Validating Wicked AI Problem Trends

Given a candidate AI problem space (or none, to discover one), produce a research matrix row:

1. Generate 8–12 research questions probing friction points in that domain (use https://www.researchquestionsgenerator.com/ or equivalent ideation).
2. Cross-reference each against real demand signals (GitHub issue/star velocity, Reddit/HN thread frequency, funding rounds, job postings, citation recency).
3. Kill false positives with a counter-narrative test.
4. For surviving problems, state the traditional consensus solution, then propose one non-standard methodology that breaks a single core assumption.
5. Stress-test that methodology adversarially.
6. Score trend probability and output a deployment blueprint.

Output format is always the 7-column research matrix (see Output Structure below) — never freeform prose.

Progress:

• Phase 1: Ingestion/Scoping — generate research questions, define domain boundaries
• Phase 2: Triaging — validate demand signals, eliminate false positives
• Phase 3: Conceptualization — draft traditional consensus + non-standard methodology + fragmented logic blocks
• Phase 4: Adversarial Stress-Testing — counter-argument, rebuttal, unproven assumptions
• Phase 5: Execution/Monitoring — deployment blueprint, feedback loop, utility multiplier
• Phase 6: Score and rank — assign trend probability with reasoning

Phase 1: Ingestion/Scoping

Generate 8–12 research questions targeting emerging AI friction points. Questions should be specific enough to map to a domain (e.g., "agent memory persistence," "multi-model eval drift") not generic ("AI is hard").

Phase 2: Triaging (Signal Validation)

For each question, pull demand evidence from at least 3 independent signal classes:

• Developer activity: GitHub stars/issues/PR velocity on relevant repos
• Discussion velocity: Reddit/HN thread count and recency
• Capital signals: funding rounds, VC thesis mentions
• Labor signals: job postings mentioning the specific pain point
• Academic signals: citation recency (last 12 months weighted higher)

Apply False Positive Dissection: for every signal, write the strongest counter-narrative that would debunk it as seasonal hype or bot-driven noise. If the counter-narrative isn't convincingly refuted, drop the problem.

Phase 3: Conceptualization

For each surviving problem:

1. State the traditional expert consensus — the industry-standard fix, named specifically.
2. Propose a non-standard methodology that explicitly challenges ONE core assumption underlying the consensus (not everything — precision beats reinvention).
3. Decompose into 3–5 fragmented logic blocks (discrete, independently combinable components). For each block specify:
   • [Constraint] — what limits this block's operation
   • [Assumption] — what must be true for it to work
   • [Data Requirement] — what input it needs
   • [Verification Method] — how you'd know it worked

Phase 4: Adversarial Stress-Testing

For each methodology:

1. Generate the strongest counter-argument a traditional domain expert would raise.
2. Explain why the counter-argument is valid (steelman it — don't strawman).
3. Explain why the methodology withstands or transcends it anyway.
4. Explicitly flag which parts are theoretically sound but practically unproven.

Phase 5: Execution/Monitoring (Deployment Blueprint)

For the highest-confidence problems only:

• Minimal viable scope: the smallest slice where this could be deployed today
• Feedback loop: metrics that would prove/disprove the methodology, and how variance refines the next discovery cycle
• Utility multiplier: who benefits (developer/enterprise/end-user), at what scale, and why they'd switch from status quo

Phase 6: Score

Assign a trend probability confidence score (Low/Medium/High or 0–100) with explicit reasoning tied to signal strength from Phase 2, not vibes.

Example 1: Input: "Find wicked AI problems in agentic tool-use with real demand signals." Output row (abbreviated):

• Problem: Agents fail silently on multi-step tool chains with no rollback
• Demand Evidence: GitHub issues on LangChain/AutoGen repos tagged "silent failure" up 340% (6mo), HN thread "why do agents lie about tool calls" 800+ comments (verified date), 2 funding rounds citing "agent reliability" (Crunchbase, last 90 days)
• Traditional Consensus: Add more structured output validation / retries
• Non-Standard Methodology: Treat tool-call chains as probabilistic state machines requiring adversarial self-audit agents rather than deterministic retry logic — challenges the assumption that failures are detectable at the call boundary
• Adversarial Dissection: Expert counter: "this adds latency/cost for marginal reliability gain." Rebuttal: cost is justified in high-stakes agentic workflows (finance, infra) where silent failure cost >> latency cost. Unproven: audit-agent false-positive rate at scale
• Deployment Viability: MVP = single-domain agent (e.g., infra remediation bots), feedback metric = silent failure rate reduction %, utility multiplier = enterprise ops teams avoiding incident cost
• Trend Probability: 72% — strong multi-signal convergence, funding + dev pain confirmed independently

Example 2: Input: A signal that turns out to be false positive. Output: Problem flagged and dropped — "AI-generated code review demand" showed high Reddit thread volume but counter-narrative check revealed 80% of threads traced to a single marketing campaign within a 2-week window; excluded from matrix per False Positive Dissection rule.