[Question] How do we prepare for final crunch time?

[Crossposted from Musings and Rough Drafts.]

Epistemic status: Brainstorming and first draft thoughts.

Inspired by something that Ruby Bloom wrote and the Paul Christiano episode of the 80,000 hours podcast.]

One claim I sometimes hear about AI alignment [paraphrase]:

It is really hard to know what sorts of AI alignment work are good this far out from transformative AI. As we get closer, we’ll have a clearer sense of what AGI /​ Transformative AI is likely to actually look like, and we’ll have much better traction on what kind of alignment work to do. In fact, MOST of the work of AI alignment is done in the final few years (or months) before AGI, when we’ve solved most of the hard capabilities problems already so we know what AGI will look like and we can work directly, with good feedback loops, on the sorts of systems that we want to align.

Usually, this is said to argue that to value of the alignment research being done today is primarily that of enabling, future, more critical, alignment work. But “progress in the field” is only one dimension to consider in boosting and unblocking the work of alignment researchers in this last stretch.

In this post I want to take the above posit seriously, and consider the implications. If most of the alignment work that will be done is going to be done in the final few years before the deadline, our job in 2021 is mostly to do everything that we can to enable the people working on the problem in the crucial period (which might be us, or our successors, or both) so that they are as well equipped as we can possibly make them.

What are all the ways that we can think of that we can prepare now, for our eventual final exam? What should we be investing in, to improve our efficacy in those final, crucial, years?

The following are some ideas.

[In this post, I’m going to refer to this last stretch of a few months to a few years, “final crunch time”, as distinct from just “crunch time”, ie this century.]


For this to matter, our alignment researchers need to be at the cutting edge of AI capabilities, and they need to be positioned such that their work can actually be incorporated into AI systems as they are deployed.

A different kind of work

Most current AI alignment work is pretty abstract and theoretical, for two reasons.

The first reason is a philosophical /​ methodological claim: There’s a fundamental “nearest unblocked strategy” /​ overfitting problem. Patches that correct clear and obvious alignment failures are unlikely to generalize fully, they’ll only constrain unaligned optimization to channels that you can’t recognize. For this reason, some claim, we need to have an extremely robust, theoretical understanding of intelligence and alignment, ideally at the level of proofs.

The second reason is a practical consideration: we just don’t have powerful AI systems to work with, so there isn’t much that can be done in the way of tinkering and getting feedback.

That second objection becomes less relevant in final crunch time: in this scenario, we’ll have powerful systems 1) that will be built along the same lines as the systems that it is crucial to align and 2) that will have enough intellectual capability to pose at least semi-realistic “creative” alignment failures (ie, current systems are so dumb, and live in such constrained environments, that it isn’t clear how much we can learn about aligning literal superintelligences from them.)

And even if the first objection ultimately holds, theoretical understanding often (usually?) follows from practical engineering proficiency. It seems like it might be a fruitful path to tinker with semi-powerful systems: trying out different alignment approaches empirically, and tinkering to discover new approaches, and then backing up to do robust theory-building given much richer data about what seems to work.

I could imagine sophisticated setups that enable this kind of tinkering and theory building. For instance, I imagine a setup that includes:

  • A “sandbox” that afford easy implementation of many different AI architectures and custom combinations of architectures, with a wide variety easy-to-create, easy-to-adjust, training schemes, and a full suite of interpretability tools. We could quickly try out different safety schemes, in different distributions, and observe what kinds of cognition and behavior result.

  • A meta AI that observes the sandbox, and all of the experiments therein, to learn general principles of alignment. We could use interpretability tools to use this AI as a “microscope” on the AI alignment problem itself, abstracting out patterns and dynamics that we couldn’t easily have teased out with only our own brains. This meta system might also play some role in designing the experiments to run in the sandbox, to allow it to get the best data to test it’s hypotheses.

  • A theorem prover that would formalize the properties and implications of those general alignment principles, to give us crisply specified alignment criteria by which we can evaluate AI designs.

Obviously, working with a full system like this is quite different than abstract, purely theoretical work on decision theory or logical uncertainty. It is closer to the sort of experiments that the OpenAI and Deep Mind safety teams have published, but even that is a pretty far cry from the kind of rapid-feedback tinkering that I’m pointing at here.

Given that the kind of work that leads to research progress might be very different in final crunch time than it is now, it seems worth trying to forecast what shape that work will take and trying to see if there are ways to practice doing that kind of work before final crunch time.


Obviously, when we get to final crunch time, we don’t want to have to spend any time studying fields that we could have studied in the lead-up years. We want to have already learned all the information and ways of thinking that we’ll want to know, then. It seems worth considering what fields we’ll wish we had known when time comes.

The obvious contenders:

  • Machine Learning

  • Machine Learning interpretability

  • All the Math of Intelligence that humanity has yet amassed [Probability theory Causality, etc.]

Some less obvious possibilities:

  • Neuroscience?

  • Geopolitics, if it turns out that which technical approach is ideal hinges on important facts about the balance of power?

  • Computer security?

  • Mechanism design in general?

Do other subjects come to mind?

Research methodology /​ Scientific “rationality”

We want the research teams tackling this problem in final crunch time to have the best scientific methodology and the best cognitive tools /​ habits for making research progress, that we can manage to provide them.

