Executive Summary

AI research professionals—from 3,000,000 practicing AI/ML engineers, researchers, and product managers to academic labs—are drowning in new work: major conferences and preprint servers produce thousands of papers and technical posts each year, and practitioners cannot realistically read more than a small fraction without missing critical advances. The result is delayed adoption, duplicate work, and wasted time that disproportionately affects fast-moving teams who must translate research into products. A viable product would deliver conference- and paper-level 5-minute summaries with clear takeaways, one-paragraph TL;DRs, citation-linked claims, confidence scores, and team-focused relevance filters, available via a consumer app, Slack integration, and an API for enterprise workflows; a human-in-the-loop verification layer and provenance tracking would offset automated errors. The core pipeline would combine retrieval-augmented LLMs, specialist extractors for datasets/methods, and lightweight editorial review to keep per-summary cost low while maintaining trust. This is an attractive market now: we estimate a $6.0B addressable market (3,000,000 professionals × $2,000 ACV) driven by an explosion of publications, falling LLM inference costs, and a shift toward continuous learning in engineering teams. Those same trends also create real challenges—model hallucination, citation errors, and legal/IP concerns—that must be managed up front. To stand out against medium-strength competition, focus on measurable quality (ground-truthable claims, explicit citations), conference-aware taxonomies, and enterprise features like team libraries, searchable archives, and SLA-backed accuracy guarantees; the main trade-offs will be the ongoing cost and complexity of verification and building initial trust. With disciplined evaluation metrics and a pragmatic pricing strategy, a lean team can carve a defensible niche by becoming the trusted source of concise, verifiable research summaries for paying teams.

Executive Summary

Market Opportunity

Information overload in AI research — 5-minute summaries across conferences targets a $6.0B = 3,000,000 AI/ML professionals x $2,000 ACV (individual + team/enterprise subscriptions) total addressable market with medium saturation and a year-over-year growth rate of 25% = growth in AI tooling, research consumption, and enterprise knowledge subscriptions.

Key trends driving demand: Explosion in AI publications -- creates overwhelm and demand for curation; LLM quality & cost improvements -- enable automated concise summaries at scale; Shift to continuous learning workflows -- teams expect on-demand, consumable research; Open-source-first distribution -- GitHub enables viral discovery and contributor growth.

Key competitors include Elicit (Ought), Scholarcy, Semantic Scholar (Allen Institute for AI), arXiv Sanity Preserver (plus social channels: X/Twitter & Reddit).

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Information overload in AI research — 5-minute summaries across conferences

Executive Summary

Information overload in AI research — 5-minute summaries across conferences

Executive Summary

Market Validation

Market Opportunity

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