After building numerous AI tools, Craig Hewitt realized many popular applications (e.g., AI avatars, voice cloning) are worthless novelties. He pivoted from creating flashy tech demos to focusing only on building commercially viable products that solve tangible business problems for customers.
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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.
Most companies are not Vanguard tech firms. Rather than pursuing speculative, high-failure-rate AI projects, small and medium-sized businesses will see a faster and more reliable ROI by using existing AI tools to automate tedious, routine internal processes.
AI lowers the barrier to entry, flooding the market with "whiteboard founded" companies tackling low-hanging fruit. This creates a highly competitive, consensus-driven environment that is the opposite of a "good quest." The real challenge is finding meaningful problems.
For companies with jaw-dropping technology, it's easy to chase 'wow moments' and PR instead of solving real problems. Synthesia instills a core value of 'utility over novelty,' obsessing over delivering value for enterprise customers rather than getting lost in the novelty of their own tech.
Successful AI products follow a three-stage evolution. Version 1.0 attracts 'AI tourists' who play with the tool. Version 2.0 serves early adopters who provide crucial feedback. Only version 3.0 is ready to target the mass market, which hates change and requires a truly polished, valuable product.
Teams that become over-reliant on generative AI as a silver bullet are destined to fail. True success comes from teams that remain "maniacally focused" on user and business value, using AI with intent to serve that purpose, not as the purpose itself.
Instead of building a single-purpose application (first-order thinking), successful AI product strategy involves creating platforms that enable users to build their own solutions (second-order thinking). This approach targets a much larger opportunity by empowering users to create custom workflows.
Technical implementation is becoming easier with AI. The critical, and now more valuable, skill is the ability to deeply understand customer needs, communicate effectively, and guide a product to market fit. The focus is shifting from "how to build it" to "what to build and why."
Companies racing to add AI features while ignoring core product principles—like solving a real problem for a defined market—are creating a wave of failed products, dubbed "AI slop" by product coach Teresa Torres.
