James van Geelen Of Citrini research brought up a good point on a podcast this week, that designing the molecules is perhaps 10-20% of the effort in bringing a drug to market. We have more possible drug designs than ever get developed.
Whilst AI drugs might be more likely to pass phase 2 tests than others and AI might be applied to the rest of the process there are still big bottlenecks in the way of AI creating a radical change.
https://podcasts.apple.com/gb/podcast/monetary-matters-with-...
And nothing I've said here is an original thought or piece of insight.
Further, [we are getting much better at simulating drug interactions.](https://pubmed.ncbi.nlm.nih.gov/41155903/) I strongly suspect AI will offer new probabilistic models to funnel novel drugs into more targeted trials which are much more likely to succeed. If we had 90% certainty of success and high certainty of lower adverse reactions I think we could allow much less stringent human trials. That means much lower cost and much faster turn-around.
We already see that with software. It will be exactly the same with drug design.
My daughter is a leading biochem at one of the big pharma companies and that’s their principal worry about this. It’s really easy to create a lot of information these days which compromises the entire process.
> If Claude is to significantly increase the rate of new cures, we need to work with many more biotechnology firms and integrate Claude more deeply into their development processes.
Given all that, the wording here feels very self-serving. Cautious optimism is probably warranted.
I think it will look more like Claude just helping every expert along the way do their job better. Analyzing and synthesizing data to make better experimental suggestions and design new equipment (or totally new experiments based on unconceived-of apparatuses?). Managing the flow of information and building bespoke knowledge bases and tools. The more it is integrated into all the systems and researchers involved the faster it can put things together.
Although, on second thought, maybe there even is a near future where it takes our models and understanding and just galaxybrains a better model. Has anyone tried feeding it some collection of datasets and saying "can you make a more detailed model of a cell?"
Isn't this exactly what startups in this area are doing - automated experiments connected to AI. Of course at some stage things will "graduate" to human review but it still seems to have a lot of potential.
Biology grad students can spend 40+ hours per week in the lab, on top of all their course work. It's not so bad though, since there's plenty of time to grade papers or work on your thesis while waiting for reactions to occur.
Genuine question because I don't know the answer, I've just anecdotally heard all about the promise and nothing about real world results.
They can put it onto robots / automatic labs, so this step is easily solvable. Humans in a modern lab are largely unnecessary. Just look at China.
The problem here is that Anthropic claims to have solved this all. It is time that they actually prove their claim. Right now it reads like an advertisement to inflate the stock of Anthropic.
You can automate the setup and mechanical process. You could increase the physical capacity for parallel tests. You could increase the rate of acquiring raw materials.
But how much you cut down the time depends on how much of the trial-and-error feedback loop depends on feedback and iteration vs cast a wider initial net.
(One of the interesting things to me about the dominant software development agent pattern across all of Claude, Codex, and Cursor is that the approach is still extremely local and iterative, human-mimicking, rather than big-brain-one-shotting a novel or perfectly-fit-for-purpose software design. The performance of the agents on the initial "jump to conclusions" step does not appear superhuman in the same way that the speed-of-iteration is clearly superhuman.)
Care to elaborate, explain?
That is utterly untrue. It stems from the notion that biology only needs big data for us to solve all its mysteries.
Beyond that, while automated labs for cell biology are plausible and active, and the same fit organoid biology, the same really isn’t true at organism level. Labs already are artificial environments that make organisms have odd phenotypes, and odd interactions which bias the data away from what you’d find in the natural world.
You add in robotic manipulation, and you add a new confound.
Drones in the wild are an obvious counterbalance to this, though even in that scenario, the drone can be a confound unless well hidden.
In the end though, this kind of systemic breakdown of biology, while much needed raw material to improve our theories, won’t solve the whole of biology. Human hands and human minds will be needed for a good long while.
As always Anthropic is saying literally anything if it gets them closer to the giant bucket of money that is the IPO.
Oooops I mean, the agent "escaped" and hack the goverment... definetely not training it to become it's own state agent thing... pinky promise....
"Cautious optimism is probably warranted" strongly negativism advised
Actually creating them requires physically manipulating the inputs to the process, which requires humans doing it or humans making machines that can do it and allowing them to be controlled by the model.
In general the damage potential only goes as far as the agency extended to the models. The problem is that we've already seen how incredibly cavalier most people will be about security, and now we can envision situations where one bad actor could threaten billions.
