Why I’m Optimistic About Near-Term AI Risk

I’m not worried about AI posing an existential risk in the next 10-20 years. Recent developments in AI capabilities actually make me feel more optimistic about this. The fact that relatively simple models can perform a wide array of tasks suggests that we can build satisfactory AI without the need to use sophisticated, potentially dangerous agents in the near-term.

My expectation for how AI will develop over the next decade is that companies will continue to focus on transformer-based foundation models. The general capability of these models will increase for a while simply by using more data, improving training procedures, and leveraging specialized hardware. Eventually, companies will start hitting bottlenecks in the amount of data required for optimal training at a given capability level. But before that, deployment of these systems will favor smaller, faster, and more auditable models leading companies to focus on distilled models specializing in specific tasks.

These specialized models will be oriented towards augmenting human productivity, producing entertainment, or automating specific tasks. The slow pace at which industries change their practices and utilize the benefits of a new technology will moderate the adoption of AI. As adoption increases, these AI services will gain autonomy, producing more value at lower cost. Continued specialization will result in mostly autonomous AI’s derived from generally capable foundation models that are distilled down for variety of tasks.

I’m not claiming that these Tool AI’s won’t eventually be dangerous, but I can’t see this path leading to high existential risk in the next decade or so.

I think most people in the AI safety field would agree with me on this, so why write it up?

I want to make this point explicit and foster a discussion about near-term AI safety. If AI will become dangerous soon, the field needs to act very quickly. Researchers would have to consider eschewing movement building, trading goodwill for influence, and gambling on near-term approaches. People who would take more than a decade to have an impact would have less reason to join in the first place while investments in infrastructure in the field would become less valuable.

It’s important for those concerned to be on the same page about near-term risks in order to avoid the unilaterialist’s curse. Recent, pessimistic takes about AI risk make it seem superficially as if the consensus has shifted, but I don’t think this is representative of the field as a whole. I remain optimistic that innovations in foundation models will produce a lot of value without a large increase in risk, providing more time to build.