Carl Shulman - Intelligence Explosion, Primate Evolution, Robot Doublings, & Alignment
Today I have the pleasure of speaking with Carl Shulman. Many of my former guests, and this is not an exaggeration, have told me that a lot of their biggest ideas have come directly from Carl especially when it has to do with the intelligence explosion and its impacts. So I decided to go directly to the source and we have Carl today on the podcast. He keeps a super low profile but is one of the most interesting intellectuals I've ever encountered and this is actually his second podcast ever. We're going to go deep into the heart of many of the most important ideas that are circulating right now directly from the source. Carl is also an advisor to the Open Philanthropy project which is one of the biggest funders on causes having to do with AI and its risks, not to mention global health and well being. And he is a research
associate at the Future of Humanity Institute at Oxford. So Carl, it's a huge pleasure to have you on the podcast. Thanks for coming. Thank you Dwarkesh. I've enjoyed seeing some of your episodes recently and I'm glad to be on the show. Excellent, let's talk about AI. Before we get into the details, give me the big picture explanation of the feedback loops and just general dynamics that would start when you have something that is approaching human-level intelligence. The way to think about it is — we have a process now where humans are developing new computer chips, new software, running larger training runs, and it takes a lot of work to keep Moore's law chugging (while it was, it's slowing down now).
And it takes a lot of work to develop things like transformers, to develop a lot of the improvements to AI neural networks. The core method that I want to highlight on this podcast, and which I think is underappreciated, is the idea of input-output curves. We can look at the increasing difficulty of improving chips and sure, each time you double the performance of computers it’s harder and as we approach physical limits eventually it becomes impossible. But how much harder? There's a paper called “Are Ideas Getting Harder to Find?" that was published a few years ago. 10 years ago at MIRI, I did an early version of this analysis using
data mainly from Intel and the large semiconductor fabricators. In this paper they cover a period where the productivity of computing went up a million fold, so you could get a million times the computing operations per second per dollar, a big change but it got harder. The amount of investment and the labor force required to make those continuing advancements went up and up and up. It went up 18 fold over that period. Some take this to say — “Oh, diminishing returns. Things are just getting harder and harder and so that will be the end of progress eventually.” However in a world where AI is doing the work, that doubling of computing performance, translates pretty directly to a doubling or better of the effective labor supply. That is, if when we had that million-fold compute increase we used it to run artificial intelligences who would replace human scientists and engineers, then the 18x increase in the labor demands of the industry would be trivial. We're getting more than one doubling of the effective labor
supply than we need for each doubling of the labor requirement and in that data set, it's over four. So when we double compute we need somewhat more researchers but a lot less than twice as many. We use up some of those doublings of compute on the increasing difficulty of further research, but most of them are left to expedite the process. So if you double your labor force, that's enough to get several doublings of compute. You use up one of them on meeting the increased demands from diminishing returns. The others can be used to accelerate the process so you have your first doubling take however many months, your next doubling can take a smaller fraction of that, the next doubling less and so on. At least in so far as
the outputs you're generating, compute for AI in this story, are able to serve the function of the necessary inputs. If there are other inputs that you need eventually those become a bottleneck and you wind up more restricted on this. Got it. The bloom paper said there was a 35% increase in transistor density and there was a 7% increase per year in the number of researchers required to sustain that pace. Something in the vicinity, yeah. Four to five doublings of compute per doubling of labor inputs. I guess there's a lot of questions you can delve
into in terms of whether you would expect a similar scale with AI and whether it makes sense to think of AI as a population of researchers that keeps growing with compute itself. Actually, let's go there. Can you explain the intuition that compute is a good proxy for the number of AI researchers so to speak? So far I've talked about hardware as an initial example because we had good data about a past period. You can also make improvements on the software side and when we think about an intelligence explosion that can include — AI is doing work on making hardware better, making better software, making more hardware. But the basic idea for the hardware is especially simple in that if you have an AI worker that can substitute for a human, if you have twice as many computers you can run two separate instances of them and then they can do two different jobs, manage two different machines, work on two different design problems. Now you can get more gains than just what you would get by having two instances. We get improvements from using some of our compute not just to
