[Relevant context/COI: I'm CEO at the Forecasting Research Institute (FRI), an organization which I co-founded with Phil Tetlock and others. Much of the below is my personal perspective, though it is informed by my work. I don't speak for others on my team. I’m sharing an initial reply now, and our team at FRI will share a larger post in future that offers a more comprehensive reflection on these topics.]

Thanks for the post — I think it's important to critically question the value of funds going to forecasting, and this post offers a good opportunity for reflection and discussion.

In brief, I share many of your concerns about forecasting and related research, but I'm also more positive on both its impact so far and its future expected impact.

A summary of some key points:

1. Much of the impact of forecasting research on specific decision-makers is not public. For example, FRI has informed decisions on frontier AI companies' capability scaling policies, has advised senior US national security decision-makers, and has informed research at key US and UK government agencies. But, we are not able to share many details of this work publicly. However, there is also public evidence that forecasting research is widely cited and informs discourse and some decision-making (some examples below).
2. AI timelines, adoption, and risk forecasts play a huge role in both individual career decisions and the broader AI discourse. Forecasting research still seems like one of the best tools available for getting specific and accountable beliefs on these topics. For example, comparing 'AI safety' community forecasts to more ‘typical’ experts’ forecasts seems especially important for understanding how much to trust each group’s views. These comparisons will become increasingly relevant for government policymakers over time, especially if there is extremely rapid AI capabilities progress that leads to major societal impacts in the short-run.
3. When evaluating the impact of FRI-style forecasting research, I think the closest relevant comparison classes are more like broad public goods/measurement-oriented research (e.g., Our World in Data, Epoch) or think-tank research (e.g. GovAI, IAPS). By its nature, the impact of this kind of research tends to be more diffuse and difficult to measure. However, I'd be interested in more intensive comparative evaluation of this type of research and agree that funders should be responsive to evidence about relative impact in these fields.
4. Forecasting research still has a ton of flaws, and its impact has been far from the dream I've long had for it. There are still big challenges around identifying accurate forecasters on questions related to AI, integrating conditional policy forecasts with actual decision-makers’ needs, and combining deep, individual qualitative research with high-quality, group-generated quantitative forecasts. 
   1. My extremely simplified narrative is: Tetlock et al. established the modern judgmental forecasting field and created a proof of concept for better forecasts on important topics (“superforecasting”)---this work was largely academic; some forecasting platforms were created to build on that work and apply it to a range of important issues; targeted efforts to make forecasting more directly useful to decision-makers are relatively nascent (i.e., have largely begun in the last few years), and are accumulating impact over time, but still have room for improvement.
   2. FRI’s research, in particular, aims to close many of the gaps left by prediction markets and historical forecasting approaches: it is particularly focused on conditional policy forecasts, medium-to-long-run forecasts that do not get much detailed engagement on prediction markets/platforms, and systematically eliciting forecasts from experts who would not typically participate in forecasting platforms but whom decision-makers want to rely on (while also eliciting forecasts from generalists with strong forecasting track records).
5. However, some factors make the future potential impact of this work look more promising:
   1. AI-enhanced forecasting research is a huge factor that will unlock cheaper, faster, high-quality forecasts on any question of one's choosing. 
   2. The next few years of forecasting AI progress/adoption/impact seem critical, and like they'll deliver a lot of answers on whose forecasts we should trust. It seems good to be ready to support decision-makers during this time.
   3. Leaders in the AI space seem particularly interested in using forecasting in their decision-making; they tend to be both quantitative and open-minded. This creates more potential for forecasting to be useful. More minorly, prediction markets and forecasting are generally becoming more credible within governments. 

More detail on some select points below. This comment already got very long (!), so I’ll reserve more elaboration for a future, more comprehensive post.

Examples of impact

Forecasting research has informed some very important decisions. Unfortunately, many of the details of the relevant evidence here cannot be made public. However, there is evidence of substantial public citation of this research, and some public evidence of affecting particular decisions.

A few examples of relevant impact include:

• Forecasting has been particularly relevant for decision-making around capability scaling policies. The near-term magnitude of AI-biorisk, how growing AI capabilities may increase it, and what safeguards need to be in place to respond to it, are highly uncertain. Frontier AI companies, the EU AI Code of Practice, and other governments are trying to track and respond to AI impacts on biorisk, cybersecurity, AI R&D, and other domains. We’ve had substantial engagement with the relevant actors, including some focused partnerships, and believe our work in this area has affected important decisions, though we unfortunately cannot share many of the details publicly.
• Our work on ForecastBench, a benchmark of AI’s ability to do forecasting, showed that AI-produced forecasts could catch up to top human forecasters in roughly the next year if trends persist. This generated interest among senior decision-makers in U.S. national security. We cannot share details, but this is another example of important decision-makers paying attention to and using forecasts.
• We have completed commissioned research to directly inform grantmaking at Coefficient Giving, and also have indirectly affected grantmaking. For an example of the latter, our work on the Existential Risk Persuasion Tournament (XPT) partially inspired Coefficient Giving (formerly Open Philanthropy) to launch an RFP on improved AI benchmarks. The XPT forecasts predicted that most existing benchmarks would likely saturate in the next few years, and showed that progress on these benchmarks was not crux-y for disagreements about AI impact. We were told that this played a role in the launch and conception of the RFP, and the XPT is cited in the public write-up. 

