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.

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.

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.

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.

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.

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.

Gavin holds a large put position on QQQ (the Nasdaq 100 ETF) as a hedge against broad market downside. While he is bullish on specific AI infrastructure companies, he is bearish on the general market, believing the QQQ index may decline, and uses puts to express that view and protect against systemic risk.

Unity Software is a world model builder with deep physics and 3D rendering capabilities. As AI moves toward AGI and humanoid robots, simulated environments for training become critical, and Unity's engine is one of the best platforms for creating these virtual datasets, making it a unique AI play.

As AI enables more sophisticated impersonation and cyber attacks, investors should over-weight cybersecurity to protect against these threats; the speaker is actively overinvesting in the space.

DRAM companies trade at mid-single-digit earnings multiples while semicap equipment trades at 40x, an unsustainable gap; DRAM demand is structurally boosted by AI, especially HBM, which will improve memory business models, and the shortage dynamics should drive earnings and multiple expansion.

Tech risk/reward attractive: valuations broadly below Covid and Deepseek lows while token consumption and GPU rental prices go vertical; quality secular growth names at mid-single-digit multiples on 27/28 numbers.

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.