The Three Vectors of the AI Race
Increasingly, I believe there are three primary vectors in the AI race:
- Raw IQ: Who has the smartest model?
- IQ + Cost: Which model is the smartest per token dollar?
- IQ + Cost + Speed: Which model is the smartest per token dollar at the highest tokens-per-second (toks)?
While raw IQ is the frontier race everyone pays attention to, I think IQ + cost is the most interesting thing to watch in AI right now.
But before we get there, let’s talk about raw IQ. The consensus (and my personal POV) puts Fable on top, with Sol behind, then a fairly big gap to number three. Broadly, though, this class of model should be reserved for your toughest problems where the costs don’t matter that much. If the AI is solving something truly valuable, then the $10/50 (input/output cost per million tokens) you spend on Fable shouldn’t matter that much. The problem, of course, is that the way most tokens are getting consumed today are in coding harnesses like Claude Code and Codex, and when you’re doing that kind of work, a lot of people just leave the smart model on and let it cook. Luckily, these harnesses, particularly Claude Code, are getting smarter about offloading easier tasks to cheaper models in the form of subagents. A lot of people dream of routers to solve this, but I think for the most part we’ll be good with this smart orchestrator, dumb-agent architecture.
More interesting, though, is that even today I’d argue the vast majority of work can now be done by much cheaper models (Luna, particularly in Max mode, is an amazing workhorse that’s worth spending more time with). Which brings us to bucket two: IQ + cost. Lots of people are talking about this as the Pareto Frontier, and I think it’s the most interesting space to watch right now. Segment co-founder Calvin French-Owen had a great post titled “Small Models Have Arrived” where he discussed the value of this class of model:
I think demand for "frontier-level" models is going to keep compounding. Especially for fields that require novel breakthroughs or discovery (engineering, hard science, model training).
But I also think the demand for "fast/cheap/good-enough" models is just about to take off.
Think of the people you interact with on a daily basis: coworkers, vendors, and customers. Nine times out of ten, you want someone who is super responsive, and just handles things for you. Most of the "human tokens" at companies today are spent this way — hiring skews heavily toward the fast/cheap/good-enough archetype.
