Archived version

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Shortly after the outbreak of the Second World War, Eli Heckscher delivered a lecture at the Stockholm School of Economics on the fate of “the West”. His diagnosis was, above all, technological. He argued that early industrial machinery had left ample room for individual freedom. However, it was the network systems that followed – railways, the telegraph, power lines and, finally, radio – that changed society. As networks could only be controlled from their centre, Heckscher noted, they strengthened those in control, first large firms and then the state (Heckscher 1939).

The digital economy of the 2020s has so far followed Heckscher’s predictions. Cloud platforms, app stores, and frontier AI models are network systems with a single control room. Their fragility has been repeatedly exposed, for instance when a defective CrowdStrike update disabled 8.5 million Windows devices in July 2024.

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By 2050, Europe should strive to become the world’s most capable mesh economy: a dense network of small and medium-sized enterprises and municipalities that leverage interoperable, open, and increasingly local digital infrastructure rather than renting intelligence from a few central labs. I refer to this vision as “Small is beautiful 2.0”, in reference to E. F. Schumacher’s (1973) call for technology on a human scale.

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Schumacher lost the argument in the 1970s because mass production economics were against him. With generative AI, however, the conditions are changing. Open-weight models running on local hardware can now accurately answer most chat and reasoning queries.

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Concentrated economic power rarely remains purely economic. Firms that control the infrastructure on which administrations and markets rely acquire influence over the rules that govern them. Heckscher’s warning applies to both sides: the same networks that empower large firms also empower the central state.

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If the mesh is the destination, the choices made about digital policy now will be crucial.

• Firstly, ensure that switching is the default option at every layer of the emerging AI economy. From January 2027, the Data Act will abolish cloud switching charges, and since March 2024, Article 6(7) of the Digital Markets Act has obliged gatekeepers to grant interoperability with features of their operating systems. The same logic should apply to AI: firms should be permitted to transfer fine-tuned models, agent configurations, and associated data between providers and onto their own hardware.

• Secondly, write procurement rules with the mesh in mind. Public buyers account for around 15% of the EU’s GDP. The Commission’s proposed Public Procurement Act contains several provisions that could benefit SMEs, including a requirement for suppliers to grant buyers the licenses needed to operate, adapt, and maintain deliverables (Article 63), and a cap on turnover requirements (Article 27). However, open-source solutions are only mentioned as an option for buyers pursuing innovation objectives (Article 61). The co-legislators should require an assessment of exit costs for every digital purchase and give preference to solutions that buyers can run on infrastructure they control.

• Thirdly, measure intelligence per watt rather than gigawatts. Capacity targets for gigafactories count inputs. A 2050 benchmark should measure outcomes, such as the proportion of AI workloads that European firms and public bodies can run on their own infrastructure and the cost of switching. Therefore, part of public compute funding should go to distributed edge capacity and shared public inference services. The Swiss open model Apertus, released in 2025 and offered through an international public AI inference utility, demonstrates how such a service could function.

• Finally, fund diffusion rather than European champions. More than half of EU Horizon funding currently goes to mid-tech firms and consultancies with limited growth potential. In contrast, single-recipient SME instruments, such as the EIC Accelerator, have a more lasting effect, but only for small, independent firms (Fuest et al. 2025). A European AI extension service modelled on the US Smith-Lever Act of 1914, which took university expertise to farms, could deliver applied AI knowledge to small firms via universities and regional innovation hubs (Frazier 2025). A more proactive competition policy plays a role, too. If their innovations and teams are simply absorbed by the hubs through acquisitions and “acqui-hires” that fall below merger thresholds, public money spent on small firms is ultimately wasted.

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Scale as a means, not an end in itself

Critics will argue that a continent without its own frontier model cannot afford to be romantic about small units. However, the argument is not that Europe should avoid large-scale projects. Rather, it is that Europe should not mistake scale for the destination. Learning from Heckscher, Europe’s success in 2050 should be measured by how many of its firms and citizens can build, adapt, and switch AI on their own terms.

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How do I cast my vote for this?