The standard for measuring large compute deals has shifted from number of GPUs to gigawatts of power. This provides a normalized, apples-to-apples comparison across different chip generations and manufacturers, acknowledging that energy is the primary bottleneck for building AI data centers.

As AI agents become more sophisticated, they will autonomously seek out and use the cheapest decentralized services for tasks like storage and processing. This creates a relentless, 24/7 market pressure that will continuously drive down the fundamental costs of computing for everyone.

The dominant use of AI compute is moving from training massive models to running inference tasks. This shift fundamentally alters the market, enabling broader enterprise adoption via cheaper, open models and changing the demand profile for compute hardware beyond the absolute cutting edge.

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.

While AI compute demand seems limitless, its price is not infinitely elastic. As inference becomes a core cost of goods sold (COGS) for AI products, excessively high compute prices will break the business models of infrastructure customers, ultimately limiting demand.

The intense computational demand and latency of AI models are compelling enterprises to use multiple cloud providers. Rather than vendor loyalty, companies now prioritize performance, switching between clouds like AWS and Azure to find the fastest available capacity for their AI workloads, reshaping the cloud market.

Unlike cable or power companies that benefit from regional monopolies, AI intelligence is a globally competitive, frictionless market. This dynamic is 'so much worse' for business because it allows for perfect arbitrage, driving the price of intelligence toward zero and making it incredibly difficult to build a sustainable, high-margin business on the infrastructure layer.

While the idea of distributed compute pools is appealing, it's not feasible for AI training due to high latency demands; GPUs must be physically co-located. However, AI inference is less sensitive to this lag, making a distributed network of compute (like home GPUs) a much more viable and exciting model.

Drawing a parallel to AWS's history, AI inference costs are expected to continuously decrease over time. As usage skyrockets, providers will be incentivized to lower prices to capture market share, making fears of escalating costs for startups unlikely to materialize.