BluePi

Agree on decision traces — but the deeper break is architectural. Agents don’t need better access to systems of record. They need memory, not storage. Call it a graph or don’t — the real shift is a purpose-built cognitive layer outside the model. Existing systems of record were never designed for that.

A practical AI system maturity ladder: Single prompt Prompt chains / DAGs Single agent + tools (RAG, function calls) — large gap — Multi-Agent System Advance only when observability, reliability, or expressiveness break at the prior layer.

We’re now applying the same thinking to AI systems. More agents ≠ more intelligence. More autonomy ≠ better outcomes. In many cases, it just means: • More state • More non-determinism • Fewer guarantees

This mirrors an older infra mistake. Kubernetes was adopted for small CRUD services because hyperscalers used it. The result: operational drag, not leverage. Powerful abstractions only pay off once failure modes, scale, and variability demand them.

Not yet @_mohansolo 😄 Gemini 3 might be too smart. I gave it a GitHub issue and asked “should I build this?” It replied: 💡 Recommendation: If your goal is differentiation & sales — pause this. Focus on what actually differentiates. Nothing like an LLM casually challenging your product sense.

Didn't take long did it?

Still waiting on my side — hasn’t rolled out to me yet. Curious to see how it holds up once I can actually put it through real workloads.

The Great Transition Honestly, I don’t think the human brain is wired for what’s coming over the next 36 months. We are about to enter a period of profound cognitive dissonance that will be felt in every corner of the globe. open.substack.com/pub/pronam…

For four decades, humans contorted their brains to think like machines. We learned their rigid syntax, navigated their endless menus, and structured our thoughts into queries just to get a straight answer. We bent over backwards to accommodate the computer’s limitations. That era is ending. The dynamic has finally flipped. We are done learning their language. Now, the machine has to learn ours.

There is a flaw in the premise: Current AI benchmarks measure the ability to retrieve and recombine existing human knowledge (Interpolation). They do not measure the ability to discover new physics or biology (Extrapolation). ​An AI that answers every question 'perfectly' based on 2024 textbooks is useful, but it isn't the 'end of technology.' The end state isn't an AI that knows everything humans know; it's an AI that can run experiments to learn what humans don't know. We aren't even on that S-curve yet.