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There is no universally accepted pass/fail test for artificial general intelligence (AGI), and no company can establish the claim with a single benchmark score. A sound evaluation starts by defining exactly what is being claimed, examining performance across a broad set of abilities and conditions, separating capability from autonomy and safety, and checking whether independent evaluators can reproduce the result.
The practical question is not simply “Did this model score highly?” It is “What proposition was tested, under what conditions, against which human baseline, with what failures and independent scrutiny?” That approach works for present AGI announcements and for more speculative claims about superintelligence.
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Who decides when AGI is attained?
No regulator, standards body or scientific panel currently has authority to declare that a system has achieved AGI. The term is used differently by companies and researchers, so an announcement must be evaluated against its stated definition rather than an assumed universal threshold.
The OECD’s 2025 report introduces a potential framework for defining and measuring AGI, describing it as AI that matches the full range of human cognitive and social abilities. “Potential framework” is important: the indicators help organize evidence, but they do not create an agreed final certification test.
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Geoffrey Hinton, as quoted by the Associated Press in 2024, said: “I use it to mean AI that is at least as good as humans at nearly all of the cognitive things that humans do.” That is Hinton’s personal usage, not a binding definition. Other claims may mean human-level performance in one occupation, broad competence across cognitive tasks, autonomous completion of long projects, or economic impact comparable to a large share of human labor. Those are materially different propositions.
Start with the claim, not the headline
Before reviewing charts or demonstrations, write down the exact proposition. Record:
- the company making the statement and the speaker’s role;
- the model, version, system prompt and tools involved;
- the date and whether the system is publicly released, an internal prototype or a forecast;
- what “AGI” or “superintelligence” means in that statement;
- whether the claim concerns capability, autonomy, economic effect, or a combination;
- the domains and languages covered, and any exclusions.
Do not treat a forecast about a future model as evidence that a current model already meets the forecast. Likewise, a system that performs at a human level in one job has not thereby demonstrated general intelligence.
Use a multidimensional definition of ability
The OECD AI Capability Indicators describe nine areas that can be used to map breadth:
| Indicator area | Questions to ask |
|---|---|
| Language | Can the system understand and produce language accurately across genres, languages and levels of ambiguity? |
| Social interaction | Does it interpret social context, intentions, norms and disagreement without relying on scripted cues? |
| Problem solving | Can it formulate and revise plans for unfamiliar problems rather than match a memorized pattern? |
| Creativity | Can it generate original, useful work under constraints and explain meaningful design choices? |
| Metacognition and critical thinking | Can it detect uncertainty, critique its own reasoning and change course when evidence conflicts? |
| Knowledge, learning and memory | Can it acquire new information, retain it appropriately and avoid confidently repeating errors? |
| Vision | Can it interpret images, diagrams, spatial relationships and visual changes reliably? |
| Manipulation | Can it control or reason about physical objects and environments with precision? |
| Robotic intelligence | Can it perceive, plan and act in the physical world under changing conditions? |
Nine areas are a map, not a score. A company may report excellent language and coding results while providing little evidence about physical manipulation, social interaction or metacognitive reliability. Compare the individual profiles instead of accepting a blended “intelligence” number.
What a convincing evidence package contains
A credible claim should let an outside reader reconstruct what happened. For every reported result, look for the following information.
Tasks and modalities
Identify the exact tasks, prompts, input types and permitted outputs. A text-only test cannot establish visual or robotic ability. A short answer task cannot establish sustained planning. Ask how many examples, languages, environments and difficulty levels were included.
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Find the scoring rule, aggregation method and human comparison. “Human-level” is meaningless without identifying which humans, performing which version of the task, with what time limit, training and access to reference material. If a benchmark combines unrelated subtasks into one average, inspect the component scores and the spread between best and worst areas.
Configuration and conditions
Record the model version, context window, sampling settings, system instructions, retrieval sources, external tools, number of attempts and time allowed. A result obtained with unlimited retries or hidden human intervention is not comparable with a one-shot result.
Contamination and test exposure
Ask whether the test set or close variants could have appeared in training. Strong performance on a familiar format can reflect memorization. Useful safeguards include newly created tasks, held-out data, contamination analysis and evaluations conducted after the model’s training cutoff.
