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.
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.
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.
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.
Don't see why it should! Unless there's a harness like in math for other fields.
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 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.
I’ll start - Claude for finance.