Some examples of more diffuse impacts — e.g., impact on public understanding of AI and research for policymakers or philanthropists, include:

• FRI has given presentations to, and has ongoing connections and conversations with, important government agencies such as the Congressional Budget Office, US CAISI, the UK Department of Science, Innovation, and Technology, and others. We cannot share many details, but the potential to inform decisions at these organizations is highly important.
• Major reports for policymakers, like the International AI Safety Report, the AI Index, and relevant RAND reports, also prominently cite FRI research.
• FRI research is cited in places like the New York Times, The Economist, and Bloomberg to inform readers about the economic impacts of AI, AI-biorisk, general catastrophic and existential risk, AI-enhanced forecasting, and the future of AI more generally.
• Forecasts are widely cited in cause prioritization research and by experts in relevant domains: as a few examples, see citations from Ethan Mollick on AI progress, 80,000 Hours on biorisk, Dr. Richard Moulange on AI-biorisk, Tyler Cowen on the economic effects of AI, Will MacAskill on AI progress and risk, etc.

For context: FRI has been operating for a little over 3 years, and we're accumulating substantially more momentum in terms of connections to top decision-makers as time goes on.

(To be clear: I am mostly discussing FRI here since it’s what I’m most familiar with.)

AI timelines, impact, and adoption forecasts drive a huge amount of career decision-making, attention, etc. 

Forecasts about AI timelines and risk have had major effects on people’s career decisions and the broader AI discourse. AI 2027 underlies popular YouTube videos, 80,000 Hours advises people on career decisions based on timelines forecasts, Dario Amodei’s “country of geniuses in a datacenter by 2027” forecast informs a lot of Anthropic’s work and policy outreach, the AI Impacts survey on AI researchers’ forecasts of existential risk is highly cited, etc.

A major reason I got into this field is that many people are making very intense claims about the effect that AI will have on the world soon, and I want to bring as much rigor and reflection as possible to those claims. So far, it looks like most forecasters are substantially underestimating AI capabilities progress (with some exceptions, e.g. on uplift studies); the evidence on forecasts about AI adoption, societal impacts, and risk is less clear, but I expect we will have more evidence soon, particularly from the Longitudinal Expert AI Panel (LEAP), especially as some forecasters are predicting transformative change in the next few years.

As the expected impact and timing of AI progress is sharpened and clarified, talent and money can be allocated more efficiently.

Case study: Economic impacts of AI

In some cases, it looks to me like forecasting research is picking relatively low-hanging fruit.

The economic impact of AI is a prominent topic of public discussion right now, and it is likely that governments will spend many billions of dollars to address it in the coming years.

Currently, economists hold major sway in public policy about the economic impacts of AI. Perhaps you think top economists, as a group, are badly mistaken about the likely near-term impacts of AI, as some Epoch researchers and others believe. Perhaps you think they are likely to be fairly accurate, as Tyler Cowen, Séb Krier, or typical economists believe. It seems like a valuable common sense intervention to at least document what various groups believe, so that when we are making economic policy going forward we can rely on that evidence to determine who is trustworthy. I believe that studies like this one (and its follow-ups) will be the clearest evidence on the topic.

Relevant comparison class for forecasting research

When thinking about the impact and cost-effectiveness of forecasting, I think it’s more appropriate to compare this work to public goods-oriented research organizations (e.g., Our World in Data, Epoch, etc.) and policy-oriented think-tank research (e.g. GovAI, IAPS, CSET, etc.).

I’ve been disappointed by most impact evaluation of think-tanks and public goods-oriented research that I’ve seen. I believe this is partly because it is very difficult to quantify the impact of this type of work because it has diffuse benefits. But, I still think it’s possible to do better and I would like FRI to do better on this front going forward. 

That said, I still believe there are reasonable heuristics for why this research area could be highly cost-effective. There are many billions of dollars of philanthropic and government capital being spent on AI policy topics. If there is a meaningful indication that forecasting is changing people’s views on these questions (as I believe there is; see discussion above), it seems reasonable to me to spend a very small fraction of that capital on getting more epistemic clarity.

My critiques of forecasting research

Forecasting research, and FRI’s research in particular, still has major areas for improvement.

Examples of a few key issues:

• I've been underwhelmed by the accuracy of typical experts and superforecasters on questions about AI capabilities progress (as measured by benchmarks); they often underestimate AI progress (with exceptions). I think this underestimation is a useful fact to document, but it would be much more helpful if our research identified experts you should trust. We're in the process of identifying ‘Top AI forecasters' through LEAP and aim to share updates on this soon.
• I think forecasting research is at its best when combined with in-depth research reports that provide more narratives and key arguments underlying forecasts. For example, Luca Righetti’s work on estimating (certain kinds of) AI-biorisk provides a lot of valuable analysis that usefully complements our expert panel study on the topic. [Note: Luca is an FRI senior advisor and a co-author of our forecasting study.] For decision-makers to build sufficiently detailed models, and for forecasters to test their arguments, we’d ideally have detailed research like Luca’s on most major topics where we collect forecasts — ideally from a few experts who disagree with each other. Unfortunately, this research often doesn’t readily exist, but we are investigating ways to generate it.
• I have been somewhat surprised by how few experts in AI industry, AI policy, and other domains predict transformative impacts of AI similar to what are commonly discussed by AI lab leaders, people in the AI safety community, and others. This has made it harder to have a true horse-race between the ‘transformative AI’ school of thought that seems to drive a lot of discourse and decision-making vs. more gradual views of AI impacts. Though we have some transformative AI forecasters in our studies, in future work we aim to explicitly collect more forecasts from the ‘transformative AI’ school of thought in order to set up clearer comparisons between worldviews and to better anticipate what will happen if the ‘transformative AI’ school makes more accurate forecasts.