I can't tell -- has there been any real progress made? Or is this a supposed application of AI that is getting interest? What about this problem in particular makes it suitable to AI as opposed to any other?
Or is this just a matter of -- "AI can cure cancer and is Good For the World!"
The last hundred oncology drugs have all required painstaking trial and error with no way to predict which candidates succeed or fail except at the years long cost of blood sweat and tears. A.I. will not remove that price from the equation except maybe move towards those trials marginally faster towards those decade long trials.
Pharma makes bank on drugs that are patent protected. Squeezing the manufacturing cost is a small gain on top of that.
I actually don’t think this is a strong argument.
AI does very well following rules.
Proving your drug works is hard, but an LLM can help you come up with the ideas.
How many of these "we sandboxed it and said not to do Y, but then it went and did it!" stories would you consider necessary to disprove that?
It may be good at following rules 99.99% of the time but if someone launches a million times more parallel agents than they would hire humans, it might still be much worse in practice...
"We've been told we must find an effective drug candidate here, and to follow this process for evaluating the quality of the candidate, but actually we could subtly change the analysis like this to make this candidate look favorable enough..."
(And then compare that to deterministic algorithms...)
The argument here is that AI can run permutations faster than any human can. And, feeding it a company’s proprietary data about what worked, what did not, how it worked and so on can be invaluable at accelerating the discovery process of new drugs.
Plus, it can work 24/7/365.
People who behave like OpenAI and Antropic AI does when controlled by those companies get punished instead of celebrated.
Being a doctor in America has a high barrier to entry.
It’s not because medical science is difficult.
It’s because the American Medical Association (aka, doctor’s union) limits the number of new doctors and medical students each year.
If you 2x the number of medical students, resulting in 1.5x number of qualified doctors, resulting in perhaps 20-30% lower salaries, and maybe 5-10% lower cost to patients.
But no, preserving a doctor’s middle-class salary is more important than lowering patients’ costs.
Google “number medical students over time”
Then google “number residency slots over time”
Then notice how both of them have been increasing significantly over time.
Then compare the income percentile for doctors in US vs say Canada, Australia etc. all are top 1-3%. Doctors are paid well relative to median salary in every first world country.
Then google “physician percent healthcare costs” and learn that physician pay is about 8-10% of total healthcare spending. Then realize about 40% of that $ immediately goes back to the government via various taxes, so even if you paid physicians nothing you’d only save about 5% of healthcare spending.
Do you not try to verify any information you read before confidently regurgitating it at a later point? You really should…
There's no regulation on not doing a trial because your oracle machine told you it was an expensive dead end.
Maybe in 10 years when frontier-level models are easily usable and not locked down by the government and PVP between the companies and everyone else to justify their existence and take all the resources from everyone.
Maybe then it'll accelerate development.
I suspect what is happening here, though, is that Anthropic is going to take a bunch of Dunning-Kruger AI researchers who throw prompts at Claude and publish 1,000 papers worth of drug-discovery slop that will only waste the time of actual experts...
This has been one of the target AI applications since the 1960s.
(Former drug developer who will never touch the life sciences again)
"Developing a drug involves many varied tasks: doing early research to identify a target for a drug and the right type of drug to engage it, designing a molecule with the right properties, optimizing it to be manufactured reliably at scale, drafting the regulatory filings needed to run clinical trials, designing and administering those trials, and much more.
Our strategy to realize AI’s medical benefits is based on having AI increasingly involved in all of these tasks."
Optimizing molecular properties of a lead is important, but ultimately not a huge source of costs. In any case, LLMs probably have as much chance of predicting outcomes as anyone else (i.e. very small).
Manufacturing is somewhat expensive, but most of the cost sits in actually creating a production process, which is not a thing LLMs can do.
Clinical trials are where most of the costs lie, but unless you can create a magical classifier that can tell me if my drug will make it past phase 3, you're not significantly bending the cost curve.
> unless you can create a magical classifier
A generator could concentrate on pockets while remaining unable to judge most drugs. And LLM RLVR a) works well, and b) is black box optimisation and so is suited since it doesn't need system identification (which you can't do for complex biological systems). So the hypothesis is that, yes making a classifier is very very hard, but you could through some (lots?) initial interactive data, end up with our generator outputting with high probability drugs-that-end-up-passing-trials-with-probability-p. Whether these drugs thus generated are drugs we care about (i.e, whether `p` is good enough) and whether the whole pocket it generates in is large enough, and how jagged this behaviour is (i.e distribution of `p` across taxonomies of drugs), are the few questions we want answered.
