Working to reduce extreme suffering for all sentient beings.
Author of Suffering-Focused Ethics: Defense and Implications; Reasoned Politics; & Essays on Suffering-Focused Ethics.
Co-founder (with Tobias Baumann) of the Center for Reducing Suffering (CRS).
The reason this matters is that EA frequently decides to make decisions, including funding decisions, based on these ridiculously uncertain estimates. You yourself are advocating for this in your article.
I think that misrepresents what I write and “advocate” in the essay. Among various other qualifications, I write the following (emphases added):
I should also clarify that the decision-related implications that I here speculate on are not meant as anything like decisive or overriding considerations. Rather, I think they would mostly count as weak to modest considerations in our assessments of how to act, all things considered.
My claims about how I think these would be “weak to modest considerations in our assessments of how to act” are not predicated on the exact manner in which I represent my beliefs: I’d say the same regardless of whether I’m speaking in purely qualitative terms or in terms of ranges of probabilities.
In summary, people should either start stating their uncertainty explicitly, or they should start saying “I don’t know”.
FWIW, I do state uncertainty multiple times, except in qualitative rather than quantitative terms. A few examples:
This essay contains a lot of speculation and loose probability estimates. It would be tiresome if I constantly repeated caveats like “this is extremely speculative” and “this is just a very loose estimate that I am highly uncertain about”. So rather than making this essay unreadable with constant such remarks, I instead say it once from the outset: many of the claims I make here are rather speculative and they mostly do not imply a high level of confidence. … I hope that readers will keep this key qualification in mind.
As with all the numbers I give in this essay, the following are just rough numbers that I am not adamant about defending …
Of course, this is a rather crude and preliminary analysis.
Thanks! :)
Assigning a single number to such a prior, as if it means anything, seems utterly absurd.
I don’t agree that it’s meaningless or absurd. A straightforward meaning of the number is “my subjective probability estimate if I had to put a number on it” — and I’d agree that one shouldn’t take it for more than that.
I also don’t think it’s useless, since numbers like these can at least help give a very rough quantitative representation of beliefs (as imperfectly estimated from the inside), which can in turn allow subjective ballpark updates based on explicit calculations. I agree that such simple estimates and calculations should not necessarily be given much weight, let alone dictate our thinking, but I still think they can provide some useful information and provoke further thought. I think they can add to purely qualitative reasoning, even if there are more refined quantitative approaches that are better still.
You give a prior of 1 in a hundred that aliens have a presence on earth. Where did this number come from?
It was in large part based on the considerations reviewed in the section “I. An extremely low prior in near aliens”. The following sub-section provides a summary with some attempted sanity checks and qualifications (in addition to the general qualifications made at the outset):
All-things-considered probability estimates: Priors on near aliens
Where do all these considerations leave us? In my view, they overall suggest a fairly ignorant prior. Specifically, in light of the (interrelated) panspermia, pseudo-panspermia, and large-scale Goldilocks hypotheses, as well as the possibility of near aliens originating from another galaxy, I might assign something like a 10 percent prior probability to the existence of at least one advanced alien civilization that could have reached us by now if it had decided to. (Note that I am here using the word “civilization” in a rather liberal sense; for example, a distributed web of highly advanced probes would count as a civilization in this context.) Furthermore, I might assign a probability not too far from that — maybe around 1 percent — to the possibility that any such civilization currently has a presence around Earth (again, as a prior).
Why do I have something like a 10 percent prior on there being an alien presence around Earth conditional on the existence of at least one advanced alien civilization that could have reached us? In short, the main reason is the info gain motive that I explore at greater length below. Moreover, as a sanity check on this conditional probability, we can ask how likely it is that humanity would send and maintain probes around other life-supporting planets assuming that we became technologically capable of doing this; roughly 10 percent seems quite sane to me.
At an intuitive level, I would agree with critics who object that a ~1 percent prior probability in any kind of alien presence around Earth seems extremely high. However, on reflection, I think the basic premises that get me to this estimate look quite reasonable, namely the two conjunctive 10-percent probabilities in “the existence of at least one advanced alien civilization that could have reached us by now if it had decided to” and “an alien presence around Earth conditional on the existence of at least one advanced alien civilization that could have reached us”.
Note also that there are others who seem to defend considerably higher priors regarding near aliens (see e.g. these comments by Jacob Cannell; I agree with some of the points Cannell makes, though I would frame them in more uncertain and probabilistic terms).
