AI accelerates development so much that building and go-to-market activities become concurrent, not sequential. This constant pressure to be CEO, PM, and marketer all at once creates a relentless pace that can quickly lead to co-founder exhaustion.
Founders often obsess over building a desirable product but neglect the crucial challenge of reaching customers. Distribution must be treated as a core hypothesis and tested from the outset, not deferred until after finding product-market fit.
Solving your own problem provides deep conviction, but it's a trap. Your needs are just one data point, and your own conviction is the least reliable signal. You must rigorously test against people who aren't you to avoid building for an audience of one.
Polite feedback is cheap and misleading. To gauge real demand before building, create a difficult but valuable task for prospects. If they invest significant effort to overcome the friction (like providing sensitive documents), it signals a strong, authentic pull for your solution.
For products with long usage cycles (e.g., quarterly software), trust requires multiple comparative cycles against the old workflow. This "time to trust" can take months, far exceeding typical trial periods and demanding different onboarding, pricing, and financial models.
A product intended for individuals may find success through team adoption, unexpectedly flipping the sales model from a simple B2C transaction to a complex enterprise sale. This requires a different company structure and GTM strategy that may not align with the founder's vision.
Unlike traditional software, AI models are not static; providers can deprecate them with minimal notice. This instability means building a robust QA and monitoring framework is not optional—it is a critical, ongoing investment to ensure product quality and reliability.
Customers often overvalue solutions to hypothetical problems. A feature that sounds brilliant in a demo may be ignored in practice when users are time-poor and focused on their core job. Differentiate between users' stated desires and their actual, revealed preferences.
While trying to overcome an existing market anchor (like ChatGPT's low price), be careful not to create a new one with your own discounted pilot. The jump from a pilot to full price can feel like a penalty to early adopters, making the transition difficult.