We've been telling ourselves a story about the hidden cost of running llms at scale (and how to cut it in half) that no longer matches the data. Time to update.
What's changing
Skeptics will point out — correctly — that we've seen similar inflection-point claims fizzle. The honest answer is that you don't need certainty to act, just better expected value. The downside of moving too early in this category is small; the downside of moving too late is structural.
Why it matters
Skeptics will point out — correctly — that we've seen similar inflection-point claims fizzle. The honest answer is that you don't need certainty to act, just better expected value. The downside of moving too early in this category is small; the downside of moving too late is structural.
What to do about it
The shift began quietly. A handful of teams, working in parallel and mostly unaware of each other, arrived at similar conclusions: the old approach optimized for a constraint that no longer binds. Hardware got cheaper. Models got smaller. Distribution got more direct. Each individual change felt incremental — but together they reset the cost curve.
- Adopt early — the cost of waiting is higher than the cost of failing fast.
- Measure honestly — pick two metrics, ignore the rest for the first month.
- Talk to users — the gap between assumption and reality is wider than ever.
The takeaway
The biggest mistake will be treating this as a tooling question when it's actually a strategy question. Tools change. The underlying shift in customer expectations is what compounds.
