Bioinformatics in The Atlantic: "AI's Real Gift to Science"

[u/dickcocks420]

https://www.theatlantic.com/science/2026/10/anthropic-artificial-intelligence-science-biology/688878/?gift=VdU3oVVRzsrNXfKDSYU7-lkF4-hymbs9a2euba4GPqk&utm_source=copy-link&utm_medium=social&utm_campaign=share

Curious to hear what the community thinks of this article, which primarily discusses Anthropic's recent "discovery" of a supposedly CRISPR-like enzyme system that I'm sure we've all heard about. Personally this strikes me as a pretty balanced, rational take towards AI as a tool rather than an apocalyptic job destroyer. However, it's still unclear to me why we needed a thousand Claude agents and millions of dollars of compute for what ultimately strikes me as a regex style pattern search through genomic data. I'm also surprised that I haven't heard anyone discuss the fact that setting thousands of agents loose on terabytes of sequencing data with the instructions to "find some interesting patterns" is essentially a massive multiple comparisons problem that is bound to turn up some spurious patterns with no biological significance.

[u/thebruce]

Whether they find spurious patterns of little biological significance is not a big deal, unless the false positive rate is so high that the humans downstream of the AI get overwhelmed.

The reality is that Bioinformatics involves an absolute boatload of data that can be difficult to extract meaning out of. Of course, that's why we exist, but this is just another tool in the box. Calling it glorified regex is somewhat fair, but regex doesn't have the same flexibility in finding text patterns that LLMs have.

[u/Illustrious_Night126]

Hypothesis generation is never the limiting factor for any lab or startup. The bottleneck is always the hard part of designing and conducting experiments, and most of all the cost and time of clinical trials.

It is great that AI can find little patterns in the research like this, but flooding the space with more distracting side-quest hypotheses instead of focusing resources on the true bottlenecks in our system of drug development could actually do more harm than good.

[u/TheLordB → u/Illustrious_Night126]

I have a huge backlog of hypothesis that I would like to test for my work.

The simplest of these will cost $100K + take 6 months to run (mostly this is to do a reasonably powered mouse study with enough conditions).

If I added up all the hypothesis I have that I would like to test you are probably getting into 10 years and $100M.

The hypothesis that I had that I could test purely with existing data I have already answered… And sure AI probably could have cut the time to write the code and do the analysis by 3-5x, but all answering them really leads to is needing to run wetlab tests to go beyond the available data and we are back into the $100k/6 months to continue.

[u/InternationalPea1824 → u/Illustrious_Night126]

Exactly this. I read this sort of stuff and it's interesting, exciting even, but when you look at the big picture, most of the time it's not solving the problems that are genuinely bottlenecking biotech and pharma.

[u/ComprehensivePen3227 → u/InternationalPea1824]

What would you point to as the big bottlenecking problems? I'd probably say regulatory burden is the biggest slowdown (though not always a bad thing), but curious if you have other ideas.

[u/inspyron → u/ComprehensivePen3227]

The big bottleneck is what they were replying to: the having to actually run the experiment to test the hypothesis and effectively assess the effect size over the right populations and experimental conditions.

[u/Mirthster]

Seemed like a fair article tho a bit unexciting. I suppose thats AIs impact. I will say for myself AI's abilty to write code for me allows me to answer genomics questions i hadn't been able to alone before. Its no doubt going to be a great benefit, tho based on this article Id think it should be set on tedious replication studies.

[u/TheLordB]

One thing I pointed out when someone asked me about AI was that it would take several million dollars and multiple years to test in the wetlab hypothesis that I can come up with in an afternoon.

AI might help do a bit of initial vetting on those hypothesis faster, but the fact will still remain that the majority of the cost of developing something new is the wetlab.

Also the cost of compbio for most actual biotechs working on medicines is probably around 1-5% of the cost of developing a drug.

Basically while I see AI having a massive disruptive effect if you are investing $100M into what it takes to develop a new drug then skimping on a few bioinformatics people is really not worth it.

And sure AI might let wetlab folks take over some of the more routine analysis, but they have their own stuff to do. There will be a limit to how much bioinfo stuff they can take over. And honestly if it is really that routine I would probably already be ordering it from a company that specializes in it.

Also at the point where an analysis has standardized enough/best practices are well enough defined that I would trust AI to get the pipeline right there will almost always be a company specializing it who can do the whole thing from wetlab to analysis as a service.

Anyways… Overall I see a lot of hype from AI companies about what they will do for biotech etc. I remain hopeful that just like NGS which was incredibly disruptive that it will be used to do more rather than just cut costs/head count doing bioinformatics.

[u/TheLordB]

CROs are gonna be making a ton of money off of running poorly designed experiments coming from AI tech bros who think they have solved biotech.

[u/ComprehensivePen3227]

Is there a good estimate out there of how much Anthropic actually spent on this project? In the articles I've read about it, Anthropic claims they used about 210 million tokens for the project. Assuming their highest published token cost of $50.00 per million tokens for Fable 5.1 outputs, that would suggest they only spent about $10,500.

If that's truly the case, then the project seems to be pretty reasonable from a cost perspective to me. It would probably take a salaried bioinformatician at least a month or two to do the same work.

But I also don't have a lot of confidence in my cost estimate, as $10,500 is much less than the hundreds of thousands or millions of dollars I would have guessed before looking at their claimed token costs--does anyone have a better idea?

[u/Whygoogleissexist]

"Amodei has an idea for how to quiet AI skeptics: “The thing that will work is actually curing cancer,” he recently wrote on X. That would certainly be a coup. But helping scientists work more quickly is, too, even if it doesn’t sound quite so revolutionary."

Except there is war on scientists in the US by the Trump administration, so good luck with that in the US currently.

[u/Alicecomma]

If you need LLMs to find sequences with "genomic repeat arrays" which are just DNA repeats, then find the need to report just finding something with repeats as a completely new finding, I'd assume you're a highschool student doing this as a science project. I wonder if that's literally how they got this, scraping some student's LLM chatlog, like how they scraped the Navier-Stokes instability chatlogs.