Benjamin Hilton

Research Analyst @ 80,000 Hours


Benjamin is a research analyst at 80,000 Hours. Before joining 80,000 Hours, he worked for the UK Government and did some economics and physics research.


There are important reasons to think that the change by the EA community is within the measurement error of these surveys, which makes this less noteworthy.

(Like say you put +/- 10 years and +/- 10% on all these answers - note there are loads of reasons why you wouldn't actually assess the uncertainty like this, (e.g. probabilities can't go below 0 or above 1), but just to get a feel for the uncertainty this helps. Well, then you get something like:

  •  10%-30% chance of TAI by 2026-2046
  • 40%-60% by 2050-2070
  • and 75%-95% by 2100

Then many many EA timelines and shifts in EA timelines fall within those errors.)

Reasons why these surveys have huge error

1. Low response rates.

The response rates were really quite low.

2. Low response rates + selection biases + not knowing the direction of those biases

The surveys plausibly had a bunch of selection biases in various directions.

This means you need a higher sample to converge on the population means, so the surveys probably aren't representative. But we're much less certain in which direction they're biased.

Quoting me:

For example, you might think researchers who go to the top AI conferences are more likely to be optimistic about AI, because they have been selected to think that AI research is doing good. Alternatively, you might think that researchers who are already concerned about AI are more likely to respond to a survey asking about these concerns

3. Other problems, like inconsistent answers in the survey itself

AI impacts wrote some interesting caveats here, including:

  • Asking people about specific jobs massively changes HLMI forecasts. When we asked some people when AI would be able to do several specific human occupations, and then all human occupations (presumably a subset of all tasks), they gave very much later timelines than when we just asked about HLMI straight out. For people asked to give probabilities for certain years, the difference was a factor of a thousand twenty years out! (10% vs. 0.01%) For people asked to give years for certain probabilities, the normal way of asking put 50% chance 40 years out, while the ‘occupations framing’ put it 90 years out. (These are all based on straightforward medians, not the complicated stuff in the paper.)
  • People consistently give later forecasts if you ask them for the probability in N years instead of the year that the probability is M. We saw this in the straightforward HLMI question, and most of the tasks and occupations, and also in most of these things when we tested them on mturk people earlier. For HLMI for instance, if you ask when there will be a 50% chance of HLMI you get a median answer of 40 years, yet if you ask what the probability of HLMI is in 40 years, you get a median answer of 30%.

The 80k podcast on the 2016 survey goes into this too.

Thanks for this thoughtful post! I think I stand by my 1 in 10,000 estimate despite this.

A few short reasons: 

  • Broad things: First, these scenarios and scenarios like them are highly conjunctive (many rare things need to happen), which makes any one scenario unlikely (although of course there may be many such scenarios). Second, I think these and similar scenarios are reason to think there may be a large catastrophe, but large and existential are a long way apart. (I discuss this a bit here but don't come to a strong overall conclusion. More work on this would be great.)
  • On inducing nuclear war:  My estimate of the direct risk of nuclear war is 1 in 10,000, and the indirect risk is 1 in 1,000. It seems like the chances that climate change  causes a nuclear war, weighted by the extent to which the war was more likely by virtue of climate change and not e.g. geopolitical tensions unrelated to climate change is, while subjective and difficult to judge, probably much less than 10%. If it's say 1%, this gives less than 1 in 100,000 indirect x-risk from climate change. This seems a bit small, but consistent with my 1 in 10,000 estimate. Note this includes inducing nuclear war from ways other than crop failure.
  • On runaway warming: My understanding is that the main limit here is how many fossil fuels it's possible to recover from the ground - see more here. Even taking into account uncertainty and huge model error, it seems highly unlikely that we'll end up with runaway warming that itself leads to extinction. I'd also add that lots of the reduction in risk occurs because climate change is a gradual catastrophe (unlike a pandemic or nuclear war), which means that, for example, we may find other emissionless technology (e.g. nuclear fusion) or get over our fear of nuclear fission, etc., reducing the risk of resource depletion. Relatedly, unless there is extremely fast runaway warming over only a few years, the gradual nature of climate change increases the chances of successful adaptation to a warmer environment. (Again, I mean adaptation to prevent an existential catastrophe - a large catastrophe that isn't quite existential seems far far more likely.)
  • On coastal cities: I'd guess the existential risk from war breaking out between great powers is also around 1 in 10,000 (within an order of magnitude or so), although I've thought about this less. So again, while cyanobacteria blooms sounds like a not-impossible way in which climate change could lead to war (personally I'd be more worried about flooding and migration crises in South Asia), I think this is all consistent with my 1 in 10,000 estimate.

