When Models Converge, What Actually Wins?
GPT-5.4, Claude 4.6, and Gemini 3.1 launched within weeks of each other. No clear winner on benchmarks. That is the most important result.
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How AI is reshaping decisions, organizations, and the systems we rely on — the second-order effects nobody talks about. Published when ready.
These essays reflect my personal perspectives only and do not represent the views of my employer.
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Second-Order Effects: The AI Jobs Story We're Missing
Everyone asks what AI will replace. The better question is what becomes newly valuable once intelligence gets cheap.
GPT-5.4, Claude 4.6, and Gemini 3.1 launched within weeks of each other. No clear winner on benchmarks. That is the most important result.
OpenAI killed Sora because $15 million a day in inference costs dwarfed $2.1 million in lifetime revenue. It is the strongest case study yet for why delivery economics — not capability — decides what survives.
Everyone asks how smart the model is becoming. The better question is how cheap intelligence is becoming to deliver.
Most AI demos are framed as intelligence demos. ChatJimmy is more interesting as an economics demo.
In many real systems, the scarce thing is not intelligence itself. It is working memory — the cost of keeping enough of the past alive for the model to be useful in the present.
Why AI is making presence the premium — and what that means for the future of strategy work.
Most leaders still talk about AI as though it were a better search box. OpenClaw suggests the bigger shift: AI as an ambient layer that can act wherever work already happens.
Everyone asks what AI will replace. The better question is what becomes newly valuable once intelligence gets cheap.
I went all-in on vibe coding for a real project. The speed was unreal. The debugging was a nightmare. Here's the honest scorecard.
People frame AI decisions as 'human vs. machine.' The real question is where to sit on the spectrum between them — and most organizations choose poorly.
Strategy decks are easy. The hard part is who retrains the model, who handles the 2am edge case, and who decides when to override the AI.
A killer demo gets funding. It gets press. It gets a standing ovation. What it doesn't get you is product-market fit.
I learned to read code as a strategy person who'd spent a decade outside engineering. Here's the mental model that made modern codebases legible.
Every day I watch autonomous systems make life-or-death decisions they can't fully explain. Here's the framework I use to think about trust when stakes are high.