Independent analysis of economics, technology, business, and public policy. Original essays informed by academic research, historical evidence, and data-driven reasoning.
A language model asked the same factual question three times gave three confident, wrong answers. The real culprit isn't broken software — it's benchmarks that reward bluffing over honesty, a flaw retrieval-augmented generation can narrow but never actually cure.
Samsung has led DRAM for 34 years and just clawed its way back in the HBM race. The real edge isn't better chips—it's owner-led decision speed, counter-cyclical capital, and vertical integration no rival can copy.
Everyone watches the token price. It's the smallest, most-discounted, least-honest number in your stack — and it explains almost nothing about what an AI system actually costs to run.
Word embeddings don't understand language — they record the company words keep. That single fact explains both their power and why their prejudice can't be separated from their intelligence.
Vector-search benchmarks measure the one part every engine already optimized and ignore the thing that actually breaks production retrieval: filtering against live business data. That reframes the entire pgvector vs Qdrant vs Pinecone debate.
Precision is easy to report; recall requires counting every relevant document that exists, which nobody has done since the web outgrew ten blue links. The AI search industry hasn't solved that problem — it has stopped mentioning it.
Vector search misses exact strings; keyword search misses meaning. The reason hybrid retrieval works isn't that it's smarter — it's that it refuses to trust either retriever's confidence, and hands the decision to a score-blind referee.
The trick behind billion-scale AI retrieval isn't reading everything faster. It's learning what to ignore—and accepting a system that misses one match in twenty by design.
Enterprises rushed to buy standalone vector databases in 2023 and 2024, then discovered the real bill: double-write sync failures, egress surcharges, and replication costs the pricing calculator never showed. Now they're walking it back into the databases they already run.
Semantic search didn't just make search smarter. It converted language into geometry, gaining a genuinely new ability while losing the razor-sharp precision of the old index — which is why the best systems now run both.
When INP became a Core Web Vital in March 2024, it reframed the rendering debate from 'when is HTML built' to 'how much work runs on the user's device' — and client-side rendering came out the loser.
Teams pick embedding models for contracts and case law using a benchmark that measures Wikipedia retrieval, not legal recall. Here's why the small-vs-large-vs-Voyage-Law-2 debate is told in the wrong units.