If it helps at all, my subjective estimate of the risk from AI is probably around 1%, and approximately none of that comes from worrying about killer nanobots. I wrote about what an AI-caused existential catastrophe might actually look like here.

Hi! Wanted to follow up as the author of the 80k software engineering career review, as I don't think this gives an accurate impression. A few things to say:

  • I try to have unusually high standards for explaining why I believe the things I write, so I really appreciate people pushing on issues like this.
  • At the time, when you responded to <the Anthropic person>, you said "I think <the Anthropic person> is probably right" (although you added "I don't think it's a good idea to take this sort of claim on trust for important career prioritisation research"). 
  • When I leave claims like this unsourced, it’s usually because I (and my editors) think they’re fairly weak claims, and/or they lack a clear source to reference. That is, the claim is effectively is a piece of research based on general knowledge (e.g. I wouldn't source the claim "Biden is the President of the USA”) and/or interviews with a range of experts, and the claim is weak or unimportant enough not to investigate further. (FWIW I think it’s likely I should have prioritised writing a longer footnote on why I believe this claim.)

    The closest data is the three surveys of NeurIPS researchers, but these are imperfect. They ask how long it will take until there is "human-level machine intelligence". The median expert asked thought there was an around 1 in 4 chance of this by 2036. Of course, it's not clear that HLMI and transformative AI are the same thing, or that thinking HLMI being developed soon necessarily means that HLMI will be made by scaling and adapting existing ML methods. In addition, no survey data pre-dates 2016, so it's hard to say that these views have changed based solely on survey data. (I've written more about these surveys and their limitations here, with lots of detail in footnotes; and I discuss the timelines parts of those surveys in the second paragraph here.)

    As a result, when I made this claim I was relying on three things. First, that there are likely correlations that make the survey data relevant (i.e., that many people answering the survey think that HLMI will be relatively similar to or cause transformative AI, and that many people answering the survey think that if HLMI is developed soon that suggests it will be ML-based). Second, that people did not think that ML could produce HLMI in the past (e.g. because other approaches like symbolic AI were still being worked on, because texts like Superintelligence do not focus on ML and this was not widely remarked upon at the time despite that book’s popularity, etc.). Third, that people in the AI and ML fields who I spoke to had a reasonable idea of what other experts used to think and how that has changed (note I spoke to many more people than the one person who responded to you in the comments on my piece)!

    It's true that there may be selection bias on this third point. I'm definitely concerned about selection bias for shorter timelines in general in the community, and plan to publish something about this at some point. But in general I think that the best way, as an outsider, to understand what prevailing opinions are in a field, is to talk to people in that field – rather than relying on your own ability to figure out trends across many papers, many of which are difficult to evaluate, many of which may not replicate. I also think that asking about what others in the field think, rather than what the people you're talking to think, is a decent (if imperfect) way of dealing with that bias.

    Overall, I thought the claim I made was weak enough (e.g. "many experts" not "most experts" or "all experts") that I didn't feel the need to evaluate this further.
  • It's likely, given you’ve raised this, that I should have put this all in a footnote. The only reason I didn't is that I try to prioritise, and I thought this claim was weak enough to not need much substantiation. I may go back and change that now (depending on how I prioritise this against other work).

This looks really cool, thanks Tom!