I will save other thoughts on how forecasting, and FRI’s research, could be made more useful to decision-makers for a future post.

But, to be clear: I have a lot of genuine uncertainty about whether forecasting research will be sufficiently impactful going forward. There are promising signs, and increasing momentum, but to more fully deliver on its promise, more improvements will be necessary.

Some notes on FRI-style forecasting research vs. other forecasting interventions

On the value of FRI-style forecasting research in particular:

• Prediction markets do not have good ways to collect causal policy forecasts, but in our experience, conditional policy forecasts (e.g., how much would various safeguards reduce AI-cyber risk) are often the most helpful forecasts for decision-makers. 
• Similarly, prediction markets do not create good incentives for longer run forecasts or low-probability forecasts, and incentivize against sharing the rationales behind forecasts. Directly paying and incentivizing relevant experts and forecasters to answer questions is often more useful.
• Typical forecasting platforms do not get forecasts from the kinds of experts that policymakers typically rely on, and aren't the kind of evidence that can easily be cited in government reports. (This may be unfortunate, but it is the current state of the world.)

Reasons for optimism about future impact

Finally, there are a few factors that have the potential to dramatically change the field going forward:

1. It looks like AI may soon make it >100x cheaper and faster to get high-quality forecasts on any topic of one’s choosing. Policy researchers will be able to ask the precise question they’re interested in, will be able to upload confidential documents to inform forecasts (something we’ve heard is especially important to decision-makers), and will be able to get detailed explanations for all forecasts. AI-produced forecasts will also be much easier to test for accuracy due to the volume of forecasts they can provide, and it will be easier to generate ‘crux’ questions since AI will not get bored of producing huge numbers of conditional forecasts (which are necessary for identifying cruxes). Building benchmarks and tooling to harness AI-produced forecasts will be a much larger part of our work going forward.
2. The next few years seem very unusual in human history: very thoughtful researchers are predicting “Superhuman Coders” by 2029, with attendant large impacts. There is a spectrum of views, but the scope for disagreement among reasonable people about what the world will look like in 2030 is huge. This is a particularly important time to make predictions testable, update on what we observe, and make better policy and personal decisions on the basis of this information.
3. People working in the AI space seem particularly interested in using forecasting, perhaps due to a mix of being quantitatively oriented and because they’re facing unusual degrees of uncertainty. This bodes well for forecasting being useful in the coming years. More minorly, it appears that there is a broader cultural change around forecasting-related topics. Prediction markets are increasingly being cited by government officials, and the public is paying more attention to them than ever before. Much of the impact for prediction markets specifically seems negative (e.g. via incentivizing gambling on low-value topics), but the broader cultural shift suggests there may be an opportunity for better uses of forecasting to enter public consciousness as well.

I don’t disagree with some of the fundamentals of this post. Before diving into that, I want to correct a factual error:

“the Swift Centre have received millions of dollars for doing research and studies on forecasting and teaching others about forecasting”

The Swift Centre for Applied Forecasting has not received millions in funding. The majority of our earnings have been through direct projects with organisations who want to use forecasting to inform their decisions.

On your wider argument. I think forecasting has probably received too much funding and the vast majority of that has misallocated on platforms and research. I believe some funding (hundreds of thousands) to maintain core platforms like Metaculus as a public good of information. Though, services like Polymarket can probably fill most of this need in the future (but many useful, informative markets would never reach the necessary volume to be reliable).

Where I think we disagree most is in the application of forecasting and some of the achievements. We’ve worked with frontier AI labs to inform their decisions, are currently advising a U.K. Minister’s team on a central piece of their policy, and are about to start a secondment where I will be advising one of the most influential decision making committees in the country to help improve their scenario analysis and forecasting.  Forecasting, and specifically, the science of decision making that it is built on, has the ability to structurally improve decisions in institutions. Significantly better than asking two or three of your smartest friends. That was just never funded, so instead we conclude forecasting is not useful.

I think this post significantly overstates its conclusion and is plausibly poorly calibrated on the relative value of forecasting.

