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 massive capital expenditure by hyperscalers on AI will likely create an oversupply of capacity. This will crash prices, creating a golden opportunity for a new generation of companies to build innovative applications on cheap AI, much like Amazon utilized the cheap bandwidth left after the dot-com bust.

Unlike compute-rich giants, AppLovin's bootstrapped culture enforces extreme efficiency in its AI infrastructure. Engineers don't have unlimited GPUs, forcing them to optimize code and models for cost and performance. This constraint-driven approach leads to significant cost savings and a lean operational model.

The primary bear case for specialized neoclouds like CoreWeave isn't just competition from AWS or Google. A more fundamental risk is a breakthrough in GPU efficiency that commoditizes deployment, diminishing the value of the neoclouds' core competency in complex, optimized racking and setup.

The vast network of consumer devices represents a massive, underutilized compute resource. Companies like Apple and Tesla can leverage these devices for AI workloads when they're idle, creating a virtual cloud where users have already paid for the hardware (CapEx).

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.

A primary risk for major AI infrastructure investments is not just competition, but rapidly falling inference costs. As models become efficient enough to run on cheaper hardware, the economic justification for massive, multi-billion dollar investments in complex, high-end GPU clusters could be undermined, stranding capital.

According to advisor Bradley Tusk, the massive electricity consumption of AI data centers is causing consumer energy bills to rise, creating political backlash. This pushback from voters and politicians creates a significant market opportunity for startups focused on energy-efficient chips and alternative on-site power generation.

The current AI investment boom is focused on massive infrastructure build-outs. A counterintuitive threat to this trade is not that AI fails, but that it becomes more compute-efficient. This would reduce infrastructure demand, deflating the hardware bubble even as AI proves economically valuable.