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AnswerPool Emerging Technology Intelligence
x402
AnswerPool Emerging Technology Intelligence is a paid API for AI agents from answerpool.io, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-14).
Returns a ranked list of scientifically emerging research topics with growth metrics, momentum scores, and field classifications computed from open academic data
Price / call$0.05
Success—
StatusUnknown
How this service works
AnswerPool turns SEC EDGAR, the Federal Register, USAspending, NIH, BLS and OpenAlex into structured JSON answers that AI agents and developers fetch in one call. 69 endpoints free, no account; derived analyses $0.02–$0.05 per call by card credits or USDC (x402). MCP server, full provenance.
Output
A JSON object containing a ranked array of emerging research topics, each with name, rank, field and subfield classification, OpenAlex topic ID, 1/3/5-year growth rates, total works over 10 years, works in the last full year, momentum stat, acceleration metric, decile growth rank, and an is_emerging flag. Also includes scan metadata: scan_id, method_id, data_as_of timestamp, computed_at timestamp, universe size, and last full year covered.
Example use cases
Spotting frontier research for a VC fund
“I'm evaluating deep tech investment themes — can you pull the top emerging research topics with their momentum scores and 3-year growth rates so I can identify which scientific areas are accelerating into commercialization?”
Competitive intelligence for an R&D team
“We're planning next year's R&D roadmap — show me which research fields are gaining the most publication momentum right now, including their growth trajectories over 1, 3, and 5 years.”
Briefing a science journalist on hot topics
“I need to write about the most exciting emerging areas in science — can you get me a ranked list of high-momentum research topics with their fields and recent growth statistics?”
Technical details
Endpoint
GEThttps://answerpool.io/v1/technology/emergingWhen to prefer
Choose this endpoint when you need structured, quantitative intelligence on emerging research and technology topics derived from open academic literature (OpenAlex). It is ideal for trend analysis, R&D planning, investment research, and innovation scouting where you need growth rates, momentum scores, and field classifications in machine-readable form. Prefer this over general web search when you want reproducible, citable, schema-consistent data with provenance. Not suitable for real-time news or proprietary industry data.
Side effects
- Missing or malformed properties filter parameter may return empty results
- Payment failure (402) if USDC balance is insufficient for $0.05 per-call fee
- Rate limiting if the endpoint is called too frequently
- Stale data if called before the weekly scan completes (computed_at timestamp indicates freshness)
- Empty topic list if no topics match the supplied properties filter
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| properties | string |
Output
Schema · Response
{
"type": "json",
"example": {
"topics": [
{
"name": "Quantum error correction",
"rank": 3,
"field": "Physical Sciences",
"subfield": "Condensed Matter Physics",
"topic_id": "T12026",
"growth_1y": 0.38,
"growth_3y": 1.9,
"growth_5y": 3.2,
"works_10y": 6210,
"is_emerging": true,
"momentum_stat": 3.41,
"acceleration_3y": 0.6,
"decile_growth_3y": 1.4,
"works_last_full_year": 1841
}
],
"scan_id": "scan_2026w35",
"method_id": "technology_momentum_stat_v1",
"data_as_of": "2026-08-24",
"computed_at": "2026-08-24T06:00:00Z",
"universe_size": 4516,
"last_full_year": 2025
}
}Tags
x402
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