My main "directional" issues with the post as it's currently written:

• I think it overstates the amount of funding devoted to forecasting on a "worldview" basis.
  • Most forecasting funding is (iiuc) not going to neartermist causes or particularly fungible with neartermist causes, so pointing to a bunch of neartermist causes to justify better funding options seems irrelevant.
  • From my perspective, it seems like:
    • Within Animal welfare fungible money, very little goes into forecasting e.g. less than $2M per year
      • Tbh - I would probably prefer that more money went into some kinds of forecasting on the margin. For example, I think that people are generally too bullish on clean meat, and Linch/Open Phil's work investigating the difficulty of clean meat has plausibly resulted in better allocation of millions of dollars because there are, in fact, good alternatives (like cage-free campaigns).
    • Within Longtermist/AI fungible money, maybe $10M/year goes into forecasting, which seems pretty reasonable to me but i think to get to 10M you need to be including projects that seems very promising to me for different reasons to mainstream forecasting infrastructure e.g. AI 2027, METR.
      • I think the strongest version of the argument would be attacking AI evals but I'm unsure about whether those are in-scope for this post - my impression is that evals are useful for forecasting capabilities are a pretty great bet relative to other funding opportunities within the AI space.
    • So the argument actually seems to be "longtermist funding is not as cost-effective as neartermist funding" which is not totally unreasonable, but clearly needs to engage with the long/neartermist worldview (e.g. moral size of the future) as opposed to just engaging with tangible short-term impact indicators.
• I'm less convinced than the OP that funders in particular are overrating forecasting - I just don't see much effort going into forecasting grantmaking compared to ~every other grantmaking area.
• My impression is that a lot of forecasting dollars are funded by organisations that are incentivised to use the money well (e.g. AI companies paying FRI to produce forecasts around safety and capability evaluations for safety planning). I see that others have weighed in on this already so not planning to elaborate on this more.

I agree with some of the post's vibes and think it's pointing at real cultural traits of rationalist communities. Though tbh, I think OP is too bearish on the usefulness of betting/making falsifiable predictions for people in EA-spaces. I suspect that OP seen lots of people getting very distracted by futarchy/manifold etc. (and I do think this is a risk), but culturally I think EA should be pretty into "betting/making falsifiable predictions" and that cluster of epistemic traits AND I think forecasting infrastructure has a meaningful effect on this. E.g In two office spaces (out of three that I've spent substantial time in), I think Manifold/prediction markets have very clearly made the communities more forecasting-y, and this has had tangible effects on people's research/choice of projects - this is probably the most explicit example, though most changes are harder to hyperlink.

I work at Founders Pledge, which has made many forecasting-related grants, some of them quite recently. Like Marcus, I’ve been fairly successful at forecasting — I am a so-called superforecaster a — but have a fair amount of skepticism. My views here are personal ones, not FP’s.

I have some agreements and disagreements with this post. The main point of agreement I have is with Marcus’ “vibe” here: I think forecasting’s apparent status and prominence among EAs outstrip either its prima facie promisingness or the to-date empirical support for its use.

I’m not sure that I agree that too much has been spent on forecasting, and I definitely don’t agree that enough time has passed that we’d know by now whether this work has been useful. We’re talking about a very short period of time here.

I think we’re at risk of conflating a bunch of different kinds of forecasting work:

• Investments in calibration: Funding new techniques or experiments in more effective forecasting
• Investments in diffusion: Broadly, attempts to “make forecasting a thing” by supporting e.g. new platforms
• Investments in capacity: Attempts to propagate or institutionalize formal forecasting at influential institutions
• Investments in public goods: Supporting institutions that do good forecasting and which the broader EA ecosystem finds useful.

I hope it’s clear that these are very different kinds of effort and should be considered differently promising. One fairly strongly held view I have is that further investments in precise calibration are probably not worthwhile: as far as I know, there are no consequential institutions that are able to usefully differentiate between the ways they’d respond to a 63% forecast vs a 65% one. 

Finance, of course, could benefit from such an edge. But here’s where I find Marcus’ vibe most compelling: if this were really so useful at the moment, then good human forecasters would be much better-paid.

At this point, I think it’s critical to draw another distinction. In funding forecasting work, effective giving orgs are essentially trying to purchase an outcome. I think the best case for forecasting work is that we’re not trying to purchase well-calibrated forecasts but rather institutional forms that generate well-calibrated forecasts.

I investigated FP’s most recent large grant in this space, which seeded a forecasting practice at an international security-focused think tank. Almost everyone I spoke to for that investigation viewed good forecasts as something like an incidental side-effect of the process required to generate them: generating useful questions, formally surfacing critical disagreements, identifying critical paths, decomposing reasoning, generating anchors that can be updated as events progress, making individuals’ judgment intercomparable.

As forecasting proponents have been arguing for years, judgmental forecasting is something like “institutionalized good judgment” — Brier scores are sort of like an OKR for org epistemics. And if you talk to people at the kinds of institutions where EAs are enthusiastic to see forecasting implemented, you’ll find either (a) an eagerness to see these kinds of norms and guardrails put in place or (b) an epistemic posture that makes the need for these guardrails self-evident.

My overall feeling here is one of sympathy for Marcus’ view: I think there is a there there, but I agree that EAs’ native enthusiasm for this kind of work has outrun our rigorous thinking about its usefulness, and I think we could probably use more discipline in that regard.
 

For context: I founded Inkling Markets in 2006 out of Y Combinator running internal prediction markets for companies and governments, and then Cultivate Labs in 2014, which has participated in some of the projects this community has funded. So I've watched this play out for 20 years. Before IARPA ACE and Tetlock's Superforecasters, before FRI, before most of what's mentioned in this thread existed.

Two things I think this debate is missing:

1) On whether the OP/CG money was wasted. Several commenters imply specific grants were boondoggles and this was recently mentioned in Nuño’s forecasting newsletter as well. The stated goal of much of that funding was to influence decision-making inside governments, particularly the US Government.

