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Granted, I feel like munging gigabytes of text data (i.e. G, A, T and Cs) would be something LLMs would be good at

How do you think? I think with words.

AI is way beyond conventional LLM architecture now. It combines LLMs with search + RL. The traditional LLM architecture hit a wall around GPT-4o. Arc AGI evals show this.

I don't think anyone knows, not even the LLMs.

I mean, the subtlety of the neural network weights that emerge from training are not fully comprehended by anyone, man or machine.

Every individual calculation is understood, and every step of training is understood, but the exact nature of those weights that divide the responsibility of responding to subtle changes of input in intelligent ways is beyond me.

I imagine it's writing a story about a character doing those things and then reading the story and acting on it.

It's really good at pattern recognition.

So I'm not sure how it knows to be 'surprised' that alone is pretty fascinating.

Did anyone read the blogpost? They did publish a pre-print:

  > Our work to understand the primary function of ARTs is ongoing. However, we think it is important to share such findings early, both to demonstrate Claude’s capabilities and to give the broader community insight into what we’re working on. We have released a pre-print (here) that discusses this in more detail.

Medical advances are more important than post docs

> The market for entry-level programmers has already declined, but at least they were somewhat in demand and made reasonable salaries. Now what happens to post-docs who already make almost nothing and often get treated like crap?

Waiting for frontier labs to get into Political Science to show that SOTA models can be vastly better politicians...

It's not odd, it's a for profit company that's using it for marketing.

No, it's not odd; IMO. It's by design.

I see all of this leading to a setup for: We did cure Cancer, everyone else (Healthcare, Gov., Rx) etc... has just not caught up or even worse; "you just don't have access top that model/version".

I have seen several times on HN recently how people don't see the impact of AI/more code etc... and I believe this is because its following the K-shape of the current economy.

At the top where most of us aren't but CAN see via stock market news etc...; they are making more money by adding efficiencies etc...

At the bottom; efficiencies are being applied at a scale that they could not before such that social and Gov. programs are more manageable and optimized at scale.

Novel discoveries are now marketing ads.

> Now what happens to post-docs who already make almost nothing and often get treated like crap?

This sort of discoveries are what gets postdocs funded lmao.

Every new idea like this creates several years worth of highly specialized work to test out derivative ideas, productizing it, and connecting dots to existing work.

> Now what happens to post-docs who already make almost nothing and often get treated like crap?

At least in the US, that particular brain drain has already been happening due to Trump's administration. The best of the best are exiting to other countries that will gladly have them, and then there will be far fewer people getting into the field. Science in general has taken a massive hit under the current administration and it going to take decades to fix if it's even possible.

I’m curious, what differentiates this from a preprint given the assumption it’s sufficient for publication? It didn’t read like marketing, they don’t seem to sell anything, and there's a link to a not-anthropic.com hosted paper.

It's not odd at all. Every single "AI did this cool thing" type post is an Ad. Remember AI outputs slop and never produced anything valuable that wasn't heavily assisted by humans or is a lie.

Every day now I expect that they or somebody alike will discover in our genes that we (ie life) are lambda terms, combinators or something similar to Universally Programmable Intelligent Matter https://web.eecs.utk.edu/~bmaclenn/UPIM/UPIM3.pdf or chemSKI https://imar.ro/~mbuliga/talks/chemski-with-tokens.html

All it takes is to identify the syntax, so to say.

It's really a matter of iterating on the problem and validation right?

Models make progress on coding and math because they can write tests and proofs to an extent. Many industries that are more 'physical' and require performing experiments lack that instant feedback loop. Find a way to close that loop and AI begins to look useful.

But try and convince companies to invest on closing that loop just to see if the current models work well on their problems or not? Tough sell. So Anthropic just shows them, hey look, this is possible and if you don't do it I will.. so they fold.

