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.
If you don't tackle the societal level problems, nobody will care about research breakthroughs.
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.
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.
But something tells me either they can't or they won't, so no trust will be built.
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.
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.