Alexandr Wang, head of Meta Superintelligence Labs, believes AI agents could fundamentally change how startups compete, making ambitious ideas and strong leadership more valuable as the work needed to execute them becomes easier. The shift could allow smaller companies to challenge established businesses with significantly greater resources.
Speaking with Garry Tan, chief executive of startup accelerator Y Combinator, during Startup School 2026 in July, Wang argued that access to intelligence and the ability to take action would become increasingly widespread. TOKENPOST reports that a recording of their conversation appeared on July 29.
Vision becomes the advantage
Wang sees a future in which founders gain access to powerful execution capabilities without needing the large teams traditionally required to build ambitious products. He estimated that AI agents could make execution 10 or 100 times easier compared with conditions a decade earlier.
That change could reshape the competitive landscape. Previously, startups often needed to identify narrow opportunities where smaller teams could compete against much larger organizations. Agent-powered businesses could operate with considerably more capacity, changing the balance between emerging companies and established corporations.
Wang described the potential result as a smaller company enhanced by agents competing against a conventional corporate giant. Founders who use these systems ambitiously could pursue projects that previously demanded substantially greater organizational resources.
Meta offers an example
Wang pointed to internal agent groups at Meta as evidence of what coordinated AI systems might achieve. He explained that companies operate through feedback loops: attracting customers, improving their experience and using growth to expand their workforce and reach additional customers.
People traditionally perform much of this repeated work. However, Wang said Meta had observed groups of agents producing more work than a 100-person engineering team when supplied with an appropriate feedback loop and a measurable objective to optimize.
Adoption remains the obstacle
Despite the productivity potential, Wang identified distributing AI throughout society and helping people adjust as the central bottleneck. The technology's capabilities alone will not guarantee that businesses and institutions can use it effectively.
He also encouraged builders to help companies and governments adapt while addressing risks associated with biosecurity and cybersecurity. These areas require attention as increasingly capable AI systems become more widely integrated into organizational operations.
Builders must think systematically
Wang expects work to evolve away from people directly writing code and toward coordinating groups of AI agents. As those groups grow, organizing their activities and maintaining rigorous reasoning will remain essential.
He encouraged younger people to develop a clear understanding of possible future changes and recognize long-lasting exponential trends. Wang used the progress of AI as a contemporary example, comparing its significance with the earlier technological period shaped by Moore's law, which describes the historical growth in computing power.
Why it matters
Wang's argument points to a change in what makes a startup competitive. If agents substantially reduce the effort required to execute projects, access to large engineering teams may become less decisive. The ability to identify valuable problems, set ambitious goals and coordinate automated work could matter more.
For Meta, its internal agent experiments offer one example of the productivity potential Wang describes. But the broader challenge extends beyond building capable systems: businesses and governments must learn to deploy them responsibly, while managing risks and adapting their existing processes. The next important measure will be how effectively organizations turn agent capabilities into sustained, real-world results.