During a major technology shift like AI, the most valuable initial opportunities are often the simplest. Founders should resist solving complex problems immediately and instead focus on the "low-hanging fruit." Defensibility can be built later, after capitalizing on the obvious, easy wins.
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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.
The founder predicts that hyper-specific vertical AI solutions are too easy to replicate. While they may find initial traction, they lack a durable moat. The stronger, long-term business is building horizontal tools that empower users to solve their own complex problems.
Unlike traditional software development, AI-native founders avoid long-term, deterministic roadmaps. They recognize that AI capabilities change so rapidly that the most effective strategy is to maximize what's possible *now* with fast iteration cycles, rather than planning for a speculative future.
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.
To increase the odds of success, Moonshot AI's founder advises choosing a startup path that operates in "easy mode." This framework involves selecting a market you're passionate about, leveraging the core strengths of the founding team, and aligning with strong market tailwinds. While no startup is easy, this approach simplifies key variables.
In a gold rush like AI, the shared 'why now' forces many founders into a pure speed-based strategy. This is a dangerous game, as it often lacks long-term defensibility and requires an incredibly hard-charging approach that not all teams can sustain.
Early-stage founders should not prematurely optimize for defensibility. The primary focus must be on solving a real problem and building something people want. Moats are a defensive strategy that only becomes relevant once a startup has created value worth protecting.
ElevenLabs' CEO sees their cutting-edge research as a temporary advantage—a 6-12 month head start. The real, long-term defensibility comes from using that time to build a superior product layer and a robust ecosystem of integrations, workflows, and brand. This strategy accepts model commoditization and focuses on building durable value on top of the technology.
The rapid pace of AI innovation means today's cutting-edge research is irrelevant in three months. This creates a core challenge for founders: establishing a stable, long-term company vision when the underlying technology is in constant, rapid flux. The solution is to anchor on the macro trend, not the specific implementation.
The key differentiator for companies succeeding with AI isn't technical prowess but mastery of core behaviors: flexibility, targeted incremental delivery, being data-led, and cross-functional teams. Strong fundamentals are the prerequisite for benefiting from advanced technology.
