Steve Hou
Head of Research at Silicon Data, AI Compute Markets
Alphabet overtaking Nvidia as the world's most valuable company seems inevitable, because Google's TPUs serve AI inference and applications on top of all its other thriving businesses, while Nvidia's GPUs center on AI training. Per @BrianSozzi, Nvidia's market cap is $4.85 trillion versus Alphabet's $4.66 trillion. x.comÂ
AI infrastructure bottlenecks could give eight stocks as much as 18x upside. Nvidia supplies frontier-model GPUs; Broadcom and Marvell supply custom silicon and networking; TSMC makes advanced chips; Micron supplies sold-out HBM; Vertiv provides power and cooling; Palantir monetizes enterprise AI; Alphabet spans TPUs, research, cloud and apps. The list tracks where AI infrastructure spending is flowing. x.com x.comÂ
Alphabet's ownership of the whole AI stack, from chips to cloud to distribution, gives it a structural cost advantage rivals can't easily copy. It alone designs its own TPUs, runs a global cloud and owns Search, Android, YouTube and Workspace, so aggressive inference pricing is a strategic weapon. As inference overtakes training, case studies show moving workloads from GPUs to TPUs can cut operating costs by over 50%. Microsoft and Amazon are building custom chips, but Alphabet is years ahead. … Show more
Google's allocation of massive TPU capacity to Anthropic strains its own AI effort while its researchers face compute constraints. CNBC reported internal tension; Anthropic competes with Gemini. Google should keep more TPUs for its own AI and buy more NVIDIA GPUs to rent to cloud customers, serving CUDA developers alongside Gemini. Selling TPU systems changes the equation, but Alphabet does not disclose TPU margins, so NVIDIA-like economics cannot be assumed. x.comÂ
Google's TPUv7 inference efficiency supports the bullish case for Alphabet. Per SemiAnalysis' first TPUv7 FP8 vLLM benchmark submission, Google's TPUs delivered 67% more AI inference performance per dollar than Nvidia's GB300 NVL72, even with Nvidia's system using FP4 precision. x.com x.comÂ
AI software and hardware advances make GPUs and power more valuable, creating huge long-term tailwinds for compute providers and AI model and app businesses. Each new software generation increases a GPU's value; each hardware generation increases the value of a gigawatt of power. That favors Nvidia, Broadcom, Intel and AppLovin. x.comÂ
Alphabet's frontier-model effort is deteriorating quickly, and Jeff Dean's exit matters because he masterminded TPUs and Google's distributed systems, not only models. Google failed to foresee this year's demand and sold TPU capacity to Anthropic while its model teams lacked capacity, showing divisions that don't collaborate. Anthropic is now building ASICs directly with Broadcom and running its own infrastructure. TPU and infrastructure remain strong, but confidence in Google overall is lower. … Show more
Anthropic's compute commitments point to future revenue for Alphabet, Amazon, Microsoft, Broadcom and Nvidia. Its S-1 reports 14-fold revenue growth, positive second-quarter 2026 EBIT and more than $600 billion committed to compute through 2045. The AI trade is alive and well, more than expected, provided demand keeps pace with those commitments. x.comÂ
No serious AI customer runs on a single chip vendor, so the choice was never Google's TPUs or Nvidia, but both. Per @StockSavvyShay, Anthropic contracted 2.6 GW of Nvidia infrastructure through 2028 despite leaning heavily on Google TPUs and Amazon Trainium. Per @wallstengine, Nvidia says revenue per gigawatt rises from $25B for Blackwell to $40B for Rubin, but that only works if the gigawatts can be found. x.com x.comÂ
Alphabet's 30x earnings multiple overstates how cheap it is. Stripping out investment gains, counting capex that will keep building as depreciation, and judging it on normalized free cash flow rather than P/E make it look pricier. Subsidized compute deals with OpenAI and Anthropic, circular financing that drives over half of Google Cloud, mean today's 'cheap' could look very expensive later. Relative to many other AI infrastructure names, though, Alphabet is cheaper and a much better business. … Show more
Google TPU adoption could broaden the AI hardware opportunity beyond Nvidia, with demand reaching custom ASICs, HBM, packaging, optics and testing. Per The Information, OpenAI reportedly plans to buy Google's TPUs and is hiring compiler engineers, but no purchase is confirmed. Broadcom, SK hynix, Samsung, Micron, TSMC, Coherent, Lumentum, FormFactor and Teradyne could benefit. x.comÂ
