GavinSBaker

1. THE FACT: The next 12 months will see the GB300 (Nvidia's next-gen GPU) ramp up, followed by Rubin. Models trained and inferenced on these GPUs are expected to show a dramatic leap in capability, leading to the "first significant AI" advancements. 2. THE BRIDGE: The ramp-up of advanced GPUs like GB300 and Rubin suggests a significant increase in demand for high-performance computing hardware and the companies developing and deploying advanced AI models. This implies continued strong performance for GPU manufacturers and AI infrastructure providers. 3. THE VERDICT: Long NVDA and other AI infrastructure/hardware plays due to the anticipated dramatic leap in AI capabilities driven by new GPU architectures (GB300, Rubin) over the next 12 months.

1. THE FACT: The next 12 months will see the GB300 (Nvidia's next-gen GPU) ramp up, followed by Rubin. Models trained and inferenced on these GPUs are expected to show a dramatic leap in capability, leading to the "first significant AI" advancements. 2. THE BRIDGE: The ramp-up of advanced GPUs like GB300 and Rubin suggests a significant increase in demand for high-performance computing hardware and the companies developing and deploying advanced AI models. This implies continued strong performance for GPU manufacturers and AI infrastructure providers. 3. THE VERDICT: Long NVDA and other AI infrastructure/hardware plays due to the anticipated dramatic leap in AI capabilities driven by new GPU architectures (GB300, Rubin) over the next 12 months.

Micron is a leading memory manufacturer essential for AI workloads. Given the massive demand for memory chips from AI training and inference, and the company's recent 10x market cap growth to over $1 trillion, the memory bottleneck creates a huge opportunity for Micron.

SpaceX’s AI compute business is massively underappreciated by public markets. The company has already shown it can bring on huge GPU clusters faster and cheaper than anyone, and recent product launches (Grok 4.5, Cursor acquisition) suggest accelerating fundamentals. Consensus revenue estimates of $73 billion for next year look far too low given that a single gigawatt of compute can generate around $50 billion in revenue; reports of 8 GW of capacity would imply triple-digit billions. The market is pricing in a commoditizing spot market and a bearish short case, but execution history suggests the opposite—very little upside to the compute story is in the stock.

Cerebras designs specialized inference chips that are crucial as the AI industry shifts from pre-training to post-training and reasoning. Inference compute demand is estimated to be 5–10x larger than pre-training, and Cerebras is positioned to capture that growth alongside other inference-focused companies.

Astera Labs is miscategorized as a copper loser when it is actually a switch company that benefits from both copper and optics connectivity; the stock has many bears but is well-positioned in the AI infrastructure buildout with a long-term track record.

1. THE FACT: Gavin Baker is "deeply amused" by commentary that datacenters in space don't work, highlighting Elon Musk's experience with large GPU clusters and SpaceX's capabilities in mass to orbit and operating Starlink. He then details Starlink v3's 20kW power, Elon's plan for 100kW AI satellites (nearly a Blackwell rack), and Starship's ability to lift 10-15 megawatts to orbit. 2. THE BRIDGE: This suggests a strong belief in the viability and future potential of space-based data centers for AI, driven by SpaceX's technological advancements. Companies involved in space infrastructure, satellite technology, and potentially AI hardware designed for space deployment could benefit. 3. THE VERDICT: Long AI and space infrastructure plays, particularly those related to SpaceX's vision for space-based data centers, given the technical feasibility and potential for massive power delivery to orbit.

AI data center buildout is creating huge demand for power equipment and turbines; jet engines are being pulled from old planes and repurposed as turbines for data centers, and Caterpillar, Cummins, GE Vernova and Siemens Energy are all expanding capacity fast to meet the demand.

AI data center buildout is creating huge demand for power equipment and turbines; jet engines are being pulled from old planes and repurposed as turbines for data centers, and Caterpillar, Cummins, GE Vernova and Siemens Energy are all expanding capacity fast to meet the demand.

AI data center buildout is creating huge demand for power equipment and turbines; jet engines are being pulled from old planes and repurposed as turbines for data centers, and Caterpillar, Cummins, GE Vernova and Siemens Energy are all expanding capacity fast to meet the demand.

AI data center buildout is creating huge demand for power equipment and turbines; jet engines are being pulled from old planes and repurposed as turbines for data centers, and Caterpillar, Cummins, GE Vernova and Siemens Energy are all expanding capacity fast to meet the demand.

Hyperscale cloud providers (Microsoft, Meta, Amazon) saw operating cash flow accelerate from 28% to 35% in the most recent quarter, even before AI compute contracts reprice significantly higher. As installed GPU capacity reprices at spot rates, operating cash flows will grow substantially, easily funding the AI capex cycle internally and reducing reliance on credit. The selloff on fears of excess capacity and debt-financed overbuild is therefore misplaced; these stocks are undervalued given the earnings power set to materialize.

Hyperscale cloud providers (Microsoft, Meta, Amazon) saw operating cash flow accelerate from 28% to 35% in the most recent quarter, even before AI compute contracts reprice significantly higher. As installed GPU capacity reprices at spot rates, operating cash flows will grow substantially, easily funding the AI capex cycle internally and reducing reliance on credit. The selloff on fears of excess capacity and debt-financed overbuild is therefore misplaced; these stocks are undervalued given the earnings power set to materialize.

Hyperscale cloud providers (Microsoft, Meta, Amazon) saw operating cash flow accelerate from 28% to 35% in the most recent quarter, even before AI compute contracts reprice significantly higher. As installed GPU capacity reprices at spot rates, operating cash flows will grow substantially, easily funding the AI capex cycle internally and reducing reliance on credit. The selloff on fears of excess capacity and debt-financed overbuild is therefore misplaced; these stocks are undervalued given the earnings power set to materialize.

DRAM and HBM memory are the most critical AI bottleneck. Micron’s entire 2026 HBM supply is sold out, and new multi-year supply agreements with large customers feature pricing floors above prior cycle peak gross margins. The three DRAM/HBM makers—Micron, SK Hynix, and Samsung—are seeing their business models improve while still trading at cheap valuations relative to the rest of the AI supply chain. DRAM is expected to become 30–40% of hyperscaler capex, keeping the bottleneck tight for years.