Anyone who has actually tried this knows it's an extremely expensive and difficult endeavor. For example, just getting in the door to talk to the people who have budgets to spend requires former senior officials on your team to make introductions. These are some of the highest demand people in Washington because of their networks. Then if you get through the door and eventually get to yes, the procurement and contracting takes months or doesn't end up even being possible because of incompatible contract "vehicles." An $8M grant sounds like a lot until you price out what it actually costs to embed forecasting infrastructure into a federal agency's workflow. You can fairly argue the bets didn't pay off, or that an aspect of the strategy was wrong. But I can assure you the money wasn't being burned on team dinners.

2) The deeper problem is one almost no one is funding. This morning while writing this post, I pulled the data. Inkling, and then Cultivate Labs, has worked with 150+ companies, government agencies, and think tanks. Not a single engagement has been canceled because the software was inadequate, our team sucked, or the predictions weren't accurate enough. Not one. They were canceled because we couldn't get enough employees to participate, or because the predictions, accurate ones, weren't getting used. Senior leaders didn't want to listen and outputs weren't integrated into decision processes. In those instances, there was limited or no demand signal, so why should an employee/forecaster care either?

This wasn't a UX problem, a mechanism problem, or a "need for better epistemics" problem. It was culture and politics and remains so to this day. Management has one eye on company performance and one eye on their own position. If a forecast supports what they want to do, great. If it doesn't, it's dead to them. I've watched organizations ignore strikingly accurate forecasts about product demand, product launch timelines, budget overshoot, future customer service failures, competitor behavior, results on future employee satisfaction surveys, not because the forecasts were not calibrated or the Brier score was poor, but because acting on them would have required someone to a) admit they were wrong, b) surrender authority to a process that didn't flatter them or their leadership, or c) take away a modicum of control over decision making.

So when Marcus says he can't point to "this forecasting happened, and now we have made demonstrably better decisions,” he's mostly right, but the diagnosis is wrong. The forecasts existed in 150+ organizations that I personally know of, and I'm sure there are more. Many of them some of the largest, most profitable companies in the world. The decisions didn't change because organizations aren't built to be changed by forecasts, even today.

What I've increasingly come to believe, and what has shaped the work my team has been pivoting to the past couple years now, is that forecasting is most useful when it isn't the deliverable itself. Now we’re embedding live forecasts inside a broader analytic framing and decisionmakers are engaging with it because it matches how they already think. A 63% probability on a narrow question doesn't really survive contact with anyone in leadership. But the same forecast as context inside a broader narrative does, because senior people don't navigate the world at the granularity of point estimates. They navigate through a small set of plausible futures with a few pivotal factors in mind, and decision points they feel they need to make. I'm not claiming forecasting as context vs. deliverable solves the incentive problem. It doesn't fully. But if done correctly and with the involvement of others, it raises the political cost and visibility of simply ignoring forecasts, and instead brings needed rigor to a decision making process - which I think was the point of believing in forecasting in the first place.

Ultimately, I think grant-makers have been over-indexing on the easier problem unfortunately. Funding more accuracy, more platforms, more benchmarks, more tournaments...That work is tractable, measurable, and produces clean deliverables, which is part of why I assume it keeps getting funded. But that should be the domain of academia. The harder problem, and the much more important problem IMO for the practitioners who are actually trying to affect change, is the last mile: how do forecasts get embedded into decision processes that have every cultural and political incentive to ignore them? How do we create alternative management models and pre-commitment mechanisms where leaders agree in advance how a forecast will move a decision? Where are the paid studies of the rare organizations that did truly integrate forecasting and what made them different? Where is the research on forecast-as-context vs. forecast-as-deliverable and how to optimize such a system?

That's where the marginal dollar should go.

[COI: I work at the Swift Centre as a forecaster, I have worked for a prediction market, I am very involved in forecasting. It is not my current work however, which is on community notes]

A few things points attempting to say things other commenters haven't, though I largely agree with the critical comments and the things they agree with Marcus on:

I agree that the $100M doesn't seem super well allocated. Not because forecasting is useless, but because the money flowed to big institutions and platforms rather than smaller, weirder, mechanism-design bets. I like Metaculus, but it has absorbed a lot of money in the last 5 years and not clearly changed much. I don't know if I think FRI has been worth it, I am glad someone has done the research but, again, how much are we talking? I would have preferred smaller projects were funded on the margin. Coefficient's strategy in forecasting has felt poor to me, often ignoring the community who in my view come up with the most interesting projects and going for marginal spending on incumbents. 

Nobody funds mechanism design or institutional epistemics. I recently spoke to someone at a household name enormous tech company who described their institutional process. It was almost unbelievably dysfunctional to me. Who is funding the work to help institutions think better? It doesn't promise near-term wins and frankly should't be the priority of any non-research org. So basically no one. Forecasting is an attempt. How much value is there in the joint stock company, or in democracy. To me, that's what we are talking about. Figuring out fundamentally better ways of making decisions. It is a problem at scale, it is neglected, and given the deregulation of prediction markets, tractable (though maybe bad, more later).