I can see how substantially lower priors than mine could be defensible, even a few orders of magnitude lower, depending on how one weighs the relevant arguments. Yet I have a hard time seeing how one could defend an extremely low prior that practically rules out the existence of near aliens. (Robin Hanson has likewise argued against an extremely low prior in near aliens.)
Thanks for your comment. I basically agree, but I would stress two points.
First, I’d reiterate that the main conclusions of the post I shared do not rest on the claim that extraordinary UFOs are real. Even assuming that our observed evidence involves no truly remarkable UFOs whatsoever, a probability of >1 in 1,000 in near aliens still looks reasonable (e.g. in light of the info gain motive), and thus the possibility still seems (at least weakly) decision-relevant. Or so my line of argumentation suggests.
Second, while I agree that the wild abilities are a reason to update toward thinking that the reported UFOs are not real objects, I also think there are reasons that significantly dampen the magnitude of this update. First, there is the point that we should (arguably) not be highly confident about what kinds of abilities an advanced civilization that is millions of years ahead of us might possess. Second, there is the point that some of the incidents (including the famous 2004 Nimitz incident) involve not only radar tracking (as reported by Kevin Day in the Nimitz incident), but also eye-witness reports (e.g. by David Fravor and Alex Dietrich in the case of Nimitz), and advanced infrared camera (FLIR) footage (shot by Chad Underwood during Nimitz). That diversity of witnesses and sources of evidence seems difficult to square with the notion that the reported objects weren’t physically real (which, of course, isn’t to say that they definitely were real).
When taking these dampening considerations into account, it doesn’t seem to me that we have that strong reason to rule out that the reported objects could be physically real. (But again, the main arguments of the post I shared don’t hinge on any particular interpretation of UFO data.)
I think it would have been more fair if you hadn’t removed all the links (to supporting evidence) that were included in the quote below, since it just comes across as a string of unsupported claims without them:
Beyond the environmental effects, there are also significant health risks associated with the direct consumption of animal products, including red meat, chicken meat, fish meat, eggs and dairy. Conversely, significant health benefits are associated with alternative sources of protein, such as beans, nuts, and seeds. This is relevant both collectively, for the sake of not supporting industries that actively promote poor human nutrition in general, as well as individually, to maximize one’s own health so one can be more effectively altruistic.
I think this evidence on personal health is relevant in the ways described. I don’t think it’s fair to say that the quote above implies that “[health benefits] will definitely happen with no additional work from you, without any costs or trade-offs”; obviously, any change in diet will require some work and will involve some tradeoffs. But I agree that it’s worth addressing the potential pitfalls of vegan diets, and it’s a fair critique that that would have been worth including in that essay (even though a top link on the blog does list some resources on this).
FWIW, in terms of additional work, tradeoffs, and maximizing health, I generally believe that it is worth making a serious investment into figuring out how to optimize one’s health, such as by investing in a DNA test for nutrition, and I think this is true for virtually everyone. Likewise, I think it’s worth being clear that all diets involve tradeoffs and risks, including both vegan and omnivore diets (some of the risks associated with the latter are hinted at in the links above: “red meat, chicken meat, fish meat, eggs and dairy”).
I didn’t claim that there isn’t plenty more data. But a relevant question is: plenty more data for what? He says that the data situation looks pretty good, which I trust is true in many domains (e.g. video data), and that data would probably in turn improve performance in those domains. But I don’t see him claiming that the data situation looks good in terms of ensuring significant performance gains across all domains, which would be a more specific and stronger claim.
Moreover, the deference question could be posed in the other direction as well, e.g. do you not trust the careful data collection and projections of Epoch? (Though again, Ilya saying that the data situation looks pretty good is arguably not in conflict with Epoch’s projections — nor with any claim I made above — mostly because his brief “pretty good” remark is quite vague.)
Note also that, at least in some domains, OpenAI could end up having less data to train their models with going forward, as they might have been using data illegally.
I think it’s a very hard sell to try and get people to sacrifice themselves (and the whole world) for the sake of preventing “fates worse than death”.
I’m not talking about people sacrificing themselves or the whole world. Even if we were to adopt a purely survivalist perspective, I think it’s still far from obvious that trying to slow things down is more effective than is focusing on other aims. After all, the space of alternative aims that one could focus on is vast, and trying to slow things down comes with non-trivial risks of its own (e.g. risks of backlash from tech-accelerationists). Again, I’m not saying it’s clear; I’m saying that it seems to me unclear either way.
