Cross-domain index

Unsolved problems, evidenced

Real constraints that have resisted solving, each tested against a single question: does AI actually remove the binding constraint, or just speed up work inside it? Every entry carries its full analysis and source trail. For problem solvers and investors looking for what's genuinely unbuilt.

The provenance registry exists and the EU will mandate passports — so why does nobody query the registry where secondhand counterfeiting actually lives? Open opportunity Fashion

What's now possible: Provenance ground truth exists at manufacture: the Aura Blockchain Consortium (LVMH, Prada, Richemont/Cartier, OTB) reports 40M+ products carrying a verifiable digital identity assigned at origin #22, and the EU's Digital Product Passport mandate is on track to make garment passports compulsory for every item sold into the EU, realistically around 2028 #75.
The gap: No evidence was found that anyone queries that registry at the point of resale — the thrift stores, consignment desks, and resale marketplaces where secondhand counterfeiting actually lives. TikTok Shop's authentication program runs on Entrupy-style post-hoc inspection, per the vendor #21. The constraint was removed upstream; the removal sits unused downstream.
Binding constraint: Not a research problem — an integration and adoption problem. The unbuilt piece is the query/verification layer at the point of resale: marketplace integrations, consignment tooling, whatever turns a registry lookup into a habit.
Why it's an opportunity, not just a flaw: The registry exists, the EU mandate will make passports universal, and whoever builds the resale-side verification layer completes the network — provenance's missing last mile — and captures the value of being the default answer to "what is this garment?" This entry was promoted to the public registry directly from the fashion article's adversarial grill session (2026-08) — the first entry sourced that way.
Verdict: Open opportunity— flagged for problem solvers and investors. Watch item: the final text of the EU textiles delegated act (if self-declared brand databases count as compliant passports, shared verifiable provenance loses its premium).

Deep dive: the report's Blindspots & open fronts section · Fashion Research Materials

Why couldn't a garment's authenticity be verified before the fact — only inspected for afterward? Genuine invention candidate Fashion

Why it was considered unsolvable: Verifying a luxury good's authenticity always required scarce human expert attention, which cannot scale as resale/secondhand volume grows. Counterfeits are estimated at ~2.5% of global trade, ~$464B #36.
Binding constraint: Expert attention doesn't scale, and authenticity historically could only be established after the fact, by inspection — there was no ground truth to check against.
Does AI remove the constraint?: Split evidence, and the split is instructive. Entrupy-style computer vision #21 automates the expert's pattern-matching — faster inspection, but still probabilistic, still after-the-fact, still purchasable by any single reseller. That's automation within the old model. The Aura Blockchain Consortium's digital product passports #22 — co-founded by LVMH, Prada Group, Richemont/Cartier, and OTB, covering 40M+ products — instead assign each item a verifiable digital identity at the point of manufacture. Authenticity is no longer inferred after the fact from an image; it's established at origin. That removes the actual constraint.
Evidence anyone is attempting it: eBay's Certilogo acquisition #20; Entrupy's adoption including TikTok Shop's handbag program #21; Aura's 40M+ products across four major luxury groups #22.
Institutional-capability formulation: Intelligence should live upstream, at manufacturing/point-of-origin, as shared infrastructure across a consortium of brands — which is exactly why it's hard to copy. It requires industry-wide cooperation, not a vendor contract a single competitor could also sign.
Verdict: Genuine invention candidate— Aura-style provenance infrastructure. The CV-inspection approach (Entrupy/Certilogo) is automation in disguise by direct contrast within the same problem.

Deep dive: Fashion Research Materials → Unsolved Problems

Can AI forecasting actually shorten fashion's demand-to-shelf loop, or does it just make the same slow loop's guess better? Automation in disguise Fashion

