The Right Models in the Right Seats

The thesis behind everything I build: specialization beats scale.

Ask the frontier labs what the future of AI looks like and the answer is always the same: bigger. More parameters, more compute, one ever-more-capable model that does everything. That vision has a convenient property: only a handful of companies on earth can build it, and you'll be renting it from them forever.

I run my AI workloads on a different thesis, and after a year of measurements on my own hardware, I believe it more, not less.

The way forward is not ever-larger all-knowing models. It's teams of highly effective, highly specialized smaller models, composed the way a good company composes a staff.

Get the right models on the bus

Jim Collins framed the difference between good companies and great ones as getting the right people on the bus, and the right people in the right seats. Nobody staffs a company by hiring one impossibly expensive generalist to do every job. You hire people whose strengths match their seats, and the organization outperforms the sum of its parts.

My inference stack runs exactly this way, and I have a year of production numbers behind it:

• A 4B-parameter model running on CPU cores classifies every incoming request in a few hundred milliseconds. It cannot write good code and never will. It doesn't need to. Its seat is triage, and it fills it for free, on hardware that would otherwise idle.
• The same class of tiny model reads a request against a list of available tools and tells a big cloud model which one to use. That two-second nudge cut my action-request latency in half, not because the small model is smart, but because it prevented an expensive model from fumbling toward a conclusion a cheap one had already reached.
• A sparse mixture-of-experts model handles the fast path at 41 tokens per second on decade-old GPUs, because activating 4B parameters out of 26B is itself specialization inside the weights.
• The big models, local or cloud, only see the work that actually needs them. In one measured 40-hour window, 8,129 requests flowed through this division of labor with a 0.17% failure rate and no human intervention.

The lesson from those numbers isn't that small models are secretly as good as big ones. They aren't, and pretending otherwise is how you build garbage. The lesson is the one from my notes that I keep coming back to: the tiers aren't a hierarchy of quality, they're a division of labor. A 4B model in the right seat outperforms a frontier model in the wrong one, on cost, on latency, and often on reliability, because narrow tasks reward consistency over brilliance.

Companies already know this pattern

Here's why I think this thesis wins on economics, not ideology. Every company already organizes its people this way. Nobody believes the optimal workforce is one superhuman consultant billing by the token. Companies win by being specialized and nimble in their personnel, and they will demand the same from their AI: a small model fine-tuned on their support history in the support seat, a compliance-tuned model in the compliance seat, a coding model that knows their codebase in the engineering seat, orchestrated by systems that route work to whoever fills the seat best.

That world is arriving through open weights. The Qwens and Gemmas I run today are the early, general-purpose versions. The trajectory points at made-to-order weights: models distilled, tuned, and owned by the companies that run them, sized to their seats, running on their hardware or commodity clouds, with their data never leaving the building. Every quarter the open releases get better at fitting into seats that used to require a frontier API call, and my own benchmarks watched it happen: models that needed 39GB of VRAM last spring were outclassed by 23GB models with better architectures by fall.

The gatekeepers always cry

Which brings me to the part of the argument that the frontier labs make for me.

Listen to how the largest AI companies talk about open weights: dangerous, irresponsible, impossible to control, surely the end of safety itself. Some of those concerns are sincere and worth engaging seriously. But notice the shape of the argument and who it benefits. The pioneers of every technology wave have warned that the tools were too dangerous to leave the temple, right up until the moment the tools left anyway: mainframe companies about personal computers, telecoms about the open internet, every incumbent about every commodity that ended their toll booth.

Gatekeepers in the pioneer phase always cry. It's what the phase sounds like. The economics underneath don't care: when capability becomes a commodity you can own instead of rent, composition becomes the differentiator, and composition is an engineering discipline, not a capital moat.

What this means for the work

If the thesis is right, the scarce skill of the next decade isn't prompting one giant model. It's the boring, measurable systems work of running teams of models well: routing, failover, quality gates, evals that catch a model drifting out of its seat, guardrails that hold when a model exceeds its authority, and the operational discipline to measure everything, because half of what you believe about your stack will be wrong within six months. I know because I measured mine, and it was.

That's the bet my basement server, my benchmarks, and this blog are all placed on. One team of specialists, on hardware nobody wanted, doing the daily work of a system that would otherwise be an expensive subscription. The right models on the bus, the right models in the right seats, and the bus is yours.