Each post here reads on its own, but they are not independent essays. They are one model, taken apart into pieces small enough to argue with. If you are new, read these three in this order:

1. AI Is Superhuman Wherever the Scoreboard Is Cheap, the capability model
2. You Cannot Own a Falling Floor, the economic model
3. The Bottleneck Migrates, the physical model

Together they say where AI capability goes next, why having it stops being worth anything, and what scarce thing the race runs into instead. The Taiwan post is the risk model underneath all three: the one assumption that, if it fails, takes the rest of the document with it.

The model itself fits in five questions. Everything on this blog, and most of the corpus, is an answer to one of them.

Where does capability go next?#

Wherever checking the answer is cheap. A training loop needs a signal, and domains split by what that signal costs: the Millisecond Loop, where a unit test or a proof checker grades millions of attempts a day for free, against the Six-Month Loop, where finding out whether the advice was any good takes half a year and an argument. Capability flows downhill toward the cheap side, which is why the same model is superhuman at competitive programming and merely fine at judgment. "Cheap" is shorthand for three properties, cheap, faithful, and hard to game, and the post stress-tests all three against autonomous driving, the strongest counterexample on offer.

The corpus grounds this in the data wall and files the one development that would break it, learned verification, as a framework-level risk.

What happens to the price of what it can already do?#

It collapses, on a schedule. Cost per token at fixed capability fell roughly 100 to 1000× in two years, which is the falling floor: pick any level of intelligence, and once it exists at all it is nearly free within about two years. The Two-Year Clock is the product-design consequence, and it runs on everybody. A moat made of model access depreciates on a published schedule.

The cost decline is close to physics. The price decline is politics, carried by open-weight competition, and the corpus tracks the two lines separately in inference economics.

Who captures the surplus?#

Mostly not the people spending the money. When every firm has to adopt and none gains an edge by adopting, spending becomes defensive: the Red Queen, the standard fate of a general-purpose technology, argued in the firms game. The surplus passes through to customers and to whoever owns the short list of things that do not get cheaper: energy, distribution, licences, liability capacity, trusted data, senior judgment.

The stable position is to own the scoreboard: be the party that defines correct, carries the liability for that judgment, and holds the data that settles the argument.

What physical constraint binds next?#

Not chips. The bottleneck migrates, silicon to electrons to permits, and by 2028 the rate-limiting step on US frontier AI is the Permit Window: interconnection queues, environmental review, turbine order books. The tell is that variance across US power markets exceeds variance across countries, which is how you know the constraint is institutional rather than physical. Grounded in energy.

The orbital post extends the migration one step, to kilograms. Terrestrial power pays the Night Tax, the firming cost that makes always-on solar a multiple of its record-cheap headline price, and the Neighbor Problem, the ratepayer, commissioner, and hearing attached to every site on the map. Orbit pays neither in that form. It still files with the FCC and shares the sky, but what actually prices the bet is launch mass, an industrial constraint rather than a local institutional one, the kind that factories and learning curves grind down.

What would break all of this?#

Two different kinds of failure, and they get separate instruments.

The first is being wrong about an assumption. The big one is Taiwan, and the trap there is the Gray Zone: the likely failure is not the invasion everyone watches for but a repricing, insurance premia and export licences, a valve rather than a switch. The general lesson is the scored versus consumed gap: the assumption you wrote down and scored is often weaker than the one your conclusions actually rest on, and the Green Dashboard will glow green all the way through the failure of the second one. That audit method comes from the Taiwan page and applies well beyond it.

The second is being wrong about a forecast, which is what the scorecard exists to catch: every claim that can be caught failing, with a probability, a resolution date, and a frozen rule.

The lexicon#

For reference, every named concept in one place. The claim column is deliberately one sentence. The post is where it earns it.

If a future post coins something and it is not on this list within a week, that is a bug in this page.