Progress to AGI

Strong on many well-defined problems, but novel reasoning and reliable verification remain inconsistent.

Models can decompose goals, but long-horizon plans still drift when tools, people, and changing conditions enter the loop.

In-context adaptation is useful. Durable learning across sessions still depends on external memory, evaluation, and retraining systems.

Models generate useful combinations across media, while originality, provenance, and judgment still require human scrutiny.

Transfer between domains is improving, but unexpected situations and recovery from errors remain difficult.

Current agents need permissions, checkpoints, and human review for consequential or extended workflows.

What AGI means to us

We define AGI as an AI system that can learn, reason, and perform at or above human level across any intellectual task, without needing task-specific training. That bar is high. Current systems are impressive but narrow.

One possibility is that broader intelligence emerges through an accumulation of capabilities rather than one breakthrough. Whether those capabilities become AGI remains uncertain.

In the meantime, we focus on reviewed workflows that can be evaluated against a concrete operating need, with explicit limits and human approval where consequences matter.