run more instances of the existing AI, but to train larger AIs. There's hardware technology, how much you can get per dollar you spend on hardware and there's software technology and the software can be copied freely. So if you've got the software it doesn't necessarily make that much sense to say that — “Oh, we've got you a hundred Microsoft Windows.” You can make as many copies as you need for whatever Microsoft will charge you. But for hardware, it’s different. It matters how much we actually spend
on the hardware at a given price. And if we look at the changes that have been driving AI recently, that is the thing that is really off-trend. We are spending tremendously more money on computer hardware for training big AI models. Okay so there's the investment in hardware, there's the hardware technology itself, and there's the software progress itself. The AI is getting better because we're spending more money on it because our hardware itself is getting better over time and because we're developing better models or better adjustments to those models. Where is the loop here? The work involved in designing new hardware
and software is being done by people now. They use computer tools to assist them, but computer time is not the primary cost for NVIDIA designing chips, for TSMC producing them, or for ASML making lithography equipment to serve the TSMC fabs. And even in AI software research that has become quite compute intensive we're still in the range where at a place like DeepMind salaries were still larger than compute for the experiments. Although more recently tremendously more of the expenditures were on compute relative to salaries.
If you take all the work that's being done by those humans, there's like low tens of thousands of people working at Nvidia designing GPUs specialized for AI. There's more than 70,000 people at TSMC which is the leading producer of cutting-edge chips. There's a lot of additional people at companies like ASML that supply them with the tools they need and then a company like DeepMind, I think from their public filings, they recently had a thousand people. OpenAI is a few
hundred people. Anthropic is less. If you add up things like Facebook AI research, Google Brain, other R&D, you get thousands or tens of thousands of people who are working on AI research. We would want to zoom in on those who are developing new methods rather than narrow applications. So inventing the transformer definitely counts but optimizing for some particular businesses data set cleaning probably not. So those people are doing this work,
they're driving quite a lot of progress. What we observe in the growth of people relative to the growth of those capabilities is that pretty consistently the capabilities are doubling on a shorter time scale than the people required to do them are doubling. We talked about hardware and how it was pretty dramatic historically. Like four or five doublings of compute efficiency per doubling of human inputs. I think that's a bit lower now as we get towards the end of Moore's
law although interestingly not as much lower as you might think because the growth of inputs has also slowed recently. On the software side there's some work by Tamay Besiroglu and collaborators; it may have been his thesis. It's called Are models getting harder to find? and it's applying the same analysis as the “Are ideas getting harder to find?” and you can look at growth rates of papers, from citations, employment at these companies, and it seems like the doubling time of these like workers driving the software advances is like several years whereas the doubling of effective compute from algorithmic progress is faster. There's a group called Epoch, they've received grants from open philanthropy, and they do work collecting datasets that are relevant to forecasting AI progress. Their headline results for what's the rate of progress in hardware and software, and growth in budgets are as follows — For hardware, they're looking at a doubling of hardware efficiency in like two years. It's possible it’s a bit better than that when you take into account certain specializations for AI workloads. For the growth of budgets
they find a doubling time that's something like six months in recent years which is pretty tremendous relative to the historical rates. We should maybe get into that later and then on the algorithmic progress side, mainly using Imagenet type datasets right now they find a doubling time that's less than one year. So when you combine all of these things the growth of effective compute for training big AIs is pretty drastic. I think I saw an estimate that GPT-4 cost
like 50 million dollars or around that range to train. Now suppose that AGI takes a 1000x that, if you were just a scale of GPT-4 it might not be that but just for the sake of example, some part of that will come from companies just spending a lot more to train the models and that’s just greater investment. Part of that will come from them having better models.You get the same effect of increasing it by 10x just from having a better model. You can spend more money on it to train a bigger model, you can just have a better model, or you can have chips that are cheaper to train so you get more compute for the same dollars. So those are the three you are describing the ways in which the “effective compute” would increase? Looking at it right now, it looks like you might get two or three doublings of effective compute for this thing that we're calling software progress which people get by asking — how much less compute can you use now to achieve the same benchmark as you achieved before? There are reasons to not fully identify this with software progress as you might naively think because some of it can be enabled by the other. When you have
