ANI, AGI and ASI: AI Capabilities and Evaluation

ANI means specialised artificial intelligence, AGI concerns general capabilities across a broad range of tasks, and ASI describes superintelligence: broadly exceeding human capabilities. A useful comparison specifies the tasks, performance level and definition being used. Application autonomy is a separate dimension.

A company can use a highly capable system in a clearly bounded process without needing an AGI classification. Equally, an interface that answers questions on many topics does not, by itself, demonstrate coverage of every relevant task. Procurement and development should seek evidence for actual work, not merely an impressive label.

The original diagrams are illustrative, not evaluations of products or customers. Photographs show creative work, document verification and software development. They do not depict AI outputs or provide evidence of general capabilities. The company-assistant examples are proposed explanatory scenarios.

ANI, AGI and ASI: which question does each term answer?

These terms appear in capability-based classifications. They do not replace classifications by technology, such as neural networks, or by task, such as text processing. A system has an architecture, a usage domain and a performance profile that should be described together.

The guide to types of artificial intelligence maps these different axes. Here, we explore what is measured when discussing specialisation, generality and superintelligence. This is not a timeline requiring every application to pass through the same compulsory stages.

In a technical presentation, ask for the definition before the conclusion. “General” may describe task breadth, while “capable” requires an outcome level and comparison. When a document uses a particular research framework, retain that framework explicitly rather than turning it into universal certification.

ANI: artificial intelligence for a defined task or domain

Specialisation does not automatically mean modest performance

A specialised system may classify documents, estimate a value or identify patterns in a category of images. Its domain and conditions are bounded. It may perform very well there, but those results do not automatically transfer to tasks that have not been evaluated.

For example, an application sorting commercial enquiries can be tested on the company's categories and language. Testing shows what it confuses and where review is requested. Working in that process does not demonstrate an ability to independently manage the company's accounting, technical design or legal relationships.

The task sheet also records the positive boundary: which outcome is accepted and under which conditions it can be used. Correct classification may support routing to the appropriate department without authorising an offer or contract change. This presentation highlights the implemented function while avoiding attribution of capabilities outside the project.

Nor does ANI establish an absolute prohibition on transfer between languages or tasks. Describe the actual scope and available evidence. An implementation based on deep learning and neural networks may be specialised; its number of layers does not, by itself, change its competence domain.

AGI: generality and performance across a broad scope

Multiple conversation topics are not a complete test

AGI discussions concern an ability to solve and learn varied tasks under the adopted definition. Evaluation should include novel situations, not merely formulations close to familiar examples. Specify the required outcome level and the population or system used for comparison.

In Levels of AGI, Meredith Ringel Morris and coauthors propose separating generality from performance level and considering autonomy during deployment. This is a research framework, not a product certificate. Historical examples in the paper remain tied to their period; they are not used to automatically classify models released later.

A large language model can support many application functions. For a company, test them separately: summarisation, retrieval, extraction or response preparation. Fluent conversation and a long feature list do not replace an outcome profile, access conditions or exception checks.

ASI: why does a domain record not mean general superintelligence?

Broadly exceeding human capabilities is a different requirement

Nick Bostrom's definition of superintelligence concerns broadly exceeding the best human capabilities in practically every relevant domain, rather than one task. The term requires breadth and level; it does not describe a computer's speed alone.

Exceptional performance in a game, very fast retrieval or exact calculation supports conclusions about those activities. These are not aggregated into a verdict about all cognitive capabilities. Similarly, connecting several applications does not automatically make the combined system satisfy an ASI definition.

For a service page or commercial project, present implemented functions and acceptance criteria. ASI is not a synonym for advanced automation. An application can be selected using concrete requirements without predicting inevitable arrival dates or attributing economic effects to a hypothetical system.

Generality and performance: two axes to read together

A profile is more informative than a single average

Generality concerns task breadth; performance concerns outcomes on those tasks. For a company, we propose a profile separating families of uses and their conditions. An average dominated by many easy cases should not conceal repeated failure in an important activity.

The teaching matrix helps readers interpret a presentation without assigning levels to real products. For a document assistant, field extraction, preservation of conditions and identification of missing information can be checked separately. Record the scope of every conclusion so a good result is not extended beyond the test.

We propose retaining both resolved cases and cases sent for review in the report. Operators should understand every status. Excluding exceptions from a denominator without explanation can change a metric's meaning, even when the remaining percentage looks favourable.

A scenario can describe the profile without invented scores: an assistant correctly summarises an ordinary document but loses a condition from its annex. The useful conclusion is that testing should include this document structure and condition verification should remain in the workflow. Performance on the first task does not become a label of competence across all company documents.

Autonomy: what may the system do on the company's behalf?

Model capability is not execution permission

A model may prepare a response or operation arguments. The application determines whether it can read data, propose a change or execute an authorised action. The same technical component can have different permissions in two roles within the same system.

The guide to AI agent types and autonomy levels explains this organisation. In a specific project, check the recipient, object and effect of the operation. Convincing wording does not approve sending, and text inside a retrieved document does not change company access rules.

Broader access expands the range of possible operations; it is not evidence of greater general intelligence. We propose boundaries, approvals and logs separately from response evaluation. The team can then review both output quality and compliance with the authority granted for that request.

A workshop can change the recipient after a draft has been approved. The application should treat the new sending operation as a change requiring appropriate control, rather than reusing approval for different details. This tests implementation and access rules. It does not independently measure cognitive generality, even when the same model is used in the writing test.

How should benchmarks and human comparisons be interpreted?

