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.

The market is rewarding companies selling scarce AI resources (power, memory, GPUs) as they can raise prices and expand margins. Conversely, the hyperscalers buying this shortage face multiple compression as their capex soars and ROI on each dollar declines, creating a clear divide between winners and losers.

Blitzscaling is taking the risk of scaling rapidly in an uncertain environment. According to its originator, Reid Hoffman, the AI sector's multi-billion dollar investments in compute, despite unproven widespread business models, perfectly embodies this principle by betting on future transformative impact.

Comparing today's AI competition to the cloud market circa 2010 suggests we'll see multiple massive winners. Just as AWS's early lead didn't prevent Azure and GCP from becoming hundred-billion-dollar businesses, the AI market is vast enough to support several dominant labs like OpenAI and Anthropic.

Specialized AI clouds (NeoClouds) like CoreWeave emerged because hyperscalers' strengths—such as custom networking and security for multi-tenancy—were detrimental to the performance of large-scale, single-tenant AI workloads. This performance gap created a significant market opening.

While model performance gains headlines, the true strategic priority and bottleneck for AI leaders is the 'main quest' of securing compute. This involves raising massive capital and striking huge deals for chips and infrastructure. The primary competitive vector has shifted to a capital war for capacity.

The current wave of AI companies is growing at unprecedented rates, far outpacing the growth curves of the mobile, social, or SaaS eras. They are becoming larger and more consequential much faster, a phenomenon described as "speed running the process of company growth."

The go-to-market for AI hardware is unlike traditional enterprise sales. Founders should focus on a small number of massive customers: the hyperscalers and emerging "sovereign clouds" in various countries. The total addressable market is maybe 50 customers, not thousands, making it a telecom-like industry.

Unlike previous tech booms built on a 'if you build it, they will come' mentality, the current AI data center buildout is racing to meet existing, booked demand. Cerebras CEO Andrew Feldman notes the demand for AI hardware and data centers already far outstrips the industry's ability to supply it, a highly unusual market dynamic.