I would never trust an AI to make a scientific conclusion, but they're very good at some things:
- Plotting with natural language: "Create a scatter plot of column A versus column B, log scale Y and use stars sized by column C." Every plotting library (except maybe ggplot2 / plotnind) sucks, and the boilerplate never gets less tedius.
- Literature search, e.g. "Have there been any peer reviewed papers where they applied X method to Y domain"
- Complex visualization, e.g. "Make a self contained HTML file with a 3D interactive visualization of this graph. Add ball and spring position optimization and let me move nodes"
- Trying random stuff that would normally be too time consuming to be worth it, eg "Here is a dataset. Try regression methods A, B, and C and report a table." In this case, usually it will screw up and overfit, but it's a good smoke test.
Note how in all of these, it's being used for tedious things that provide some value, but ultimately they are only providing information to make a decision on.
Granted, I would like to have a research assistant I could train, but if your company will not create a position then there is not much you can do.
So you're not wrong in most of these conclusions, however this is very surface level use case for these tools when it comes to scientific research.
> I would never trust an AI to make a scientific conclusion, but they're very good at some things
> In this case, usually it will screw up and overfit, but it's a good smoke test.
> it's being used for tedious things that provide some value
I see a lot of comments like this about AI use in general, and I suspect there’s a kind of learned helplessness combined with anchoring to earlier experiences. Someone tries a model, encounters a few failures, and forms a fairly permanent view of what these systems can do.
My concern is that people sometimes turn an expectation of failure into a permanent ceiling on how much they’ll even experiment with these tools. Their limited use then keeps confirming their limited expectations.
> this is very surface level use case for these tools
Correct. As of Q4 2026, in my experience they are only trustworthy for surface level use cases in the domain of computational chemistry / in-silico high throughout screening, unless you are working on a highly constrained, immediately verifiable problem like protein docking or structure-property relations.
> anchoring to earlier experiences.
I have observed the models improving over time. I am basing my post on things at the level of Opus 5.5 / GPT-6.1 Sol / Fable 5.1
> Their limited use then keeps confirming their limited expectations.
Every time a new model comes out or a harness I use gets a new feature, I do try to push it until it breaks. A swarm of GPT Lunas at max context is highly effective at finding information or condensing highly disparate data sources; that wasn't even a thing a year ago in most harnesses.
But even the smartest models like Fable will get sidetracked, or make bad decisions because they do not have enough context of the problem domain. As of Q4 2026.
I'd put their autonomous intelligence in this particular domain at the level of a well-read first year grad student. Phenomenal at knowing things, but unable to question their own assumptions or invalidate their own work unless you hold their hand in the form of a prompt, skill, or direct instruction.
Yes to all but I have trouble using them effectively for my research side projects or Kaggle competitions. Claude Code/Codex designs and runs experiments quickly, invalidates my initial idea using some obscure criteria than changes direction, and after long hours and days of experiments we end up with poor results. There must be a better agentic workflow for research.
> invalidates my initial idea using some obscure criteria than changes direction
You might be giving it too much leeway.
Opus and Fable are more trustworthy, but even today I find they very quickly go off the rails for research tasks if I don't give them short chunks of work.
They're not ready for prime time automation of research (and before someone mentions the math results, that required absurd amounts of computation time and has immediate feedback in the form of Lean)
Yes, I find DeepSeek can be very precious about its ideas and I have to insist multiple times to do the experiments I ask for despite it thinking them not worth doing. Often my experiments do give good results and I swear the model gets salty about it.
I guess when you ask these things to take on the role of a senior scientist it picks up some natural stubbornness in the training data.
When it comes to difficult optimization/search problems, doing research in a single Claude/Codex thread is definitely a struggle. There are ways to bypass some of the pitfalls by creating a agentic swarm with hard guardrails such as context resets and changing the way the harness works with memory, etc.
Don't like this style of article where the "How" is "in what manner do they use it?" and not "what techniques do they use?". I would prefer to see specific lab setups, how they use and coordinate agents, what kind of protocol they have for asking questions and verifying information, and so on.
Using privacy-preserving methods, we study 207,000 Claude and Claude Code research sessions to understand how Claude is used across scientific fields and research tasks
Artifacts are amazing, I think they are not getting enough attention. They recently cloned most of the Google Docs / Notion features that I was using those connectors for. Comments are very useful for steering.
The missing feature for docs is now GDrive, ie organizing and cataloging your artifacts to build a coherent long-lived KB. But for that you can still export.