That said, we have extremely well-refined force fields that attempt to model the underlying physics, and still fail to do this well. There's absolutely no reason to believe that a generic LLM will bring anything to the table that improves the situation. Even with the hand-wavy assertion that we (somehow) get "lots and lots of data", and somehow train a purpose-built model to do the same, it's kind of like saying that we could turn a minivan into a formula one car with enough effort. Different tools for different jobs.
I actually do believe that informatics approaches will be helpful given lots of system-specific data, but when I say that, I mean "a bespoke neural energetic model that is trained specifically for some molecular system down to customized force-field parameters", not "Claude".
So sure, maybe Anthropic will invest lots of time and money and energy into making these kinds of molecular simulation models, which they can then use for high-throughput screening experiments. But even if they do all of that, there's still no guarantee that they'll bring anything to the party that hasn't been done before...because this is the lowest-value step of drug discovery, and a model like that won't tell you if your drug is going to be toxic, or have off-target effects, or have metabolic problems, or...a million other things, which are what kill drug programs in phase 1 and beyond.
This is the fundamental problem with nearly all tech-meets-drugs startups. They almost always focus on the part of the problem where there isn't a bottleneck, because the parts where there is a bottleneck are the parts we don't understand at all.
Are they doing the thing where they develop the technology first, then hunt around for problems for it to solve and hype up potential applications?
That said, the LLM version of this is perhaps more solution-seeking-problem than past iterations. LLMs are obviously useful [1]. Scientists already use them. The labs are just desperate to find a multi-billion-dollar application outside of coding, and they’ve all decided that “drug development” is lucrative, so they're gonna apply their magic to that problem and collect profits!
[1] In the way that photocopiers are useful in drug development: as a tool. A powerful tool, but still a tool.
Re-ranking pre-clinical leads is the scientific equivalent of that old saying about opinions.
Thank you for entertaining my curiosity! I have been meaning to educate myself on this but don't know anybody in my circle who is knowledgeable in this field.
>> If Claude is to significantly increase the rate of new cures, we need to work with many more biotechnology firms and integrate Claude more deeply into their development processes
Sounds invasive.
> Sounds invasive.
If it's not working, the problem is clearly "you're not spending of your money on Anthropic's products," and the solution is to spend more.
I'm interested in seeing how the scorpion anti venom plays out.
https://www.anthropic.com/research/claude-scorpion-toxin-bin...
I'd be happy to be wrong, though. I've never tried to do biology with Claude, but I have gotten many refusals and downgrades for security work.
Biology is the complete opposite of that.
Despite the improvements, though, we still see plenty of code that is "slop", because it's directed by someone with little idea of what to do, but also because it's harder to test for (or even measure) code quality than correctness. The "solved problem" rhetoric depends very strongly on your standards.
In other fields the problem is similar, and typically worse. Plenty of AI music sounds acceptable, not clearly flawed, and even mildly "interesting" to me; but I think this is much more a commentary on music than it is on AI. Prose continues to be abhorrent in nearly every application; while I've seen examples of people getting really impressive output, it takes a level of effort that defeats the purpose of almost everyone who wants AI to assist their writing (i.e., just generate a comprehensible report on some findings and get on with their day). Art is a whole different world; the quality of results clearly strongly depends on the quality of prompting, it's much better/worse in some genres than others, humans vary widely in their ability to notice "mistakes", etc.
For instance, let's say we are doing biology on plants, which is still much easier than doing human trials. No matter how motivated claude might get, you aren't getting a corn plant to grow in 40 seconds. Real world research pipelines involve stages where even greenhouses don't do, and sacks of grain are shipped to Hawaii, or Brazil, or wherever, to try to get practical data for a given test.
If your computer simulation is so accurate you can skip a step now done with biology, great, but it gets harder the larger your problem is. I might see if a molecule kills bacteria kind of quickly, but what does a molecule do to a real human being? Not so easy.
Seeing tiny stuff doesn't magically give you enough info for Claude to reason with the system predictably (the way it can with source code).
There’s no well-labeled dataset for all this because we don’t know everything we are looking at.
Take a neuron. You can stain it so you can tell apart an axon and a dendrite. But if you don’t it’s very possible to miss or mislabel the axon, because unlike the textbook neuron, the axon isn’t always obviously oriented in a way that promotes ID.
And that’s just one example. With more complex samples, you also have a lot of edges which AI models don’t segment very well.
