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Comment by ryanschaefer

19 hours ago

I’m confused why AI companies are using agents in-house for this type of research instead of partnering externally.

I guess the improvement loop is tighter and they have more control over how discoveries can be used for marketing?

But, in my mind, it begins to feel like they are setting themselves up to be “everything” companies instead of focusing on their core product…

54 comments

ryanschaefer

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Insanity  19 hours ago

Because their core product is not a long-term sustainable business strategy. Local hardware and models will continue to improve to the point of not needing the hosted solutions. And if you do need a hosted solution, remember that the big cloud providers already offer these solutions, so signing up for OpenAI/Anthropic _and_ AWS/GCP/Azure is not a sound business decision compared to just signing up with 1 of them that offers your cloud infra + GenAI infra. (Which is why the long-term benefits for cloud companies will probably be for the likes of AWS and not the likes of OpenAI).

They'll continue to burn money for marginal model improvements in the next few years all the while having no moat _and_ having Open-Weight / Local models eat their lunch.

The only way for them to stay relevant as a company is to expand beyond simply providing the models.

  • beachy  19 hours ago

    I'm old enough to remember the arrival of RDBMS, once IBM primed the space with DB2.

    There was a pitched battle over features like row-level locking as competitors like Sybase, Ingress and Oracle scrapped it out. New features arrived on a monthly cadence, with immense engineering effort behind them. The winners (Oracle mostly) won a great moat which led to them to where they are today.

    The fact that so many AI companies can produce amazing coding tools so quickly shows there is no moat, supporting your theory.

  • mordymoop  18 hours ago

    I would phrase it slightly differently.

    The companies who control the compute resources will ~always control the greatest "amount" of intelligence. They can lease that intelligence out, or they can use it themselves. Currently the "total amount of intelligence" or perhaps "total amount of ability-to-do-stuff" is split between humans and machines at a ratio that means it still makes sense to lease the machine intelligence to the human intelligence - plus there are things that humans are still better at. In maybe 2 more years that will stop being true, due to the availability of more physical compute resources, and far greater model intelligence per unit compute. At that point, the point at which the substantial majority of ability-to-do-stuff is controlled by machine intelligence, then the entities who control all the compute will control all the ability-to-do-stuff, i.e. "the economy."

    So I agree that the core product is not long-term sustainable as a product but this is because the whole world will look so different in the near future that the framing of intelligence as a "product" breaks down.

    Open-Weight models, of course, are fine and useful, but if you have one million times less compute than your competitor (the lab), then you're not really playing the same game. You can only tackle the problems that they have decided they're not interested in.

  • user43928  6 hours ago

    Open models still don't beat February's Mythos.

    I don't know if the gap will close or rather widen with more compute coming online.

    Being half a year to one year behind could be meaningful, not to mention that competitors may not have the necessary compute to train and serve models of a certain size.

    This could be a significant advantage for OpenAI and Anthropic, and if they make breakthroughs in robotics or science, that is worth far more than mediocre coding assistants.

jackb4040  19 hours ago

A chatbot for cancer researchers to talk to is worth single-digit billions at most. Anthropic is already valued at over a trillion dollars, on the premise that they can replace the majority of jobs in most knowledge industries. All the announcements about hacking / math problems / biological science are meant to create the impression that that strategy works and is repeatable across industries.

  • sigmoid10  19 hours ago

    Cancer research is a lot harder for LLMs than math millennium problems though, because there is no fast feedback loop to iterate on. Even if you have a really good idea based on a solid theoretical insight, doing the experiments using in-vitro/mice/monkeys/humans can take years or even decades. I have no doubt that AI will help find new avenues that boost certain parts of research in these fields, but I don't see a potential for a drastic change until we at the very least give LLMs a direct way to interact with lab equipment and train them using RL on it.

    • AbsurdCensor  18 hours ago

      Yes, but initial discovery of molecules and novel mechanisms is massive. That was the last generational change in modern drug research was the movement to high throughput screening, going from the ability to screen 10's of molecules to hundreds of thousands to find 'hits'. Better and more focused models, especially ones trained internally at big pharma companies will accelerate that portion of the pipeline, or increase the hit rate of successful compounds. Several companies are already taking this approach like Novo has been. There are other more early stage companies like Recursion and others that are doing the same thing. They are more tech companies than traditional wet lab companies.

    • jackb4040  18 hours ago

      Sorry, yes I agree 100%. I don't agree with their narrative, I was just explaining it. I think it's fraudulent and based on science fiction and will lead to a significant economic crisis.

  • user43928  6 hours ago

    The global GDP is on the order of $120T, consequently a few percentage points of productivity improvements correspond to vast sums yearly.

    I don't think replacing the majority of jobs in knowledge is priced in at a 1T valuation.

  • tclancy  18 hours ago

    >A chatbot for cancer researchers to talk to is worth single-digit billions at most

    I am disappointed by your lack of Capitalism buff. What you say is true, but what is the untapped fetish market for such a thing?

dekhn  18 hours ago

They do partner externally. This work is fundamental discovery science, rather than industrial research.

the folks who run anthropic grew up reading scifi with crazy awesome biotech. However, when they look at biotech today, it's just depressing. It's incredibly slow, it takes decadfes to prove out new technologies, and they figure with this new tool, they can just point it at problems and have it emit discoveries. If they show a few high-impact discoveries, that makes a case for them to move biotech forward much faster than its current progress.