We should be doing all we can now to avoid having to face such a predicament!
But, as I see it, what’s at issue is what the best way is to avoid such a predicament/how to best navigate given our current all-too risky predicament.
FWIW, I think that a lot of the discussion around this issue appears strongly fear-driven, to such an extent that it seems to get in the way of sober and helpful analysis. This is, to be sure, extremely understandable. But I also suspect that it is not the optimal way to figure out how to best achieve our aims, nor an effective way to persuade readers on this forum. Likewise, I suspect that rallying calls along the lines of “Global moratorium on AGI, now” might generally be received less well than would, say, a deeper analysis of the reasons for and against attempts to institute that policy.
What are the downsides from slowing down?
I’d again prefer to frame the issue as “what are the downsides from spending marginal resources on efforts to slow down?” I think the main downside, from this marginal perspective, is opportunity costs in terms of other efforts to reduce future risks, e.g. trying to implement “fail-safe measures”/”separation from hyperexistential risk” in case a slowdown is insufficiently likely to be successful. There are various ideas that one could try to implement.
In other words, a serious downside of betting chiefly on efforts to slow down over these alternative options could be that these s-risks/hyperexistential risks would end up being significantly greater in counterfactual terms (again, not saying this is clearly the case, but, FWIW, I doubt that efforts to slow down are among the most effective ways to reduce risks like these).
a fast software-driven takeoff is the most likely scenario
I don’t think you need to believe this to want to be slamming on the breaks on now.
Didn’t mean to say that that’s a necessary condition for wanting to slow down. But again, I still think it’s highly unclear whether efforts that push for slower progress are more beneficial than alternative efforts.
I’m not sure what you are saying here? Do you think there is a risk of AI companies deliberately causing s-risks (e.g. releasing a basilisk) if we don’t play nice!?
No, I didn’t mean anything like that (although such crazy unlikely risks might also be marginally better reduced through cooperation with these actors). I was simply suggesting that cooperation could be a more effective way to reduce risks of worst-case outcomes that might occur in the absence of cooperative work to prevent them, i.e. work of the directional kind gestured at in my other comment (e.g. because ensuring the inclusion of certain measures to avoid worst-case outcomes has higher EV than does work to slow down AI). Again, I’m not saying that this is definitely the case, but it could well be. It’s fairly unclear, in my view.
Thanks for your reply, Greg :)
I don’t think this matters, as per the next point about there already being enough compute for doom
That is what I did not find adequately justified or argued for in the post.
I think the burden of proof here needs to shift to those willing to gamble on the safety of 100x larger systems.
I suspect that a different framing might be more realistic and more apt from our perspective. In terms of helpful actions we can take, I more see the choice before us as one between trying to slow down development vs. trying to steer future development in better (or less bad) directions conditional on the current pace of development continuing (of course, one could dedicate resources to both, but one would still need to prioritize between them). Both of those choices (as well as graded allocations between them) seem to come with a lot of risks, and they both strike me as gambles with potentially serious downsides. I don’t think there’s really a “safe” choice here.
All I’m really saying here is that the risk is way too high for comfort
I’d agree with that, but that seems different from saying that a fast software-driven takeoff is the most likely scenario, or that trying to slow down development is the most important or effective thing to do (e.g. compared to the alternative option mentioned above).
To push back a bit on the fast software-driven takeoff (i.e. a fast takeoff driven primarily by innovations in software):
Common objections to this narrative [of a fast software-driven takeoff] are that there won’t be enough compute, or data, for this to happen. These don’t hold water after a cursory examination of our situation. We are nowhere near close to the physical limits to computation …
While we’re nowhere near the physical limits to computation, it’s still true that hardware progress has slowed down considerably on various measures. I think the steelman of the compute-based argument against a fast software-driven takeoff is not that the ultimate limits to computation are near, but rather that the pace of hardware progress is unlikely to be explosively fast (e.g. in light of recent trends that arguably point in the opposite direction, and because software progress per se seems insufficient for driving explosive hardware progress).
We’re on ~10^18 FLOPS for large clusters at the moment[14], but there are likely enough GPUs available for 100 times this[15]. And the cost of this – ~$10B—is affordable for many large tech companies and national governments, and even individuals!
That actors can afford to create this next generation of AIs does not imply that those AIs will in turn lead to a hard takeoff in capabilities. From my perspective at least, that seems like an unargued assumption here.
