Harvard Business Review LogoA new operating model shrinks the time and capital needed to build companies, redefining how ventures launch and scale.Javier ReyThe startup landscape is undergoing its most profound transformation since the internet revolution. This change is powered by a convergence of forces. Large language models (LLMs) have reached a level of maturity that makes them capable of reasoning and planning tasks. When given access to relevant data and permission to act on the user’s behalf, LLMs can act as agents, taking on the role of digital assistant or even digital employee. New frameworks enable multiple agents to coordinate autonomously, creating agentic AI systems, or teams of digital colleagues. Low-cost application programming interfaces (APIs) have made it inexpensive to connect different IT systems so that they can share data and work together. And cloud costs continue to fall even as computing capabilities rise. These trends are producing a second great compression of entrepreneurship: The cost, time, and head count required to build a prototype of a product, test it, and then build an improved version have all collapsed. This compression shifts the competitive baseline. Small startups can enter, expand, or disrupt markets at a pace and cost structure that challenges incumbents to reimagine their operating models.
How Agentic AI Supercharges Startups and Threatens Incumbents
Agentic AI is reshaping entrepreneurship and raising the stakes for incumbents. Startups are deploying coordinated systems of AI agents that can plan, act, and adapt autonomously. This approach dramatically compresses the time, capital, and head count needed to launch and scale up a company. Products that once took large teams more than a year to develop can now be built and refined in weeks by a handful of people. AI-native startups build proprietary workflow knowledge by using AI agents, which creates compounding advantages. Established companies face a structural challenge: Siloed data, legacy workflows, and rigid roles limit the benefits of agentic AI. Leaders must redesign processes before automating, strengthen data quality, clarify human–AI handoffs, and prepare employees to focus on work that requires human judgment or is outside the norm.
