Post by tasq.ai

Lindy moved 100% of its traffic from Claude to DeepSeek last month, and on paper, the math worked. Cheaper, open-weight, billed by the actual token rather than a flat subscription. What used to take enterprises eighteen months of vendor diligence, three SOC2 reviews, and a procurement committee with veto power now happens over a weekend. That's actually impressive. The agility of modern AI procurement, compared to even three years ago, is genuinely worth pausing on. But the math worked differently for the evaluation layer their team had spent months building around the old model. When you swap an AI model, you don't just swap pricing. You swap its behavior, its failure modes, its error margin. The patterns the team learned to catch on Claude, the confidently-wrong moments, the overconfident summaries, the edge cases that tripped it up, those don't transfer. DeepSeek fails differently. Different overconfident summaries, different edge cases, a different shape of "almost right." The eval room rituals, the tribal knowledge of "here's what we know to look for," all of it has to be rebuilt. That cost doesn't make it onto the migration spreadsheet. The cost reckoning itself is real and overdue. Meta capped employee spend. Uber burned through its 2026 budget in four months. The Linux Foundation just launched a Tokenomics Foundation, which means AI spend is officially becoming the kind of line item that gets a board meeting attached. But cost discipline that doesn't account for new behavior, new error margins, and the trust calibration you just threw out isn't really discipline. It's half a solution. The teams that swap models cleanly are the ones whose judgment layer is model-agnostic to begin with: their evaluation rubric works on the output, not on the model's quirks, and their experts catch what was wrong rather than what the old model used to be wrong about. Token caps are the meter. The layer above the meter is what decides which outputs are worth catching, regardless of who produced them. What's emerging in the teams that do this cleanly is something quieter than discipline. It's real optionality. They can ride whatever cost curve the market produces next, because they're not paying the rebuilding tax every time the math changes. In six months, watching which companies became serial model-switchers without quality drift will tell you a lot about who actually built the layer above the meter, and who just got a new spreadsheet. #ProductionAI #AIEvaluation #TrustInAI