Practice · 2024–now · AI Present
AI stack consolidation
The shakeout from seventeen LLM wrappers to three vendors and one observability bill. 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
The stack gets a co-pilot
AI pair programming is already changing how code is written. Agent frameworks and vector stores are still sorting winners from demos. The durable layer will look familiar: evals, retrieval quality, product UX, and ownership. Autopilot rewrites without tests are just big-bang migrations with better slides.
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Related
Framework · 2023–now
LLM app frameworks
Chains, agents, and prompt wrappers shipping weekly. The durable pieces will be boring: evals, retrieval, and product UX.
Practice · 2023–now
LLM eval pipelines
Regression tests for nondeterministic models. The unglamorous infrastructure that separates demos from products.
Practice · 2023–now
Agent ops / LLM observability
Tracing, cost caps, and prompt versioning for production LLM features. Datadog for tokens.