AI startup clipping that lands model launches in the right founder feeds.

AI startup distribution lives inside the research-Twitter / Hacker News / arXiv-adjacent ecosystem because that ecosystem decides whether a new model gets cited, whether a new agent framework gets integration-tested. And whether a new evaluation harness becomes the de-facto benchmark. Creator-economy distribution patterns built around entertainment swipe behaviour produce zero traction in this ecosystem. Researchers do not discover models on TikTok FYP.they discover models on arXiv RSS, on author-followed Twitter timelines, on benchmark leaderboards, in citation graphs of papers they read last week, and in Hacker News front-page threads.

Distribution that ignores this ecosystem topology fails the AI startup before clip volume becomes the issue.

FORKOFF's AI startup distribution engine treats research-Twitter discovery as the upstream cohort that drives every downstream conversion. The strategist maps the brand's current research-graph position (whose papers cite it. Which benchmark leaderboards it appears on, which AI Twitter principals have referenced it, what the arXiv-citation half-life of the underlying technique is) and engineers cut packs that surface the model where researchers already attend. A side-by-side eval-harness output cut surfaces where benchmark threads live.an inductive-bias cut surfaces where architecture-debate threads live; a fine-tuning recipe cut surfaces where reproducibility threads live.

The cut is engineered to enter a debate already happening, not to interrupt entertainment consumption.

Demo-frame design is the second wedge. Researchers reading a 6-second clip evaluate three signals: (1) what the model produces in the example, (2) what input produces it, (3) whether the output is reproducible from the prompt and seed. Clip operators that ship founder-on-mic talking-head over a vague screenshot get zero research-cohort recognition.

The cut frame must be tight on the input prompt, the seed if applicable, the output. And the evaluation-metric overlay where one exists. The cut runs closer to a screen-record GIF that became canonical in research-Twitter discourse than to anything that looks like consumer-app advertising. Aesthetic norms here are research-Twitter native, not lifestyle creator native.

Category-vocabulary capture is the third wedge. AI startups that succeed in this ecosystem coin or co-opt vocabulary that competitors then have to use. The cut pack lays down vocabulary the brand wants embedded in research-cohort memory: a specific eval-metric name (HumanEval, GPQA, SWE-bench), a specific benchmark threshold framing, a specific architectural primitive name.

The vault of cuts reinforces consistent vocabulary so search-Twitter recall compounds. Inconsistent vocabulary across cuts dissolves the brand into the adjacent-category soup.

The discovery-to-integration funnel runs distinct from any SaaS funnel. First researcher discovers the model via an arXiv preprint or a research-Twitter QT. Second cohort cites the model in their own work.

Third cohort integration-tests the model against existing pipelines. Fourth cohort lands paid customers downstream once integration tests confirm reproducibility. The cut pack distributes against the discovery and citation tiers first.integration and paid-customer conversion arrive 3 to 9 months downstream depending on enterprise procurement cycles.

Outcome-priced means the brand pays $0.003 CPQV against a denominator that already passes research-cohort recognition signals (clip is shared inside research circles, not just FYP-skimmed by general consumers).