# v9 BRIEF — 100 discoveries that AI makes *feasible*, and 20 that it does not

The question is not "what could AI discover?" That framing produces slogans. The question is:

> **Which specific discoveries have a bottleneck that is *search* rather than *measurement* or
> *understanding* — and is that bottleneck one current AI actually relieves?**

Every entry must answer three things plainly:

1. **What is the bottleneck today?** (search space size, literature volume, inverse problem, cost per
   evaluation, human attention)
2. **What does the AI do about it?** (a specific capability: surrogate regression, generative inverse
   design, embedding search, symbolic regression, active learning, automated experiment)
3. **What does the AI NOT do?** (the physical step that remains: synthesis, measurement, proof,
   validation, or the causal inference that no model supplies)

If (3) is missing or trivial, the entry is hype and must be rewritten.

---

## Format (200–260 words)

```
### A01 — <the discovery, stated as a result>
**What becomes possible.** <one or two sentences: the concrete finding or capability>
**Bottleneck today.** <why it has not been done: size, cost, attention, inverse problem>
**What the AI supplies.** <the specific capability, not "AI" generally>
**What still needs the bench.** <the physical, experimental or proof step that AI cannot do>
**First concrete test.** <a number, a small experiment, or a dataset that would show it works>
**Hype risk.** <the most likely way this entry is wrong or oversold>
```

## Sections

| Folder | Theme | Count |
|---|---|---|
| `01_A_SEARCH` | Mining archives, literature, and unpublished data for missed relationships | 15 |
| `02_B_INVERSE` | Inverse design over spaces too large to enumerate (materials, molecules, proteins, metamaterials) | 15 |
| `03_C_SURROGATE` | Replacing expensive simulation with learned approximations, and knowing when they fail | 15 |
| `04_D_AUTOMATION` | Self-driving laboratories, adaptive experiment, closed-loop characterisation | 15 |
| `05_E_TRANSLATION` | Finding isomorphic structure between fields (same equation, different subject) | 10 |
| `06_F_SYMBOLIC` | Equation discovery, invariant finding, and the recovery of laws from data | 10 |
| `07_G_MEASUREMENT` | Instruments that get better with learned priors (calibration, denoising, super-resolution) | 10 |
| `08_H_LIMITS` | **Ten things AI will not deliver soon**, stated as honestly as the positive entries | 10 |

Total: 100.

## Hard rules

1. **No "AI will revolutionise X."** Every claim is a specific result.
2. **The physical step must be named.** If the discovery needs a synthesis or a measurement, say so.
3. **No fabricated citations, datasets, results, or benchmark numbers.**
4. **Hype risk is mandatory** and must be a real failure mode, not false modesty.
5. **Section H is adversarial.** It exists because a document with 90 optimistic entries and no
   negatives is a marketing artifact.
6. **No prohibited subjects:** human demographics, social constructs, politics, economics, climate,
   ecology, marketing.
7. **Distinctness:** no two entries share a core mechanism or a core AI capability argument.