On "feels useful when it isn't" (point 6). I don't entirely disagree. I deliberately try not to spend time forecasting unless I'm being paid to. It can be a distraction. Where I disagree is that some forecasting is genuinely mentally sharpening, at least for me as a thinking discipline. And I think it's a not unreasonable status hierarchy. Do I endorse the status that Peter Wildeford or Eli Leifland have gotten from forecasting? Yes. Frankly, who do I not endorse having got status from being a forecaster?

Why don't AI 2027 and Ajeya count? Tangible forecasting outputs that demonstrably moved discourse and decision-making. Why don't these count as valuable forecasting outputs. AI 2027 is clearly informed by judgemental forecasters and was read by (I think) the Vice President. Habryka said something like 'too much time has been wasted down the resolution criteria mines' and I disagree but even if one agrees, I'm not sure even he thinks the whole field is a waste of time. 

Prediction markets may be net-harmful, but not useless. I've said publicly I'm less sure PMs are net-positive — bankruptcies and intimate partner violence are real and huge problems that may be as large as any coordinaton benefits. But 'bad on net' and 'useless' are different claims, and the later seems more obviously incorrect to me. I would be more interested in a post entitled "EA forecasting efforts have caused massive harm".

I tend to agree with the OP, but think there are a couple of other points about subsidising prediction markets which could have had more emphasis

• Forecasts are a market in which people trade money, which makes it easy for them to function on a for profit model if there is significant interest in participation. Even if prediction markets are objectively highly valuable, it is not clear there is sufficient altruism-relevant benefit in forecasting quality coming from subsidised rather than non-subsidised platforms to justify the subsidy ^[1]
• Forecasting for profit is zero sum,^[2] which means every superforecaster is balanced out by an equal and opposite amount of money collectively lost by others who are less "well calibrated". Many people are perhaps happy to net lose money gambled for entertainment or signalling purposes (though perhaps they could part with their cash in other ways which deliver more positive outcomes...), but others may be developing gambling habits which can be extremely self destructive^[3]. I guess this links to Marcus' "feels like doing something useful where it isn't" point, but it can be much worse than simply a distraction. Negative externalities can be significant, and it is unclear if the positive externalities outweigh them.

1. ^{^}

   I guess without a platform cut/spread you get marginally more precision, but how many forecasts actually need that precision and are sufficiently liquid to get it?

2. ^{^}

   actually worse than zero sum on a for-profit exchange, obviously...

3. ^{^}

   many forms of traditional gambling relies heavily on "whales" with a mixture of non trivial amounts of money to lose and impulse control problems for much of their volume and profit; some of them ruin their lives doing so, even more so the people with the same impulse control problems and less starting money. This may not apply to niche prediction markets, but I'm sure people can become addicted to the idea of winning their money back even if they know casino "betting systems" are -EV and don't like sports or machines with flashing lights 

Hi Marcus. Thanks for the post. I broadly agree.

Coefficient Giving's (CG's) Forecasting Fund has recently been closed.

As of March 30, the Forecasting Fund is no longer active, though we continue to make key forecasting grants through other funds, such as Navigating Transformative AI. This page will be maintained until the end of 2026 as a record of the fund’s work.

I think this is more likely to make forecasting grants useful. They will presumably be assessed with the criteria used to evaluate the non-forecasting grants of the respective fund.

@NunoSempere wrote about the end of CG's Forecasting Fund in the last edition of the Forecasting Newsletter. Only paid subscribers can check the relevant section.

We are always in triage

Right.

I have at least three reasons to be hopeful:

1. I see forecasting catching on with researchers for experimental design, which could easily save a lot of money and help make more progress. Earlier this month we updated a working paper on forecasts using data from the Social Science Prediction Platform to explicitly include results demonstrating use in power calculations. If a year from now forecasts are used a similar amount to now in economics research then that would be evidence for your hypothesis but from my perspective the concept that forecasts could be used in this way has only just started to be socialized, at least in my field. I also personally know of at least a couple of large institutions seeking forecasts and am planning a RCT on how they affect decision-making in the field.
2. I think LLMs are making forecasting much cheaper and easier.
3. If humans don't take up the use of forecasts in decision-making as much as they "should", well, LLMs may be more likely to in their own pipelines.

That's not to say that every project previously funded around forecasting was a good use of money. I would probably agree with you regarding most of the projects you have in mind, while disagreeing with the title and framing which is way too broad.

Related comment I made 2 years ago and ensuing discussion: https://forum.effectivealtruism.org/posts/ziSEnEg4j8nFvhcni/new-open-philanthropy-grantmaking-program-forecasting?commentId=7cDWRrv57kivL5sCQ

As someone who has dedicated their PhD to researching forecasting, I think this article raises an important point—but states it too bluntly, which ultimately muddles its central argument.

First, some context. Broadly speaking, all decisions are made on the basis of expectations about the future. It follows that anything which shifts those expectations can affect decisions in meaningful ways. This is easy to overlook, but it matters for how we evaluate forecasting interventions.

Furthermore, it is not easy to observe the effects of research. Consider the work of the Forecasting Research Institute. Much of what FRI does is research, and most of the benefits of research accrue in the future and are not easily traced back to any single input. The same difficulty applies more generally: since we cannot observe the counterfactual world in which a forecasting intervention was never made, measuring its effect is genuinely hard. How many decisions were improved? We may never know with precision.

