This is not true. There are two companies at the center of AI direction: Anthropic and OpenAI. If there were anyone on the plant who has the ability to influence our direction then it would be Dario Amodei.

Acknowledging the grievances is a good step, but it's not enough. There needs to be a clear explanation of actions to address them, a plan to enact those actions, and commitments with consequences in failure of those actions. Tell people how you're going to make them more employable and effective and needed. Tell people how your datacenters will be carbon neutral. Tell people how financial actions resulting in a frothy market will be coming to an end. He and Sam Altman alone have this power and their inaction says everything we need to know about their intent.

Possibly, but not necessarily in a better direction. And that's the whole problem here. They should all go watch Phantasia.

Hoping that they solve those huge problems themselves is basically hoping that they will have more power.

It would be like expecting Craigslist to rescue journalism. The amount of power needed to disrupt an industry is nowhere near what's needed to fix it.

If you don't tackle the societal level problems, nobody will care about research breakthroughs.

I don't think anyone will care about the largely made up issues about electricity prices or water use in case AI literally cures cancer.

His body was healthy, aside from the bullet in his skull.

But something tells me either they can't or they won't, so no trust will be built.

I just don't buy the AI labs approach to this stuff. Like, unless we can basically simulate the entirety of human biology, I don't really see how LLMs can make progress here. Maths is different as it doesn't require a real-world interface, and programming already (by definition) can be simulated on a computer.

Without that, I can't see much (if any) progress being made on domains like biology.

Drug discovery is similar AFAIK. The space of possibilities is even larger than protein folding, but it's structurally similar enough that I think AI will help to make progress on the discovery side. Actually getting the drug tested and approved is another matter though for sure.

For one, the question for Anthropic is whether LLMs, specifically, not AI techniques more generally, can help significantly with cancer research. And here, all experience so far is that LLMs only really work when they can easily automatically verify their own outputs and self correct - such as in math (using automatic proof verifiers) or programming (using compilers and unit tests).

The second problem is that biological research speed is highly dependent on slow biological processes, such as cultures and long term studies. In programming, if an LLM could provide excellent insights and research suggestions 100x faster than a human, it would speed up the work roughly 100x. But in biology, it would only speed up the total work by a small amount - as any insight, even if absolutely brilliant and spot on, would still require months and years of actual experimentation.

If I was Dario, and I was after a cynical sales grab to go with the "actually curing cancer!" spiel, I'd probably aim for a drug repurposing strategy. I'd use the above approach to find existing drugs that might target known pathways that drive incurable cancers. I'd only screen compounds with extensive safety data and easy delivery mechanisms, and from the in silico hits, I'd throw a tonne of money at rapidly experimentally screening all those candidates in parallel, and then rapidly push those that worked into clinical trials. I'd assume that, with a small but non-negligible prior, and the money to push through hundreds and hundreds of candidate compounds through at once, I'd have a reasonable chance of getting one drug through to a "Claude Cured Cancer!" show-stopper headline.

But I think even then, with all of Anthropic's wealth, you'd need 2 years minimum to move from initial screening targets to a Phase 3 trial. And it would be an almost criminal waste of research funding to get there -- the dollars for discoveries ratio would be appallingly bad.

(If I was Dario, and I actually wanted to cure some forms of cancer? I'd just use my obscene profits to fund actual cancer research, step back, and let the researchers get on with it.)

I do agree that this kind of targeted approach makes sense.

However, discovery is not really the issue here. Running the clinical trials (1/2/3) is much much more difficult, and consumes basically all of the time in drug development, so even if LLMs perfectly automate this, the speedup will not be particularly large.

This is not correct, Alphafold predicts crystal structures of proteins. It is a known issue in the field that these folding models do not generate ensembles of protein conformations like the ensembles generated by DE Shaw Research (who built a super computer to simulate proteins). And these folding models fail when we don't have crystal structures, so they are certainly not generalizing. A great paper on the subject: https://www.biorxiv.org/content/10.1101/2025.02.03.636309v1

Drug discovery is significantly more complicated than protein folding. Small molecules with similar chemistries can adopt novel binding poses, can end up binding to off targets (PXR, hERG, etc) or simply never make it to the protein due to solubility or permeability.

I will always defer to Pat Walters who seems like the most careful and sane person in this field: https://patwalters.github.io/

Anthropic is literally creating the bubble. It is not beyond their scope of influence, it is literally what they are consciously achieving.

As for peoples jobs, same actually applies. Anthropic is selling itself on dream of replacing jobs, even or especially where they are well aware AI does not perform that well. They are actively trying to replace people quickly before management notices it does not work well.

And also, they can influence how much their data centers contribute to global warming.

Curing cancer is a societal problem. We have lots of cures for common diseases but resource allocation means people don't actually receive the treatment they need. For example, prohibitively expensive gene therapies, or HIV treatments in developing countries. A disease may have a "cure" but if people who need it don't receive it then from their perspective it may as well not exist.