Three layers of AI, and the frontier we can all map

A first version of this appeared on the Coop Chronicle. This is the reworked version, and it is where Our Weird Future starts: what the three layers are, and why the open one is worth your weekend.

Models get noticeably more capable every few months. If you want to actually take part in that, rather than just watch it happen to you, it helps to know where the improvement is coming from. It splits into three layers, and only one of them has room for you.

Three layers

Each layer moves at its own pace, is driven by different people, and leaves a different amount of room to jump in.

Layer 1: Hardware. Faster chips mean larger models trained on more data in less time. NVIDIA’s revenue tripled in a year, hyperscalers are building gigawatt-scale data centres, and national strategies hinge on chip supply chains. Can you contribute? Honestly, no. It’s a capital game and you’re on the consuming end. But it still reaches you: the model that was too expensive to run last year is cheap this year, the 8K context window is now 1M, and your setup gets better without you touching anything.

Layer 2: Model training. Architecture (transformers, mixture of experts, state space models), data curation, and the post-training that turns a raw model into something useful. Can you contribute? A bit. Fine-tuning and open-weight models (Llama, Mistral, Qwen) are within reach, but the frontier belongs to labs with thousands of GPUs. When Anthropic ships a better Claude, I benefit. I didn’t help make it better, and that’s fine.

Layer 3: Application. Everything between “a capable model exists” and “a useful thing got done.” Agent harnesses, prompt systems, tool orchestration, multi-agent patterns, and the human side of when to steer and when to delegate. This is the layer that’s wide open. You don’t need a data centre and you don’t need a PhD. You need a problem, an API key, and the willingness to poke at it.

What I learned from a chicken coop

“Vibe coding” carries a faint whiff of “not serious.” But the people messing with agent patterns right now, even on silly weekend projects, are doing real applied research. A few things I picked up just building a monitor for my chickens:

• Single-turn agent calls beat long-running conversations for autonomous loops. Continuity lives in state files, not chat history.
• Parallel sub-agents work, as long as you scope each to non-overlapping files or you get git conflicts.
• Frame diffing plus a vision model replaces an expensive video API. A cheap Pi camera gets you 90% of the way there.
• Agent personality is not just flavour. A “devoted parent” prompt notices health concerns a “neutral observer” skips.

None of that needed a PhD. It came from building a thing and paying attention. And it all lives in Layer 3, where a chicken coop and a Fortune 500 deployment are solving the same core problem: getting reliable, sustained work out of a language model.

The jagged frontier

This next part is worth sitting with. These models are not uniformly smart. They’re spiky. Brilliant at one task, hopeless at the near-identical one next to it, and you usually can’t tell which is which from the outside. Researchers have a name for this shape: the jagged frontier. The capability line is not a smooth curve you can read off a benchmark. It’s a coastline full of inlets and headlands, and the only way to learn where the edges actually sit is to walk them.

That is exactly what tinkering does. A backyard coop monitor hits a vision-model failure that no benchmark caught. Two copies of the same open model run hundreds of times apart in speed. A frontier model will describe a hen in loving detail but refuse to name her while an 8B model running on a home GPU names fourteen of fifteen. Every one of those is a point on the map that only turned up because someone was using the tool on a real problem they cared about.

This is the good news, and it is the reason this site exists. You do not have to be a lab to add to the map. Every person applying these models to their own hobby is doing two useful things at once: finding a genuine improvement in their own space, and marking one more spike or gap on the frontier that the rest of us can learn from. The garden planner, the terrain printer, the electronics bench, the writing tool. Each is a probe into where the intelligence is sharp and where it’s dull.

Nobody knows where this lands. But the people running into the spikes in their own work are figuring it out fastest, and there are a lot more hobbies than there are labs. So if you’re building something with these models, even if it feels small, even if it feels silly, write down what worked and what broke. That’s a coordinate the rest of us didn’t have.

We’re all building this. You can start with a chicken coop.