to how do you actually imbue those two properties that create the ML scaling law curve of hope and bring those over to biology so that you can take the Eroom's law curve and potentially give it the same sort of potential beneficial spin. There are a few different versions of this you could imagine. But I'll address the first point. How do you get to a place where you're actually able to generate more revenue per medicine so that potentially the outputs you're generating are more valuable, even if each output might cost a bit more? Traditionally, when we've developed medicines, we go after fairly narrow indications, meaning diseases that fairly small numbers of people get. That's actually increased, in terms of the narrow scope of what medicines are addressing, as we've gone forward in time.…

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