So, I'd say it's a pretty similar process, and yet we have the AI models now almost completely integrated into the SWE domain remediating significant parts of its complexity.
AI already can do in-silico simulation
add microscopes and tiny precision actuators, and we can build closed feedback loop in-vitro system
once we have in-vitro, it will be easier to go in-vivo
I've always thought openai could actually create an agent that can control various physical robotic arms and things like that, and then continuously iterate on solutions that can participate in the real physical environment. This could accelerate automatic iteration for many things that require human operations, such as complicated experiments, assembly adjustments, and testing.
Otherwise, AI is always trapped in virtual systems and still needs human intervention. But even though AI is already very powerful now, there is still a limit to how much humans can handle or assign tasks. Many times, AI has already finished processing something, but I don't even have time to assign the next deeper level of tasks.
People hope AI can improve productivity, but at the same time they're afraid of AI actually getting involved in reality, while the valuation of the entire AI industry is so high.
This kind of wanting to move forward but not moving forward makes humans especially uncomfortable.
finally, us
Since sequential steps are slow, I think we're going to see massive increases in parallelization. I'm not sure how the regulatory frameworks will keep up honestly.
Why? This is not an AI problem, it's an industry one. And if it was simple, it would already exist.
Let me guess. You have never stepped in a lab.
Not necessarily. Many things are simply gatekept behind the door.
We see it happening in SWE as we speak - people from and outside the tech who had no or very little experience with programming are now starting to deliver their ideas into products without devs or very few of them. We also see this within the SWE where strong domain experts are now able to deliver ideas into code easier than ever, and by themselves, and for which they would previously need a handful of programming experts. We also see it in other way too, strong programming experts are now able to pick up other domain easier than ever.
This process is something that I believe it will (and hope so) inevitably be picked up by the pharma too so the myth about the complexity will become broken.
Process improvements come from failing faster, so you spend less money on your test. Finding the actual answer without testing would also be wonderful, but LLMs might be fast, but they aren't magic. Something like Alphafold saves time in some steps, but it's not just "hand me a candidate", but find completely novel verification mechanisms, which would be just as useful for humans. Either way, parallelization is unlikely to increase.
I wonder what the unlock would be, if any, of having simulations substitute any (or all, in time) of these steps. Akin to the method used to train FSD cars, or robots.-
But I'm no biologist so if someone else is, feel free to correct me.
I wonder if, at some point, AI's will perform to such degree they are able to construct "black box" simulations as needed, that nobody understands BUT that are sufficient to empirically experiment on ...
... much in the same way LLM's function now: In an aggregate sense, they are bloack boxes.-
If that's the bottleneck maybe we need to rethink all the bureaucracy around the process instead of using AI to fill a document and then using AI to read it. That's just plain stupid.
Consider another area; a common heard complaint about why we don't see more police in the streets (where I live anyway, not the US) is that after every incident they have to spend time on doing paperwork - important administrative part of police work. Part of that work has been automated and outsourced, but not everything.
Of course, AI isn't perfect and can hallucinate things and get facts wrong which would impact a court case. But with the right guardrails, it can accellerate and partially automate this process even further.
The problem is that biology is like trying to reverse engineer alien technology to understand how it works. Math/engineering is more like building up complexity through well defined parts. Math/engineering is a heck of a lot more deterministic than biology. The ultimate bottleneck in biology in context of AI is well curated and breadth of data, not necessarily intelligence.
> It helps with bureaucracy and pipeline generation.
You can't use Claude to write a grant unless you are one of the special people because "uplift." I genuinely wish I was joking. Here are the exact passages from their report that I quoted here, https://news.ycombinator.com/item?id=49651727
My feeling on this is that math is amenable to LLM based development because the formalism of LEAN + the ability of AI to nudge research means that we basically have machines that can "research" through what is basically brute force search, but where the explored trees are ordered by AI.
But what if there were a biological system similar to LEAN for drug interaction? Could we not use AI in a similar way to nudge the exploration space based on the vibes that the AI has about drug discovery?
I agree there is a a distinction in the formalism of a proof (through LEAN) and the sort of probabilistic/chemical/physical/whatever nature of a biological organism (through some sort of future modeler for life science). But is the fact that one system is deterministic and the other probabilistic really as much of a leap as you think?
