Real-world action is rarely a matter of a single moral decision. It often involves a combination of decisions, each with its own stakes and potential for moral failure.

In an AI-mediated giving experiment I am working on, zooidfund, delegating altruistic action to an AI agent is not simply a linear continuum between receiving advice at one end and letting the agent execute a donation end to end at the other. Shortlisting potential recipients, deciding which cases merit further assessment, evaluating evidence, calibrating donation amounts, and formulating messages to recipients are all distinct decisions with moral components. Relying on AI may improve the outcome of each. Delegating some of these decisions to AI may therefore be a moral choice in its own right, if we believe we should use the best available tools to increase the impact of our giving.

I therefore think there is a strong case for developing a practice of real-world moral delegation now, through well-defined and bounded decisions within pro-social action. This need not wait until alignment is solved. Practical experience of delegating parts of complex pro-social action to AI may itself generate useful evidence about where delegation works, where it fails and why, and ultimately contribute to alignment.

I am not seeing it mentioned in the post, and I assume you do this, but one important addition is a simple decision log. Things need to move fast and decisions need to be taken quickly, but institutional memory can disappear surprisingly fast around what was decided, by whom and when. That can make both handover and any later postmortem much harder.

Great post. There may be relevant lessons here to learn from the aid localisation push. One specific and well-established failure mode is capacity building that improves an organisation’s ability to satisfy donor requirements, rather than its ability to deliver. In the worst case, organisations become more legible and fundable at the expense of effectiveness.

We are dealing with severely misaligned incentives. And when incentives point the wrong way, awareness does not fix much.

Anyone working at a frontier lab is presented with an extraordinary asymmetry: enormous and immediate personal rewards from pushing capabilities forward, against a probabilistic risk borne by everyone else. People are extremely good at persuading themselves that what benefits them is also the right thing to do, especially when the rewards are this high.

The US government, even if it understands the risks, is locked into strategic competition with China that makes slowing down impossible. A serious warning confirms the danger, but can make frontier AI seem even more important, increasing the pressure to get there first.

And internationally, the institutions that might once have helped manage this kind of competition are all being torched just as they are most needed. Communication, verification, restraint and collective action that helped prevent WWIII are all being made harder, leaving fewer ways to slow or manage the race.

So we are heading to a situation where the labs, governments and even the public understand the danger quite well, but the incentives of those who control development still push them toward acceleration, and the mechanisms for managing this are gone.

Should the reduced search and assessment costs on the side of employers also count towards impact? The selection by talent programs creates a signal about candidate quality that could save potential employers effort and resources they would otherwise have to invest in identifying and assessing the right candidates.
In that sense, some of what looks like a selection effect from the participant side may still actually be a benefit created by the program.

There is probably an architecture shape that could help solve this: interactions with AI advisor remain private but the AI advisor also helps the applicant create and maintain a live profile, that you can then base your prioritization decisions on, while applicant still controls what goes in.
This can then be a continuous screening process rather than a discreet application-decision cycle.

Many of the issues you mention may come from organizations not actually being ready to hire for senior roles.

A genuinely senior position requires giving someone meaningful decision-making authority, not just hiring a more experienced person to execute the founders' decisions. Younger founders in small organizations can struggle with this. Delegation can be quite uncomfortable for an inexperienced manager, especially when the person being hired may disagree with them or change how things are done. 

That can manifest as never-ending hiring rounds, repeated “culture fit” rejections, or eventually hiring someone who is not really qualified for a senior role but is unlikely to challenge the founders or demand much autonomy. 

You are right, measuring AI maintenance burden without accounting for maintenance that had to be done regardless of AI deployment would not get you anywhere.

Interesting you are have Claude identifying opportunities to simplify/reuse/streamline, this could actually mean the per system maintenance burden will go down the more systems you connect. Pretty much the opposite of what I was thinking. Thank you.

Is the proposal to evaluate country prospects, or does it include also weighing these against further expansion in your existing countries?
With my admittedly very limited understanding of your work, it looks like the factors determining the success for your interventions can be quite local. India alone is huge with a significant variety of local conditions and your existing intervention is addressing a small subset of that diversity.

If this succeeds are you considering making the AI advisor the "allocation layer" for your human advisors? Human advisor attention is scarce so you programme has to be selective. What you currently rely on for this selection is probably mostly what's in the application. If you can augment this by the knowledge accumulated by the AI advisor, it could make the quality of allocating your human advisors' attention a lot better.

This is provided human advisors still retain some advantage over ai going forward.