Toby Ord Profile picture
Sep 18, 2020 11 tweets 3 min read Read on X
What is moral uncertainty?

Philosophers seek objective principles or theories that tell us how we should act. They come up with candidates and debate which ones (if any) are best. This is thought to help a conscientious person act rightly. But it neglects moral uncertainty.
1/11
People tend to consider the principles and theories and choose the one they find best overall, acting as if we they certain of it. This would be fine if the process of moral philosophy were over and we knew the right answers, but we aren’t there yet…
2/11
Instead, we each face moral uncertainty: we aren't 100% sure of any one answer. We might have our favourites, but we are aware that all theories give counterintuitive advice in some cases, and some very smart people disagree with our choices.
3/11
Simply acting as if you were 100% certain of your leading theory is an approach to moral uncertainty that has become known as My Favourite Theory (MFT). Once we have explicitly formulated it, we can easily see how it can go wrong.
4/11
For example, what if you had very little credence in any individual theory? Let’s say one is twice as plausible as any other, but there are many contenders (e.g. 20% credence in one and 10% in eight others).
5/11
Now suppose you face a choice and your favourite theory says A is right and B is wrong, while all the others say the reverse. MFT leads you to choose an act you believe is 80% likely to be wrong, (when an alternative is only 20% likely to be wrong).
6/11
My Favourite Theory is actually very similar to the First Past the Post voting system — both in terms of where it goes wrong, and that it is a system that seems obvious at first, but holds up badly once you consider alternatives.
7/11
There are many alternative approaches to acting under moral uncertainty. Unlike MFT many of these are sensitive to what the non-favourite theories say. e.g. do the act that has the greatest chance of being right.
8/11
After a lot of thought, I think the best approach is something like how we deal with regular uncertainty: maximising the expected ‘choiceworthiness’ (the degree to which that act is to be preferred to others according to a moral theory).
9/11
But there are many new challenges in applying this principle: such as when there is no obvious way to compare choiceworthiness between theories. So the full account has to explain how to best deal with such challenges.
10/11
My new book with @willmacaskill and Krister Bykvist, Moral Uncertainty, develops such an account and explores its practical upshots.
moraluncertainty.com
11/11

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More from @tobyordoxford

Feb 4
Some great new analysis by @gushamilton shows that AI agents *don't* obey a constant hazard rate / half-life. Instead they all have a declining hazard rate as the task goes on.
🧵
@gushamilton This means that their success rates on tasks beyond their 50%-horizon are better than the simple model suggests, but those for tasks shorter than the 50% horizon are worse than it suggests.
@gushamilton I had suggested a constant hazard rate was a good starting assumption for how their success rate at tasks decays with longer durations. It is the simplest model and fits the data OK.
Read 17 tweets
Dec 22, 2025
Are the *costs* of AI agents also rising exponentially?

We all know the graph from METR showing exponential growth in the length of tasks AI can perform. But the costs to perform these tasks are growing quickly too.
Indeed, it looks like they are growing even faster:
🧵 Image
In my view, the key question is:
How is the ‘hourly’ cost of AI agents changing over time?
If this hourly cost is increasing, then these cutting-edge AI systems would be getting less cost-competitive with humans over time.
If so, the METR trend could be misleading. Part of the progress would be from more lavish expenditure on compute so it would be diverging from what is economical.
It would be becoming more like the Formula 1 of AI performance — showing what is possible, but not what is practical.
Read 14 tweets
Oct 20, 2025
New post on RL scaling:
Careful analysis of OpenAI’s public benchmarks reveals RL scales far worse than inference: to match each 10x scale-up of inference compute, you need 100x the RL-training compute. The only reason it has been cost-effective is starting from a tiny base.
🧵 Image
But now RL has grown to nearly the size of pretraining and scale-ups beyond this reveal its inefficiency. I estimate it would take a 1,000,000x scale-up from its current level to add the equivalent to a 1,000x scale-up of inference or a 100x scale-up in pretraining.
This is a big deal. Pretraining scaling has already stalled and RL-scaling was the new hope for scaling up training compute. By going beyond imitation learning it also offered the best hope for blasting past the human-range of abilities — but it just scales too poorly.
Read 5 tweets
Oct 3, 2025
Evidence Recent AI Gains are Mostly from Inference-Scaling
🧵
Here's a thread about my latest post on AI scaling...
1/14 Image
Scaling up AI using next-token prediction was the most important trend in modern AI. It stalled out over the last couple of years and has been replaced by RL scaling.
This has two parts:
1. Scaling RL training
2. Scaling inference compute at deployment
2/
Many people focus on (1). This is the bull case for RL scaling — it started off small compared to internet-scale pre-training, so can be scaled 10x or 100x before doubling overall training compute.
3/
Read 14 tweets
Sep 25, 2025
Evaluating the Infinite
🧵
My latest paper tries to solve a longstanding problem afflicting fields such as decision theory, economics, and ethics — the problem of infinities.
Let me explain a bit about what causes the problem and how my solution avoids it.
1/20
Decision theory, economics and ethics all involve comparing different options. Sometimes the relevant sums or integrals that we'd hope would provide the value of an option instead diverge to +∞, making comparison between such options impossible.
2/
This problem arises because the standard approach to evaluating infinite sums and integrals uses a very coarse-grained system of infinite numbers, where there is only one positive infinite number (+∞). To assign values to such options, we need a more fine-grained system.
3/
Read 20 tweets
Sep 19, 2025
The Extreme Inefficiency of RL for Frontier Models
🧵
The switch from training frontier models by next-token-prediction to reinforcement learning (RL) requires 1,000s to 1,000,000s of times as much compute per bit of information the model gets to learn from.
1/11
Next-token prediction (aka pre-training) gives the model access to one token of ground-truth information after each token the model produces.
RL requires the model to produce an entire chain of thought (often >10,000 tokens) before finding out a single bit of information.
2/
So the shift from scaling up compute used for pre-training to scaling up compute used for RL comes with a major reduction in the information-efficiency of the training method.
3/
Read 11 tweets

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