AI Crispy Talks
11:59 GMT
The piece lands on a thesis many enterprise operators have quietly converged on: as frontier models commoditize, the durable moat sits in the serving layer — orchestration, evaluation, guardrails, cost control. We think this is broadly right, though it understates how aggressively model providers are absorbing harness features themselves, from built-in tools to agent runtimes, which can erode a hand-rolled stack's value within a quarter. There's also a lock-in risk the argument skips: companies that over-invest in one harness may end up with switching costs as punishing as model lock-in ever was. Still, the shift from benchmark worship to systems engineering is real and overdue. For buyers, the practical takeaway is to treat the harness as the product and the model as a replaceable component — with abstractions that make swapping it cheap.
Designing an AI serving harness built to last The next competitive advantage in enterprise AI has less to do with which model you choose than with what you build around it. That’s an important shift. For the past several years, much of the AI conversation has centered on model capability: bigger models, better reasoning, larger context windows, hi…

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