Artist's co-founder warns that the biggest mistake founders make is building technology too early. Her team validated their text-based learning concept by manually texting early users, confirming the core hypothesis and user engagement before committing significant engineering resources.
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Many teams wrongly focus on the latest models and frameworks. True improvement comes from classic product development: talking to users, preparing better data, optimizing workflows, and writing better prompts.
Founders often get stuck endlessly perfecting a product, believing it must be flawless before launch. This is a fallacy, as "perfection" is subjective. The correct approach is to launch early and iterate based on real market feedback, as there is no perfect time to start.
For AI products, the quality of the model's response is paramount. Before building a full feature (MVP), first validate that you can achieve a 'Minimum Viable Output' (MVO). If the core AI output isn't reliable and desirable, don't waste time productizing the feature around it.
To avoid over-engineering, validate an AI chatbot using a simple spreadsheet as its knowledge base. This MVP approach quickly tests user adoption and commercial value. The subsequent pain of manually updating the sheet is the best justification for investing engineering resources into a proper data pipeline.
In AI, low prototyping costs and customer uncertainty make the traditional research-first PM model obsolete. The new approach is to build a prototype quickly, show it to customers to discover possibilities, and then iterate based on their reactions, effectively building the solution before the problem is fully defined.
Non-technical founders using AI tools must unlearn traditional project planning. The key is rapid iteration: building a first version you know you will discard. This mindset leverages the AI's speed, making it emotionally easier to pivot and refine ideas without the sunk cost fallacy of wasting developer time.
Crisp.ai's founder advocates for selling a product before it's built. His team secured over $100,000 from 30 customers using only a Figma sketch. This approach provides the strongest form of market validation, proving customer demand and significantly strengthening a startup's position when fundraising with VCs.
To truly validate their idea, Moonshot AI's founders deliberately sought negative feedback. This approach of "trying to get the no's" ensures honest market signals, helping them avoid the trap of false positive validation from contacts who are just being polite.
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.
