RESEARCH
Where primary sources meet original analysis — long-form research on markets, technology, and economics.
Why “research” means something specific here
"Research" is one of the most abused words in online publishing. A summary of three threads is not research. Here it means something specific: original analysis built on primary sources, with the methodology visible and the conclusions held open for revision.
What we publish
Every piece starts from the source material, not someone else’s summary of it.
- AI coverage drawn from arXiv preprints, lab technical reports, and peer-reviewed work — read in full before we write about a model or capability.
- Technology analysis that tracks patents, regulatory filings, and corporate disclosures, and follows the researchers whose work reaches production years later.
- Economic analysis that engages academic economics as a tool — covering forecasts with explicit attention to methodology and historical accuracy.
Our standards
Rigor is the difference between an opinion and a finding.
- Methodology is visible: we show how a conclusion was reached, not just the conclusion.
- Disagreement is specific: when we reject the mainstream reading of a result, we say exactly why.
- Uncertainty is honest: we are explicit about what we do not yet know.
What we don’t do
Long-form is the default — and length is earned, never padded.
- No rewriting of other people’s takes.
- No summarizing threads and calling it research.
- No padding a 1,200-word idea to 4,000 for word count — or compressing a 4,000-word subject to fit a template.
Latest Deep Dives
The Social State Encoder: Trust-Shaped Robot Motion
A diffusion transformer can generate fluent motor trajectories and still approach a person the wrong way. The missing layer is not more pixels — it is a relational state vector that conditions every denoising step.
Key Takeaways
- Fluent robot motion is not the same as socially appropriate motion — a policy that optimizes only for task success can still invade personal space or approach too fast.
- A Social State Encoder projects a compact relational state vector (trust, valence, arousal, phase, resonance) into a token that conditions the motor diffusion model at every denoising step.
- The useful architecture is multi-rate: language context at ~0.5 Hz, social cross-attention at ~1–10 Hz, and a hard safety envelope at ~50–200 Hz that can bypass the policy.
Offline Accuracy Is a Trap: Build Evals That Predict Production
Your model scores 91% offline and users still churn. The gap is not the model — it is how you measure.
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The Inference Cost Paradox: Token Prices Fall, AI Spend Explodes
The price of a token collapses every year. The total bill goes up anyway. Both are true at once, and the reason is older than computing.
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Building AI Systems That Actually Work: The Architecture Nobody Talks About
The model is not the system. Most builders focus on the wrong layer and pay for it in production.
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Retrieval Architecture That Doesn't Degrade: Why RAG Fails at 10x Usage
RAG breaks at exactly the point where you can't rebuild it. Design for degradation now.
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Routing Queries to Models: The Cost Decision Tree Nobody Writes Down
Your biggest cost lever is not inference optimization. It's routing. 80% of products skip it.
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