George Popescu Shares Paris Reflection on AI Limits, Robotics Opportunities, and the Importance of Stability

George Popescu: AI is limited pattern-matching. Robotics offers real opportunity. Predictable stability is vital for long-term innovation.

BROOKLYN, NY, UNITED STATES, December 3, 2025 /EINPresswire.com/ -- George Popescu, an MIT-trained engineer, entrepreneur, and serial successful entrepreneur , recently recorded a detailed reflection while in Paris, examining the current direction of artificial intelligence, the emerging opportunity in humanoid robotics, and the crucial role that stability and predictability play in long-term innovation. His remarks provide a grounded, unvarnished view of today’s technology landscape and the conditions required for building ambitious companies.

George Popescu challenges one of the loudest assumptions driving today’s technology markets: the belief that the rapid expansion of AI models and data centers automatically translates into greater intelligence and breakthrough capabilities. He cautions that much of the current enthusiasm surrounding AI is built on an assumption he considers deeply flawed. According to him, the idea that simply making models “bigger and bigger” will result in smarter, more capable systems has become a kind of collective delusion, repeated so often that it is now taken for granted.
Popescu offers an analogy to make the point clear. He compares modern AI systems to a trained monkey or a trained dog. A dog can learn to fetch a ball and perform a series of impressive behaviors, but it will not spontaneously invent a new game and teach it to its owner. The performance is impressive, but fundamentally limited, because it does not originate from independent creativity, discovery, or problem definition. Popescu argues that current AI systems operate in a similar way: sophisticated pattern-matching machines that can imitate and respond, but cannot originate new concepts or independently discover novel ideas.

In his definition, intelligence is not the ability to regurgitate patterns, repeat training data, or execute well-defined tasks at scale. True intelligence, in his view, is the ability to create things that did not previously exist—ideas, solutions, connections, inventions—and to do so in situations that are undefined, unpredictable, or ambiguous. He does not believe that the present generation of AI models is capable of crossing that threshold.
Despite the scale of ongoing investment, Popescu states he has not invested in AI and does not plan to. He believes the current direction is too expensive, too late, and too poorly aligned with the fundamental qualities that define real intelligence. He does acknowledge one significant achievement: today’s AI systems have made the human–computer interface more intuitive and human-friendly. But he stresses that this is an interface improvement, not the emergence of independent intelligence.

Popescu then shifts to a different technological frontier—one he believes is underappreciated: humanoid robotics and advanced home automation. He argues that robotics may offer more practical opportunities in the coming years than AI model scaling. According to him, computers excel at tasks that are precise, repetitive, and well-defined: calculating, performing exact operations, and following clear instructions. But they struggle with “not well-defined” problems—tasks with irregular structure, unexpected variation, and no strict predictability.

Folding laundry is his example. Clothes arrive in different shapes, textures, directions, states of inside-out, and often in mismatched pairs. For a computer trained on uniform, well-labeled data, this type of real-world inconsistency becomes extremely difficult. However, Popescu believes that recent advances in machine vision and AI-driven communication interfaces can finally be applied to these messy, real-world problems. He sees significant opportunity in humanoid robots that can perform tasks which lie between manual labor and strict automation.

In his assessment, robotics can fill labor gaps in households, small businesses, and industrial environments. It is an area where incremental improvements in dexterity, perception, and adaptability could produce concrete economic gains—lowering costs, improving efficiency, and reducing dependence on hard-to-find labor. Popescu notes that while AI hype dominates headlines, robotics may be the domain where real innovation and practical value emerge.
After discussing technology, Popescu turns toward a broader societal reflection. He explains that he has been thinking extensively about what allows societies to succeed across long periods of time—decades or even centuries. In his view, the decisive factor is stability and predictability. To illustrate this, he contrasts historical examples. The Spanish Empire, despite immense wealth, repeatedly undermined its own stability by borrowing money, failing to repay creditors, and jailing wealthy individuals who challenged sovereign demands. This behavior eroded trust, drove out capital, and created an environment where long-term planning became impossible.

By contrast, England and Holland developed systems where rules were followed consistently, expectations were predictable, and entrepreneurs had confidence that their investments would not be arbitrarily destroyed. According to Popescu, this predictability allowed them to grow into stable, prosperous societies.

George Popescu expresses concern that modern societies are losing this commitment to stability. When governments, regulators, or institutions behave unpredictably—changing rules quickly, ignoring norms, or failing to provide clarity—innovators, investors, and asset owners tend to leave. They move to environments where the rules are clearer, safer, and more predictable. Over time, this migration drains a society of the very people and resources that generate progress. Popescu notes that this pattern has repeated many times throughout history.

He also connects this directly to entrepreneurship. Building a company requires time, money, and the confidence that conditions will remain reasonably stable for years. Popescu explains that when founders do not know what laws, taxes, or trade conditions will look like in six months, they hesitate. The cost, risk, and uncertainty become too high. He gives the example of wanting to build a humanoid robotics company in the United States. Such an effort would require five to ten years of consistent rules to justify the investment. If the environment is unpredictable, he will not start.

Popescu states plainly that he himself is currently staying put, not engaging in major new ventures. Instead, he is watching, learning, and using this time to rest and reflect. For him, this period in Paris provides clarity, distance, and the ability to analyze the shifts underway without rushing into a volatile environment.

His reflection offers a rare combination: technological skepticism, historical awareness, and a founder’s practical understanding of risk. It highlights the gap between AI hype and real capability, the overlooked promise of robotics, and the foundational role that stability plays in enabling meaningful innovation.

Amy Sterling
NOHO Photo Studio NYC
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AI Isn’t What You Think — George Popescu Breaks It Down

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