AI can generate and transform text and other media, help with demanding research and coding tasks, and complete some structured computer actions—but its abilities are uneven, and a fluent answer is not necessarily a correct one. A strong score on one test does not establish general reliability. Treat AI as a set of task-specific capabilities: check what a particular system was tested on, what tools it can use, and how much human review the task needs.
What does “AI can do” mean in practice?
There is no single score that captures what AI can do. A system may be strong at language, image analysis, or a narrowly defined planning problem and still struggle with a seemingly simple task. The OECD’s beta AI Capability Indicators separate nine areas: language, social interaction, problem solving, creativity, metacognition, knowledge and memory, vision, manipulation, and robotic intelligence. The OECD says its ratings reflect the state of the art in November 2024, so they are a framework for thinking about capabilities—not a current ranking of every product. OECD AI Capability Indicators
It also matters whether a system is only generating a response or can use tools, view images, or act in a software environment. A result from one model, version, language, or test setting does not automatically transfer to another. Product features such as memory or live access to information need to be checked separately; they should not be assumed from the label “AI.”
What can AI do well today?
Generate and transform content
Generative systems can produce and transform language and other media. NIST’s GenAI program evaluates generators and detectors across text, image, code, audio, and video. Generating content, however, does not establish that it is true, original, or authentic. NIST GenAI program
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Help with demanding, well-defined tasks
Stanford HAI’s 2026 AI Index reports that several frontier models meet or exceed human baselines on PhD-level science questions, multimodal reasoning, and competition mathematics. These are results on particular evaluations, not evidence that a model can reliably do all expert work in those fields. The same report says performance on SWE-bench Verified rose from 60% to near 100% in a year. That result concerns the benchmark’s software-engineering tasks; it is not a measure of success across the full range of professional software development. Stanford HAI, 2026 AI Index
Complete some structured computer tasks
On OSWorld, a structured computer-use benchmark, AI agents achieved about 66% task success, according to Stanford HAI’s 2026 report. In that evaluation they still failed roughly one in three attempts. This is meaningful progress for bounded tasks, but not a guarantee that an agent will safely or correctly operate arbitrary software on a user’s behalf. Stanford HAI, 2026 AI Index
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Excel at narrow specialist problems
Some symbolic AI systems have achieved superhuman performance in narrow areas such as logistics planning and model checking, the OECD notes. Such results show the value of specialized systems; they do not imply broad, human-like competence across unrelated situations. OECD overview of AI capabilities
What can AI not reliably do?
Guarantee that an answer is true
AI can produce a confident, plausible-sounding response that contains invented or incorrect information. Stanford HAI’s 2026 Responsible AI chapter reports hallucination rates ranging from 22% to 94% across 26 top models on one new accuracy benchmark. Those figures describe that benchmark, not the chance that any given AI response is wrong. The OECD also identifies hallucination as a persistent challenge across the capability areas it reviewed. Stanford HAI, 2026 Responsible AI chapter · OECD overview
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Apply a benchmark win to every ordinary task
Stanford HAI illustrates uneven, or “jagged,” capability with a striking contrast: while recent systems scored strongly on demanding mathematics evaluations, the top model’s accuracy on analog-clock reading was 50.1%. That is a result on the report’s clock-reading evaluation, not a general measure of visual ability. Success on a difficult test does not mean a system will handle every simpler-looking task well. Stanford HAI, 2026 AI Index
Learn continuously from everyday conversations by default
The OECD framework characterizes leading large language models as pretrained, non-adaptive systems and discusses dynamic learning as a limitation in the capabilities it assessed. An AI service may offer product-specific memory or update features, but whether it does—and how those features work—must be verified for that service rather than assumed. The OECD’s description is based on its November 2024 assessment, not a fresh 2026 product-by-product check. OECD AI Capability Indicators
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Be uniformly robust to dialects or adversarial inputs
Stanford HAI reports that several leading models lost close to half their accuracy on a Slovenian commonsense test when evaluated in a regional dialect. Its Responsible AI chapter also reports that adversarial prompts weakened safety performance on tested models. These findings point to vulnerabilities in those evaluations; they do not establish how every model performs in every language or under every attack. Stanford HAI, 2026 Responsible AI chapter
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can AI be trusted, and can detectors prove content is genuine?
Trust should depend on the task, the evidence available, and the cost of an error—not on how polished an answer sounds. Stanford HAI’s 2026 AI Index reports 362 documented AI incidents in 2025, up from 233 in 2024, citing the AI Incident Database. Incident counts describe recorded events; they do not tell you the probability that a particular AI interaction will cause harm. The same report says responsible-AI benchmark reporting is much less common than capability benchmark reporting, limiting what benchmark scores alone can say about safety. Stanford HAI, 2026 Responsible AI chapter
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Nor should an AI detector be treated as universal proof of authorship or authenticity. In NIST’s text-summarization pilot, three generators fooled every detector in that test. That result is specific to the pilot’s generators, detectors, and task; it shows why a detector result should not settle an authorship dispute by itself. NIST describes its program as rigorous, science-based testing of generators, detectors, and prompting across multiple modalities. NIST GenAI program
How should you use AI when accuracy matters?
Match review to the consequences of being wrong. For brainstorming or a low-stakes draft, a quick plausibility check may be enough. For factual, financial, legal, medical, safety-critical, or irreversible decisions, verify the important claims with reliable evidence and keep a qualified person responsible for the decision.
- Define the task narrowly. Ask for a draft, explanation, comparison, or bounded action rather than treating a broad request as a guarantee of a complete answer.
- Check consequential facts. Verify names, dates, quotations, calculations, citations, and claims against dependable sources. A citation supplied by a model should itself be checked.
- Test the actual conditions. If language, dialect, image quality, unfamiliar inputs, or a tool-enabled workflow matters, evaluate those conditions rather than relying on a result from a different setting.
- Review actions before they take effect. For computer-use agents, inspect changes and confirm consequential steps instead of assuming a completed task is a correct one.
- Keep a human accountable. Use AI to assist judgment, not to outsource decisions where errors could materially affect people, money, rights, or safety.
How do you read AI benchmark results responsibly?
A benchmark score is evidence about a defined test, not a universal measure of intelligence, safety, or usefulness. Before relying on a result, ask what was evaluated and when, which model or system was tested, what counted as success, and how closely those conditions match your use. The OECD indicators span nine distinct capability domains and are explicitly beta; Stanford’s 2026 figures are more recent snapshots but remain tied to their named tests. NIST’s program emphasizes ongoing, adversarial evaluation across modalities. OECD indicators · Stanford HAI, 2026 AI Index · NIST GenAI program
When comparing systems for a real job, compare the task and modality, accuracy and kinds of errors, performance on unfamiliar or adversarial inputs, language and dialect coverage, tool access, required human oversight, and evaluation date or version. A single headline score rarely answers all of those questions, and available benchmark evidence is not a complete current product-by-product comparison.
The OECD AI Capability Indicators’ language-scale authors—Yvette Graham, Arthur Graesser, and Swen Ribeiro—write that “Today’s most advanced LLMs, such as that used by ChatGPT, are roughly at level 3.” This is their language-scale assessment in a beta framework based on the state of the art in November 2024; it is not a current rating of every model or a score for every kind of language task. OECD AI Capability Indicators
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