"We gave Claude a prompt to search through a massive database of DNA sequences for interesting new examples of RTs. Our involvement was limited to the initial prompt and the lab work, while Claude agents combed through the database, investigated the distinct RT families, and used their own judgement to identify interesting candidates. After 21 hours spent searching this data by roughly 950 agents using 210 million tokens, one of the agents spotted something remarkable: a repeating pattern of DNA sequences that occurs next to the gene for an odd-looking RT. After further analysis and testing in our lab, we recognized that this pattern marked a previously uncharacterized enzyme system found in bacteriophages (the viruses that infect bacteria) that we call array-associated reverse transcriptases (ART)."

We've all used LLMs. It was certainly somebody using Claude.

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.

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.

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.

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.

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

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).

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?

> 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.

External partners don't trust the AI companies I think to not steal their work and the partner's moat

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.

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

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

If an amazing drug or other therapeutic falls out of this I bet they would be happy to own it outright.

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.

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

"Everything" is the core product if you are developing AGI

the core product has no moat and they're racing to find the actual product

Relevant: "Anthropic quietly sets up biology lab as it ramps AI drug program"

https://www.reuters.com/world/anthropic-quietly-sets-up-biol...

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.

Yea, I think that there's pretty much a ceiling with day-to-day models that have already been hit months ago. Maybe you need SOTA for reviews, high-level planning, or research, but long running tasks like writing out a feature, testing, getting feedback and making refactors can be done for low-end models (like luna). And the margins on those models are basically evaporating.

The article talks about the lab, so it's not quiet at all.

apparently big labs are also pitching profit sharing arrangements to biopharma companies in exchange for privileged access to the top internal above-the-api capability models .... repeat this in every industrial vertical and it could turn out that much denied Dario claim may as well have been true for all intents and purposes

Yeah, I hope so as well. I want to see this tech being used for good stuff and not just spam and inducing fear ...

So they're already threatening their customers Amazon-style?

Excellent. Now every pharma company, plus any kind of company that wants to own a market through innovation, will need a "world-class" AI research team that actually has spectacular AI budgets.

Really now, everything is bad? Trying to cure diseases? Talking about it? That’s bad somehow! Let me sit here on my ass and do nothing instead.

Dario would like to ‘cure’ aging. He’s got some personal experience with bad illnesses, but aging isn’t that. I also have reduced trust for people who want to live forever and don’t have kids.

Cancer can be cured in a lot of cases, its just so damn expensive and the treatment is beyond torturous that some patients cannot handle it. Stem cells are amazing. But we need cheaper technology to replicate them into the cancer destroyers they need to be, as well as find ways to ease the pain of that internal battle.

Yep, my prediction is that Anthropic is going to use Claude's reputation to "launder" known solutions to aging, cancer, and other things that society hasn't accepted quite yet. But maybe with the right marketing we'll try those things!

https://news.ycombinator.com/item?id=49329717

> Dario believes that the solution to AI's PR problem is to cure cancer

He isn’t wrong. But selling potential cures for cancer won’t cut it.

As they should because things like this get people thinking even if it something small. Once you get people thinking about things you tend to get solutions.

Also the general public might find the implications of AGI so distasteful even if everything goes well that we might stall out or get the Butlerian Jihad before we can cure cancer. Artists and Software Engineers, now also Mathematicians, already have existential crises, but the public still thinks AI is fake. I can't imagine the backlash when the realize what's coming even in the good ending.

> Real world testing takes a long time and is an unavoidable part of the process.

Not if it's a virus

>> the solution to AI's PR problem is to cure cancer

I think its much simpler than that. Anything actually useful for people would be a good solution.

Obviously image gen and code gen is not the case, as though it does increase productivity, it doesn't make anyone's life actually better. If it led to 4 day work week - sure. Otherwise it could easily be net negative.

Real world approvals for drugs are accelerating through, even with all the steps. Think about it, Moderna went from zero, to approved vaccine in 10 months. While COVID vaccines were the exception, not the rule, there are ways to accelerate the process if there is will and $$$. In the last 20 years, the number of new drug approvals per year in the US has doubled, and the length of time to get approval has been cut in half.

