Much recent commentary on artificial intelligence (AI) has focused on the prospect of a company or a country winning a race for artificial general intelligence (AGI) or more-than-human “superintelligence.” However, that goal, which seems rather more religious than technological, is both elusive and, should it ever be achieved, fragile (see SIGnal, “Mutual Assured Malfunction,” March 13, 2025). Investors are focusing instead on “little tech” and firm-level or industry-level AI that uses specific data sets to engineer specific productivity gains. In SIG’s view, this more modest course seems both economically more promising and politically much more sustainable. But it definitely does have risks of its own.
The appeal of “little tech” AI is partly that it leaves to one side the many serious questions about data privacy and other more existential matters that are posed by AGI. Smaller AI systems can run on the contained, often proprietary data sets involved in industrial processes, especially in manufacturing. The goal is not to replicate the human brain but to make industrial processes more efficient, raising productivity. It is a type of automation, using new technology yet still familiar enough from the history of industrial production.
With little-tech AI, startups can focus on specific problems whose solutions will provide a payoff in the relatively short term. In other words, AI would be monetizable. This has an obvious appeal not just to startup investors but also to industrial incumbents whose processes would be improved and whose productivity would be raised in competition with their rivals. Startups are not alone in this sphere. The German giant Siemens, for example, has put industrial AI at the core of its offering.
Politically, this approach to AI is much more appealing to most governments, only a few of which (the US, China) can have much hope of achieving global dominance by winning a race for AGI, at which point they might well regret getting what they wished for. Leaving aside the large question of AI data-center electricity demands, it offers the attractive prospect of raising productivity while reducing carbon use — because your factory in Texas, enhanced by AI, will no longer have to source so many of its components from East Asia, with all the carbon-using transport that entails. The little-AI approach also means states would not have to expose their citizens’ data to foreign tech multinationals, possibly based in hostile or overweening states, in order to participate in the later 21st century. That would be a gain for state sovereignty; and given that so many of the tensions around globalization have had to do with the way it threatens sovereignty and the democratic (or otherwise) accountability of governments to citizens, the little-AI approach could conceivably enhance global stability and the prospects for peace. Little AI, by improving productivity within a given national domestic workforce, could help states that are facing demographic stagnation — which is pretty much all industrialized states and many less-industrialized ones — to nonetheless grow on the basis of domestic labor (see SIGnal, “AI Family Values,” May 3, 2024). As Marc Andreessen and Ben Horowitz wrote in July 2024, “little tech” could make it possible “to reconstruct the American manufacturing sector around automation and AI, reshoring entire industries and creating millions of new middle class jobs” while also having green benefits. Technology could, in effect, provide the “labor” that would solve the biggest challenge facing President Trump’s vision of a more self-sufficient US: the lack of workers operating at a sufficient level of productivity (see SIGnal, “Trade Wars and US Labor,” April 11 2025).
Less carbon use, stabilization of the international sovereign-state system, a growing middle class, a renewal of rich-world domestic manufacturing but with higher wages and less grim manual work…What could possibly go wrong?
AI-enhanced production aimed at reshoring manufacturing to high-wage economies would square the circle of productivity growth and de-globalization. It would revive the pre-1975 global industrial status quo with the crucial addition of China (but not so much India or Southeast Asia). If you have the good fortune to live and work in a benefitting state, this would be a positive outcome. It could, however, also fuel techno-nationalism in the rich world (plus China) and make growth outside the AI-enhanced nations highly problematic. One key issue raised by the US-China struggle — a protected US market deprives non-American producers of consumers, while a protected Chinese economy, likewise deprived, dumps its production for the pre-tariffs US market onto the rest of the world’s economies — would be gravely worsened as the world’s two largest economies reduce their dependence on the rest of the world for both supply and demand.
AI-enhanced de-globalization could, in short, reverse the global redistribution of labor productivity that led to the greatest poverty reduction in human history. In theory, the gains from little AI could be more equally distributed. After all, the AI enhancements that would lift an underemployed person in Oklahoma or eastern Germany into the middle class of his or her domestic economy could do the same for a person in Nigeria or Thailand. But that outcome is not the goal for the people, states, and companies that are driving the growth in AI monetization. Their goal is nearly the opposite. For investors, the greatest gains will come from identifying companies and sectors best positioned to gain from AI-enhanced de-globalization.