Firms like OpenAI and Meta claim a compute shortage while also exploring selling compute capacity. This isn't a contradiction but a strategic evolution. They are buying all available supply to secure their own needs and then arbitraging the excess, effectively becoming smaller-scale cloud providers for AI.

OpenAI's CFO highlights a key dynamic: the cost of raw compute inputs (power, memory) is rising, but the cost to produce a unit of intelligence is falling dramatically, citing a 97% cost reduction from GPT-4 to 5.4. This deflationary curve is central to their financial modeling, allowing them to price future capacity and value creation more aggressively.

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 chip fabrication is complex, the most binding constraint for AI compute providers is physical infrastructure. The entire industry's growth is bottlenecked by the availability of powered data center buildings, a problem projected to persist for at least another 15-18 months.

OpenAI's aggressive partnerships for compute are designed to achieve "escape velocity." By locking up supply and talent, they are creating a capital barrier so high (~$150B in CapEx by 2030) that it becomes nearly impossible for any entity besides the largest hyperscalers to compete at scale.

The value unlocked by frontier AI models is expanding so rapidly that there isn't enough hardware to meet demand. This scarcity ensures that not just the top lab (like OpenAI), but also second and third-tier competitors, will operate at full capacity with strong margins.

Instead of viewing compute as a cost center, OpenAI treats it as a revenue generator, analogous to hiring salespeople. The core belief is that demand for AI capabilities is so vast that they can never build compute fast enough to satisfy it, justifying massive, forward-looking infrastructure investments.

A theory suggests Sam Altman's massive, multi-trillion dollar spending commitments are a strategic play to incentivize a massive overbuild of AI infrastructure. By driving supply far beyond current demand, OpenAI could create a 'glut,' crashing the price of compute and securing a long-term strategic advantage as the primary consumer.