AI Economy Faces Hard Physical Limits

The AI boom is running into hard physical limits, with chip shortages, energy constraints, and the need for more data and simulation capabilities posing significant challenges.

Experts Warn of Supply Limitations

At the Milken Institute Global Conference in Beverly Hills, five experts discussed the constraints of the AI boom. Christophe Fouquet, CEO of ASML, expressed his "strong belief" that the market will be supply limited for the next two to five years, despite a huge acceleration of chip manufacturing. This means that hyperscalers, such as Google, Microsoft, and Amazon, won't be able to get all the chips they're paying for.

Google Cloud's COO, Francis deSouza, highlighted the issue's significance, noting that Google Cloud's revenue crossed $20 billion last quarter, growing 63%, while its backlog nearly doubled in a single quarter.

Energy Constraints

The energy problem is also real. DeSouza confirmed that Google is exploring data centers in space as a serious response to energy constraints. "You get access to more abundant energy," he noted. However, even in orbit, it isn't simple. DeSouza observed that space is a vacuum, so eliminates convection, leaving radiation as the only way to shed heat into the surrounding environment.

"Nothing can be priceless," Fouquet said. The industry is investing extraordinary amounts of capital, driven by strategic necessity. But more compute means more energy, and more energy has a price.

Alternative AI Architecture

While the rest of the industry debates scale, architecture, and inference efficiency within the large language model paradigm, Bodnia is building something very different. Her company, Logical Intelligence, is built on so-called energy-based models (EBMs), a class of AI that doesn't predict the next token in a sequence but instead attempts to understand the rules underlying data.

Language is a user interface between my brain and yours, she said. The reasoning itself is not attached to any language. Her largest model runs to 200 million parameters, compared to the hundreds of billions in leading LLMs, and she claims it runs thousands of times faster. More importantly, it's designed to update its knowledge as data changes, rather than requiring retraining from scratch.*

Physical AI and National Sovereignty

Younis offered what may have been the panel's most geopolitically charged observation, which is that physical AI and national sovereignty are entangled in ways that purely digital AI isn't. Autonomous vehicles, defense drones, mining equipment, and agricultural machines manifest in the real world in ways governments can't ignore, raising questions about safety, data collection, and who ultimately controls systems that operate inside a nation's borders.

Almost consistently, every country is saying: We don't want this intelligence in a physical form in our borders, controlled by another country, he told the crowd. Fewer nations can currently field a robotaxi than possess nuclear weapons.

Optimism Despite Challenges

The experts were optimistic about the future of AI, despite the challenges. DeSouza pointed to the scale of problems that more powerful tools might finally let humanity address, such as neurological diseases and greenhouse gas removal. Shevelenko made a more pragmatic point, noting that the entry-level job may be disappearing, but the ability to launch something independently has never been more accessible.

The constraint is your own curiosity and agency, he said. Younis drew the sharpest distinction between knowledge work and physical labor, pointing out that physical AI isn't displacing willing workers, but rather filling a void that already exists.