We currently have disparate datasets that allows us to make some predictions on how drugs will interact with molecules. And we have studies on how these molecules might affect our organs/bodies. But all of this data are at different resolutions, sparsity, and have different experimental context. It is extremely hard to reconcile for a holistic model.
DNA is the great unifier in molecular biology as it is the digital anchor/foundation that everything above relies on. But there is just so much complexity above it. An analogy in the AI space would be model distillation where you are trying to discover all the weights in a LLM by giving it a prompt and seeing what the results are. With biology, we know the prompt (DNA sequence, genotype) and we can kind of quantify the result (phenotype). And we have to figure out all the weights in between (how DNA becomes a fully functioning organism).
Seems kind of stupid, because AI companies aren't cuddly benevolent family members, they want to eat everyone's lunch.
“Generate scientist” … is that a real job like “prompt engineer”
Either that or six degenerate scientists. Both options work just fine
The company is called Generate Biomedicines, and they reference it in short as Generate.
So as long as you can test things on cell culture level? You can do a lot with a high throughput setup.
Other large companies and foundations have copied this model and I would presume have given much more latitude in coloring outside the lines without red tape. Beyond that I am curious what will be innovative about this (sure, claude is amazing, but there's nothing stopping another grant making organization from saying here's a bunch of money and compute - hire great people and go wild)
Also Anthropic: “we’ve given AI a biological lab to develop chemicals that impact humans!”
(No one please propose automated mice testing or the EAs will have an aneurysm, haha.)
A lot less these days tho.
And what we need is not simply more biological phenotypical data (such as genomics, biomarkers, etc.) that we can plug into a model. We actually need that AND labeled trial outcomes, which is a challenge in and of itself because pharma companies will often hide negative trial results (which are advantageous for training the model layer) in order to exacerbate the perception of drugs that make it to market (search for "missing data phenomenon pharma"). The clinical trials are an extremely costly (and necessary) market inefficiency that leads to a bunch of bad behavior that is unfortunately self-reinforcing. I spoke on the phone with a biotech entrepreneur who told me to think of the Gavin Belson (from Silicon Valley the TV show) quote where he says "I don't know about you, but I don't want to live in a world where someone other than me makes the world a better place better than me", and to just remember that's how many people think in pharma and biotech + pharma.
Augmenting how pharma companies work is a great first step. Augmenting how candidate drugs are engineered is a good first step. But that does not change the fact that we simply do not have deterministic governing laws (as we do in physics) to understand the complete pharmacokinetics/dynamics of a drug across a large patient population. You just have to run the test. It is true that AI will help, but only as much as we have a reasonable way to predict that candidate drugs will lead to positive trial outcomes and a large market.
Note that I am not a doomer here. I think AI will have a profoundly disruptive and positive impact on the pharma industry. I'm just not sure starting with drug design alone leads to hope unless there are things happening in the background between pharma + the labs (very possible) that could lead to better trial outcomes.
Automating or streamlining human/animal trials with 24/7 monitoring and AI check-ins would be huge.
Yikes
I know Anthropic are brilliant at knowing when to focus where, but I genuinely expected them to be talking about 3-5x more serious scaled up drug discovery plays at this stage.
Is this not a focus, or did the big plays not want to be part of a roundup post?
Neural network driven drug candidate creation has been around since the 1980s. The blockbuster GLP-1 agonists were actually derived from the peptide in Gila monster venom by algorithmic search.
But you can't, and I can't emphasize this enough, just go "Claude make me a virus" and have it work. It just doesn't work that way. And the only reason why people think it does is because they haven't spent enough time in a lab, https://news.ycombinator.com/item?id=49620521
Don't get me wrong, this technology is worth fighting for because it will have incredible impact on the long time scale. But just not in ways they're talking about and expect. Nor the ways in which they're trying to dictate top down.
Some, do what it says on the tin.
Some...not so much.
The issue with any new chemical is full, comprehensive, regulated testing.
Dodgy folks aren't really so good, with that last part. They use their customers as guinea pigs.
Same applies to construction projects, BTW.
I don't believe there's any shortage of _potential_ new medicines being developed, but very few get to the stage where they are proven to be both safe and effective.
If anything Claude might just increase the downstream bottlenecks.
I won't judge which way, but I am getting tired of the repetitive nature here. Quite frankly, science works in large parts by others (independent) folks investigating any findings. Low quality or fake had been retracted in the past - see https://www.thelancet.com/journals/lancet/article/PIIS0140-6... from 2016.
It is time for others to do a more systematic research of anything coming out from Anthropic here.
