Blaine Dillingham

In 1962, the Executive Branch spent roughly $544 for every $1 the Legislative Branch spent By 2024, that number doubled to $984 to $1 I am increasingly concerned about worlds the legislative and judicial branch are unable to oversee an AI-accelerated executive branch It seems more important than ever to figure how to empower congress and the courts with AI tools And its important to figure out how congress and the courts should oversee the executive branch once a significant fraction of the USG is automated

For a moment, substitute the notion of “believing in short AGI timelines” for: “acknowledging the idea of AGI as an ill-defined thing that will nonetheless probably exist within a strategically relevant timeframe, the pursuit of which will produce importantly capable artifacts along the way, whose arrival will be even sooner than the so-called ‘AGI,’ and so we don’t really need to quibble all that much about exact AGI definitions and timelines, because the mega-capable artifacts already kinda resemble ‘AGI,’ have national security implications, and seem like they’re going to keep improving rapidly, so functionally we just have to accept that we live in AGI world now, regardless of whether one’s personal definition of AGI is satisfied in 2028, 2035, or, indeed, 2026.” If this was your view—and it is mine—then it is not so much about short timelines to AGI as it is “short timelines to the importantly useful artifacts produced along the path to AGI, so capable that maybe in some ways they blend into AGI.” Thus “Mythos” or “the latest frontier model” can be substituted for “AGI” in many debates about timelines. The rhetoric of “we should sell China more chips” or “AI is the next internet platform business and it should we regulated exactly like prior waves of internet platform businesses, which is to say ‘basically not at all’” pivots not so much on short timelines to AGI but instead short timelines to models that *matter,* national-security-wise. The fatal flaw in the 2024-era accelerationist view, epitomized by Jensen, was that models would never matter in this way; or at least, that you should not think so much about the world in which models mattered in that way. It is much easier to justify “doing what we have been doing” if you don’t believe neural networks will ever truly matter to national security. Basically all AI debates hinge not so much on “AGI timelines” but on “will LLMs ever matter, really, to national security.” The near-term existence of LLMs with national-security-relevant capabilities can therefore be thought of as, to borrow a phrase, an inconvenient truth.

Accelerationist AI policy is losing ground, and the current strategy does not give moderates a pro-AI case for 2028. Without it, they'll get pulled apart by anti-AI sentiment. Today, I argue accelerationism needs to change, or its defeat will make AI policy worse for everyone.

Days after voting in favor of a new data center in Indianapolis, Councilman Ron Gibson says his home was struck by 13 gunshots while he and his family were asleep. He says a handwritten note reading “No data centers” was found under the doormat.