Automate YouTube post-production: AI editing, captions, thumbnails, SEO

Independent creators, small agencies and in-house media teams — part of the roughly 100 million global creators — spend disproportionate time on routine post-production tasks like trimming long-form footage, creating captions, producing thumbnails and optimizing metadata, often 4–10 hours per episode. That friction limits creators’ ability to scale output and monetize through sponsorships, memberships and cross-platform distribution. You could build an AI-native, end-to-end YouTube post-production platform that automates speech-to-text with diarization, scene detection and highlight extraction, generates multiple short-form cuts and thumbnail/metadata variants, and hooks into the YouTube API for automated publishing and A/B testing, with human-in-the-loop review for quality-critical assets. The market is attractive now: we estimate a $30 billion addressable market (100M creators × $300/year), momentum in automated editing, and rising pressure to repurpose long-form into shorts to capture platform distribution and sponsorship windows. Market and revenue scores (92/100 and 88/100) reflect high demand but also indicate sizable opportunity to capture value through differentiated execution. To stand out you must focus on YouTube-specific signals — optimizing thumbnails, titles and chapters against creator analytics, offering workflow integrations for creators with >10k subscribers and a tiered human-review service for high-stakes videos — rather than a generic editor that competes on features alone. Real challenges include earning creator trust on quality and safety, navigating platform policies and building reliable AI pipelines; early pilots should aim to demonstrably reduce routine editing time (target ~70%) while acknowledging 10–20% of outputs will require human touch initially.