I haven't read the report in full (just the short summary) - but I have some initial scepticism, and I'd love to answers to some of the following questions, so I can figure out how much evidence this report is on takeoff speeds. I've put the questions roughly in order of subjective importance to my ability to update:

  • Did you consider Baumol effects, the possibility of technological deflation, and the possibility of technological unemployment, how they affect the profit incentive as tasks are increasingly automated? [My guess is that this effect of all of these is to slow takeoff down, so I'd guess a report that uses simpler models will be noticeably overestimating takeoff speeds.]
  • How much does this rely on the accuracy of semi-endogenous growth models? Does this model rely on exponential population growth? [I'm asking because as far as I can tell, work relying on semi-endogenous growth models should be pretty weak evidence. First, the "semi" in semi-endogenous growth usually refers to exogenous exponential population growth, which seems unlikely to be a valid assumption. Second, endogenous growth theory has very limited empirical evidence in favour of it (e.g. 1, 2) and I have the impression that this is true for semi-endogenous growth models too. This wouldn't necessarily be a problem in other fields, but in general I think that economic models with little empirical evidence behind them provide only very weak evidence overall.]
  • In section 8, the only uncertainty pointing in favour of fast takeoff is "there might be a discontinuous jump in AI capabilities". Does this mean that, if you don't think a discontinuous jump in AI capabilities is likely, you should expect slower take-off than your model suggests? How substantial is this effect?
  • How did you model the AI production function? Relatedly, how did you model constraints like  energy costs, data costs,  semiconductor costs,  silicon costs etc.? [My thoughts: looks like you roughly used a task-based CES model, which seems like a decent choice to me, knowing not much about this! But I'd be curious about the extent to which using this changed your results from Cobb-Douglas.]
  • I'm vaguely worried that the report proves too much, in that I'd guess that the basic automation of the industrial revolution also automated maybe 70%+ of tasks by pre-industrial revolution GDP. (Of course, generally automation itself wasn't automated - so I'd be curious on your thoughts about the extent to which this criticism applies at least to the human investment parts of the report.)

That's all the thoughts that jumped into my head when I read the summary and skimmed the report - sorry if they're all super obvious if I'd read it more thoroughly! Again, super excited to see models with this level of detail, thanks so much!

I agree with (a). I disagree that (b) is true! And as a result I disagree that existing CEAs give you an accurate signpost.

Why is (b) untrue? Well, we do have some information about the future, so it seems extremely unlikely that you won't be able to have any indication as to the sign of your actions, if you do (a) reasonably well.

Again, I don't purely mean this from an extreme longtermist perspective (although I would certainly be interested in longtermist analyses given my personal ethics). For example, simply thinking about population changes in the above report  would be one way to move in this direction. Other possibilities include thinking about the effects of GHW interventions on long-term trajectories, like growth in developing countries (and that these effects may dominate short-term effects like DALYs averted for the very best interventions). I haven't thought much about what other things you'd want to measure to make these estimates, but I would love to see someone try, and it seems pretty crucial if you're going to be doing accurate CEAs.

Sure, happy to chat about this!

Roughly I think that you are currently not really calculating cost-effectiveness. That is, whether you're giving out malaria nets or preventing nuclear war, almost all of the effects of your actions will be affecting people in the future.

To clarify, by "future" I don't necessarily mean "long run future". Where you put that bar is a fascinating question. But focusing on current lives lost seems to approximately ignore most of the (positive or negative) value, so I expect your estimates to not be capturing much about what matters.

(You've probably seen this talk by Greaves, but flagging it in case you haven't! Sam isn't a huge fan, I think in part because Greaves reinvents a bunch of stuff that non-philosophers have already thought a bunch about, but I think it's a good intro to the problem overall anyway.)

I'm curious about the ethical decisions you've made in this report. What's your justification for evaluating current lives lost? I'd be far more interested in cause-X research that considers a variety of worldviews, e.g. a number of different ways of evaluating the medium or long-term consequences of interventions.

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