mon
Independent Analyst, High-Risk Small-Cap & Growth Investing
AI agents scaling to hundreds of millions of users will need a large layer of infrastructure, and the buildout is still early. Agents need CPUs to run browsers, code, tools and background tasks, adding a major new source of server CPU demand for Intel and AMD. They also require more memory, compute and data center capacity, benefiting neoclouds like IREN and Nebius. Underneath it all, more data centers mean more electricity. x.com
Owning AI bottlenecks is the best way to play the next 12-18 months of scaling, because the Jevons paradox means better models and cheaper inference drive far more usage. Astra was pretrained on over 100,000 GPUs, and more agents and tokens keep infrastructure needs rising. The plays span compute and data-center power (IREN, Nebius, CoreWeave, Cipher, TeraWulf), GPUs (Nvidia, AMD, Broadcom), memory, networking, optics, power and cooling, with the best odds where supply is hardest to add. x.com
AI agents' productivity over human labor should lift GPU demand and rates for neoclouds. The bottom looks in for CoreWeave, Nebius, Boost Run, Sharon AI and IREN. Gavin Baker says token prices can fall as revenue per GPU rises; paraphrasing Elon, even $38 per GPU-hour can pay off. CoreWeave's five-year fixed contracts may lag current rates; Boost Run has less capacity locked in. If SpaceX takes more growth, slower cash burn could rerate neoclouds, though smaller customers may pay less. x.com … Show more
AI's biggest bottleneck is power and cooling, not memory, making Vertiv, GE Vernova, Bloom Energy and Eaton buys. Musk expects a prolonged compute shortage; Nvidia, Broadcom, Micron, SK hynix, TSMC, Arista, AMD, Marvell, Credo, Lumentum, ASML and Astera Labs could benefit massively. Grok Bot's 800,000 paid weekly users have more than doubled in a month; Musk says agents could eventually use more compute than any other part of SpaceX's Super Intelligence business, benefiting Dell and Sandisk too. … Show more
AI suppliers' demand may not justify today's investment. Roughly $3 trillion in revenue absent last year is needed to support the spending, yet GDP growth is weak and many companies may be seeing only incremental gains. If the estimate that Anthropic uses two-thirds of its compute for training is right, it could triple inference supply. The most likely outcome for many companies, roughly half the S&P 500, is slowing growth, compressed margins and multiples; when the bubble ends is uncertain. … Show more
Faster-than-expected AI computing price increases challenge fears of too much capacity too soon. Nebius reported that pricing trend. x.com
Premium-model inference revenue could support much higher compute pricing for AI infrastructure providers. Evercore models over $120M/MW in potential lab revenue while labs pay roughly $30-50M/MW for compute. With power and GPUs scarce, SpaceX AI, Nebius, IREN and CoreWeave can capture the biggest uplift over historical pricing if they deliver contracted high-density capacity. x.com x.com
Nebius' business model faces pressure once GPU supply and demand normalize, perhaps within five years. It faces enormous supplier power, more competitors and likely rising buyer power, and lenders and equity investors may view it differently once utilization and pricing stop being exceptional. China could flood global markets with low-cost compute, and the market already seems to price a very long AI boom. The case weakens if AI demand proves durable, like an 18-year real estate cycle. x.com
AI's physical buildout looks set to last to 2030 and beyond because financing, memory supply and hyperscaler spending still point to sustained demand. Nebius has $3.5B in financing, IREN $2.4B at 9% and SPCX $40B for GPU equipment. SK hynix expects shortages through at least 2030 and Samsung says HBM4 is sold out; memory companies are making money with no sign of slowing. Google expects 2027 capex to rise significantly. Pullbacks can happen; be cautious and buy quality. x.com
Nebius' software efficiency and a possible end to heavy AI financing support a re-rating. Per @DrTomsLens, its engineers got about 43% more video output from the same GPUs, cutting compute cost per second by 30%; newer hardware alone is not enough without capable software teams. Oracle expects to fund growth from free cash flow in FY2029, suggesting capital-heavy AI providers may eventually outgrow debt and dilution. Google Cloud reported 103% revenue growth amid strong AI infrastructure demand. … Show more
NVIDIA and Intel face a constraint money cannot fix, the time needed to turn capital into physical capacity. NVIDIA's MOUs with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to mobilize $500B+ make GPUs collateral, but token revenue, CUDA longevity and fungibility are also failure modes, and MOUs are not money. Only ~5 GW of ~12 GW of 2026 US AI capacity is under construction, with interconnection taking 4–7 years. Intel's $15B raise buys fabs that take 3–5 years. x.com
A rising value of compute per watt should benefit AI cloud, chip, connectivity and power suppliers. Elon Musk says that value will rise over time. Nebius has Microsoft and Meta deals; Marvell benefits from custom silicon demand; Credo connects GPUs; Vistra has long-term nuclear supply deals with Amazon and Meta; Nokia's Nvidia partnership could help it capture data-center networking growth. x.com x.com
