Three Approaches to Slowing AI Down

It's not as hopeless as it may seem

Source: The Guardian

Earlier this year I wrote a Substack piece on “Slowing Down the AI Train.” After years of thinking there was no reason for serious concern about AI development, I decided that as a society we need to figure out some way to limit the pace of development. I was hardly the first to do so, but since then many more voices have joined this particular chorus. Two weeks ago, for example, 1100 AI leaders and professionals signed a letter calling for “an international effort to develop the technical and governance tools needed to deliberately pace the frontier of automated AI development.” Last month, 200 economists—including 16 Nobelists—and executives signed a letter arguing for safeguards against large-scale job loss from AI. In addition, organizations like the Future of Life Institute, Pause AI, Stop AI, and several others have raised substantial concerns about the uncontrolled pace of AI development. Recent automated hacks by systems from Anthropic, OpenAI, and Meta haven’t helped the image of generative AI models. The anti-AI chorus is becoming increasingly loud, although there are still plenty of anti-AI regulation boosters (“accelerationists”) out there.

Political Levers

As I stated in my piece last February, there aren’t a lot of sure-fire methods to slow down the development of AI. But there are some potential levers that could help. The most commonly discussed are legal and regulatory approaches. Since the Trump administration’s only consistent AI policy is to favor Big Tech firms and owners who support the administration financially, it seems unlikely that there will be federal regulation in the US on AI for the next several years. Although Trump signed an executive order prohibiting states from passing their own regulation, that appears to be unconstitutional and hasn’t stopped states from proposing (47 pieces in total) or enacting (8 thus far) legislation regulating AI, according to this tracking map. There may be more bipartisan consensus across states on regulating AI than on any other issue! Therefore, if you want to reach out to legislators to express your desire to slow AI down, you’re probably better off contacting state officials rather than federal ones.

Of course, what we really need is global AI regulation, since multiple countries are developing AI and any problems with it are likely to have global implications. Alas, however, meaningful global regulation is highly unlikely. A survey of AI experts released recently by the Council on Foreign Relations found that 80% of the experts believe that regulation will continue to be fragmented, and that 70% believe the most likely trigger for change would be a major AI accident. Yes, that’s a bit depressing.

Economic Levers

There are also various economic levers that could slow down AI development. The primary vendors seem hell-bent on investing and circular funding themselves into bankruptcy, so there may be no need for further economic intervention. But in case they don’t pop their own bubble, consumers can slow down the train by simply spending less on AI subscriptions and tokens. Taking advantage of the increasing number of open-weight models is one way to do that. Not using AI for highly frivolous purposes is another.

Many businesses have already realized that generative AI costs a lot and yields economic value—particularly in terms of individual productivity—that is difficult to measure. So they (82% of them according to an EY survey) are already cracking down on tokenmaxxing and using AI routers to find the cheapest generative model that will get the job done. The fact that this newfound thriftiness takes place just as AI vendors are doubling down on capital expenditures may also by itself bring down startup valuations and the resulting pace of development.

I realize that generative AI can be useful, so I’m not advocating any sort of boycott—not that it would succeed if I did. I don’t want AI to go away—I just want fewer new data centers, a new model once every few months instead of every day, and greater attention paid to testing and guardrails.

Technology Levers

Technology got us into this mess, and there’s a good chance it can help get us out if we want it to. The “hair of the dog that bit you” approach attempts to use software to help us use AI in the right—and often less aggressive—ways. Software, for example, can monitor whether we are using AI as a cognititive crutch or a Band-Aid. Skillbench, for example, is an early-stage startup cofounded by my friend Matt Beane to help software developers use AI to build their skills rather than deskill them. In education, tools like Cherry Pot help students learn complex problem-solving domains like advanced math. As they work on problems they receive real-time, line-by-line feedback on their reasoning and methodology. Even the most commonly-used models can act as a coach to inform users how they can write better, for example. I hope we will see many new possibilities along these same lines.

There are a variety (more than I can easily name) of AI governance-oriented software offerings that provide usage controls and guidelines. Some are primarily oriented to preserving data privacy and cybersecurity, while others focus on limiting AI costs. While these are generally a good idea, it’s important for organizations to strategize about what governance objectives are most important, and to remember that only some of these tools will slow down AI use and hence AI development.

A final type of software that’s still emerging focuses on one of the AI industry’s primary rationales for unbridled acceleration: if we don’t do it, China will. It measures compliance with specific rules—as yet largely undeveloped—that would govern international competition in AI model capabilities. If, for example, the US and Chinese governments agreed on rules about what models from each country could and couldn’t do, this software—from startups like Lucid Computing and Amodo Design—would assess compliance with the rules. This category of software is described in an unlikely location—an article in Time magazine.

What’s Not Allowed

To be clear, I am absolutely opposed to approaches to AI slowdown that are generally illegal and to me immoral. That includes threatening or harassing AI company leaders, which is already happening and will only reflect poorly on the AI slowdown movement. Threats to sabotage AI-related facilities like data centers are also increasing. Such activities are a great way to find your way into jail, but are the antithesis of calm, reasoned discussion about potential harms of AI and how to prevent them.