Weekly|AI4S, Gemini 4, TSM & ASML, MRVL and OCS, Muse, CRWV

Google is back in the frontier race, and we think a new training cycle is beginning. As we argue in this week’s column, Gemini 4 is evidence that scaling still works; the next test is how much post-training improves coding and long tasks in Gemini 4.1. With OpenAI, Anthropic, Google, Meta and xAI all investing to defend or improve their positions, we remain bullish on the networking and optical interconnects needed to keep larger clusters productive.

AI for science gives us another reason to be optimistic about where that compute can go. OpenAI’s latest mathematics release still needs independent scrutiny, but progress in problems with verifiable answers could help AI take on more research and engineering work. Drug discovery, materials and energy research offer much broader potential benefits. Physical experiments and clinical trials will still take time; our optimism is about expanding the ideas scientists can test and the connections they can make across disciplines.

The supply chain is preparing for that investment. We see TSMC’s growth increasingly governed by how quickly capacity comes online, and have raised our ASML EUV shipment forecasts again. Marvell’s Investor Day targets are moving closer to our forecasts, backed by longer-term supplier planning. OCS adds another source of optical demand as content per accelerator rises, although deployment beyond Google remains an important assumption in our longer-term model.

On the application side, we are watching retention and cost together. Muse’s improving retention gives Meta a reason to expand acquisition once the economics support it, while computer use opens a much larger pool of work to automation. Our expert interviews also underline the obstacles: high token consumption can limit enterprise adoption, and physical capacity still depends on manufacturing yields, equipment and power. Better models and more infrastructure need to translate into reliable, affordable work.

This Week’s Reports

Going the Way of Go: stronger models make the next training cycle worth watching. Our new column connects AI’s progress in mathematics and programming with Google’s return to the frontier, and explains why we remain optimistic about AI for science and optical interconnect demand.

TSM & ASML: capacity expansion sets the pace. We expect TSMC’s advanced-node expansion to support further growth and now forecast ASML total EUV shipments of 100/125 units in 2027/28, as both logic and memory customers need more equipment.

MRVL & OCS: more optical content as AI clusters grow. Marvell’s new targets move closer to our forecasts, while our OCS work points to more than $20b of annual demand by 2030, conditional on broader adoption that includes Nvidia from late 2028.

META: Muse’s retention is improving before the next growth push. We expect adoption to advance in steps as Meta improves retention and balances user acquisition against the substantial compute cost of serving a personal agent.

Computer use: a much larger market for AI providers. Our long-term framework points to roughly $1tn of additional annual revenue and automatable work equivalent to about 180mn full-time roles, an estimate of task hours rather than a forecast of layoffs.

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