Its going to be an uphill battle. Every story about job losses, consequences to the community from building a datacenter (real or perceived), eminent domain case that blows up, plus all the slop on every platform. Not to mention a lot of normies think techbros are obnoxious, and that is who is hyping ai.

They'll need to show their goal is to help humanity and that all the other peoole arent acceptable collateral damage. Since those other people get to vote.

> Even RSI will not be able to get new medical treatments to market instantly. Real world testing takes a long time and is an unavoidable part of the process.

Is it unavoidable, though?

please no more doomer talk, can we just be excited that we have ai doing science

Given his background (biophysics PhD, postdoc at Stanford School of Medicine) and that medicine was the core of Machines of Loving Grace back in 2024 it reads less like PR and more like a long-held goal, no? Personally I'm actually surprised it took so long.

Agree trials won't compress much with AI in the near future. But they're starting with basic discovery rather than therapeutics – that part can move fast.

I'd also judge it less by what result is and more by the rate of change – even a year ago ~1k agents running ~1d on single prompt producing wet-lab-verifiable leads wasn't really a thing.

You can't say this. We have no idea. There is nothing about the law of physics that pushes cancer cure a long time away. A lot of people would have told you AI was decades away, yet here we are. We are still on track for possible strong take off.

Now on real world testing, you think the rule applies? I tell you it doesn't. Human life might be precious, but human life in practice is also not precious. We waste so much of it. In some countries regulations will stop/slow it, but there are plenty of places around the world that will turn a blind eye for a fistful of dollars. Countries will go to those locations if it means gaining an edge.

It’s a blog post about work an LLM did though?

The pre print clearly states it’s a well defined problem limited by the man hours required to sift through the data. I think everyone knows it’s not setting the world alight?

Thank you for sharing, that provided some good context for how to interpret this

Post content:

_____

I wish we didn’t need these again, but here is the honest version of Anthropic’s biology announcement (Caveat: I haven’t worked in bioinformatics for many years.) The good: Anthropic ran ~950 Claude agents over a large biological sequence database. Claude searched, wrote code, compared sequences and genomic neighborhoods, and found an interesting pattern that apparently had not been noticed before: a known reverse transcriptase associated with another gene and a repetitive DNA array.

That is cool. Automating this kind of open-ended bioinformatics search at scale is useful, and Claude may have found a lead a human would have missed.

But: Claude did not do a biological experiment. It searched databases and analyzed data.

Humans then took the candidate into the wet lab. And the wet-lab result so far is modest: they showed that the repeat array produces short RNAs.

We still don’t know what the system does. No function, mechanism, phenotype, targeting, defense activity, or programmability has been demonstrated.

This is also where the CRISPR framing gets ahead of the result. Right now, “it has some features reminiscent of known programmable systems” is a hypothesis for what to investigate next, not a discovery that it behaves like CRISPR.

And there is a missing baseline: bioinformatics has had tools for finding unusual gene neighborhoods and candidate systems for years. The interesting comparison is 950 Claude agents vs. an expert using the best existing computational pipelines - not Claude vs. someone manually looking through 200,000 sequences.

So my honest announcement would be:

Claude autonomously found an interesting candidate for a previously uncharacterized biological system. A small human wet-lab experiment confirmed that part of the candidate is expressed. We don’t yet know what it does.

That is a good result.

But in a regular biology lab, this isn’t the finished paper. It is the result you show at lab meeting and say: “This looks interesting. Now we need to figure out what the hell it does.”

Maybe that next step leads to a major discovery. But that discovery hasn’t happened yet.

https://xxcancel.com/ziv_ravid/status/2102844800345251858

It looks like the researchers just wrote the agentic harness and the rest of the work really was done autonomously by Claude with only extremely limited guidance after.