a lot of compute you can do more experiments and find algorithms that work better. We were talking earlier about how sometimes with the additional compute you can get higher efficiency by running a bigger model. So that means you're getting more for each GPU that you have because you made this larger expenditure. That can look like a software improvement because this model is not a hardware improvement directly because it's doing more with the same hardware but you wouldn't have been able to achieve it without having a ton of GPUs to do the big training run. The feedback loop itself involves the AI that is the result of this greater effect of compute helping you train better AI or use less effective compute in the future to train better AI? It can help with the hardware design. NVIDIA is a fab-less chip design company. They
don't make their own chips. They send files of instructions to TSMC which then fabricates the chips in their own facilities. If you could automate the work of those 10,000+ people and have the equivalent of a million people doing that work then you would pretty quickly get the kind of improvements that can be achieved with the existing nodes that TSMC is operating on and get a lot of those chip design gains. Basically doing the job of improving chip design that those people are working on now but get it done faster. While that's one thing I think that's less important for the intelligence explosion. The reason being that when you make an improvement to chip design it only applies to the chips you make after that. If you make an improvement in AI software,
it has the potential to be immediately applied to all of the GPUs that you already have. So the thing that I think is most disruptive and most important and has the leading edge of the change from AI automation of the inputs to AI is on the software side At what point would it get to the point where the AIs are helping develop better software or better models for future AIs? Some people claim today, for example, that programmers at OpenAI are using Copilot to write programs now. So in some sense you're already having that feedback loop but I'm a little skeptical of that as a mechanism. At what point would it be the case that the AI is contributing significantly in the sense that it would almost be the equivalent of having additional researchers to AI progress and software? The quantitative magnitude of the help is absolutely central. There are plenty of companies
that make some product that very slightly boosts productivity. When Xerox makes fax machines, it maybe increases people's productivity in office work by 0.1% or something. You're not gonna have explosive growth out of that because 0.1% more effective R&D at Xerox and any customers buying the machines is not that important. The thing to look for is — when is it the case that the contributions from AI are starting to become as large as the contributions from humans? So when this is boosting their effective productivity by 50 or 100% and if you then go from like eight months doubling time for effective compute from software innovations, things like inventing the transformer or discovering chinchilla scaling and doing your training runs more optimally or creating flash attention. If you move that from 8 months to 4
months and then the next time you apply that it significantly increases the boost you're getting from the AI. Now maybe instead of giving a 50% or 100% productivity boost now it's more like 200%. It doesn't have to have been able to automate everything involved in the process of AI research. It can be that it's automated a bunch of things and then those are being done in extreme profusion. A thing AI can do, you can have it done much more often because it's so cheap. And so it's not a threshold of — this is human level AI, it can do everything a human can do with no weaknesses in any area. It's that, even with its weaknesses
it's able to bump up the performance. So that instead of getting the results we would have with the 10,000 people working on finding these innovations, we get the results that we would have if we had twice as many of those people with the same kind of skill distribution. It’s a demanding challenge, you need quite a lot of capability for that but it's also important that it's significantly less than — this is a system where there's no way you can point at it and say in any respect it is weaker than a human. A system that was just as good as a human in every respect but also had all of the advantages of an AI, that is just way beyond this point. If you consider that the output of our existing fabs make tens of millions of advanced GPUs per year. Those GPUs if they were running AI software that was as efficient as humans, it is sample efficient, it doesn't have any major weaknesses, so they can work four times as long, the 168 hour work week, they can have much more education than any human. A human, you got a PhD,
it's like 20 years of education, maybe longer if they take a slow route on the PhD. It's just normal for us to train large models by eat the internet, eat all the published books ever, read everything on GitHub and get good at predicting it. So the level of education vastly beyond any human, the degree to which the models are focused on task is higher than all but like the most motivated humans when they're really, really gunning for it. So you combine the things tens of millions of GPUs, each GPU is doing the work of the very best humans in the world and the most capable humans in the world can command salaries that are a lot higher than the average and particularly in a field like STEM or narrowly AI, like there's no human in the world who has a thousand years of experience with TensorFlow or let alone the new AI technology that was invented the year before but if they were around, yeah, they'd be paid millions of dollars a year. And so when you consider this — tens of millions of GPUs.