Data is not a show-stopper either. Sure, ~all the text on the internet might’ve already been digested, but Google could readily record more words per day via phone mics[16] than the number used to train GPT-4[17]. These may or may not be as high quality as text, but 1000x as many as all the text on the internet could be gathered within months[18]. Then there are all the billions of high-res video cameras (phones and CCTV), and sensors in the world[19]. And if that is not enough, there is already a fast-growing synthetic data industry serving the ML community’s ever growing thirst for data to train their models on.
A key question is whether this extra data would be all that valuable to the main tasks of concern. For example, it seems unclear whether low-quality data from phone conversations, video cameras, etc. would give that much of a boost to a model’s ability to write code. So I don’t think the point made above, as it stands, is a strong rebuttal to the claim that data will soon be a limiting bottleneck to significant capability gains. (Some related posts.)
The time for talking politely to (and working with) big AI is over.
This is another claim I would push back against. For instance, from a perspective concerned with the reduction of s-risks, one could argue that talking politely to, and working with, leading AI companies is in fact the most responsible thing to do, and that taking a less cooperative stance is unduly risky and irresponsible. To be clear, I’m not saying that this is obviously the case, but I’m trying to say that it’s not clear-cut either way. Good arguments can be made for a different approach, and this seems true for a wide range of altruistic values.
I agree that the reduction of s-risks is underprioritized, but it’s unclear whether the aim of reducing s-risks would render research into the nature of sentience a high priority; and there are even reasons to think that it could be harmful.
I’ve tried to outline what I see as some relevant considerations here.
By “I am confused by your argument against scaling”, I thought you meant the argument I made here, since that was the main argument I made regarding scaling; the example with robots wasn’t really central.
I’m also a bit confused, because I read your arguments above as being arguments in favor of explosive economic growth rates from hardware scaling and increasing software efficiency. So I’m not sure whether you believe that the factors mentioned in your comment above are sufficient for causing explosive economic growth. Moreover, I don’t yet understand why you believe that hardware scaling would come to grow at much higher rates than it has in the past.
To be clear, I don’t mean to claim that we should give special importance to current growth rates in robotics in particular. I just picked that as an example. But I do think it’s a relevant example, primarily due to the gradual nature of the abilities that robots are surpassing, and the consequent gradual nature of their employment.
Unlike fusion, which is singular in its relevant output (energy), robots produce a diversity of things, and robots cover a wide range of growth-relevant skills that are gradually getting surpassed already. It is this gradual nature of their growth-related abilities that makes them relevant, imo — because they are already doing a lot of work and already contributing a fair deal to the growth we’re currently seeing. (To clarify, I mostly have in mind industrial robots, such as these, the future equivalents of which I also expect to be important to growth; I’d agree that it wouldn’t be so relevant if we were only talking about some prototypes of robots that don’t yet contribute meaningfully to the economy.)
I agree with premise 3. Where I disagree more comes down to the scope of premise 1.
This relates to the diverse class of contributors and bottlenecks to growth under Model 2. So even though it’s true to say that humans are currently “the state-of-the-art at various tasks relevant to growth”, it’s also true to say that computers and robots are currently “the state-of-the-art at various tasks relevant to growth”. Indeed, machines/external tools have been (part of) the state-of-the-art at some tasks for millennia (e.g. in harvesting), and computers and robots in particular have been the state-of-the-art at various tasks relevant to growth for decades (e.g. in technical calculations and manufacturing). And the proportion of tasks at which machines have been driving growth has been gradually increasing (the pictures of Model 2 was an attempt to illustrate this perspective). Yet despite superhuman machines (i.e. machines that are superhuman within specific tasks) playing an increasing role in pushing growth over the past decades, economic growth rates not only failed to increase, but decreased almost by a factor of 2. That is, robots/machines have already been replacing humans at the growth frontier across various tasks in the way described in premises 1-4, yet we still haven’t seen growth increase. So a key question is why we should expect future growth/displacement of this kind to be different. Will it be less gradual? If so, why?
In short, my view is that humans have become an ever smaller part of the combined set of tools pushing growth forward — such that we’re in various senses already a minority force, e.g. in terms of the lifting of heavy objects, performing lengthy math calculations, manufacturing chips, etc. — and I expect this process to gradually continue. I don’t expect a critical point at which growth rates suddenly explode because the machines themselves are already doing such a large share of the heavy lifting, and an increasing proportion of our key bottlenecks to growth are (already) their bottlenecks to faster growth (which must again be distinguished from claims about absolute room for growth; there may be plenty of potential for growth in various domains without there being an extremely fast way to realize that potential).