Also, anthropic has so much capitalization right now that it's simply easiest to invest it in a wide portfolio that includes both internal and external research.

GolfPopper  19 hours ago

Because the goal is marketing and maxxing the IPO, not real-world results.

ndriscoll  19 hours ago

I was actually thinking the other day that it makes perfect sense for AI companies to develop a professional services oriented software development arm. Imagine that you want to develop a training pipeline for "tasteful" programming: you might make a reward metric for that does some obvious stuff (nothing that anyone could easily agree is a bug like a crash, good performance, perhaps minimize LoC), but you really want to also want to also track "bugs" where the feature was discovered to be missing some unspecified nuance that was only discovered through product use, or train on ability to keep a small codebase while also keeping diffs small (essentially, "maintainability") as real new requirements come in.

So then you want a training set full of real product requirements and product evolution, which is something you could get if you offered custom software development, with a lot more control than you'd get trying to do the same by scraping random FOSS projects on github.

Other industries are perhaps similar. If you offer a service directly, you have much more ability to build collection of training data into the process. Want to make the best law bot? Buy a law firm, offer legal services, and integrate extremely deeply into their workflows. If their models turn out to be as good as they hype up, they should be able to scale to be a major player in any endeavor they move into with a relatively small number of staff and develop a strong feedback loop (not that that would be good for the rest of us).

akersten  19 hours ago

If your core service is getting more expensive to provide and competitors are busy eating your margins, why let someone else taste your secret sauce and only get paid for the tokens, when you can keep the good stuff (bio capability) for yourself, and net both the profit and the fame?

jryle70  16 hours ago

I'm confused of why this is a question. First of all everyone is doing something because it benefits them. You and I included. Second of all as long as it's a real discovery, it will be beneficial to us all eventually (after benefiting Anthropic for sure).

Perhaps you're not on HN long enough, but there have been many posts where someone bemoaned the lack of basic science research by corporations, that IBM and Microsoft were the only a few remaining companies with any science research. Guess what? they do it for their own benefits as well.

  • ryanschaefer  16 hours ago

    What area of basic research are they bemoaning?

    Because as I see it, there are a lot of already established labs that could take research like this a lot further with the help of AI instead of just throwing more agents at the problem.

    That’s my confusion around this topic. Does the strategy change when you can throw a bonkers amount of compute at the problem with fewer guardrails?

    • solenoid0937  11 hours ago

      Because you're not understanding the goal. The goal isn't to assist humans in making the discovery. The goal is to develop a system that can autonomously make the discoveries, as this is way more scalable.

consumer451  18 hours ago

> I’m confused why AI companies are using agents in-house for this type of research instead of partnering externally.

As an outsider, here is how I explain that behavior:

1. Truly risky models are very useful.

2. Truly risky models should not be released, according to AI safety standards. I think Antrhopic genuinely believes in AI safety. (see: standing up against automated kill chains, no matter the impacts to the company)

3. Truly risky models face regulatory pressures, if released to the public.

This all leads to "let's just do this in-house." I believe that might end up being the answer to every application of AI eventually. It seems unavoidable, and very depressing.

6thbit  18 hours ago

Lands as an active threat. Maybe they're serious about this research or not, but for sure medical companies doing this sort of research will consider upping their AI budget and connecting their labs, etc. to avoid "falling behind".

So, the AI labs benefit either from achieving something they could market or from the peer-pressure imposed to companies in the sectors they get their nose in.

baxtr  19 hours ago

They’re trying to pivot into verticals because being a "dumb model provider" has no real moat any longer.

Sol-  18 hours ago

I think the "everything company" vision has become apparent for a while now. Doesn't even have to be sinister - I think Anthropic simply believes on one else can be trusted with this power. Another point of leverage they have is that they can keep their internal models for themselves.

amelius  17 hours ago

> “everything” companies instead of focusing on their core product

Aren't all large companies like that? Apple makes hardware, software, platforms, ...

mock-possum  19 hours ago

Because clients don’t know how to use the tools.

I run into this all the time - we have such powerful functionality available to our users, and further we provide the elements that undergird all of it, so it’s totally possible for clients to take the services they buy from us and reconfigure them to make their own tools, better even than the ones we have built, purpose-built for their workflows…

And 9/10 clients will just click on the one thing they know and recognize and are familiar with and comfortable with… and then stop thinking about it.

It’s crazy how much of our job is not only building our product, but interrogating our clients over what they need, so we can demonstrate how our tools solve their problem. The users simply are not interested in figuring it out for themselves.

  • stillpointlab  19 hours ago

    This is my speculation as well. For the time being, knowing how to use Claude extremely effectively probably beats out industry insider status. And Anthropic can attract whatever expertise it needs to build scrappy research teams in house. I'm guessing this kind of work doesn't need 100+ people, maybe just a dozen highly specialized people.

    Given the prestige of the AI labs, the recent explosion of math proofs, the literal millions they can throw around, it seems very likely they can attract then fund small research projects across a broad range of science. And like startup math, it only takes one or two ground breaking results from a hundred attempts to pay back in the PR/hype.