Each is doing the work of 40, maybe more of these existing workers, is like going from a workforce of tens of thousands to hundreds of millions. You immediately make all kinds of discoveries, then you immediately develop all sorts of tremendous technologies. Human level AI is deep, deep into an intelligence explosion. Intelligence explosion has to start with something weaker than that. Yeah, what is the thing it starts with and how close are we to that? Because to be a researcher at OpenAI is not just completing the hello world Prompt that Copilot does right? You have to choose a new idea, you have to figure out the right way to approach it, you perhaps have to manage the people who are also working with you on that problem.
It's an incredibly complicated portfolio of skills rather than just a single skill. What is the point at which that feedback loop starts where you're not just doing the 0.5% increase in productivity that an AI tool might do but is actually the equivalent of a researcher or close to it? Maybe a way is to give some illustrative examples of the kinds of capabilities that you might see. Because these systems have to be a lot weaker than the human-level things, what we'll have is intense application of the ways in which AIs have advantages partly offsetting their weaknesses.
AIs are cheap so we can call a lot of them to do many small problems. You'll have situations where you have dumber AIs that are deployed thousands of times to equal one human worker. And they'll be doing things like voting algorithms where with an LLM you generate a bunch of different responses and take a majority vote among them that improves some performance. You'll have things like the AlphaGo kind of approach where you use the neural net to do search and you go deeper with the search by plowing in more compute which helps to offset the inefficiency and weaknesses of the model on its own. You'll do things that would just be totally impractical for humans because of the sheer number of steps, an example of that would be designing synthetic training data.
Humans do not learn by just going into the library and opening books at random pages, it's actually much much more efficient to have things like schools and classes where they teach you things in an order that makes sense, focusing on the skills that are more valuable to learn. They give you tests and exams. They're designed to try and elicit the skill they're actually trying to teach. And right now we don't bother with that because we can hoover up more data from the internet. We're getting towards the end of that but yeah, as the AIs get more sophisticated they'll be better able to tell what is a useful kind of skill to practice and to generate that. We've done that in other areas like AlphaGo. The original version of AlphaGo was booted up with
data from human Go play and then improved with reinforcement learning and Monte-carlo tree search but then AlphaZero, a somewhat more sophisticated model benefited from some other improvements but was able to go from scratch and it generated its own data through self play. Getting data of a higher quality than the human data because there are no human players that good available in the data set and also a curriculum so that at any given point it was playing games against an opponent of equal skill itself. It was always in an area when it was easy to learn. If you're just always losing no matter
what you do, or always winning no matter what you do, it's hard to distinguish which things are better and which are worse? And when we have somewhat more sophisticated AIs that can generate training data and tasks for themselves, for example if the AI can generate a lot of unit tests and then can try and produce programs that pass those unit tests, then the interpreter is providing a training signal and the AI can get good at figuring out what's the kind of programming problem that is hard for AIs right now that will develop more of the skills that I need and then do them. You're not going to have employees at Open AI write a billion programming problems, that's just not gonna happen. But you are going to have AIs given the task of producing the enormous number of programming challenges. In LLMs themselves, there's a paper out of