This is complete nonsense. There is already plenty of ML in drug research, the bottleneck is not coming up with experiments its the testing and implementation of that testing (capital!!) Llms cannot contribute anything to this process that traditional ML hasn't already.
> We’ve also created the Life Sciences Verification Program to allow trusted organizations to use our most powerful models with more permissive versions of our safeguards, which are necessary to prevent misuse but which can impede legitimate biological research
You also can't get verified as a student, let alone as an independent learner or someone seeking to change careers, or heck just an entrepreneur with scientific interests.
Somewhere around March, Anthropic disabled their sales@ account as well. So you can't even email their sales.
I work a lot with younger scientists and people interested in science, and watching them navigate this ridiculous gatekeeping genuinely makes me sad. You can't use Claude to write grants without special dispensation from Anthropic, which is great if you're in California, but less so if you're in Tanzinia, Kazakhistan, Nepal, Bangladesh, India, South Africa, Brazil, Chile...
It's next to impossible for them to get support.
But at the same time, Anthropic can and does have a disproportionate impact on their lives. You can see that in what they brag about. Anthropic has actively bragged about blocking real research to make better painkillers and study a neglected tropical disease.
This is from their "Detecting and countering misuse of AI: September 2026" report. https://news.ycombinator.com/item?id=49647300
Here's one case study they cited, this one was called, "Case study 3: Covert frontier model access for orthopoxvirus research"
> In May 2026, our biological safety classifier blocked a request for Claude’s assistance in authoring a grant application for scientific funding. The work discussed in the application involved gain-of-function research (that is, research that genetically alters an organism to create a new or enhanced biological property) on the chikungunya virus. This gain of function research was aimed at the virus’ transmissibility and immune evasion properties.
"blocked a request for Claude’s assistance in authoring a grant application"
> Chikungunya virus is a mosquito-borne virus that causes debilitating symptoms (such as severe pain and fever) that can last for weeks or months, and has no licensed therapeutic. And because chikungunya circulates naturally, a deliberate release (as part of a bioweapon) would be difficult to distinguish from a natural outbreak. The grant sought to identify enhancing mutations in the chikungunya virus, engineer them into infectious clones, and select for virulence in vivo. In other words, the virus would become progressively more harmful as it repeatedly infected live animals, with researchers keeping the most disease-causing variants in each round. Similar research could certainly be used in the development of better vaccines and therapeutics for the virus—but it could also be used to make the pathogen more dangerous.
Note, "The grant sought to identify enhancing mutations in the chikungunya virus, engineer them into infectious clones, and select for virulence in vivo" [..] and then, "Similar research could certainly be used in the development of better vaccines and therapeutics"
It was most likely vaccine development. They stopped the study of a neglected tropical disease and vaccine development.
No Claude for the developing world, I guess.
For maximum charitability, they emphasize that they can't be sure because,
> One of the reasons we were inclined to think this research was less innocuous was that the institutional affiliation associated with the grant was also a cause of concern. Although information within the application suggested that the research was pursued by civilian researchers, it was intended to be performed at a military research institute.
In most parts of the world, there's either strict military control over BSL-4 labs, or a mixed military-civilian hybrid model.
I doubt that researchers working in the military side of these labs looking to weaponize things are writing grants with Claude.
I really want to be charitable here, but in general, it seems that they stopped people writing grants and reports for vaccine and therapeutics research and are claiming it as "possible efforts to build biological weapons."
Because – and this is the most charitable read here – are you claiming that you stopped a nation state - one with a large military - that has an institutional BSL-4 lab staffed with biologists from making bioweapons by blocking a scientist from using Claude to do grammar checks?
It's somewhat insulting that they termed a working scientist using their chatbot to write a grant as "uplift."
Because what is Anthropic saying here? Are they saying that the only thing stopping a military from a country like India, Russia, China, Iran, South Africa etc from making bioweapons --- a technology that's more than a century old, btw --- is whether or not they can use a Claude account to write grants?
I would be more than OK with their right to create such restrictions if they weren't lobbying the US Government and other Governmetns to restrict others, thereby ensuring that they control humanity's intellectual commons well into the future.
Because who are they to say what I can learn or can't learn? Who are they to control what I can or can't do with these tools?
Who are they to say who can use Claude to generate drug discovery targets or not?
-
edit - I encourage you to paste the URL to this thread into a Claude Fable session. You will be blocked for "safety."
Lol that cracked me up :D I love that job title!