I posted the original comment, but there are multiple reasons why math success won't translate into drug development or materials engineering success. It is easy to create verifiable rewards environments for math and coding, and use the resulting data for RLVR post-training. For other domains you need machines or robots creating and running experiments. This is simply something that's never been attempted at scale.

Getting IPO level monies should enable the amount of capital needed to sustain such efforts, and if I were a betting man, that is going to be Anthropic's actual endgame given their nascent efforts in automating bio research.

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kaffekaka an hour ago | parent | next [-]

Do you think their endgame is achievable? Honest question, your reasoning seems balanced.

	▲	LarsDu88 an hour ago \| parent [-]
		I work in biotech. Honestly, I think they will run into massive hurdles. Amodei's academic background is relevant for protein engineering. Hence the recent press releases Anthropic has released which all concern prototype proof-of-concepts related to binding efficacy. For a large number of therapeutic relevant indications, the bottleneck is actually clinical trials where you must contend with the sideeffects of any given therapeutic. Almost every drug I've worked on involves very well known mechanisms where knockdowns with targeted compounds will work in a petri dish, but can have catastrophic consequences when dosed on a live human. As far as I can see there is no way to accelerate clinical trials in humans. Even the most fantastically large and detailed perturbation datasets will struggle to capture this dynamic in a full human. This is why tech oligarchs are investing in virtual cell models. With a large automated research lab running perturbations 24/7 and capturing massive amounts of data, perhaps we will get as confident with predicting outcomes in cells as we now are confident with many coding projects. However, this is a scientific question, not a technological one. Public companies rarely give the people running them leeway to try and fail on big scientific question marks as opposed to well established or soon-to-be well established technology.

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chasd00 37 minutes ago | parent | prev [-]

> This is simply something that's never been attempted at scale.

isn't that the point though? You invest in a company so they can make these attempts. If it works you make money if it doesn't you lose money, that's the risk.