In AI-dominated times, is it still feasible for startups to establish a competitive advantage?

In the rapidly evolving landscape of artificial intelligence (AI), a startup's success is not solely dependent on the AI software they build, but rather on how they strategically leverage this technology to create something tangible and enduring. This is the key to building a moat, a business's ability to maintain competitive advantages that protect long-term profits and market share, as defined by Warren Buffett.

In industries like the utility sector, gaining access to and making sense of vast datasets can be transformative. For instance, in the home energy automation space, companies that deploy a hardware hub to monitor and manage devices gain a distinct advantage due to smarter optimization for cost savings and resilience.

Hardware, despite introducing higher costs and operational complexity, can serve as a stake in the ground in today's rapidly moving software landscape. A durable way to create a moat is by incorporating a hardware component, as hardware brings both recurring revenue and customer stickiness. This strategy has been successfully implemented by tech giants like Apple, OpenAI, and Meta. Apple controls 65% of the US smartphone market and has exclusivity deals with OpenAI, which holds 80% of the AI chatbot market share, giving them a competitive advantage by limiting competitors' data access. Meta, on the other hand, invests billions in AI infrastructure, recruits top AI researchers, and develops advanced conversational AI to maintain leadership despite antitrust challenges.

However, in the AI era, building a defensible business is complex, as the democratization of AI tools has lowered the barrier to entry. The challenge isn't just building with AI; it's building moats that protect against its commoditization. Startups must identify their customer, understand why AI makes the solution uniquely valuable, and solve a problem in a way no one else can.

AI built for a specific use case often outperforms general-purpose tools. To improve the performance of AI models, startups must secure ownership or access rights to data, and invest in making it clean, structured, and useful. The more users interact, the smarter and more tailored the product becomes, potentially producing better outcomes, attracting more users, and leading to a defensible advantage.

In domains like legal advice, infrastructure design, and finance, a human in the loop to review, edit, or assure quality adds confidence and builds trust. The path to durability in AI starts with strategic clarity. The startups that succeed will be the most thoughtful and strategic, recognizing that in a world where AI is everywhere, context matters most.

Startups that secure early distribution advantages through partnerships can lock in access to users, making it harder for competitors to incentivize a switch. Another powerful moat is access to proprietary, high-quality data, especially about user engagement, as it can be trained and refined in ways others can't duplicate.

Anna Demeo, the managing partner at Clean Tech Strategy Advisors, emphasizes that the opinions represented in this article are solely hers, and do not reflect the views of the website or any of its staff.

In conclusion, in the AI era, the key to building a sustainable competitive advantage lies in strategic thinking and a deep understanding of the specific industry and its needs. By leveraging AI in ways that feel natural and necessary, startups can gain a competitive edge and build moats that protect against commoditization.

In AI-dominated times, is it still feasible for startups to establish a competitive advantage?