At Stanford Graduate School of Business, Altman urged young professionals and aspiring founders to pursue fresh entrepreneurial ideas instead of defaulting to once-fashionable career paths, arguing that AI can make products and companies possible that were previously difficult to build.

He said AI will eventually perform many tasks better, faster and more cheaply, but expects that transition to unfold over time rather than happen all at once.

Altman said the opportunity is not to copy OpenAI, but to build businesses using technology it has released; he suggested those companies could become larger and more important than OpenAI itself, as technological shifts can give new entrants a chance to challenge established businesses.

That call comes amid record business formation: U.S. entrepreneurs filed 5.7 million applications to start businesses in 2025, the highest annual total in the two decades the Census Bureau has tracked the measure.

Some entrepreneurs are already using AI to run million-dollar businesses with few or no employees, applying it to coding, customer service, marketing, refunds and routine operations.

Citadel CEO Ken Griffin has similarly argued that AI can help founders launch quickly and take on established companies with teams of only a few people, rather than 30 to 40.

Griffin said an agentic AI system can produce complex financial research in two to three hours, compared with the six to eight weeks employees with advanced degrees might typically need.

Other entrepreneurs also see AI as a competitive advantage: Alexis Ohanian says AI-native companies benefit from having the technology built into their organizational DNA, while Mark Cuban has urged founders to use AI to work faster and make smarter decisions.

The enthusiasm is not universal among younger workers: a 2026 Glassdoor analysis found positive views of AI among 21% of Gen Z women and 42% of Gen Z men, while a Gallup survey found 48% of employed Gen Zers thought AI’s workplace risks outweighed its benefits and 15% believed the benefits were greater.

Those concerns are tied to pressure on early-career work: a 2026 Stanford study found employment declines concentrated in occupations where AI is used more to automate tasks than to complement workers.

If AI reshapes tasks before eliminating entire occupations, employers may need to reconsider entry-level hiring and training, while business schools may need to teach students how to build with AI; the record pace of business applications also signals growing interest in AI-native ventures.

Altman also expects some work to remain human-centered, noting that people may not want an AI consultant to arrive and tell them what to do.