Moving from a science-focused research phase to building physical technology demonstrators is critical. The sooner a deep tech company does this, the faster it uncovers new real-world challenges, creates tangible proof for investors and customers, and fosters a culture of building, not just researching.
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Instead of guarding prototypes, build a library of high-fidelity, interactive demos and give sales and customer success teams free reign to show them to customers. This democratizes the feedback process, accelerates validation, and eliminates the engineering burden of creating one-off sales demos.
To de-risk innovation, teams must avoid the trap of building easy foundational parts (the "pedestal") first. Drawing on Alphabet X's model, they should instead tackle the hardest, most uncertain challenge (the "monkey"). If the core problem is unsolvable, the pedestal is worthless.
For leaders overwhelmed by AI, a practical first step is to apply a lean startup methodology. Mobilize a bright, cross-functional team, encourage rapid, messy iteration without fear, and systematically document failures to enhance what works. This approach prioritizes learning and adaptability over a perfect initial plan.
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.
Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.
Small firms can outmaneuver large corporations in the AI era by embracing rapid, low-cost experimentation. While enterprises spend millions on specialized PhDs for single use cases, agile companies constantly test new models, learn from failures, and deploy what works to dominate their market.
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.
Unconventional AI operates as a "practical research lab" by explicitly deferring manufacturing constraints during initial innovation. The focus is purely on establishing "existence proofs" for new ideas, preventing premature optimization from killing potentially transformative but difficult-to-build concepts.
Even at SpaceX, many engineers first heard from customers during a company all-hands. This feedback revealed the setup process was a huge pain point, leading to a dedicated team creating first-party mounting options. This shows that fundamental user research is critical even for highly technical, 'hard tech' products.
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.