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Uncertainty and failures
Prefer repeated trials, confidence intervals or other variance estimates over one impressive run. Require a list of failed tasks, refusal cases, hallucinations, unsafe actions and conditions that caused performance to degrade. A claim that reports only successes is incomplete.
NIST’s AI measurement and evaluation program supports the development of metrics and evaluation methods. NIST does not provide a single AGI certification checklist; its work is best used as a reason to inspect measurement design rather than to substitute a government seal for analysis.
Why one benchmark cannot prove general intelligence
A benchmark is evidence about the tested task under the tested conditions. It is not a direct measurement of every ability associated with AGI. Narrow optimization, test familiarity, prompt engineering and selective reporting can all produce a high score without broad competence.
Stanford HAI’s 2026 AI Index illustrates both the speed of progress and the danger of over-extrapolation. It reports that frontier-model performance on Humanity’s Last Exam gained 30 percentage points in one year. The same edition describes SWE-bench Verified performance rising from 60% to near 100% in a single year. Those are substantial movements on named evaluations, but neither benchmark is a universal AGI threshold. Coding performance, even at a very high level, does not establish social reasoning, physical control, dependable long-horizon action or safe deployment.
Check transfer, not just score
Ask whether the system can carry a skill to unfamiliar tasks and conditions:
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- different domains, vocabulary, cultures and languages;
- changed tools, interfaces and environmental constraints;
- longer tasks that require monitoring, memory and recovery from mistakes;
- adversarial, ambiguous and incomplete information;
- multiple independent runs with consistent outcomes.
Report capability as a profile. A model that is exceptional on one axis and unreliable on another should be described that way, even if its average score is high.
A repeatable workflow for evaluating an AGI announcement
- Pin down the proposition. Copy the statement, date, model version and definition. Mark whether it is a capability claim, an autonomy claim, an economic forecast or a prediction about a future system.
- Inventory the evidence. List every benchmark, task, modality, score, baseline, tool and human comparison. Separate company-run tests from independent evaluations.
- Audit the design. Check test dates, training cutoffs, contamination controls, sampling settings, number of attempts, time limits and whether hidden operators intervened.
- Inspect the distribution. Request component scores, variance, confidence intervals and failure examples. Look for a few spectacular results masking weak performance elsewhere.
- Test breadth and transfer. Organize evidence against the OECD’s nine areas and add unfamiliar tasks, changed conditions and longer sequences of work.
- Measure autonomy separately. Record how much initiative the system had, whether it could use tools, how long it operated, how often a person corrected it and how it handled failure.
- Review safety and responsibility. Check evaluations for privacy, security, misuse, bias, robustness and human oversight. Note missing results as unknowns, not as proof of either safety or danger.
- Seek independent scrutiny. Prefer public methods, external replication, red-team findings and evaluators without a commercial stake. Ask what evidence would falsify the company’s conclusion.
- State the narrowest supported conclusion. Say exactly what the evidence demonstrates, what it suggests and what remains untested. Do not upgrade “strong performance on these tasks” into “AGI achieved.”
Keep capability, autonomy and deployment distinct
A model can answer difficult questions yet fail to plan, monitor its own work, use tools reliably or operate safely for days. Conversely, an automated workflow may complete a long task by combining modest models with rigid scripts and frequent human checks. Those are different achievements.
The proposed “Levels of AGI for Operationalizing Progress on the Path to AGI” framework treats capability and behavior alongside deployment considerations such as autonomy and risk. It is a proposed analytical lens, not an official consensus classification. Use it to ask:
- What can the model do in a controlled test?
- What can it do without step-by-step human direction?
- How long can it maintain a goal and recover from errors?
- What permissions, tools and real-world access does it have?
- What happens when it encounters uncertainty or conflicting instructions?
- What safeguards, monitoring and shutdown mechanisms surround deployment?
Do not count tool access as intelligence automatically. Record which abilities come from the model and which come from retrieval systems, code execution, databases, simulators or human supervisors.
