AI vs AGI vs ASI - crisp definitions for 2026

Let's get the words straight. People argue about this stuff all day. We do not have to. Here is a clean set of working definitions that match how teams actually build and ship systems in 2026.

What is AI? Narrow AI handles specific tasks with strong but bounded skills. Think language models, code assistants, vision models, and agent frameworks that chain tools. They can look broad because they use many tools, but each stack still runs inside constraints.
What is AGI? A system that performs a wide range of cognitive tasks at human level or better, across domains, with transfer, planning, autonomy, reliability, and ongoing learning. No sentience required. This aligns with recent definitions used by industry analysts and builders.
What is ASI? A hypothetical system that beats top human performance across most domains. It could be sentient, it could not. There is no solid evidence we have ASI today.

Yes, today's LLMs feel broad. They code, write, reason, and use tools. But they are still narrow stacks, tuned for specific workflows. They struggle with long-horizon planning, robust autonomy, and calibration under real-world constraints. That is why ChatGPT-like systems are advanced AI, not AGI.

For a concrete framing, David Jayatillake sums it up cleanly: AI is narrow by task, AGI is human-level across many tasks without needing sentience, and ASI refers to a powerful superintelligence beyond human ability. He also notes debates about whether LLMs can get us there and whether we should expect sentience at all.

Where LLMs and agent frameworks fit

LLMs are the core model. Agent frameworks wrap planning, memory, tool use, and sometimes full OS control. Tool stacks make LLMs look more general, but they remain brittle at the edges. They hallucinate under pressure, miscalibrate confidence, and fail when requirements shift mid-run. That gap is the difference between "smart tool" and "general intelligence."

How to tell 'general' from 'specialized': evaluation criteria (and the moving goalpost)

Here is the test I use when someone shouts "that looks like AGI." Forget vibes. Use operational criteria.

  • Breadth across domains: not just code or copy, but many fields with different patterns.
  • Transfer and generalization: can it apply what it learned in one area to a new one with minimal scaffolding.
  • Long-horizon planning: can it plan over hours or days, recover from mistakes, and still hit the goal.
  • Tool use: reliable, safe use of external tools, APIs, and files without constant human babysitting.
  • Autonomy: can it choose subgoals, monitor progress, and self-correct without looping forever.
  • Robustness and calibration: low hallucination rates, honest uncertainty, and safe fallback behavior.

Why the goalpost keeps moving: once a capability lands, we tend to call it "just AI." Machine translation, image labeling, coding help, even computer control. The word AGI moves up a level to whatever we do not have yet. That is human nature. It does not help teams decide what to ship.

Case in point: full-computer-control tools like OpenClaw have re-ignited the debate. They can drive the mouse, type, open apps, and complete multi-step tasks. Impressive autonomy. Also brittle heuristics. They get stuck in UI edge cases, miss context, and still need audit and recovery. Great agents, not AGI.

Watch out: OS control feels like intelligence because it looks like a person working. It is still pattern execution. If an agent cannot explain tradeoffs, calibrate risk, and recover cleanly on novel screens, it is not general. It is a powerful macro with a brainy front end.

Use operational tests, not vibes

  1. Define the task set: cover multiple domains and modalities, not just a single workflow.
  2. Constrain and measure: set SLAs, error budgets, and safe tool permissions. Log everything.
  3. Force novelty: change requirements mid-run. Add unseen data. Watch adaptation and calibration.
  4. Score autonomy: track how long the agent runs without human fixes, and how it handles recovery.
  5. Stress reliability: evaluate hallucinations, safety violations, and cascading errors under load.

Some investors call long-horizon agents that work for hours while making and fixing mistakes "functionally AGI," highlighting pre-training for knowledge, inference-time compute for reasoning, and iteration for self-correction. I get the argument. In practice, these systems still break on messy reality and need scaffolding. Useful, yes. General, not yet.

AI vs AGI vs ASI - side-by-side comparison

Here is the quick reference you can share with your team.

Dimension AI (Narrow) AGI (General) ASI (Superintelligence)
Scope Task-specific systems and agent stacks Human-level or better across many domains Above top human in most domains
Autonomy Constrained autonomy with guardrails Plans and self-corrects over long horizons Strategic planning at superhuman scale
Reliability Good on known paths, brittle at edges Stable, calibrated, safe under novelty Unclear, depends on alignment and control
Examples LLMs, code copilots, vision models, OS-control agents Not in production today Hypothetical only
Availability Production-ready in many workflows Research target, debated timelines Speculative
Risk & Governance Tool permissions, audit, human review Stronger safety, oversight, evals, policy Existential and geopolitical concerns
Timeline Here "Next few years" per some leaders, longer per skeptics Unknown

Where are we now? 2026 reality check on AGI claims

We are living in the weird middle. Models are strong. Agents are getting bold. Yet the gaps are obvious when you push them.