That said, I do think the author raises a valid concern. We should be highly skeptical of the large investments of time and money by philanthropic actors and others in this space. Since many people find forecasting inherently enjoyable, there is a real risk that enthusiasm is doing some of the work that evidence should be doing—the elephant in the brain. I share that worry.

Where I part ways with the author is on the framing. The central argument of this piece should be: "We should rigorously review EA funding related to forecasting." That is a claim I agree with strongly. But the article instead reaches for a bolder headline—"Forecasting is overrated"—and there I disagree entirely. Most people in the world have never heard of structured forecasting, even though it could help a substantial number of them make better decisions in both business and personal life. Forecasting is not overrated. If anything, it is profoundly underrated.

When talking about forecasting, people often ask questions like “How can we leverage forecasting into better decisions?” This is the wrong way to go about solving problems. You solve problems by starting with the problem, and then you see which tools are useful for solving it.

I definitely have my own gripes about EA/rationalist attitudes towards forecasting (see here), but maybe your objection is a level confusion:

1. I think when people talk about "leveraging forecasting into better decisions", they're saying: "'Better' decisions just are decisions guided by the normatively correct beliefs. Namely, they're decisions that make reasonable-seeming tradeoffs between possible outcomes given the normatively correct beliefs about the plausibility of those outcomes. So our decisions will be more aligned with this standard of 'better' if our beliefs are formed by deferring to well-calibrated forecasts."
   1. E.g. they're saying, "When navigating AI risk, we'll make decisions that we endorse more if those decisions are guided by the credences of folks who've been unusually successful at forecasting AI developments."
   2. (At least, that's the steelman. Maybe I'm being too charitable!)
2. Whereas you seem to be asking something like: "We already know which beliefs are reasonable. Do these beliefs tell us that 'plug forecasts into some decision-making procedure' seems likely to lead to good outcomes (i.e., that this is a 'useful tool')?"

I think one should distinguish between several things here:

• Prediction markets that make a lot of $ and don't really need more because they do just fine with the profit motive
• People spending a lot of time on prediction markets to prove they are a good forecaster
• Infrastructure integrated into specific use cases, such as: when a funder is interested in a question so much that they will pay for forecasts, to inform and improve their other funding, or when for structural reasons the institution that has reason to benefit from the forecasts cannot fund them itself (such as some other decision-makers who do not have the mandate to support forecasting and are restricted from spending money to support it but would use forecasts in their work flow), or basic research with positive externalities.

This post really belabours the first and second bullet point, perhaps because that is where a lot of money has gone to, but there can be a lot of value in the third.

I suspect that the main use of forecasting is if you need a probability for something and you don't really have time to look into it yourself or you wouldn't trust your judgement even if you did.

Curious what readers here think! 

Ideally read/skim both @Marcus Abramovitch's post and @Jhrosenberg's response (currently top comment) before voting.  

Note this will obviously not be representative, it's just a quick opinion poll. 

This isn't something I've thought a ton about but I think forecasting should plausibly still receive funding in a specific way:

Funders should either pay forecasters to make predictions on important questions, or subsidize prediction markets on those questions.

I don't think forecasting is a "solution seeking a problem." There are tons of important but hard-to-predict questions that I'd like better forecasts on! The problem is that the ecosystem hasn't done a great job of turning dollars into good forecasts.

For example, most of my Metaculus questions are things I wanted answers to, but I tended not to update on the results because the questions usually don't receive a lot of forecasts. If someone wanted to pay money to get more predictions on questions, I'd learn something useful!

I'm not sure how valuable this is compared to other uses of money (I wouldn't pay for it myself) but at least it's better than more general-purpose research on forecasting.

"I don’t think there is very much in the way of “this forecasting happened, and now we have made demonstrably better decisions regarding this terminal goal that we care about”."

I assume some people disagree with this strong claim. One example I've heard was AGI timelines and their influence on AI safety field priorities - though I guess one could answer that certain reports or expert opinions where disproportionately more useful than prediction markets.

On a different point, I appreciated Eli Lifland's past comment on many intellectual activities (such as grantmaking) being forms of forecasting.

I strongly agree with the author's viewpoint, and I also strongly agree that long-term predictions in chaotic systems (such as predictions about events three years in advance) are, in most cases, a form of self-comfort, a resistance to the uncertainty of the future. Essentially, it's a psychological comfort of seeking certainty, rather than a rigorous, systematic argument. 

Specifically, in complex dynamics, there's the concept of the Lyapunov exponent, a classic application in meteorology. Any weather forecast exceeding 14 days is almost indistinguishable from a random walk or simply looking up past daily average temperatures.

 However, in the multidimensional and complex scenarios of real human society and technological iteration, this prediction window won't be much longer (theoretically). We cannot rigorously verify the effectiveness of our predictions beforehand. (That is, rigorously proven predictive power surpasses random sampling. This is unrelated to logical quality; it's determined by the nature of chaotic systems.)

I agree with some of this. But let me attempt a conciliatory take: less of forecasting money and effort should go to platforms and tournaments, but more should go to identifying existing, nascent forecasts (people using the word "probably" or "unlikely" about empirical matters) and creating markets (even unsubsidized Manifold markets would be helpful on the margin). I think it would be very helpful for someone to go through popular EA forum posts and org research documents and do this systematically.