Version, protocol and resources are part of the result

Read a score alongside the tasks, data and test conditions. Inspect permitted tools, attempt counts, allocated resources and how responses were selected. Compare results obtained under compatible conditions, rather than isolated percentages from different presentations.

In its September 2026 analysis of ARC-AGI-3, ARC Prize explains that saturating the benchmark does not demonstrate the achievement of AGI. The authors also distinguish different testing-interface conditions. We use this clarification to keep conclusions within the protocol's scope, without reproducing product scores or rankings here.

Human comparisons require comparable tasks and conditions

A person solving a task without documents is not an equivalent baseline for a system with a complete library. The evaluation sheet specifies instructions, time, information and available tools. Participant preparation should be relevant to the question being tested.

We propose a verification appendix retaining the metric definition, evaluated subset and conditions. It allows a result to be reviewed without publishing company-confidential data. When the protocol changes, identify the new series separately rather than concealing the change in a continuous chart that implies identical measurement.

Also specify what counts as an unsuccessful case: an incorrect response, no response, timeout or request for intervention. These situations may have different process effects and are reported distinctly. Evaluators should be able to reconstruct the decision from retained evidence rather than receiving only successful demonstration examples.

Generalisation, learning and memory are not the same thing

Check what actually changes between tasks

Memorising examples, using contextual information and learning parameters are distinct mechanisms. A system can answer from an available document without retraining its model. The guide to context windows, memory and RAG explains where information is retained and how it is accessed within an application.

François Chollet proposes examining intelligence through skill-acquisition efficiency, distinguishing it from performance on familiar tasks alone. The practical question is which experience, clues and examples were available before testing. This helps interpret generalisation without confusing a memorised solution with a newly demonstrated ability.

In an evaluation framework presented by Google DeepMind in March 2026, the authors propose cognitive capability profiles with separate tasks and human comparisons. Learning and metacognition are among the areas considered. This is an evaluation proposal, not a universal classification already applied to every product.

A company exercise can change an explicit instruction rule, rather than merely a name in an example. Check whether the new condition is applied and justified through the permitted source. The exercise provides evidence about that bounded behaviour; it is not presented as a universal intelligence test or a measurement of every learning ability.

Does AGI automatically mean consciousness or human emotions?

Task-solving capabilities and subjective experience are different questions. AI consciousness research examines dedicated mechanisms and indicators; fluent dialogue or a reasoning score does not replace that assessment. This article preserves the distinction without attributing inner states to a product.

For a company application, describe observable behaviours: identifying missing information, requesting clarification or respecting approval boundaries. These criteria are not replaced by claims that a system “feels” responsibility or “understands” the company like a person. Interfaces and documentation should preserve the operational meaning of such terms.

What matters to a company selecting an AI application?

Turn broad claims into acceptance tasks

Start with the actual work and a verifiable outcome. For a commercial assistant, choose permitted documents, enquiry types and conditions that must be preserved in the draft. Also check a missing-information case. An AI class name does not replace this specification.

In software application development, evaluation also includes integration: identifiers, roles, statuses, logs and intervention. The model output can be correct while the system save is wrong. Reports should locate the difference between capability, source and operation.

Training through employee AI skills connects these controls to daily work. Users should know what to check and when to request the responsible person's intervention. Training adapts to the actual role and application, not an AGI label used in a presentation.

Which questions should precede a pilot or purchase?

Ask for the tested domain, representative examples and important errors. Establish data access, usage conditions, complete cost and the operational owner. Distinguish already measured results from functions proposed for development. A demonstration has its own scenario and conditions, retained in the records.

For proposal comparisons, use the same task sheet and user permissions. Separate functions available in the proposed configuration from those requiring additional integration or licensing. The team can then compare concrete deliverables and operating costs without inferring feature access solely from a model name or a demonstration using another account.

For a pilot, we propose a dataset agreed before testing and a comparison with the existing process. Include incomplete and out-of-scope requests. Illustrative data is labelled, while real evidence has controlled access. Criteria are not changed after seeing the results merely to preserve a favourable score.

At handover, establish who can stop the feature, how issues are reported and which procedure applies until resolution. Retain the application version and corresponding training material. When a connector or source is added, recheck access and affected cases rather than assuming the earlier evaluation automatically covers the new configuration.

For custom applications with AI integration, these requirements become deliverables: workflow, data, evaluation, interface and maintenance. The company can use them to decide what to expand and what to retain under review. Value connects to correctly completed work, not the label attached to the technology.

Frequently asked questions about ANI, AGI and ASI

Does ANI mean a weak system?

No. It describes specialisation, not a compulsory performance level. A system may perform very well within a bounded area, which should be evaluated under its actual conditions.

Is a chatbot answering questions on many topics automatically AGI?

Broad conversational coverage is not, by itself, a complete evaluation. Define the tasks, required level and conditions, then inspect the outcome and generalisation profile.

Does a high benchmark score prove AGI?

The score supports conclusions within that protocol. Check coverage, version, resources and comparison. Evaluating a limited area does not automatically certify every general capability.

Is an autonomous agent more intelligent than one requiring human approval?

Autonomy describes permitted deployment operations. The same model can be used with different permissions. Task quality and execution authority are assessed separately.

Is ASI the same as superhuman game performance?

No. General superintelligence concerns broadly exceeding human capabilities. A game or bounded-domain result remains evidence about that area.

Should a company wait for AGI before using AI?

A project can start from a current, verifiable need. Select appropriate methods, data and controls, measure the pilot outcome and decide on expansion for that specific process.