When you have two different technologies A and B where A works better now, but B is getting better faster than A, then there will predictably be a big jump in the use of B once it exceeds A
Not if B is gradually getting better than A at specific growth-relevant tasks, and if B is getting produced and employed roughly in proportion to how fast it is getting better than A at those specific tasks. In that case, familiar rates of improvement (of B over A) could imply familiar growth rates in the production and employment of B in the future.
But I’m just not seeing why you don’t expect a ton of scaling
Just to be clear, in one sense, I do expect to see a ton of scaling compared to today, I just don’t expect scaling growth rates to explode, such that we see a doubling in a year or faster. In a future robot population that is much larger than the current one, consistent 12 percent annual growth would still amount to producing more robots in a single year than had been produced throughout all history 20 years earlier.
Regarding explosive growth in the amount of hardware: I meant to include the scale aspect as well when speaking of a hardware explosion. I tried to outline one of the main reasons I’m skeptical of such an ‘explosion via scaling’ here. In short, in the absence of massive efficiency gains, it seems even less likely that we will see a scale-up explosion in the future.
Incidentally, the graphs you show for the decline in innovations per capita start dropping around 1900 … which is pretty different from the 1960s.
That’s right, but that’s consistent with the per capita drop in innovation being a significant part of the reason why growth rates gradually declined since the 1960s. I didn’t mean to deny that total population size has played a crucial role, as it obviously has and does. But if innovations per capita continue to decline, then even a significant increase in effective population size in the future may not be enough to cause a growth explosion. For example, if the number of employed robots continues to grow at current rates (roughly 12 percent per year), and if future robots eventually come to be the relevant economic population, then declining rates of innovation/economic productivity per capita would mean that the total economic growth rate still doesn’t exceed 12 percent. I realize that you likely expect robot populations to grow much faster in such a future, but I still don’t see what would drive such explosive growth in hardware (even if, in fact especially if, it primarily involves scaling-based growth).
Also, I’m a bit skeptical of the graph showing a 5x drop.
That makes sense.
On the other hand, it’s perhaps worth noting that individual human thinking was increasingly extended by computers after ca. 1950, and yet the rate of innovation per capita still declined. So in that sense, the decline in progress could be seen as being somewhat understated by the graphs, in that the rate of innovation per dollar/scientific instrument/computation/etc. has declined considerably more.
I wrote earlier that I might write a more elaborate comment, which I’ll attempt now. The following are some comments on the pieces that you linked to.
I disagree with this series in a number of places. For example, in the post “This Can’t Go On”, it says the following in the context of an airplane metaphor for our condition:
We’re going much faster than normal, and there isn’t enough runway to do this much longer … and we’re accelerating.
As argued above, in terms of economic growth rates, we’re in fact not accelerating, but instead seeing an unprecedented growth decline across doublings. Additionally, the piece, and the series as a whole, seems to neglect the possibility that growth rates might simply continue to decline very gradually, as they have over the last 5-6 decades.[1]
Relatedly, I would question some of the “growth-exploding” implications that the series implicitly assumes would follow from PASTA (“AI systems that can essentially automate all of the human activities needed to speed up scientific and technological advancement”). I’ve tried to explain some of my reservations here (more on this below).
This post relates to the PASTA point above; I guess the following is the key passage in this context:
AI potentially offers a way to convert money into researchers. Money = build more AIs = more research.
If this were true, then once AI comes around – even if it isn’t much smarter than humans – then as long as the computational power you can invest into researching a given field increases with the amount of money you have, hyperbolic growth is back on. Faster growth rates means more money means more AIs researching new technology means even faster growth rates, and so on …
I think this is by far the most plausible conjecture as to how growth rates could explode in the future. But I still think there are good reasons to be skeptical of such an explosion. For one, an AI-driven explosion of this kind would most likely involve a corresponding explosion in hardware (e.g. for reasons gestured at here and here), and there are both theoretical and empirical reasons to doubt that we will see such an explosion.
Indeed, growth rates are declining across various hardware metrics, and yet here we are not just talking about growth rates in hardware remaining at familiar levels, but presumably about an explosion in these growth rates. That seems to go against the empirical trends that we are observing in hardware, and against the impending end of Moore’s law that we can predict with reasonable confidence based on theory.
