/AI3h ago

Self-Improving AI Faces Recursive Limit from Reality's Feedback Delays

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Carlos E. Perez@IntuitMachine#1596inAI

Intelligence can outrun everything except the world's own answer.

A self-improving system can accelerate every step that precedes feedback — hypotheses, simulations, designs — but it cannot accelerate feedback itself. Some truths are only deposited by elapsed time (κ-latency), and some judgments have no single target to optimize toward (ω is monadic — felt quality, not formal truth).

Compute drives those to zero only in the domains where the world answers cheaply and clearly: code, games, math. Everywhere else, the loop still has to wait for reality and still can't sense from the inside what it hasn't yet touched.

In QPT's own words, this is the Recursive Limit: self-improvement can reduce the latency of needing the world, never the need.

Or, most compressed:

Compute is the bottleneck engineers can see. Reality is the one they can't — because it's made of time and taste, and neither compiles.

3:28 AM · Jun 5, 2026 · 333 Views
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Carlos E. Perez@IntuitMachine

Intelligence can outrun everything except the world's own answer.

A self-improving system can accelerate every step that precedes feedback — hypotheses, simulations, designs — but it cannot accelerate feedback itself. Some truths are only deposited by elapsed time (κ-latency), and some judgments have no single target to optimize toward (ω is monadic — felt quality, not formal truth).

Compute drives those to zero only in the domains where the world answers cheaply and clearly: code, games, math. Everywhere else, the loop still has to wait for reality and still can't sense from the inside what it hasn't yet touched.

In QPT's own words, this is the Recursive Limit: self-improvement can reduce the latency of needing the world, never the need.

Or, most compressed:

Compute is the bottleneck engineers can see. Reality is the one they can't — because it's made of time and taste, and neither compiles.

3hViews 655Likes 14Bookmarks 8