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Use identical comparison axes and mark unavailable information explicitly as “not stated.” A side-by-side scorecard prevents one company’s polished demo from being compared with another’s audited evaluation.
| Axis | Evidence to compare | Common mistake |
|---|---|---|
| Breadth | Coverage of the OECD ability areas, with component scores | Using one language or coding score as a proxy for all intelligence |
| Human baseline | Who participated, task version, time limit and access to tools | Calling a vague or outdated baseline “human level” |
| Transfer | Unfamiliar tasks, domains, languages and environments | Testing only benchmark-like prompts |
| Reliability | Repeated trials, variance, calibration and failure rates | Publishing the best run only |
| Autonomy | Task length, initiative, tool permissions and human interventions | Equating a fluent answer with independent operation |
| Transparency | Methods, prompts, dates, model version and contamination controls | Accepting an unverifiable chart |
| Safety | Responsible-AI benchmarks, red-team results and oversight | Assuming no reported failure means no failure |
| Independent review | Replication, external audits and published limitations | Treating a press release as peer review |
Stanford HAI’s 2026 AI Index says almost all leading frontier-model developers report capability-benchmark results, while reporting on responsible-AI benchmarks remains spotty. That asymmetry matters: capability evidence can be plentiful while evidence about reliability, misuse and harm is sparse.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Warning signs and positive signals
Warning signs
- The definition of AGI changes between the headline, technical report and product page.
- Only a single aggregate score is disclosed.
- The model, prompt, tools, test date or number of attempts is hidden.
- Human comparison groups, time limits or assistance are unspecified.
- Demonstrations are live but not independently repeatable.
- Failures, uncertainty and safety incidents are omitted.
- A present benchmark result is used to validate a prediction about superintelligence.
Positive signals
- A precise claim with a named model version and date.
- Public tasks, scoring code, prompts or detailed methods.
- Held-out or newly created evaluations with contamination analysis.
- Component results, repeated trials and disclosed failure cases.
- Independent replication and criticism acknowledged by the company.
- Separate reporting for capability, autonomy, reliability and safety.
- A willingness to state what the evidence does not establish.
Superintelligence is a different kind of claim
Superintelligence generally refers to a system that would greatly exceed human cognitive performance across many important domains. A current benchmark score cannot by itself confirm that a future system will reach that state, improve its own design, or trigger an intelligence explosion.
Nick Bostrom’s Superintelligence: Paths, Dangers, Strategies, published by Oxford University Press, is useful background on possible routes to superintelligence and their implications. It is a conceptual and historical source, not a current benchmark standard or evidence that any present system is superintelligent.
Evaluate a superintelligence statement in two layers:
- Present evidence: What does the existing system demonstrably do, under what tests and safeguards?
- Future inference: Which assumptions connect those results to a forecast about recursive improvement, strategic power or societal impact?
Label the second layer as a forecast. Its uncertainty should not be hidden inside a present-tense capability score.
A practical evidence record
For each announcement, keep a one-page record with these fields:
- Claim: the exact wording and intended definition.
- System: model version, release status, tools and configuration.
- Scope: domains, languages, modalities and task duration.
- Tests: benchmark names, dates, prompts, scoring and baselines.
- Integrity: contamination controls, repeatability and variance.
- Failures: representative errors, refusals, unsafe outputs and recovery behavior.
- Autonomy: human interventions, permissions, monitoring and shutdown controls.
- Safety: responsible-AI evaluations and unresolved risks.
- Independence: external replication, audit or review.
- Conclusion: the narrow claim supported, plus explicit unknowns.
This record makes competing announcements comparable and exposes missing information without pretending that absence of data proves failure.
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Public evaluations can establish that a specified system achieved specified results on specified tasks under specified conditions. They can show progress, reveal weaknesses and support carefully bounded comparisons.
They cannot, by themselves, establish a universal AGI threshold, prove that every human cognitive and social ability has been matched, guarantee dependable autonomy in the open world, or validate a forecast of superintelligence. The OECD framework organizes measurement; NIST advances evaluation methods; Stanford HAI documents benchmark trends; proposed AGI-level frameworks separate capability and deployment; and reporting about definitional disagreement shows why none of these sources is a final arbiter.
The strongest conclusion is therefore conditional: a company’s claim is credible only to the extent that its definition is clear, its measurements are broad and reproducible, its failures and safety evidence are visible, and independent reviewers can test the same proposition.
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