This maybe includes skills or methods in the domains of:

  • Ways to notice as early as possible if you’re following an ultimately-fruitless research path

  • Noticing /​ Resolving /​Avoiding blindspots

  • Effective research teams

  • Original seeing /​ overcoming theory blindness /​ hypothesis generation

  • ???


One obvious thing is to spend time now, investing in habits and strategies for effective productivity. It seems senseless to waste precious hours in the final crunch time due to procrastination or poor sleep. It is well worth in to solve those problems now. But aside from the general suggestion to get your shit in order and develop good habits, I can think of two more specific things that seem good to do.

Practice no-cost-too-large productive periods

There maybe trades that could make people more productive on the margin, but are too expensive in regular life. For instance, I think that I might conceivably benefit from having a dedicated person who’s job is to always be near me, so that I can duck with them (or have them “hold space” for me) with 0 friction. I’ve experimented a little bit with similar ideas (like having a list of people on call to duck with), but it doesn’t seem worth it for me to pay a whole extra person-salary to have the person be on call and in the same building, instead of on-call via zoom.

But it is worth it at final crunch time.

It might be worth it to spend some period of time, maybe a week, maybe a month, every year, optimizing unrestrainedly for research productivity, with no heed to cost at all, so that we can practice how to do that. This is possibly a good thing to do anyway, because it might uncover trades that, on reflection, are worth importing into my regular life.

Optimize rest

One particular subset of personal productivity, that jumps out at me: each person should figure out their actual optimal cadence of rest.

There’s a failure mode that ambitious people commonly fall into, which is working past the point when marginal hours of work are negative. When the whole cosmic endowment is on the line, there will be a natural temptation to push yourself to work as hard as you can, and forgo rest. Obviously, this is a mistake. Rest isn’t just a luxury: it is one of the inputs to productive work.

There is a second level of this error in which one, grudgingly, takes the minimal amount of rest time, and gets back to work. But the amount of rest time required to stay functional is not the optimal amount of rest, the amount the maximizes productive output. Eliezer mused years ago, that he felt kind of guilty about it, but maybe he should actually take two days off between research days, because the quality of his research seemed better on days when he happened to have had two rest days preceding.

In final crunch time, we want everyone to be resting the optimal amount that actually maximizes area under the curve, not the one that maximizes work-hours. We should do binary search now, to figure out what the optimum is.

Also, obviously, we should explore to discover highly effective methods of rest, instead of doing whatever random things seem good (unless, as it turns out, “whatever random thing seems good” is actually the best way to rest).

Picking up new tools

One thing that will be happening in this time, is there will be a flurry of new AI tools that can radically transform thinking and research, perhaps increasingly radical tools coming at a rate of once a month or faster.

Being able to take advantage of those tools and start using them for research immediately, with minimal learning curve, seems extremely high leverage.

If there are things that we can do that increase the ease of picking up new tools and using them to their full potential (instead of, as is common, using only the features afforded by your old tools and only very gradually

Some thoughts (probably bad):

  • Could we set up our workflows, somehow, such that it is easy to integrate new tools into them? Like if you already have a flexible, expressive research interface (something like Roam? or maybe, if the technology is more advanced by then, something like neurolink?), and you’re used to regular changes in capability to the backed of the interface?

  • Can we just practice? Can we have a competitive game of introducing new tools, and trying to orient to them and figure out how to exploit them creatively as possible?

  • Probably it should be some people’s full time job to translate cutting edge developments in AI into useful tools and practical workflows, and then to teach those workflows to the researchers?

  • Can we design a meta-tool that helps us figure out how to exploit new tools? Is it possible to train an AI assistant specifically for helping us get the most out of our new AI tools?

  • Can we map out the sort of constraints on human thinking and/​or the the sorts of tools that will be possible, in advance, so that we can practice with much weaker versions of those tools, and get a sense of how we would use them, so that we’re ready when they arrive?

  • Can we try out new tools on psychedelics, to boost neuroplasticity? Is there some other way to temporarily weaken our neural priors? Maybe some kind of training in original seeing?

Staying grounded and stable in spite of the stakes

Obviously, being one of the few hundred people on whom the whole future of the cosmos rests, while the singularity is happening around you, and you are confronted with the stark reality of how doomed we are, is scary and disorienting and destabilizing.

I imagine that that induces all kinds of psychological pressures, that might find release in any of a number of concerning outlets: by deluding one’s self about the situation or slipping sideways into a more convenient world, by becoming manic and frenetic, by sinking into immovable depression.

We need our people to have the virtue of being able to look the problem in the eye, with all of its terror and disorientation, and stay stable enough to make tough calls, and make them sanely.

We’re called to cultivate a virtue (or maybe a set of virtues) of which I don’t know the true name, but which involves courage and groundedness, and determination-without-denial.

I don’t know what is entailed in cultivating that virtue. Perhaps meditation? Maybe testing one’s self at literal risk to one’s life? I would guess that people in other times and places, who needed to face risk to their own lives and that of their families, and take action anyway, did have this virtue, or some part of it, and it might be fruitful to investigate those cultures and how that virtue was cultivated.

Any more ideas?