Why it was considered unsolvable: Fashion demand is volatile and trend-driven, but production lead times (often months, often overseas) force commitment decisions long before real demand is known. The mismatch drives markdowns and waste — an estimated 2.5–5B excess items worth $70–140B in 2023 alone #45 (an industry estimate, not an audited figure — see the domain's Known Gaps).
Binding constraint: A slow feedback loop — the gap between a demand signal appearing and a garment reaching the shelf — not a lack of prediction skill per se.
Does AI remove the constraint?: Mostly not yet. Most deployed "AI forecasting" #8 improves the accuracy of a prediction made months in advance — a better guess within the same slow production cycle. That's accelerating work within the constraint, not removing it. 75% of executives are prioritizing AI forecasting for exactly this #45, which is telling: the industry default is "predict better," not "shorten the loop." The one model that would actually attack the constraint — short lead times plus AI reading in-season sell-through — is the Inditex/Zara story, but the specific KPI figures behind it are unverified trade-press numbers #10.
Evidence anyone is attempting it: PVH×OpenAI's demand-planning scope #1 #2 is framed as forecasting-layer automation, not lead-time compression.
Institutional-capability formulation: Intelligence shouldn't live in a better one-shot forecast — it should live in a continuous sense-and-respond loop spanning design, sourcing, and store-level sell-through, short enough that decisions can be revised mid-season. That requires re-architecting the supply chain itself, not adding a forecasting model on top of an unchanged one.
Verdict: Automation in disguiseas most commonly deployed today — with a plausible-but-unconfirmed invention-candidate exception. Needs primary-source verification before that exception can be asserted confidently.

Deep dive: Fashion Research Materials → Unsolved Problems

Could a garment ever fit a real, individual body instead of a population average — and could that data shape design itself? Promising, unproven Fashion

Why it was considered unsolvable: Individual body-shape variation is combinatorially large; standard sizing charts are population averages built for manufacturing convenience, not individual fit. Retailers historically had no cheap way to capture a shopper's actual 3D body data.
Binding constraint: No scalable, cheap way to capture individual body data — fit was always a guess against an average.
Does AI remove the constraint?: Partially, and the evidence is genuinely encouraging rather than automation dressed up as invention: Zalando's AI-built 3D avatars report up to a 40% return-rate reduction in an early pilot #6; ASOS attributes part of a 160bps returns-rate cut to try-on tech #7. A shopper's actual body shape is now informing a fit decision that used to be pure average-based guesswork. Caveat: bias evidence found elsewhere (runway/imagery representation narrowing to 0.3% plus-size #25; generative models reproducing training-data body-type skew #26) raises an open, unverified question of whether these 3D body models and their training data are themselves representative. No study was found answering this specifically for fit/sizing algorithms.
Evidence anyone is attempting it: Zalando #6, ASOS×AIUTA #5 #7, Stitch Fix Vision #11.
Institutional-capability formulation: The compounding version isn't "sell a try-on widget" — that's currently automation, purchasable from vendors like AIUTA by any retailer. It's closing the loop from fit/return data back into design and pattern-making, so garments themselves are shaped by accumulated body data over time. No evidence was found of any retailer doing this yet.
Verdict: Promising but unproven— real signal, currently deployed as commoditized automation; graduates to a genuine invention candidate only if a retailer closes the design loop, which hasn't been observed.

Deep dive: Fashion Research Materials → Unsolved Problems

Why do E2E tests always rot as the app underneath them changes? Solved (reference case) Quality engineering · reference case

Why it was considered unsolvable: Automated UI test scripts encode the exact steps of one implementation. Every UI refactor breaks them, so teams either drown in maintenance or quietly stop trusting their test suite.
Binding constraint: Scripts encode implementation details, not the tester's actual intent — so there's nothing stable for the script to track when the implementation changes.
Does AI remove the constraint?: Yes — when intelligence lives in understanding intent (what the test is actually trying to verify) rather than in a fixed sequence of clicks and selectors, the test can survive a UI refactor because it's no longer coupled to the old implementation's shape. The obvious path — AI generating test scripts from natural language — was a legitimate productivity win, but generated scripts couldn't keep pace with application change: same constraint, faster output.
Institutional-capability formulation: Intelligence should live in intent understanding, not script replay. This is the reference case the Two Hills framework itself was built from, kept here as the calibration example for every domain that follows.
Verdict: Solved— genuine capability, not automation dressed up as one.

Deep dive: Methodology — the Two Hills framework's reference case. This entry predates the domain pipeline, so it links to Methodology rather than a domain page.

Greyed-out filters are planned domains with no entries yet — shown so it's clear this list will grow, never to imply they have entries today. Nothing here ranks one domain against another.