Anthropic called Constitution AI where they basically had the program just talk to itself and say, "Is this response helpful? If not, how can I make this more helpful” and the responses improved and then you train the model on the more helpful responses that it generates by talking to itself so that it generates it natively and you could imagine more sophisticated or better ways to do that. But then the question is GPT-4 already costs like 50 million or 100 million or whatever it was. Even if we have greater effective compute from hardware increases and better models, it's hard to imagine how we could sustain four or five orders of magnitude greater effective size than GPT-4 unless we're dumping in trillions of dollars, the entire economies of big countries, into training the next version. The question is do we get something that
can significantly help with AI progress before we run out of the sheer money and scale and compute that would require to train it? Do you have a take on that? First I'd say remember that there are these three contributing trends. The new H100s are significantly better than the A100s and a lot of companies are actually just waiting for their deliveries of H100s to do even bigger training runs along with the work of hooking them up into clusters and engineering the thing. All of those factors are contributing and of course mathematically yeah, if you do four orders of magnitude more than 50 or 100 million then you're getting to trillion dollar territory. I think the way to look at it is at each step along the way, does it look like it makes sense to do the next step? From where we are right now seeing the results with GPT-4 and ChatGPT companies like Google and Microsoft are pretty convinced that this is very valuable. You have talk at Google and Microsoft
that it's a billion dollar matter to change market share in search by a percentage point so that can fund a lot. On the far end if you automate human labor we have a hundred trillion dollar economy and most of that economy is paid out in wages, between 50 and 70 trillion dollars per year. If you create AGI it's going to automate all of that and keep increasing beyond that. So the value of the completed project Is very much worth throwing our whole economy into it, if you're going to get the good version and not the catastrophic destruction of the human race or some other disastrous outcome. In between it's a question of — how risky and uncertain is the next step and how much is the growth in revenue you can generate with it? For moving up to a billion dollars I think that's absolutely going to happen. These large tech companies have R&D
budgets of tens of billions of dollars and when you think about it in the relevant sense all the employees at Microsoft who are doing software engineering that’s contributing to creating software objects, it's not weird to spend tens of billions of dollars on a product that would do so much. And I think that it's becoming clearer that there is a market opportunity to fund the thing. Going up to a hundred billion dollars, that's the existing R&D budgets spread over multiple years. But if you keep seeing that when you scale up the model it substantially improves the performance, it opens up new applications, that is you're not just improving your search but maybe it makes self-driving cars work, you replace bulk software engineering jobs or if not replace them amplify productivity. In this kind of dynamic you actually probably want to employ all the software engineers you can get as long as they are able to make any contribution because the returns of improving stuff in AI itself gets so high. But yeah, I think that can go up to a hundred billion. And at a hundred billion you're using a significant fraction of our existing fab capacity. Right now the revenue of NVIDIA is 25 billion, the revenue of TSMC is over 50 billion. I checked in 2021, NVIDIA was maybe 7.5%, less than 10% of TSMC revenue. So there's a lot of room and
most of that was not AI chips. They have a large gaming segment, there are data center GPU's that are used for video and the like. There's room for more than an order of magnitude increase by redirecting existing fabs to produce more AI chips and they're just actually using the AI chips that these companies have in their cloud for the big training runs. I think that that's enough to go to the 10 billion and then combine with stuff like the H100 to go up to the hundred billion.
Just to emphasize for the audience the initial point about revenue made. If it costs OpenAI 100 million dollars to train GPT-4 and it generates 500 million dollars in revenue, you pay back your expenses with 100 million and you have 400 million for your next training run. Then you train your GPT 4.5, you get let's say four billion dollars in revenue out of that.
