# WHAT AI ACTUALLY CHANGES

*The frame for the 100 entries, and the reason 10 of them are negative*

---

## The claim being tested

"AI could discover X" is usually a statement about *possibility*, not about *mechanism*. The useful
version is narrower:

> **A discovery becomes feasible when its bottleneck is search, and search becomes cheap.**

That is a real and large class. It is also a *bounded* class, and most of the disappointment in this
field comes from mistaking the boundary.

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## Where the bottleneck is search (AI helps, a lot)

| Bottleneck | Why AI relieves it | What it still cannot do |
|---|---|---|
| The literature is too large to read | Embedding retrieval, table extraction, cross-lingual search | Judge whether a paper is right |
| A design space is too large to enumerate | Generative models, Bayesian optimisation, active learning | Synthesise the candidate |
| One evaluation costs hours of compute | Surrogate regression | Extrapolate outside the training region |
| The inverse problem is ill-posed | Learned priors, regularised inversion | Recover information the instrument did not record |
| The pattern is invisible in high dimensions | Representation learning, clustering | Tell a real cluster from a sampling artefact |
| The experiment is slow and manual | Closed-loop automation | Discover something outside its search space |

In every row, the AI removes a *combinatorial* obstacle. None of them removes a *physical* one.

---

## Where the bottleneck is not search (AI does not help much)

- **Nothing has been measured.** No amount of learning recovers a quantity nobody recorded. The model
  will interpolate smoothly and be wrong with confidence.
- **The data are outside the training distribution.** Surrogates fail silently; the failure mode is a
  plausible number, not an error message.
- **Causality is required.** Observational data identifies associations unless an intervention, an
  instrument, or a strong assumption supplies the direction.
- **The claim needs a proof.** A model can suggest a theorem; it cannot certify one.
- **The claim needs a synthesis.** A generative model proposes a molecule; a chemist makes it.
- **The effect is below the noise floor.** A network that "denoises" below the floor is inventing
  structure, not recovering it.

These are the ten negative entries in `08_H_LIMITS`, and they are there because a document with
ninety optimistic entries and no negatives is a marketing artifact, not a research plan.

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## The honest asymmetry

The two lists above are not symmetric in difficulty. The positive ones depend on **compute and data**,
both of which improve. The negative ones depend on **information theory and thermodynamics**, which do
not.

That is the whole argument in one line: *AI makes search cheap, and search is not the same thing as
knowledge.* A discovery that needs a better search is now cheap. A discovery that needs a measurement,
a synthesis, or a proof is exactly as expensive as it was, and the model's confidence about it is
unrelated to its difficulty.

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## What the 100 entries are

Ten themes, ten or fifteen each:

- **Search** (A) — mining archives and literature for relationships nobody has joined.
- **Inverse design** (B) — spaces too large to enumerate.
- **Surrogates** (C) — what becomes possible when a calculation is 10⁶ times cheaper.
- **Automation** (D) — closed loops, and what they optimise that you did not intend.
- **Translation** (E) — the same mathematics in two fields.
- **Symbolic** (F) — recovering laws and invariants from data.
- **Measurement** (G) — instruments with learned priors.
- **Limits** (H) — ten things that will not happen soon, and why.

Every entry names the bottleneck, the capability, **and the step that remains physical**. An entry
without the third is not in this collection.

---

## The one test that separates the real from the promotional

Ask: *if the AI were perfect at its stated job, what would still be missing?*

- If the answer is "nothing" — the entry is wrong, because something is always missing.
- If the answer is "a measurement", the entry is an experiment proposal wearing a model's clothes,
  which is fine and should be labelled as such.
- If the answer is "a synthesis" or "a proof", the entry is a design target, which is also fine.
- If the answer cannot be stated — it is promotional.

Applied to all 100 entries, that test is what this folder is for.