As we await an M5-based Mac Studio refresh for our own local-LLM projects, seems we are on an inescapable near-term path toward (the bulk of?) inference happening at the edge. Proprietary LLM providers are adjusting pricing models to their evolving cost of compute. As the providers close that gap, many (most?) users may find outsourced AI increasingly unaffordable. Efforts to develop in-house AI expertise and capabilities may grow, while a fresh crop of service providers may arise to assist in the buildout. No small surprise that coders, heretofore seemingly dominant users of frontier AI compute and therefore source of cost, are at the forefront of the local-compute trend.
At a minimum, this evolution should increase focus on return on investment, rationalize compute budgets, and influence the ways both enterprise and casual users incorporate AI into work and daily life. In that sense, the trend adds immediacy to broader questions surrounding the timing, scale and ultimate returns on AI-related capital expenditure. This trend might prove lasting even over the long-term, though prior shifts to the cloud (first storage, then compute), while historically presenting a radically different profitability dynamic, leave us open to a wide range of long-term scenarios.
While three of the U.S.-based hyperscalers (Alphabet, Amazon and Microsoft) and many of the larger non-U.S. hyperscalers (Alibaba, Baidu, Huawei and Tencent), in our view, already have and will further develop natural use cases and potentially profitable revenue models for their in-house compute capacity (Meta might well figure something out, too), it seems increasingly likely that the stand-alone frontier model/agentic systems providers (predominantly Anthropic and OpenAI) will be challenged to square the investment/profit circle. Presuming as seems increasingly true that durable moats are unlikely at the model level, further development in agentic expertise may provide the means for differentiation, but TBD if agents can provide immutable business model defense and thereby serve as a basis for competitive longevity.
Setting aside the fantasy of AI in space, it likely will prove fortuitous, then, that data center construction projects are measured in years (not in months or even weeks, as it seems many thought to have been the case), as incremental gains in inference efficiency should extend the lives not only of freshly purchased hardware for which there presently is no available electrical outlet, but also otherwise idle and aging silicon that can be repurposed for AI-related tasks. Even as the local story reads strong to us, then, it also seems the jury is out re: the extent to which incremental local hardware expense will be required.
As we’ve discussed, Apple may have (inadvertently?) arrive at this same conclusion more than a few quarters ago.
None of this is novel, and the thinking seems unlikely to dent excitement for the troika of AI-Summer IPOs, but these threads already are finding increasing weight in the broader discussion re: ongoing AI capital investment and investability.
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