That's where the feedback group of revenue comes from. Where you're automating tasks and therefore you're making money you can use that money to automate more tasks. On the ability to redirect the fab production towards AI chips, fabs take a decade or so to build. Given the ones we have now and the ones that are going to come online in the next decade, is there enough to sustain a hundred billion dollars of GPU compute if you wanted to spend that on a training run? Yes, you definitely make the hundred billion one. As you go up to a trillion dollar run and larger, it's going to involve more fab construction and yeah, fabs can take a long a long time to build. On the other hand, if in fact you're getting very high revenue from the AI systems and you're actually bottlenecked on the construction of these fabs then their price could skyrocket and that could lead to measures we've never seen before to expand and accelerate fab production. If you
consider, at the limit you're getting models that approach human-like capability, imagine things that are getting close to brain-like efficiencies plus AI advantages. We were talking before a cluster of GPU supporting AIs that do things, data parallelism. If that can work four times as much as a highly skilled motivated focused human with levels of education that have never been seen in the human population, and if a typical software engineer can earn hundreds of thousands of dollars, the world's best software engineers can earn millions of dollars today and maybe more in a world where there's so much demand for AI. And then times four for working all the time. If you can generate close to 10 million dollars a year out of the future version H100 and it cost tens of thousands of dollars with a huge profit margin now. And profit margin could be reduced with large production. That is a big difference that that chip pays for itself almost instantly
and you could support paying 10 times as much to have these fabs constructed more rapidly. If AI is starting to be able to contribute more of the skilled technical work that makes it hard for NVIDIA to suddenly find thousands upon thousands of top quality engineering hires. If AI hasn't reached that level of performance then this is how you can have things stall out. A world where AI progress stalls out is one where you go to the 100 billion and then over succeeding years software progress turns out to stall. You lose the gains that you are getting from moving researchers from other fields. Lots of physicists and people from other areas of computer science have been going to AI but you tap out those resources as AI becomes a larger proportion of the research field. And okay, you've put in all of these inputs, but they just haven't yielded
AGI yet. I think that set of inputs probably would yield the kind of AI capabilities needed for intelligence explosion but if it doesn't, after we've exhausted this current scale up of increasing the share of our economy that is trying to make AI. If that's not enough then after that you have to wait for the slow grind of things like general economic growth, population growth and such and so things slow. That results in my credences and this kind of advanced AI happening to be relatively concentrated, over the next 10 years compared to the rest of the century because we can't keep going with this rapid redirection of resources into AI. That's a one-time thing.
If the current scale up works we're going to get to AGI really fast, like within the next 10 years or something. If the current scale up doesn't work, all we're left with is just like the economy growing 2% a year, we have 2% a year more resources to spend on AI and at that scale you're talking about decades before just through sheer brute force you can train the 10 trillion dollar model or something. Let's talk about why you have your thesis that the current scale up would work. What is the evidence from AI itself or maybe from primate evolution and the
evolution of other animals? Just give me the whole confluence of reasons that make you think that. Maybe the best way to look at that might be to consider, when I first became interested in this area, so in the 2000s which was before the deep learning revolution, how would I think about timelines? How did I think about timelines? And then how have I updated based on what has been happening with deep learning? Back then I would have said we know the brain is a physical object, an information processing device, it works, it's possible and not only is it possible it was created by evolution on earth. That gives us something of an upper bound in that this kind of brute force was sufficient. There are some complexities like what if it was a freak accident and that
didn't happen on all of the other planets and that added some value. I have a paper with Nick Bostrom on this. I think basically that's not that important an issue. There's convergent evolution, octopi are also quite sophisticated. If a special event was at the level of forming cells at all, or forming brains at all, we get to skip that because we're choosing to build computers and we already exist. We have that advantage. So evolution gives something of an upper bound, really intensive massive brute force search and things like evolutionary algorithms can produce intelligence. Isn’t the fact that octopi and other mammals got to the point of being pretty intelligent but not human level intelligent some evidence that there's a hard step between a cephalopod and a human? Yeah, that would be a place to look but it doesn't seem particularly compelling. One source of evidence on that is work by
Herculano-Houzel. She's a neuroscientist who has dissolved the brains of many creatures and by counting the nuclei she's able to determine how many neurons are present in different species and has found a lot of interesting trends in scaling laws. She has a paper discussing the human brain as a scaled up primate brain. Across a wide variety of animals, mammals in particular, there's certain characteristic changes in the number of neurons and the size of different brain regions as things scale up. There's a lot of structural similarity there and you can explain a lot of what is different about us with a brute force story which is that you expend resources on having a bigger brain, keeping it in good order, and giving it time to learn. We have an unusually long childhood. We spend more compute by having a larger brain than other animals,
