![Gavin Baker - Nvidia v. Google, Scaling Laws, and the Economics of AI - [Invest Like the Best, EP.451]](https://megaphone.imgix.net/podcasts/d97fee14-d4d7-11f0-8951-8324b640c1c1/image/d3a8b5ecbf3957de5b91f278a191c9ff.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress)
When power (watts) is the primary constraint for data centers, the total cost of compute becomes secondary. The crucial metric is performance-per-watt. This gives a massive pricing advantage to the most efficient chipmakers, as customers will pay anything for hardware that maximizes output from their limited power budget.
The biggest risk to the massive AI compute buildout isn't that scaling laws will break, but that consumers will be satisfied with a "115 IQ" AI running for free on their devices. If edge AI is sufficient for most tasks, it undermines the economic model for ever-larger, centralized "God models" in the cloud.
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.
As the current low-cost producer of AI tokens via its custom TPUs, Google's rational strategy is to operate at low or even negative margins. This "sucks the economic oxygen out of the AI ecosystem," making it difficult for capital-dependent competitors to justify their high costs and raise new funding rounds.
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.
Pre-reasoning AI models were static assets that depreciated quickly. The advent of reasoning allows models to learn from user interactions, re-establishing the classic internet flywheel: more usage generates data that improves the product, which attracts more users. This creates a powerful, compounding advantage for the leading labs.
Established SaaS firms avoid AI-native products because they operate at lower gross margins (e.g., 40%) compared to traditional software (80%+). This parallels brick-and-mortar retail's fatal hesitation with e-commerce, creating an opportunity for AI-native startups to capture the market by embracing different unit economics.
AI progress was expected to stall in 2024-2025 due to hardware limitations on pre-training scaling laws. However, breakthroughs in post-training techniques like reasoning and test-time compute provided a new vector for improvement, bridging the gap until next-generation chips like NVIDIA's Blackwell arrived.
NVIDIA's complex Blackwell chip transition requires rapid, large-scale deployment to work out bugs. XAI, known for building data centers faster than anyone, serves this role for NVIDIA. This symbiotic relationship helps NVIDIA stabilize its new platform while giving XAI first access to next-generation models.