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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 2026-10-27).
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
Rank the fastest-accelerating research topics across ~4,500 OpenAlex topics, weekly — returns rank, momentum z-score, growth, acceleration and citation-quality growth per topic, filterable by field. Use to find what is accelerating.
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/emerging?utm_source=zero.xyzWhen 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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