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
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.
Key Takeaways
- Offline golden-set accuracy is a necessary gate, not a launch decision. It measures a frozen slice of yesterday's traffic against labels that drift the moment you ship.
- A production evaluation stack has four layers: offline golden sets, shadow traffic, online outcome metrics, and slice-level failure analysis. Skip any layer and you promote models that look good and fail users.
- LLM-as-judge is a measurement tool, not ground truth. Without human calibration and inter-rater checks, you optimize for the judge's taste, not the user's outcome.
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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Verification Is Not Optional: How to Stop Shipping Wrong Answers at Scale
Every AI product hallucinates. Winners catch it before the user does.
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