Understanding the Impact of Artificial Intelligence on Economy

It’s a privilege to discuss artificial intelligence and its potential effects on the economy. In 1987, Robert Solow famously remarked that “you can see the computer age everywhere but in the productivity statistics.” The same observation can be made today, as AI advances at a remarkable speed, yet its aggregate impact is still barely visible in the data.

Investment in AI

Over the past year, global corporate investment in AI reached $252 billion, and private AI firms raised a record $100 billion. Five leading US investors in terms of capital expenditure are now companies that focus heavily on AI, none of which were among the top ten investors a decade ago. While some view this surge as temporary exuberance, history offers many examples of intense investment waves that ultimately left behind transformative technologies that reshaped economies for decades.

Cycles of Investment and Productivity

The key question is not whether there are cycles, but how long it will take before the enduring productivity benefits become visible. There are reasons to believe AI could spread faster and deliver tangible economic gains sooner than previous technology waves. If that is the path we are on, Europe needs to position itself accordingly and remove obstacles that stop it from embracing this transformation.

Lessons from History

To understand what is at stake, it is useful to look at history. Earlier general-purpose technologies, such as electricity, computers, or the internet, followed a recognizable trajectory: disruption arrived early, with broad-based productivity gains emerging slowly. For example, it took around thirty years before the impact of electricity showed up clearly across the economy.

Comparing AI to Previous Technologies

If Europe’s AI wave resembles the spread of electricity in the 1920s, annual productivity growth could be about 1.3 percentage points higher. However, if it follows the US digital boom of the late 1990s, the boost would be closer to 0.8 points. Even that lower bound would be significant for Europe, marking a clear step up from recent trend productivity.

Potential for Faster Progress

AI has features that could compress this cycle and push forward even greater productivity gains. Two features – innovation and diffusion – point to a faster path. The first is that frontier innovation may accelerate because of the recursive nature of AI. AI systems can use their own output to enhance their performance in a continuous loop, lowering not only the cost of producing goods and services but also the cost of generating new ideas.

Accelerating Discovery

For instance, in fifty years, science resolved approximately 200,000 protein structures. AI achieved over 200 million protein structure predictions in about one year, vastly expanding the knowledge frontier. This represents a significant change in the inputs to research and development, allowing downstream discovery to compound sooner.

Europe’s Opportunities and Challenges

What does this mean for Europe? The stakes could be extraordinarily high. With the United States and China ahead of the field, Europe has already missed the opportunity to be a first mover in AI. However, Europe can still emerge as a strong second mover if it acts decisively, focusing on rapid adoption and smart use of existing AI technologies across its wide-ranging industries.

Turning Late Start into Competitive Edge

Europe’s economy is highly diversified, with the top ten firms in the EU accounting for no more than 18% across almost twice as many sectors as in the US. European firms are already adopting generative AI on a similar scale to those in the United States. Initiatives like Manufacturing-X and Catena-X in the automotive sector foster collaboration in data sharing, enabling companies to leverage broad anonymized datasets.

Removing Barriers to AI Adoption

To turn these benefits into a competitive advantage, Europe needs to connect data across sectors and diversify critical parts of the AI supply chain. This includes maintaining a minimum capacity in foundational layers like compute capacity based on chips and data centers and leveraging the power of the Single Market to enforce interoperability and open standards.

Overcoming Old Barriers

Europe must overcome familiar barriers that have prevented it from being a first mover in the past, such as high energy costs, fragmented regulations, and failure to integrate capital markets. If these issues are not addressed, AI will diffuse more slowly, leading to a further loss of competitiveness for many European sectors and industries.

Conclusion

The potential scale and speed of the AI revolution are immense, with some experts suggesting it could be ten times bigger and faster than the Industrial Revolution. The question is no longer whether this new frontier will arrive but how soon. Europe must act now to clear the obstacles that would slow AI diffusion and delay prosperity for all Europeans in the decades ahead.