Five AI Economy Architects on Where It’s Failing

Earlier this week, five influential figures spanning the entire AI supply chain gathered at the Milken Institute Global Conference in Beverly Hills. They joined this editor to discuss a wide range of critical issues, including chip shortages, orbital data centers, and the unsettling possibility that the entire architectural foundation of modern AI might be fundamentally flawed.

The panel featured Christophe Fouquet, CEO of ASML, the Dutch firm holding a monopoly on the extreme ultraviolet lithography machines essential for producing advanced chips; Francis deSouza, COO of Google Cloud, who is managing one of the largest infrastructure investments in corporate history; Qasar Younis, co-founder and CEO of Applied Intuition, a $15 billion physical AI company that began in simulation and now operates in defense; Dmitry Shevelenko, chief business officer of Perplexity, an AI-native search-to-agents company; and Eve Bodnia, a quantum physicist who left academia to challenge mainstream AI architecture with her startup, Logical Intelligence. (Earlier this year, Yann LeCun, Meta’s former chief AI scientist, became founding chair of its technical research board.)

Here is what the five experts had to say:

The bottlenecks are real

The AI boom is encountering hard physical limits, and these constraints start much further down the stack than many realize. Fouquet was the first to address this, describing a “huge acceleration of chips manufacturing” while expressing his “strong belief” that despite all that effort, “for the next two, three, maybe five years, the market will be supply limited.” The implication is clear: hyperscalers like Google, Microsoft, Amazon, and Meta will not receive all the chips they are paying for, full stop.

DeSouza underscored just how large and fast-growing this issue is, reminding the audience that Google Cloud’s revenue surpassed $20 billion last quarter, growing 63%. Its backlog, representing committed but not yet delivered revenue, nearly doubled in a single quarter from $250 billion to $460 billion. “The demand is real,” he said with striking composure.

For Younis, the primary constraint lies elsewhere. Applied Intuition builds autonomy systems for cars, trucks, drones, mining equipment, and defense vehicles. His bottleneck is not silicon but data that can only be gathered by sending machines into the real world and observing outcomes. “You have to find it from the real world,” he explained, noting that no amount of synthetic simulation fully closes that gap. “There will be a long time before you can fully train models that run on the physical world synthetically.”

The energy problem is also real

If chips are the first bottleneck, energy is the next one looming behind it. 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 is not simple. Space is a vacuum, so convection is eliminated, leaving radiation as the only way to shed heat into the surrounding environment. That process is much slower and harder to engineer than the air and liquid cooling systems data centers rely on today. Still, Google is treating it as a legitimate path.

DeSouza also made a deeper argument about efficiency through integration. Google’s strategy of co-engineering its full AI stack, from custom TPU chips through to models and agents, pays dividends in flops per watt, or more computation per unit of energy, that a company buying off-the-shelf components simply cannot replicate. “Running Gemini on TPUs is much more energy efficient than any other configuration,” he said, because chip designers know what is coming in the model before it ships.

Fouquet echoed a similar point later in the discussion. “Nothing can be priceless,” he said. The industry is in a strange moment, investing extraordinary amounts of capital driven by strategic necessity. But more compute means more energy, and more energy has a price.

A different kind of intelligence

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 based on so-called energy-based models (EBMs). This class of AI does not predict the next token in a sequence. Instead, it attempts to understand the rules underlying data, an approach she argues is closer to how the human brain actually works. “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 is designed to update its knowledge as data changes, rather than requiring retraining from scratch.

For chip design, robotics, and other domains where a system needs to grasp physical rules rather than linguistic patterns, she argues EBMs are the more natural fit. “When you drive a car, you’re not searching for patterns in any language. You look around you, understand the rules about the world around you, and make a decision.” It is an interesting argument and one likely to attract more attention in the coming months, as the AI field begins to question whether scale alone is sufficient.

Agents, guardrails, and trust

Shevelenko spent much of the conversation explaining how Perplexity has evolved from a search product into something it now calls a “digital worker.” Perplexity Computer, its newest offering, is designed not as a tool a knowledge worker uses but as staff that a knowledge worker directs. “Every day you wake up and you have a hundred staff on your team,” he said of the opportunity. “What are you going to do to make the most of it?”

It is a compelling pitch, but it also raises obvious questions about control. When asked, Shevelenko’s answer was granularity. Enterprise administrators can specify not just which connectors and tools an agent can access but whether those permissions are read-only or read-write. That distinction matters enormously when agents are acting inside corporate systems. When Comet, Perplexity’s computer-use agent, takes actions on a user’s behalf, it presents a plan and asks for approval first. Some users find the friction annoying, Shevelenko said, but he considers it essential, especially after joining the board of Lazard. There, he found himself unexpectedly sympathetic to the conservative instincts of a CISO protecting a 180-year-old brand built entirely on client trust. “Granularity is the bedrock of good security hygiene,” he said.

Sovereignty, not just safety

Younis offered what may have been the panel’s most geopolitically charged observation, arguing that physical AI and national sovereignty are entangled in ways that purely digital AI is not. The internet initially spread as American technology and faced pushback only at the application layer, with companies like Uber and DoorDash, when offline consequences became visible. Physical AI is different. Autonomous vehicles, defense drones, mining equipment, and agricultural machines manifest in the real world in ways governments cannot 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.” Fewer nations, he told the crowd, can currently field a robotaxi than possess nuclear weapons.

Fouquet framed it a little differently. China’s AI progress is real. DeepSeek’s release earlier this year sent something close to a panic through parts of the industry. However, that progress is constrained below the model layer. Without access to EUV lithography, Chinese chipmakers cannot manufacture the most advanced semiconductors. Models built on older hardware operate at a compounding disadvantage, no matter how good the software gets. “Today, in the United States, you have the data, you have the computing access, you have the chips, you have the talent. China does a very good job on the top of the stack, but is lacking some elements below,” Fouquet said.

The generation question

Near the end of the panel, someone in the audience asked the obvious uncomfortable question: Is the era of AI going to impact the next generation’s capacity for critical thinking? The answers were optimistic, as you would expect from people who have staked their careers on this technology. DeSouza immediately pointed to the scale of problems that more powerful tools might finally let humanity address. Think neurological diseases whose biological mechanisms we do not yet understand, greenhouse gas removal, and grid infrastructure that has been deferred for decades. “This should unleash us to the next level of creativity,” he said.

Shevelenko made a more pragmatic point: The entry-level job may be disappearing, but the ability to launch something independently has never been more accessible. “For anybody who has Perplexity Computer… the constraint is your own curiosity and agency.”

Younis drew the sharpest distinction between knowledge work and physical labor. He pointed out that the average American farmer is 58 years old and that labor shortages in mining, long-haul trucking, and agriculture are chronic and growing, not because wages are too low but because people do not want those jobs. In those domains, physical AI is not displacing willing workers. It is filling a void that already exists and looks only to deepen from here.