Prediction markets seem to be a great business (mostly gambling with all the problems associated with it) so “funding” in the sense of investing in them could be sensible while “funding” in the donation sense not. (And then later donation to AMF or similar). 

In general, I’m hesitant to donate to stuff that’s plausibly just a really good business in its own right. 

Solution Seeking a Problem
When talking about forecasting, people often ask questions like “How can we leverage forecasting into better decisions?” This is the wrong way to go about solving problems.

I'm reconsidering this point. It seems intuitive, but what is the strongest argument that this is "wrong"?

I share the expressed concern but respectfully disagree with the major suggestion.
First, «overrating» is a perception problem, not purely an industry problem. People are free to believe in things, and sometimes they overrate them. The forecasting community did not force anyone to fund platforms over applied work. That was a series of decisions made by funders who could have chosen differently. Blaming the field for how it was funded seems like misplaced accountability.
Second, I am genuinely troubled by the premise of «tangible result delivery» as the primary criterion. In Belarus, the local dictator Lukashenko reduced fundamental research to a single standard: «defend a dissertation, put something on the table.» A literal table — something you could eat, drink, or watch. This same person later advised the population to treat COVID-19 with vodka and hockey. I raise this not as a rhetorical flourish but as a real example of where the demand for immediate, demonstrable utility leads. Expertise that cannot show a product on the table gets defunded. What replaces it is not better expertise, but motivational speakers and empty calories dressed as insight.
Third, and most importantly, the kind of non-ideological, procedural, deliberate thinking about existential challenges that forecasting at its best represents needs to be funded precisely because it is not self-funding. It will not produce a table. It will not generate clicks or headlines. It will not confirm anyone's priors. That is exactly why it needs institutional support — and why defunding it in favor of things with tangible short-term outputs is a mistake we will recognize too late.

Forecasting is a dangerous activity, particularly because it is a fun, game-like activity that is nearly perfectly designed to be very attractive to EA/rationalist types because you get to be right when others are wrong, bet on your beliefs, and partake in the cultural practice.

I like bets involving donations, and investments as alternatives to forecasting without money on the line.

Off topic, but one additional thing I noticed about this list:

I can point to you hundreds of millions of chickens that lay eggs that are out of cages, and I can point to you observable families that are no longer living in poverty. I can show you pieces of legislation that have passed or almost passed on AI. I can show you AMF successes with about 200k lives saved and far lower levels of malaria, not to mention higher incomes and longer life expectancies, and people living longer lives that otherwise wouldn’t be because of our actions.

Is the glaring lack of tangible advances in technical AI safety. It's a different case from your post, as it's about a problem rather than a tool; but I think it still shows something about whether we understand the dangers of AI and the systems they stem from enough to do anything about it.

It’s not the case that forecasting/prediction markets are merely in their infancy.

Counterargument: the internet had its theoretical underpinnings start approximately 1959-1960, with first grants for ARPANET in 1966-1969.

The whole thing was then not very useful until the 1990's.

You could pick earlier dates for theroetical underpinnings of the internet if you wanted, too.

I think prediction markets are more similar to the internet than to cryptocurrency: they require a mix of technology and infrastructure but also a change in human habits. Theoretically, you'd always expect this type of very different systems technology to be a multi-generational thing before there's widespread societal payoffs on it.

It's not exactly a 1-for-1 comparison, but the similar date for prediction markets in a comparison to the internet is probably 1988 with the Iowa Prediction Markets. So, it's been 38 years. It seems to me that it's right on schedule to be a useful systems technology for society. We're now in the "Wild West" days of it and it's going to be messy, like when the internet was emerging into the mainstream.

I do agree about the "feels useful but isn't" criticism — I got very into finding mis-priced bets and arbitrages on PredictIt a few years back. It can be a terrible time sink due to being so interesting and intellectually engaging. And the type of person who can do a good job at this (I'm one of them, not on your level but also pretty good) — is certainly capable of doing much more high impact work with that same type of cognition.

So I agree with that criticism on an individual level, but I don't think it's right to extrapolate that to the emerging systems technology. A cautionary note that if you're a skilled forecaster it might be a dangerous time sink I'd fully agree with, but I don't think it makes sense to throw the baby out with the bathwater as to whether this will have societal-level impact over time.

A bit tangential to the main thrust of this post, but I have been wondering lately about some the regulatory aspects around prediction markets. Recently there was the scandal of a soldier who allegedly made $400,000 from insider information about the Maduro raid. There is particular interest in the US on banning sports betting, which is seen (accurately) as another form of gambling. Minnesota might ban prediction markets entirely.

Stepping back from the merits of this specific proposal, I see it as a part of a troubling broader anti-innovation trend. We have also seen various political factions (usually the same factions) find nothing but negativity in data centers, human spaceflight, and cryptocurrency and pursue whatever legislative avenues they can to restrict these things. At some point, a person has to identify the pattern.

As a "layperson" (I don't put my own money into prediction markets, nor is my research specifically related to them), I do find prediction markets to be a helpful source of information in the way that commodity futures are a helpful source of information. But I dislike having to sift through a bunch of predictions on sports matches and short-term crypto movements. That, and my libertarian-ish fears about the heavy hand of government, make me reluctant to throw the baby out with the bathwater.

100% agreed! Just had to comment even after pressing on the ✔️ icon for some reason!