Inside HubSpot's Quiet, Profitable AI Rollout
What Anthropic's latest move means for data cleanup in the year ahead.
If you spend enough time watching the AI industry, you stop reacting to launches and start tracking patterns. Inside HubSpot's Quiet, Profitable AI Rollout is one of those patterns.
The skeptical read is that we are watching a feature, not a platform. The optimistic read is that fine-tuning is exactly the kind of feature that becomes a platform when nobody is paying attention.
Teams that win with RAG-as-a-service tend to share a habit: they write the evals before they write the prompts. Everything else follows from that.
Eval harnesses, once an afterthought, are becoming the most important piece of code in many AI projects. Figma's team treats theirs the way an SRE team treats a runbook.
Eval harnesses, once an afterthought, are becoming the most important piece of code in many AI projects. Intercom's team treats theirs the way an SRE team treats a runbook.
What Google DeepMind actually shipped with Gemini 3 Pro is less a single capability and more a cluster of small, compounding improvements — the kind that only show up when you put a real workflow on top.
Eval harnesses, once an afterthought, are becoming the most important piece of code in many AI projects. Duolingo's team treats theirs the way an SRE team treats a runbook.
The cost curve matters here. GPT-5.1 is roughly an order of magnitude cheaper per token than the equivalent model 18 months ago, and that changes which problems are worth automating at all.
None of this guarantees a clean story. Google DeepMind could ship a model next month that rearranges the assumptions in this piece. But the direction of travel, for now, is clear enough to plan around.