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What I Think Is Happening to AI

A grounded view of the shift from model access to agent action, continuity, evaluation and personal intelligence infrastructure.

The public story of AI is still mostly told as a race between models.

Who has the highest score. Who has the longest context window. Who can reason for the most tokens. Who can code the benchmark faster. Those differences matter, but model performance is already clustering at the top, while competition is increasingly shifting toward cost, reliability, control and performance inside specific domains.

That changes what is worth building.

When intelligence is scarce, access is the advantage. When intelligence becomes broadly available, orchestration becomes the advantage: what the system knows, what it can use, how it evaluates itself, what it retains and whether it becomes more aligned with the real work over time.

I think AI is moving through three shifts.

From answers to action#

Models are becoming components inside systems that use tools, modify files, browse environments, call APIs and coordinate work. The unit of value moves from a good response to a reliable outcome.

From context to continuity#

A large context window can hold more information, but holding information is not the same as knowing what deserves to persist. Continuity requires selection, consolidation, contradiction handling, scope and forgetting. A system needs a memory policy, not only more tokens.

From generic capability to personal intelligence#

The strongest model may know more about the world than any individual. It still does not automatically know what *you* mean by good. Personal intelligence is the layer that connects general capability to specific judgment, history, constraints and intent.

This is where Catalyst sits.

I do not expect one permanent model to win everything. Open-weight and hosted systems will coexist. Frontier APIs will remain valuable when they offer the strongest capability and lowest operational burden. Open models will matter when control, customization, privacy, local execution and independence matter more.

The model becomes replaceable infrastructure. The accumulated understanding around the model becomes the durable asset.

That is the direction Catalyst is building toward: an intelligence layer that can survive model changes, learn from real use and remain controlled by the person or team it represents.