✅ Progress that matters

  • Multimodal reasoning and tool use are real and useful.
  • Code generation is productive enough to change team structure.
  • OS-control agents can execute long task chains.
  • Feedback loops improve results over hours, not just seconds.

❌ Gaps holding us back

  • Hallucinations still appear at the worst times.
  • Calibration is off, agents sound certain when they are not.
  • Long-horizon plans drift, loops form, context gets lost.
  • Self-directed learning without heavy scaffolding is weak.

Here is my read. The reliability gap is the blocker. If an agent cannot stay calibrated under novelty, it cannot be trusted with general autonomy. That is not a small bug. That is the core difference between a brilliant assistant and a general intelligence you can hand a department to.

And yes, tools like OpenClaw make people say "this is it." I get it. Watching an agent control a whole computer is spooky. But when it hits a modal dialog it has never seen, or a network hiccup, or a half-loaded page, you see the truth. It is a careful stack of skills, not a general mind.

Key Takeaways:
  • Today's systems deliver value in production, across many tasks.
  • AGI's operational bar includes breadth, autonomy, and reliability we do not have yet.
  • ASI remains speculative and should not drive day-to-day product plans.

What leaders are saying about timelines (with quotes and sources)

Leaders disagree, loudly. That is useful signal if you listen for the details, not the hype.

"AGI will be here in the next few years." - Demis Hassabis and Dario Amodei, Davos interview

In a January 2026 Davos interview, Google DeepMind's Demis Hassabis and Anthropic's Dario Amodei both said AGI is likely within the next few years. Amodei went further, predicting that almost all coding could be done by AI by the end of the year, and that most other digital tasks will be automated by the end of 2028. You can watch their debate here: Google's Demis Hassabis, Anthropic's Dario Amodei Debate the World After AGI.

"By the end of the year, almost the entire codebase can be written by AI, and by 2028 most digital tasks will be automated." - Dario Amodei, Anthropic (Davos 2026)

Others push back hard. Stanford AI experts have publicly said AGI will not appear in 2026. Geoffrey Hinton warns of rapid capability jumps and safety risks. Sam Altman has hinted at shorter timelines. Yann LeCun argues that AGI is the wrong framing or decades away, and that LLMs are not the path to ASI. Demis Hassabis disagrees, saying AGI can indeed emerge from LLM-centric systems. I have whiplash just writing that, but the disagreement is the point.

My take: treat timelines as scenario planning, not truth. Build for value now, keep an option on faster progress, and evaluate claims with hard tests. Evidence beats predictions.

What to do now: pragmatic playbook for entrepreneurs and AI‑agent users

Enough theory. Here is how to drive value with today's agents while staying safe.

  • Start where reliability is high: code assistance, content ops, research copilots, data wrangling, and support triage.
  • Wrap agents with strong guardrails: permissions, scopes, and human review for risky steps.
  • Instrument everything: logs, traces, eval harnesses, and dashboards for autonomy and error rates.
  • Define SLAs and error budgets: standardize what "good enough" means, then enforce it.
  • Pilot in high-ROI, low-risk domains: expand only when metrics hold in the wild.
  • Continuously fine-tune prompts, tools, and policies: small iteration cycles beat big bang rewrites.
Pro tip: Split work by reliability tier. Let agents do 80 percent of tasks end-to-end, route the last 20 percent to human experts, and feed their fixes back into evals. You will get compounding gains without risking critical outputs.
Watch out: Full OS control is not a free upgrade. Use policy constraints, time-boxed runs, and a kill switch. Log screenshots and actions for audit. Never grant broad filesystem or payment access without a human checkpoint.

An evaluation loop you can ship this quarter

  1. Choose 10 tasks your team repeats weekly. Include structured and messy ones.
  2. Record ground truth for accuracy, latency, and cost with humans only.
  3. Deploy the agent with read-only access first. Measure deltas.
  4. Add write access behind a review gate. Track autonomy length and recovery quality.
  5. Graduate to semi-autonomy once it meets SLA for two weeks straight.

Stack choices that reduce pain

  • Use a proven LLM with consistent tool-use behavior for your domain.
  • Pick an agent framework with clear policy controls and action logs.
  • Prefer structured tool schemas over free-text commands to cut errors.
  • Add retrieval for stable facts and push non-determinism into review lanes.

Quick note on the "functionally AGI" claim from investors: long-horizon agents that iterate for hours are big progress. But the reliability bar for AGI is higher. Until agents handle novel situations with calibrated judgment and minimal scaffolding, call them what they are. Powerful AI. Not general.

Why the definition will keep changing

Let's be honest. The word AGI is a moving target. OpenClaw-style computer control moved the line again. By decade's end, I expect most teams will call the old AGI definitions broken. That is fine. Use operational bars. If your system delivers cross-domain performance at human level with reliable long-horizon autonomy and safe tool use, most people will call it AGI, whatever the label of the month is.