Over the past year, humanoid robots have become a focal point for expectations around embodied AI. The fascination with JARVIS from Iron Man is often mistaken for a desire for conversation. What people actually imagine is a physical executor that does not fail. That expectation conflicts with how generative AI
Recent discussion around AI infrastructure has focused heavily on scale. Power consumption figures, capital expenditure, and data center counts dominate the narrative. These metrics are not incorrect, but they obscure a more consequential shift. The defining feature of large-scale AI workloads is not their magnitude, but their timing. Systems designed
A strange mismatch has taken shape around artificial intelligence. At conferences and in interviews, the people building AI repeat the same urgent concerns: power is tight, compute is stretched, and expansion is hitting physical limits. Yet, everyday use tells a completely different story. When people open AI tools today, they
Some arrangements, once they are put into operation, no longer treat exit as a reversible move. Not because departure is forbidden, but because returning after a position is lost becomes prohibitively expensive. Power purchase agreements extend across decades. Transformer orders sit in queues measured in years. Grid connection permits, once
Not all AI scales by code. Some of it scales by permits, steel, and time. Sequence Cannot Be Skipped AI is often discussed as software, but parts of its development now resemble heavy industry. Not in appearance, but in constraint. Progress is no longer gated by models alone, but by
Markets continue to rely on familiar signals such as growth, margins, execution, and competitive position. What has become less stable is the context in which these signals are interpreted. Increasingly, they are assessed alongside constraints that sit outside the firm’s control, and policy-driven uncertainty is one of them. The
The Supreme Court case over Trump’s emergency trade powers is not only about tariffs. It is about whether temporary legal tools can be converted into standing economic policy. At stake is the interpretation of the International Emergency Economic Powers Act, or IEEPA, a statute passed in 1977 to give
Most discussions about AI still begin with capability. Accuracy improves. Reasoning lengthens. Benchmarks move. In deployment, capability is rarely the binding constraint. Systems using models of similar maturity often grant AI very different roles. Some allow continuous autonomous decisions. Others restrict AI to advisory functions, even when performance appears sufficient.
Most public conversations about AI still begin with capability. Model size, benchmark scores, reasoning depth, and the pace at which new thresholds are crossed form the dominant vocabulary. Progress is framed as something that can be measured, compared, and ranked. When Ren Zhengfei spoke with ICPC contestants at Huawei’s
Alpha Arena, at first glance, resembled a familiar experiment. Six large language models were each given $10,000 and two weeks to trade crypto perpetual futures. Some ended with gains, others with significant losses. Qwen and DeepSeek finished near the top, while GPT-5 and Gemini ended deep in the red.
Florida, 2025. A driver rear-ends another vehicle while Autopilot, part of Tesla’s Full Self-Driving (FSD) system, is engaged.His hands are on the wheel. The system does not issue a takeover alert. The court assigns Tesla 33 percent of the liability. The ruling is not about whether the system
Forecasts for AI’s contribution to global GDP cluster between fifteen and twenty trillion dollars by 2030. The number shifts from report to report. The assumption beneath it does not. Each projection treats AI as if it will scale the way software has always scaled. Elon Musk often talks about