Contrary to the popular belief that failing to adopt AI is the biggest risk, some companies may be harming their value by developing AI practices too quickly. The market and client needs may not be ready for advanced AI integration, leading to a misallocation of resources and slower-than-expected returns.

Large enterprises navigate a critical paradox with new technology like AI. Moving too slowly cedes the market and leads to irrelevance. However, moving too quickly without clear direction or a focus on feasibility results in wasting millions of dollars on failed initiatives.

In the fast-evolving AI space, traditional moats are less relevant. The new defensibility comes from momentum—a combination of rapid product shipment velocity and effective distribution. Teams that can build and distribute faster than competitors will win, as the underlying technology layer is constantly shifting.

In the current AI landscape, knowledge and assumptions become obsolete within months, not years. This rapid pace of evolution creates significant stress, as investors and founders must constantly re-educate themselves to make informed decisions. Relying on past knowledge is a quick path to failure.

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.

In rapidly evolving markets like AI, founders often fall into psychological traps, such as feeling they are too late or that funding has dried up. However, the current environment offers unprecedented organic user demand and technological leverage, making it an ideal time to build if you can ignore the noise.

AI drastically accelerates the ability of incumbents and competitors to clone new products, making early traction and features less defensible. For seed investors, this means the traditional "first-mover advantage" is fragile, shifting the investment thesis heavily towards the quality and adaptability of the founding team.

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.

While moats like network effects and brand develop over time, the only sustainable advantage an early-stage startup has is its iteration speed. The ability to quickly cycle through ideas, build MVPs, and gather feedback is the fundamental driver of success before achieving scale.