more than three times as large as chimpanzees, and then we have a longer childhood than chimpanzees and much more than many, many other creatures. So we're spending more compute in a way that's analogous to having a bigger model and having more training time with it. And given that we see with our AI models, these large consistent benefits from increasing compute spent in those ways and with qualitatively new capabilities showing up over and over again particularly in areas that AI skeptics call out. In my experience over the last 15 years the things that people call out are like —”Ah, but the AI can't do that and it's because of a fundamental limitation.” We've gone through a lot of them. There were Winograd schemas, catastrophic forgetting, quite a number
and they have repeatedly gone away through scaling. So there's a picture that we're seeing supported from biology and from our experience with AI where you can explain — Yeah, in general, there are trade-offs where the extra fitness you get from a brain is not worth it and so creatures wind up mostly with small brains because they can save that biological energy and that time to reproduce, for digestion and so on. Humans seem to have wound up in a self-reinforcing niche where we greatly increase the returns to having large brains. Language and technology are the obvious candidates. You have humans around you who know a lot of things and
they can teach you. And compared to almost any other species we have vastly more instruction from parents and the society of the [unclear]. You're getting way more from your brain than you get per minute because you can learn a lot more useful skills and then you can provide the energy you need to feed that brain by hunting and gathering, by having fire that makes digestion easier. Basically how this process goes on is that it's increasing the marginal increase in reproductive fitness you get from allocating more resources along a bunch of dimensions towards cognitive ability. That's bigger brains, longer childhood, having our attention be more on learning. Humans play a lot and we keep playing as
adults which is a very weird thing compared to other animals. We're more motivated to copy other humans around us than the other primates. These are motivational changes that keep us using more of our attention and effort on learning which pays off more when you have a bigger brain and a longer lifespan in which to learn in. Many creatures are subject to lots of predation or disease. If you're mayfly or a mouse and if you try and invest in a giant brain and a very long childhood you're quite likely to be killed by some predator or some disease before you're actually able to use it. That means you actually have exponentially increasing costs in a
given niche. If I have a 50% chance of dying every few months, as a little mammal or a little lizard, that means the cost of going from three months to 30 months of learning and childhood development is not 10 times the loss, it’s 2^-10. A factor of 1024 reduction in the benefit I get from what I ultimately learn because 99.9 percent of the animals will have been killed before
that point. We're in a niche where we're a large long-lived animal with language and technology so where we can learn a lot from our groups. And that means it pays off to just expand our investment on these multiple fronts in intelligence. That's so interesting. Just for the audience the calculation about like two to the whatever months is just like, you have a half chance of dying this month, a half chance of dying next month, you multiply those together. There's other species though that do live in flocks or as packs. They do have a smaller version of the development of cubs that play with each other. Why isn't this a hill on which they could have
climbed to human level intelligence themselves? If it's something like language or technology, humans were getting smarter before we got language. It seems like there should be other species that should have beginnings of this cognitive revolution especially given how valuable it is given we've dominated the world. You would think there would be selective pressure for it. Evolution doesn't have foresight. The thing in this generation that gets more surviving offspring and grandchildren is the thing that becomes more common. Evolution doesn't look ahead and think oh in a million years you'll have a lot of descendants. It's what survives and reproduces now. In fact, there are correlations where social animals do on average have larger brains and
part of that is probably the additional social applications of brains, like keeping track of which of your group members have helped you before so that you can reciprocate. You scratch my back, I'll scratch yours. Remembering who's dangerous within the group is an additional application of intelligence. So there's some correlation there but what it seems like is that
in most of these cases it's enough to invest more but not invest to the point where a mind can easily develop language and technology and pass it on. You see bits of tool use in some other primates who have an advantage compared to say whales who have quite large brains partly because they are so large themselves and they have some other things, but they don't have hands which means that reduces a bunch of ways in which brains can pay off and investments in the functioning of that brain. But yeah, primates will use sticks to extract termites, Capuchin monkeys will open clams by smashing them with a rock. But what they don't have is the ability to sustain culture. A particular primate will maybe discover one of these tactics and it'll be copied by their immediate group but they're not holding on to it that well. When they see the other animal do it they can copy it in that situation but they don't actively teach each other in their population. So it's easy to forget things, easy to lose information and in fact they remain technologically stagnant for hundreds of thousands of years.
