10 resources for investigating an AI claim
A paper, a benchmark score and an adoption survey answer different questions. Choose the evidence that fits the decision in front of you.
To investigate an AI claim, identify what it rests on. For a research paper, check the task, comparison and measured result. For a benchmark, read the evaluation method. For an adoption survey, find out who was surveyed and what counted as using AI.
Say someone claims a new model can take over a substantial part of a company's work. Ask what it actually completed, under which conditions, and how anyone checked the result. A strong research result can be a reason to try something; the business decision still needs evidence from the work you intend to change.
For a capability claim, use arXiv or ACL Anthology to locate the research and OpenReview to look for public discussion. For a model comparison, read the benchmark's methodology. For an industry trend, start with Epoch AI or the Stanford AI Index.
1. arXiv, to find the experiment behind a headline
arXiv is useful when a capability claim points back to a research paper. Open the paper and find the task, the comparison and the result the authors actually measured. The abstract may describe a broad ambition; the experiment gives you a more precise starting point.
arXiv curates submissions but does not peer-review their contents. A paper there gives you access to the authors' work; it does not show that their conclusions have survived independent scrutiny. Use it to sharpen the question for a trial: which part of this experiment could you reproduce on work that matters to you?
2. OpenReview, to read the questions other researchers asked
OpenReview helps when the paper leaves you unsure about a result. Where a venue makes the discussion public, the reviews and author responses show how the method or interpretation was challenged.
Availability depends on the venue's review policy, so a paper's presence on OpenReview is not a uniform seal of quality. Read one specific objection and its answer. If it concerns an assumption your planned use also depends on, it belongs in your own evaluation.
3. ACL Anthology, to follow research on language tasks
The ACL Anthology collects publications in computational linguistics, speech and natural language processing. For work on language tasks it is a more focused place to look than a general feed.
If a company is considering document or language work, start from the task: extracting information, retrieving a relevant passage or evaluating an answer. Read what the authors counted as success and which data they used. A result on one collection of documents does not tell you how a system will handle the company's own material, and each venue and paper still needs to be read on its own terms.
4. Hugging Face Papers, to see which papers practitioners discuss
Hugging Face Papers brings together papers submitted and discussed by the research community. It helps you notice an approach or find a paper you would otherwise miss.
Follow the link to the original work before it supports a decision. Votes and discussion show attention; they do not show that anyone has reproduced the result. Ask whether the paper changes an option you are weighing, and note that option and why it deserves a look.
5. Semantic Scholar, to find the research around a paper
Semantic Scholar supports paper search, citation exploration and research recommendations. Use it once you have found a promising paper and want to see the work around it.
Follow a relevant citation into the actual paper and check whether it supports the claim you care about, challenges it or only discusses the same subject. A citation count cannot tell you which. The trail can also turn up an alternative explanation or another approach before you treat the first result as the only route.
6. Epoch AI, to check the data behind an industry trend
Epoch AI's data collections cover AI models, benchmarks, compute and related infrastructure. They help when a claim concerns the direction of AI development and the resources behind it.
Some infrastructure datasets use estimates based on sources such as permits or company disclosures, so read what an entry represents and how it was constructed before you compare it with anything. The collections can inform a view of the industry. They do not establish the cost or benefit of introducing AI into a particular company, which depends on that company's workload and actual operating requirements.
7. Stanford AI Index, to put a claim in context
The Stanford HAI AI Index brings together research and data on AI's development and its place in the economy. It helps when a single announcement is treated as a description of the whole market.
Find the measure behind a statement, its period and the population it describes. An adoption figure tells you what surveyed organisations report doing; it cannot tell you whether your proposed use will deliver a worthwhile result. Use the context to decide which assumptions deserve attention, then return to the evidence your company's decision needs.
8. MLCommons AILuminate, to see which risks a safety benchmark covers
AILuminate is a family of safety and security benchmarks. Its published scope distinguishes tests of ordinary model behaviour from adversarial attempts to make a system behave differently.
Read the specific benchmark's tasks, language and input types before you apply a result, then ask which risks that matter to you remain outside its scope. A model-level test can inform the choice of a component. It cannot establish that a complete workflow, with its documents and permissions, is safe to operate; those conditions need their own check.
9. Artificial Analysis, to see what an AI benchmark score combines
The Artificial Analysis benchmarking methodology describes the evaluations and weighting behind its Intelligence Index. Read it when a single score is used to justify a model choice.
Look at the tasks that resemble your intended work and the conditions of the evaluation. The index is primarily text-based and English-language; other input types and languages have separate evaluations. A comparison can narrow the models worth trying. Then test the chosen model on the actual assignment, including the effort a person needs to check and correct its work.
10. METR's time horizons, to read a claim about how long an agent can work
METR's task-completion time horizons measure task difficulty by the time a human expert would need, at a stated predicted success rate for an AI agent. The definition matters when someone describes a model as capable of doing hours of work.
The measure is not simply how long an agent can run unattended. Read the reliability level and the tasks behind the estimate before you extend the conclusion to other work. This page is no longer actively updated, so use it to understand the method rather than to choose the latest model. For a company, the question is whether the agent completes the relevant assignment reliably enough with an acceptable amount of supervision, which the resources for checking an AI agent's work help you test.
How to choose the evidence for an AI decision
Choose the claim that would change a decision you are making. Find its original evidence, identify what it leaves unresolved and decide what you need to test on your own work.
If the claim concerns a change to how your company works, I can help turn it into a practical investigation through AI advisory. The starting point is the responsibility you want the system to help carry, and what a satisfactory result would look like.
Sources
- arXiv
- OpenReview
- ACL Anthology
- Hugging Face Papers
- Semantic Scholar
- Epoch AI
- Stanford HAI AI Index
- MLCommons AILuminate
- Artificial Analysis
- METR
Sources checked on 26 October 2026.