Core Expertise & Vision

How George Thinks About AI, Markets, and Embodied Intelligence

George approaches artificial intelligence not as a pure technologist, but as a builder who has lived through multiple cycles of hype and value creation. His perspective on AI sits at the intersection of computer science, financial markets, and real-world products.

AI as Interface, Not Oracle

Drawing on his experience in computer vision and data-heavy businesses, George sees today’s large models primarily as a new human–computer interface layer—powerful, but far from true general intelligence.

✓ Focus on usability and workflow gains

✓ Skeptical of “smarter by just scaling” narratives

✓ Looks for real economic impact, not slogans

Reading the AI Bubble

As someone who has built and exited companies through different macro cycles, George is acutely aware of how narratives, leverage, and infrastructure spending can inflate bubbles. His AI writing asks where capital is truly productive—and where it is simply speculative.

✓ Connects AI stories to macro stability

✓ Uses operator and investor lenses

✓ Advocates for sober, long-term thinking

From Text Models to Embodied Intelligence

George believes the most interesting future for AI lies where algorithms meet the physical world: robots, automation, and systems that solve messy, human-scale problems. His work in robotics and computer vision informs his conviction that embodiment, not just scale, is where genuine breakthroughs will emerge.

✓ Bridges theory with hardware and physical constraints

✓ Focus on practical, human-centered use cases

Philosophy on Innovation, Stability & Responsible AI

George believes that meaningful technological progress depends on more than clever models: it requires stable institutions, predictable rules, and the willingness to challenge comforting narratives. His AI perspective is shaped by watching how policy, credit cycles, and infrastructure spending interact with innovation over decades.

He is cautious about the assumption that bigger models and larger data centers will automatically yield “smarter” systems, and instead focuses on where AI can genuinely change workflows, science, and robotics. His writing emphasizes transparency, clear incentives, and honest accounting of both upside and risk.

By grounding AI debates in physics, economics, and human behavior, George aims to encourage a calmer, more constructive conversation—one that is skeptical of bubbles, but still optimistic about what well-designed, embodied systems can do for everyday life.