How to build a micro SaaS with AI

There are roughly 200-300 micro-SaaS tools explicitly marketed as 'AI-powered' launched in the last 18 months, with new ones appearing weekly on ProductHunt. The common failure pattern is clear: founders build feature-rich, AI-integrated tools without solving a specific, painful problem—they chase the trend instead of the customer. Most collapse because they can't find repeatable unit economics or they're competing on commodity AI features (ChatGPT wrappers) where margins evaporate. The actual gap exists in vertical-specific AI automation: tools that solve one narrow problem brilliantly for a specific industry (e.g., AI content generation for SaaS copywriting, AI code review for early-stage startups, AI customer support for e-commerce). What separates winners from burnouts is ruthless focus on one job-to-be-done, pre-selling before building, and using AI as a cost-reduction engine—not a feature list. New entrants should identify problems where AI significantly cuts time or cost for a defined audience, validate with 5-10 paying customers first, then scale. The shipper who gets to $5k MRR in 90 days will outpace someone who spends six months perfecting a Swiss Army knife that nobody needs.