Researchers
1 Curated Dialogues
Ed H. Chi
Key Views & Dialogues
Model capabilities will eventually commoditize; AI’s endgame is capability over cost | A conversation with Google DeepMind VP of Research Ed Chi
Ed Chi says model capabilities may gradually commoditize, though not universally, shifting value toward application stickiness and experience, and token-factory competition on energy, chips, and memory-computation costs. Google’s full stack may offer the lowest-cost tokens and a potential moat, while assistants face permission and reliability limits; their interface with AI Mode Search may clarify in 6 months to 1 year.
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- Ed Chi’s view is that model capabilities may gradually commoditize, but not all capabilities will. Google is a full-stack company spanning models, applications, hyperscaling, token factories, chips and energy. He says Google can deliver tokens at the lowest cost, which “could become the most important moat going forward.” “All economic questions ultimately come down to capability over cost. There’s no question about it.”
- As gaps in model capabilities narrow, value creation may shift toward applications and token factories—a directional call for founders. On the application side, the moat is user lock-in, stickiness and experience. On the hyperscaler side, the questions are about cost: energy consumption, chip design, the relationship between memory and computation, and how much runs on-device. “These are all open questions, and they’re very exciting.”
- General-purpose assistants are still at a very early stage, and he expects the interface between them and AI Mode Search to become clearer in 6 months to 1 year. Products such as Instinct and Muse Spark aim to move beyond Q&A into getting things done—booking flights, buying things—but run up against permissions and reliability. People worry that if they hand a general-purpose assistant their credit card, it will go on a spending spree; he says there is still no real new data on the confirmation layer. His “Grandma Test” is that the era of general-purpose assistants will have truly arrived when Grandma points at a robot and says, “Why are you so stupid?”
- One reason open models caught up with closed models is a Chain of Thought paper he co-authored: open models use CoT to surface and replicate reasoning methods, while major labs are trying to hide chain of thought to make copying harder. He says it is too early to call this settled: distillation has made some capabilities more commoditized, but there is no sign yet that capabilities such as multimodality will commoditize.
- DeepMind’s recent small models are mainly for applications, while larger models remain in development; Google abandoning model development is “completely impossible.” Users expect Google to have its own technology, with something of a technological-sovereignty dimension to it. Resource allocation between small and large models “is a difficult problem even inside large companies.” He describes Bay Area “AGI-pilled” tech bros as believers that software took over the world and that good coding can take over the world—which explains their love of ultra-size models. He personally does not want applications to depend entirely on ultra-size models, given the energy required to run them.
- Memory is a bottleneck that researchers have yet to fully understand: Transformer context buffers can currently reach around 1 million or 2 million tokens, which he considers nowhere near enough. His core framework is that memory is not just storage and retrieval; it is also a ranking problem—when you think of Paris, which memories come to mind is a matter of ranking, rather than every related memory flooding in at once. From a cognitive perspective, memory is also a program for programming AGI machinery. Asked about possible shortages of DRAM, NAND and HDD, he says memory and computation can substitute for each other; companies with long-term investment plans are less likely to worry about a short-term memory shortage.
- The bubble is localized, not market-wide: expectations for physical intelligence may be unrealistic, and many models’ visual reasoning capability is “terrible.” If you believe that agents solving software means solving everything, much of what he has said does not apply; if you do not, “there’s still plenty of room to play.” In 3–4 years, reasoning has gone from elementary-school math problems—models had just 33% accuracy then—to solving Navier–Stokes equations. For the next exciting monetization opportunity, he is bullish on consumer: consumer services have always been a Google strength, and that is why he stayed at Google.