Gamma used AI to fix its low activation rate by solving the 'blank page' onboarding problem. They discovered this wasn't just an onboarding fix; it was the core value proposition customers wanted, solving the user's primary 'job to be done' and unlocking true product-market fit.

Gamma's success ($100M ARR with 52 employees) proves an 'AI-first' approach can challenge giants. By rethinking core products like presentations from the ground up with AI, startups can create delightful, hyper-efficient products and achieve massive scale with a tiny headcount.

Unlike traditional SaaS, the AI market moves so rapidly that the concept of "finding product-market fit and then scaling" no longer applies. PMF is a fleeting state. Founders must build organizations that can adapt and evolve at a historically fast rate, assuming the future will look very different.

AI companies are showing that rapid, fundamental business pivots are no longer just for pre-product-market-fit startups. In the fast-moving AI landscape, the ability to constantly evolve core product strategy is a prerequisite for staying relevant and successful, even for established players.

During a tech shift like AI, the biggest opportunity for startups isn't direct competition. It's identifying the space between two established players who are cautiously bolting AI onto legacy products. This "in-between" space allows a startup to define a new category without being benchmarked against a 20-year-old feature set.

The rapid evolution of AI makes it difficult for established startups with existing teams and processes to adapt. It can be trickier for a company with "legacy stuff" to pivot its workforce and culture than for a new, agile founder starting with a clean slate.

Application-layer AI companies can pivot rapidly with model improvements because they serve sticky end-customers. Infrastructure companies face a pickier developer audience that is more likely to churn completely to the next hot tool, making pivots riskier.

Companies focused on ML before the GenAI boom built robust platforms and workflows around their models. When new, more powerful models emerged, they could integrate them as an upgrade, leveraging their existing battle-tested infrastructure to scale faster than new, AI-native competitors starting from scratch.

Supabase, founded before the AI boom, found exponential growth by becoming the default database for agentic AI products. This shows a powerful strategy for pre-AI companies: instead of pivoting entirely to AI, they can 'co-attach' their existing product to a new AI-driven workflow, capturing immense value from the tailwinds.