Anthropic added a while back the ability to let clients provide their own API key, again I think that’s under-explored and will become more relevant as AI gets more penetration.
Building out quick SPAs, HTML pages, etc is a great use, I think the innovation will be making these things supportable and usable for more than one-shot single-user artifacts. Eg user login, analytics, etc.
AI in science is a lot different than AI in progamming in some ways. It seems pretty harmless in programming a lot of the times because it's used to speed up rote tasks like writing boilerplate. But in science, if it does give scientists a boost in discovery, I think it's likely to lead to an even more cavalier attitude about the power that such discoveries can have, and thus lead to much more abuse of power by the wealthy and malicious actors.
> Using privacy-preserving methods, we study 207,000 Claude and Claude Code research sessions to understand how Claude is used across scientific fields and research tasks.
Did they get permission to use researchers' data or did they monitor and extract data without consent? Considering the kerfuffle OpenAI generated with their math proofs, you'd think Anthropic would take a hint and be clear that they did the research with consent, though that assumes they got consent.
Seriously, all the industry researchers at these companies went through IRB training during their PhD; have they just conveniently forgotten?
Am I the only one who finds Claude overly assertive and confident for research? I tend to get more value out of other models (e.g. Gemini 4 Argon) even if they make mistakes, and then use Claude to revise and polish.
I agree, LLMs in general tend to misuse words and concepts like "proof" - when they have only partially or sloppily carried out an (heuristic) argument, or in situations where a proof is not possible at all, only empirical evidence. This has improved with recent models but has not been entirely solved
They are very good research assistants.
I would never trust an AI to make a scientific conclusion, but they're very good at some things:
- Plotting with natural language: "Create a scatter plot of column A versus column B, log scale Y and use stars sized by column C." Every plotting library (except maybe ggplot2 / plotnind) sucks, and the boilerplate never gets less tedius.
- Literature search, e.g. "Have there been any peer reviewed papers where they applied X method to Y domain"
- Complex visualization, e.g. "Make a self contained HTML file with a 3D interactive visualization of this graph. Add ball and spring position optimization and let me move nodes"
- Trying random stuff that would normally be too time consuming to be worth it, eg "Here is a dataset. Try regression methods A, B, and C and report a table." In this case, usually it will screw up and overfit, but it's a good smoke test.
Note how in all of these, it's being used for tedious things that provide some value, but ultimately they are only providing information to make a decision on.
Granted, I would like to have a research assistant I could train, but if your company will not create a position then there is not much you can do.
So you're not wrong in most of these conclusions, however this is very surface level use case for these tools when it comes to scientific research.
> I would never trust an AI to make a scientific conclusion, but they're very good at some things
> In this case, usually it will screw up and overfit, but it's a good smoke test.
> it's being used for tedious things that provide some value
I see a lot of comments like this about AI use in general, and I suspect there’s a kind of learned helplessness combined with anchoring to earlier experiences. Someone tries a model, encounters a few failures, and forms a fairly permanent view of what these systems can do.
My concern is that people sometimes turn an expectation of failure into a permanent ceiling on how much they’ll even experiment with these tools. Their limited use then keeps confirming their limited expectations.
> this is very surface level use case for these tools
Correct. As of Q4 2026, in my experience they are only trustworthy for surface level use cases in the domain of computational chemistry / in-silico high throughout screening, unless you are working on a highly constrained, immediately verifiable problem like protein docking or structure-property relations.
> anchoring to earlier experiences.
I have observed the models improving over time. I am basing my post on things at the level of Opus 5.5 / GPT-6.1 Sol / Fable 5.1
> Their limited use then keeps confirming their limited expectations.
Every time a new model comes out or a harness I use gets a new feature, I do try to push it until it breaks. A swarm of GPT Lunas at max context is highly effective at finding information or condensing highly disparate data sources; that wasn't even a thing a year ago in most harnesses.
But even the smartest models like Fable will get sidetracked, or make bad decisions because they do not have enough context of the problem domain. As of Q4 2026.
I'd put their autonomous intelligence in this particular domain at the level of a well-read first year grad student. Phenomenal at knowing things, but unable to question their own assumptions or invalidate their own work unless you hold their hand in the form of a prompt, skill, or direct instruction.
Yes to all but I have trouble using them effectively for my research side projects or Kaggle competitions. Claude Code/Codex designs and runs experiments quickly, invalidates my initial idea using some obscure criteria than changes direction, and after long hours and days of experiments we end up with poor results. There must be a better agentic workflow for research.
