To find the right market, Gamma's seven-person team spent six months building and using two different products in parallel: a virtual office and an AI presentation tool. This macro A/B test on the company's entire direction helped them commit to the idea with the highest potential.
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Before finalizing an offer, create and promote two distinct lead magnets. The one that outperforms reveals your audience's true pain point and can pivot your entire business strategy. This approach transforms a list-building tactic into a powerful market research tool for finding product-market fit.
A company with modest growth experimented with niche content for a small user segment, revealing a massive, underserved market. This led to a second, separate app that quickly surpassed the original product's revenue and drove hyper-growth, challenging the "focus on one thing" dogma.
Instead of a full product overhaul, Gamma bet the company on perfecting the initial 30-second user experience. By making onboarding so magical that users felt compelled to share it, they unlocked true organic, viral growth that had previously been missing.
To scale effectively, resist complexity by using the 'Scaling Credo' framework. It mandates radical focus: pick one target market, one product, one customer acquisition channel, and one conversion tool. Stick to this combination for one full year before adding anything new.
Historically, resource-intensive prototyping (requiring designers and tools like Figma) was reserved for major features. AI tools reduce prototype creation time to minutes, allowing PMs to de-risk even minor features with user testing and solution discovery, improving the entire product's success rate.
To develop your "people sense," actively predict the outcomes of A/B tests and new product launches before they happen. Afterward, critically analyze why your prediction was right or wrong. This constant feedback loop on your own judgment is a tangible way to develop a strong intuition for user behavior and product-market fit.
When Fal was debating its pivot, their investor Todd Jackson asked which idea would get to $1M ARR faster versus $10M ARR faster. This framework forced them to evaluate not just immediate traction but long-term market size and velocity. It provided the clarity needed to abandon a working product for one with a much higher ceiling.
Gamma compresses the product development cycle into a single day. They generate an idea in the morning, build a functional prototype, and use platforms like Voice Panel to run user research studies in the afternoon, yielding actionable feedback by evening. This operationalizes rapid, pre-build validation.
The misconception that discovery slows down delivery is dangerous. Like stretching before a race prevents injury, proper, time-boxed discovery prevents building the wrong thing. This avoids costly code rewrites and iterative launches that miss the mark, ultimately speeding up the delivery of a successful product.
The rapid evolution of AI makes traditional product development cycles too slow. GitHub's CPO advises that every AI feature is a search for product-market fit. The best strategy is to find five customers with a shared problem and build openly with them, iterating daily rather than building in isolation for weeks.
