The AI race has been a prisoner's dilemma where companies spend massively, fearing competitors will pull ahead. As the cost of next-gen systems like Blackwell and Rubin becomes astronomical, the sheer economics will force a shift. Decision-making will be dominated by ROI calculations rather than the existential dread of slowing down.

From a first-principles perspective, space is the ideal location for data centers. It offers free, constant solar power (6x more irradiance) and free cooling via radiators facing deep space. This eliminates the two biggest terrestrial constraints and costs, making it a profound long-term shift for AI infrastructure.

Digital computing, the standard for 80 years, is too power-hungry for scalable AI. Unconventional AI's Naveen Rao is betting on analog computing, which uses physics to perform calculations, as a more energy-efficient substrate for the unique demands of intelligent, stochastic workloads.

The narrative of energy being a hard cap on AI's growth is largely overstated. AI labs treat energy as a solvable cost problem, not an insurmountable barrier. They willingly pay significant premiums for faster, non-traditional power solutions because these extra costs are negligible compared to the massive expense of GPUs.

Despite bubble fears, Nvidia’s record earnings signal a virtuous cycle. The real long-term growth is not just from model training but from the coming explosion in inference demand required for AI agents, robotics, and multimodal AI integrated into every device and application.

OpenAI announced goals for an AI research intern by 2026 and a fully autonomous researcher by 2028. This isn't just a scientific pursuit; it's a core business strategy to exponentially accelerate AI discovery by automating innovation itself, which they plan to sell as a high-priced agent.

Beyond the well-known semiconductor race, the AI competition is shifting to energy. China's massive, cheaper electricity production is a significant, often overlooked strategic advantage. This redefines the AI landscape, suggesting that superiority in atoms (energy) may become as crucial as superiority in bytes (algorithms and chips).

Instead of relying on hyped benchmarks, the truest measure of the AI industry's progress is the physical build-out of data centers. Tracking permits, power consumption, and satellite imagery reveals the concrete, multi-billion dollar bets being placed, offering a grounded view that challenges both extreme skeptics and believers.

OpenAI now projects spending $115 billion by 2029, a staggering $80 billion more than previously forecast. This massive cash burn funds a vertical integration strategy, including custom chips and data centers, positioning OpenAI to compete directly with infrastructure providers like Microsoft Azure and Google Cloud.