BTW the first author of the paper worked in the Doudna lab studying the origins of crispr (and after their PhD, joined Anthropic). All of the authors either have, or are going to have, excellent careers. I dont' think they are worried about attribution.

Because they’re trying to pump their stock price before their economic walls come tumbling down

I'm more annoyed that they announce "CRISPR-like" to hit those SV Next Big Thing dopamine receptors but upon reading haven't done any laboratory work to determine if it has any useful applications like CRISPR-Cas9.

It's totally legitimate research worthy of publication, but Anthropic chose a hot technology in the popular imagination for a reason. Now I'm going to have to see "Claude invented a new CRISPR in 24 hours!" everywhere and trying to correct it will just turn into repetitive arguments about goalposts moving....

If it requires humans in the loop it throws cold water on the hopes of replacing millions of jobs, which is priced into their current valuation.

Is it really all that different from "deep mind beat Gary Kasparov"?

At a trade show, I met a company that was advertising a feature as powered by Claude. I asked an employee what that meant, as it seemed unlikely, and he then didn’t know how answer so he introduced me to the CEO. The CEO said that the ad meant that Claude now writes all of their code including that new feature. They are now working on having Claude handle their QA process. I wondered if any of the devs were at the booth or if the employee I spoke to first was a dev who knew it was bs.

yes and no; you cant dump DNA in the context window and call it a day, in the blog post it was a common tool calling session. you do can have actual ml models for that, that the llm could use as a tool.

Why is it fake news, and how do you know that?

If by now you still think it's all just hype, it's safe to say you've succumbed to a mind virus that renders you unable to think critically about AI. Otherwise you'd have some level of awareness of just how far this technology has developed, and you should find these developments more than plausible.

seems like mythos 5 did most of the leg work, they refer to it in the technical report linked at the end: https://www-cdn.anthropic.com/22573675ada52a8ca8a97a1a4b4326...

It could be an internal model as well.

I call Michelangelo!

I wonder how long it will take for the damage Alpöge and Buckmaster have done to the perception of these AI-driven scientific developments to fade.

Not saying that they were right or wrong, but that single moment sullied all AI-driven breakthroughs that came after it, and I don't think it was ever particularly relevant, at least not nearly to the degree that it was presented in the media. But I guess it ended up being a convenient outlet for AI anxiety in the end.

This is a very inaccurate manipulation of facts that fundamentally misrepresents everything that mathematicians wrote.

Lots of stuff gets discovered by AI bots that was always hidden in plain sight. They're remarkably good at "connecting the dots".

the article says the reverse transcriptase had been noticed before, but apparently only Claude commented on the subsequence repeating, to your point.

It's not useful science; it has the rigor of someone posting a vague shower thought on Show HN or reddit.

You really think there aren’t an equal number of Claude influencers on social media?

The study results themselves aren't really dangerous in any way I can see. This is basic microbiology, and not necessarily some kind of major breakthrough that will change the world on its own. It's possible this leads to something big like CRISPR, but most likely not. The work is more the case of noticing something that someone hasn't noticed yet. It would have gotten noticed eventually, they just did it before someone else did (assuming they didn't get a hint somehow).

A lot of molecular biology is noticing something that you can't explain or that seems weird and might be interesting. Once it's noticed the followup is often fairly straightforward and it either pans out or it doesn't. The exciting/scary/unlikely part is that the LLM on its own recognized something as being important to follow up.

From my skim of the paper, the work could only be done by someone with a pretty good understanding of the biology and an extremely good understanding of how to use LLMs and agents. LLMs are not going to take over biology yet.

Just wait until Anthropic opens up their wetlab!

The singularity, as defined by Hinton (and others) as RSI (Recursive Self Improvement) may actually be beginning already, as OpenAI has announced an AI acting as a "research intern" (!).

Yes. I would bet on the latter.

That's black and white thinking; it will be a midgularity - so neither.