I think a lot of discussion around the sucess or failure of Anthropic and OpenAI seems to hinge on their current uses, but people seem to have amnesia regarding the fact that Claude code wasn't even a thing until about 1 year ago. There are far wider markets these companies can tap on if they are well capitalized even as Chinese "actually open AI" commoditizes things.
There are moonshot projects related to superconductors, materials, and biology that these companies can claim progress on to keep valuations stratospheric.
You're just glancing over every single niche they've tried to launch and failed to get any traction on.
The only one that stuck and was revenue generating has been code and Chinese models are pretty much on par.
The situation in math is rapidly evolving. Don't see why it shouldn't eventually spill to other fields at least to a degree.
Math is an interesting case. Arguably a lot of the groundwork was already there. There’s a huge body of academic literature that’s historically been difficult for humans to draw on for various reasons. It’s relatively easier for LLMs to mine all of that for relevant prior work that it can recycle into proof segments to try. And we have theorem proving software that it can use to try out these proof segments and validate them. That creates a tight, self-contained, and relatively inexpensive feedback loop that lends itself well to a host of agents grinding away, as long as you have budget to pay for all that LLM inference.
I’m not sure the same conditions exist in other fields that deal with real world phenomena. Experiments to test hypotheses about biology or physics can take years and cost millions of dollars to run. They rarely scale down to the millipennies and milliseconds scale that makes letting a swarm of agents throw a bunch of shit at the wall and see what sticks a feasible option for finding mathematical proofs.
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.
Do you think their endgame is achievable? Honest question, your reasoning seems balanced.
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.
> 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.
There's not much money in math. The frontier labs massively outspent mathematics departments to achieve recent results.
The sort of mathematics that has been published recently is very particular. Which isn't really downplaying it but it is to say that there's a pretty massive gap between mathematics and semiconductors or wetlab work. I don't want to say anything totally deflationary like "AI will never effect research in biology" or something like that, but I think the contribution of AI in those kind of fields will be much more modest.
> Don't see why it shouldn't
Don't see why it should! Unless there's a harness like in math for other fields.
lol, no it's not, the machine that is built to find connections between concepts found connections, that's it.
What has evolved is that mathematicians are pearl clutching because they never thought it would happen to them and wondering if they can still pour all their ideas / thoughts / research into these tools after all is said and done.
That last point is the one that kills OAI / Ant going forward. Can they be trusted? Anecdotally no.
>You're just glancing over every
You're glossing over
> every single niche they've tried to launch and failed to get any traction on.
This isn't true. I personally know a company that has trained over 300 companies this year to use Claude Cowork mostly for "business operations" type tasks.
People love offloading boring, repetitive tasks to it. And the spend is spending - most start on a free plan and after 3 months have multiple people on the Teams plans.
> Chinese models are pretty much on par.
There isn't even an open source harness that is close to Cowork. ChatGPT work is closest, but everything else if coding focused and a mess for people who aren't coders.
Shall we go through the list.
I’ll start - Claude for finance.
Don't forget that they have to rent chips, Google doesn't.
This is an odd distinction.
Google still has to build and run the chips they rent to Anthropic. I think from their last earnings Google makes ~30% on it, excluding R&D costs. It's a nice business for them, but hardly the thing that is going to make or break Anthropic as a business.
So paying retail (or best case, wholesale) is better for basically the same product?
It can be, yes.
It's certainly not a clear downside.