Relatedly, there is the point that explosive economic growth likely would imply an explosion in energy production, but it is likewise doubtful whether progress in software/computation could unleash such an explosion (as argued here). More generally, even if we made a lot of progress in both computer software and hardware, it seems likely that growth would become constrained by other factors to a degree that precludes an economic growth explosion (Bryan Caplan makes similar points in response to Hanson’s Age of Em; see point 9 here; see also Aghion et al., 2019, p. 260).
Moreover, as mentioned in an earlier comment, it is worth noting that the decline in growth rates that we have seen since the 1960s is not only due to decreasing population growth, as there are also other factors that have contributed, such as certain growth potentials that have been exhausted, and the consequent decline in innovations per capita.
One thing to notice about this model is that it conflicts with the historic decline in growth rates. In particular, the model is an ever worse fit with the observed growth rates of the last four decades. Roodman himself notes that his model has shortcomings in this regard:
… recent global growth has been slower and steadier than the model predicts from previous history. … Nevertheless, I think it is a virtue, and perhaps an inspiration for further work, that this rigorous model can quantify its own shortcomings.
(Perhaps also see Tom Davidson’s reflections on Roodman’s model.)
In other words, the model is not a good fit with the last 2-3 doublings of the world economy, and this is somewhat obscured when the model is evaluated across standard calendar time rather than across doublings; when looking at the model through the former lens, its recent mispredictions seem minor, but when seen across doublings — arguably the more relevant perspective — they become rather significant.
Given the historical data, it seems that a considerably better fit would be a model that predicts roughly symmetric growth rates around a peak growth rate found ca. 1960, as can be gleaned here (similar points are made in Modis, 2012, pp. 20-22). Such a model may be theoretically grounded in the expectation that there will be a point where innovations become increasingly harder to find. One would expect such a “peak growth rate” to occur at some point a priori, given the finite potential for growth (implying that the addition of a limiting factor that represents the finite nature of growth-inducing innovations would be a natural extension to Roodman’s model). And empirical data then tentatively suggests that this peak lies in the past. (But again, peak growth rates being in the past is still compatible with increasing growth in absolute terms.)
4. Report on Whether AI Could Drive Explosive Economic Growth (you mention this one but don’t really talk about the hyperbolic growth model, just the conclusions)
I think my main comments on this model are captured above.
Thanks again for engaging, and feel free to comment on any points you disagree with. :)
E.g. when it says: “We’re either going to speed up even more, or come to a stop, or something else weird is going to happen.” This seems to overlook the kind of future that I alluded to above, which would amount to staying at roughly the same pace, yet continuing to slow down growth rates very gradually. This is arguably not a particularly “weird” or “wild” prospect. (It’s also worth nothing that the “come to a stop” would occur faster in the “speed up even more” scenario, as it would imply that we’ll hit ultimate limits earlier; slower growth would imply that non-trivial growth rates end later.)
I do not claim otherwise in the post :) My claim is rather that proponents of Model 1 tend to see a much smaller distance between these respective definitions of intelligence, almost seeing Intelligence 1 as equivalent to Intelligence 2. In contrast, proponents of Model 2 see Intelligence 1 as an important yet still, in the bigger picture, relatively modest subset of Intelligence 2, alongside a vast set of other tools.
I don’t think what Knutsson means by “plausible” is “what seems obvious to most people at first glance”. I also don’t think that’s a particularly common or plausible use of the term “plausible”. (Some examples of where “plausible” and “what seems obvious to most people at first glance” plausibly come apart include what most people in the past might at first glance have considered obvious about the moral status of human slavery, as well as what most people today might at first glance say about the moral status of farming and killing non-human animals.)
Note that Knutsson does not deny that pleasure and happiness are worthwhile in the sense of being better for a person than unpleasure and unhappiness (cf. “What about making individuals happier? Yes, we should do that.”). Nor does he deny that certain experiences can benefit existing beings (e.g. by satisfying certain needs). What he argues against is instead that pleasure and experiential happiness are something “above” or “on the other side of” a completely undisturbed state.
As for the claim about “few people” (and setting aside that majority opinion is hardly a good standard for plausibility, as I suspect you’d agree), it’s not clear that this “few people” claim is empirically accurate, especially if it concerns the idea that pleasure isn’t something “above” a completely undisturbed state. The following is an apropos quote:
(On Buddhism and Epicureanism, see e.g. Breyer, 2015; Sherman, 2017; and the recent review of minimalist views of wellbeing by Teo Ajantaival.)