The demand for AI tokens is growing faster than the supply of GPU infrastructure. This profound imbalance creates a market where not just top-tier AI labs, but also second and third-tier players will likely sell out their capacity. Superior models will command better margins, but the overall resource constraint means even lesser models will find customers.

The industry is fixated on the GPU shortage, but the proliferation of AI agents will create massive demand for general-purpose compute, leading to a CPU bottleneck. As millions of agents perform tasks, the availability of CPU cores—not just specialized processors—will become the primary constraint on growth for compute providers.

Today's AI computing demand from millions of human users is just the beginning. The real explosion in demand will come from billions of AI "agents" working 24/7. This will double the workforce and create a relentless, round-the-clock need for inference computing, dwarfing current infrastructure requirements.

Despite massive infrastructure investments, Greg Brockman believes demand for AI will consistently outstrip supply, leading to a long-term state of "compute scarcity." As AI tackles bigger problems like curing diseases, the appetite for computation will prove effectively infinite, making it a chronically scarce resource.

The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.

While historical tech cycles have featured overbuilds, the current AI boom's primary risk is a severe, prolonged undersupply of compute. This is driven by regulatory hurdles for data centers and demand that is still in its infancy, which could lead to significant price hikes for AI services.

While compute and capital are often cited as AI bottlenecks, the most significant limiting factor is the lack of human talent. There is a fundamental shortage of AI practitioners and data scientists, a gap that current university output and immigration policies are failing to fill, making expertise the most constrained resource.

The current compute crunch isn't just a supply issue. It's because new AI models are so much more capable that they unlock a total addressable market (TAM) of valuable tasks that grows exponentially, far outpacing the linear or geometric growth of compute supply.

The availability of compute from Meta and XAI doesn't indicate a market-wide surplus. Instead, it points to a compute allocation problem. Massive capacity is concentrated in the hands of companies that currently lack sufficient internal inference demand for their own models, while other parts of the market remain constrained.