Most people agree that AI will lead to dramatically improved productivity, but the gains might not accrue evenly among all.
That’s the warning from Eric Schmidt, former CEO of Google and a prominent voice in Silicon Valley’s technology circles. Speaking recently, Schmidt challenged what he calls the “abundance hypothesis”—the optimistic view that AI’s benefits will be distributed broadly across society. Instead, he argues that the technology’s inherent characteristics may concentrate gains among a select few nations, companies, and individuals, raising urgent questions about inequality in the AI age.
“In the abundance hypothesis, which we’ve talked a lot about, there may be a flaw in the argument because part of the abundance argument is that it’s abundance for everyone,” Schmidt said. “But there’s plenty of evidence that these technologies have network effects, which concentrates to a small number of winners.”
The concept of network effects—where a product or service becomes more valuable as more people use it—has been a defining feature of the digital economy. Companies like Google, Facebook, and Amazon leveraged these dynamics to achieve dominant market positions. Schmidt suggests AI may follow the same pattern, but with potentially more dramatic consequences for global inequality.
“You could, for example, imagine a small number of countries getting all those benefits in those countries. You could imagine a small number of firms and people getting those benefits. Those are a public policy question,” Schmidt noted.
Despite these concerns about distribution, Schmidt is unequivocal about AI’s wealth-generating potential. “There’s no question the wealth will be created because the wealth comes from efficiency and every company that has implemented AI has seen huge gains. If you look in biology and medicine and drug discovery, much faster drug approval cycles, much lower cost of trials. Look at materials, much more efficient and easier to build materials.”
The former Google chief emphasized the widening gap between early adopters and laggards: “The companies that adopt AI quickly get a disproportionate return. The question is, are those gains uniform, which would be our hope, or in my view, more likely, largely centered around early adopters, network effects, well-run countries, and perhaps capital.”
Schmidt’s concerns align with emerging patterns in the AI landscape. Recent data shows that investment in AI is heavily concentrated in the United States and China, which together account for the vast majority of global AI funding. Meanwhile, companies like OpenAI, Google, xAI, Microsoft, and Anthropic command enormous resources for training increasingly powerful models—capital requirements that create natural barriers to entry. In the labor market, AI specialists command premium salaries, with some researchers receiving compensation packages exceeding millions of dollars, while workers in industries vulnerable to automation face uncertain futures. The “Matthew effect”—where initial advantages compound over time—appears to be playing out in real-time as leading AI labs attract top talent, secure the most computing power, and gain access to the best data, making it increasingly difficult for newcomers to compete. Schmidt’s framing of these dynamics as “a public policy question” underscores a crucial reality: the distribution of AI’s benefits is not predetermined by technology alone, but will be shaped by deliberate choices about regulation, education, international cooperation, and economic policy. Whether governments and institutions can act swiftly enough to ensure broad-based prosperity remains one of the defining challenges of the AI era.