Empiricists think the problem is hard, AGI will show up soon, and if we want to have any hope of solving it, then we need to iterate and take some necessary risk by making progress in capabilities while we go.

This may be so for the OpenAI alignment team's empirical researchers, but other empirical researchers note we can work on several topics to reduce risk without substantially advancing general capabilities. (As far as I can tell, they are not working on any of the following topics, rather focusing on an avenue to scalable oversight which, as instantiated, mostly serves to make models generally better at programming.)

Here are four example areas with minimal general capabilities externalities [AF · GW] (descriptions taken from Open Problems in AI X-Risk [? · GW]):

Trojans - AI systems can contain “trojan” hazards. Trojaned models behave typically in most situations, but when specific secret situations are met, they reliably misbehave. For example, an AI agent could behave normally, but when given a special secret instruction, it could execute a coherent and destructive sequence of actions. In short, this area is about identifying hidden functionality embedded in models that could precipitate a treacherous turn.  Work on detecting trojans does not improve general language model or image classifier accuracy, so the general capabilities externalities are moot.

Anomaly detection - This area is about detecting potential novel hazards such as unknown unknowns, unexpected rare events, or emergent phenomena. (This can be used for tripwires, detecting proxy gaming, detecting trojans, malicious actors, possibly for detecting emergent goals.) In anomaly detection, general capabilities externalities are easy to avoid.

Power Aversion - This area is about incentivizing models to avoid gaining more power than is necessary and analyzing how power trades off with reward. This area is deliberately about measuring and making sure highly instrumentally useful/general capabilities are controlled.

Honesty - Honest AI involves creating models that only output what they hold to be true. It also involves determining what models hold to be true, perhaps by analyzing their internal representations. Honesty is a narrower concept than truthfulness and is deliberately chosen to avoid capabilities externalities, since truthful AI is usually a combination of vanilla accuracy, calibration, and honesty goals. Optimizing vanilla accuracy is optimizing general capabilities. When working towards honesty rather than truthfulness, it is much easier to avoid capabilities externalities.

More general learning resources are at this course, and more discussion of safety vs capabilities [AF · GW] is here (summarized in this video).