Practice · 2024–now · AI Present
AI stack consolidation
Seventeen LLM wrappers to three vendors, one observability bill, and framework repos going read-only. Hype cycle entering boring procurement.
AI stack consolidation mutated because enterprises cannot maintain a new framework every quarter and model APIs commoditized. The fad layer — bespoke chains per team — is dying; the stuck layer — evals, guardrails, data pipelines — is becoming table stakes. Winners look like platforms; losers look like last year's YC batch.
Context
Demos consolidate; evals remain
AI pair programming changed how code is typed faster than how it is reviewed. Agent frameworks and standalone vector stores sorted into demos versus durable plumbing; mid-market RAG folded back into Postgres. The permanent layer is familiar: evals that gate deploys, model routing for cost, tool protocols instead of plugin snowflakes, and humans who own production. Autopilot rewrites and vibe-shipped auth middleware are still big-bang migrations with better slides — and a longer on-call.
Compare with
Related
Framework · 2023–now
LLM app frameworks
LangChain-class glue gave way to vendor SDKs and thin wrappers. The durable pieces are boring: evals, retrieval, and product UX.
Practice · 2023–now
LLM eval pipelines
Regression tests for nondeterministic models that actually fail the build. The unglamorous CI that separates demos from products.
Practice · 2023–now
Agent ops / LLM observability
Tracing, cost caps, and prompt versioning for production LLM features — mostly constrained tool loops, not autonomous agents. Datadog for tokens.