> invalidates my initial idea using some obscure criteria than changes direction
You might be giving it too much leeway.
Opus and Fable are more trustworthy, but even today I find they very quickly go off the rails for research tasks if I don't give them short chunks of work.
They're not ready for prime time automation of research (and before someone mentions the math results, that required absurd amounts of computation time and has immediate feedback in the form of Lean)
Yes, I find DeepSeek can be very precious about its ideas and I have to insist multiple times to do the experiments I ask for despite it thinking them not worth doing. Often my experiments do give good results and I swear the model gets salty about it.
I guess when you ask these things to take on the role of a senior scientist it picks up some natural stubbornness in the training data.
When it comes to difficult optimization/search problems, doing research in a single Claude/Codex thread is definitely a struggle. There are ways to bypass some of the pitfalls by creating a agentic swarm with hard guardrails such as context resets and changing the way the harness works with memory, etc.
> Literature search, e.g. "Have there been any peer reviewed papers where they applied X method to Y domain"
This is something the LLMs are really good for, at least for compiler papers.
They are great computer interface assistants, but forget scientific conclusion would never trust then for committing in right git branch
Don't like this style of article where the "How" is "in what manner do they use it?" and not "what techniques do they use?". I would prefer to see specific lab setups, how they use and coordinate agents, what kind of protocol they have for asking questions and verifying information, and so on.
Because it's written for marketing and engagement, not to actually teach anyone anything.
Using privacy-preserving methods, we study 207,000 Claude and Claude Code research sessions to understand how Claude is used across scientific fields and research tasks
This article talks about the outputs being Claude artifacts. I am curious if anyone has feedback on using Claude artifacts? What do you like/not like?
As a hobby I run https://smalldocs.org / https://github.com/espressoplease/smalldocs an agent agnositc artifact layer, so I'm looking for feedback on artifact use in general.
Here are some sample scientific SmallDoc artifacts:
Palmer penguins: body mass, flipper length and species - https://smalldocs.org/s/YrJDCfCD8-6j_uRE0pNCbI#k=odJRxWOIySV...
AlphaFold 3: capabilities, evidence and operational limits - https://smalldocs.org/s/rRRuByQhmhZNBH_4RNCx1t#k=S0Jhyf72Q6M...
Artifacts are amazing, I think they are not getting enough attention. They recently cloned most of the Google Docs / Notion features that I was using those connectors for. Comments are very useful for steering.
The missing feature for docs is now GDrive, ie organizing and cataloging your artifacts to build a coherent long-lived KB. But for that you can still export.
Anthropic added a while back the ability to let clients provide their own API key, again I think that’s under-explored and will become more relevant as AI gets more penetration.
Building out quick SPAs, HTML pages, etc is a great use, I think the innovation will be making these things supportable and usable for more than one-shot single-user artifacts. Eg user login, analytics, etc.
AI in science is a lot different than AI in progamming in some ways. It seems pretty harmless in programming a lot of the times because it's used to speed up rote tasks like writing boilerplate. But in science, if it does give scientists a boost in discovery, I think it's likely to lead to an even more cavalier attitude about the power that such discoveries can have, and thus lead to much more abuse of power by the wealthy and malicious actors.
> Using privacy-preserving methods, we study 207,000 Claude and Claude Code research sessions to understand how Claude is used across scientific fields and research tasks.
Did they get permission to use researchers' data or did they monitor and extract data without consent? Considering the kerfuffle OpenAI generated with their math proofs, you'd think Anthropic would take a hint and be clear that they did the research with consent, though that assumes they got consent.
Seriously, all the industry researchers at these companies went through IRB training during their PhD; have they just conveniently forgotten?
https://arxiv.org/abs/2412.13678
Isn’t there something in the terms of service that gives them essentially blanket permission to read you conversations?
Yep, exactly, this is already covered by the privacy policy.
https://www.anthropic.com/legal/privacy
Am I the only one who finds Claude overly assertive and confident for research? I tend to get more value out of other models (e.g. Gemini 4 Argon) even if they make mistakes, and then use Claude to revise and polish.
I agree, LLMs in general tend to misuse words and concepts like "proof" - when they have only partially or sloppily carried out an (heuristic) argument, or in situations where a proof is not possible at all, only empirical evidence. This has improved with recent models but has not been entirely solved