And we can look at some human situations. There's an old paper, I believe by the economist Michael Kramer, which talks about technological growth in the different continents for human societies. Eurasia is the largest integrated connected area. Africa is partly connected to it but the Sahara
desert restricts the flow of information and technology and such. Then you have the Americas after the colonization from the land bridge were largely separated and are smaller than Eurasia, then Australia, and then you had smaller island situations like Tasmania. Technological progress seems to have been faster the larger the connected group of people. And in the smallest groups, like Tasmania where you had a relatively small population, they actually lost technology. They lost some fishing techniques. And if you have a small population and you have
some limited number of people who know a skill and they happen to die or there's some change in circumstances that causes people not to practice or pass on that thing then you lose it. If you have few people you're doing less innovation and the rate at which you lose technologies to some local disturbance and the rate at which you create new technologies can wind up imbalanced. The great change of hominids and humanity is that we wound up in this situation where we were accumulating faster than we were losing and accumulating those technologies allowed us to expand our population. They created additional demand for intelligence so our brains became three times as large as chimpanzees and our ancestors who had a similar brain size. Okay. And the crucial point in relevance to AI is that the selective pressures against
intelligence in other animals are not acting against these neural networks because they're not going to get eaten by a predator if they spend too much time becoming more intelligent, we're explicitly training them to become more intelligent. So we have good first principles reason to think that if it was scaling that made our minds this powerful and if the things that prevented other animals from scaling are not impinging on these neural networks, these things should just continue to become very smart. Yeah, we are growing them in a technological culture where there are jobs like software engineer that depend much more on cognitive output and less on things like metabolic resources devoted to the immune system or to building big muscles to throw spears. This is kind of a side note but I'm just kind of interested. You referenced Chinchilla scaling at some point. For the audience this is a paper from DeepMind which describes if you have a model of a certain size what is the optimum amount of data that it should be trained on? So you can imagine bigger models, you can use more data to train them and in this way you can figure out where you should spend your compute. Should you spend it on making the model bigger or should you spend it on training it for longer? In the case of different animals, in some sense how big their brain is like model sizes and they're training data sizes like how long they're cubs or how long their infants or toddlers before they’re full adults. I’m curious, is there some kind of scaling law?
Chinchilla scaling is interesting because we were talking earlier about the cost function for having a longer childhood where it's exponentially increasing in the amount of training compute you have when you have exogenous forces that can kill you. Whereas when we do big training runs, the cost of throwing in more GPU is almost linear and it's much better to be linear than exponentially decay as you expend resources. Oh, that's a really good point. Chinchilla scaling would suggest that for a brain of human size it would be optimal to have many millions of years of education but obviously that's impractical because of exogenous mortality for humans. So there's a fairly compelling argument that relative
to the situation where we would train AI that animals are systematically way under trained. They're more efficient than our models. We still have room to improve our algorithms to catch up with the efficiency of brains but they are laboring under that disadvantage. That is so interesting. I guess another question you could have is:
Humans got started on this evolutionary hill climbing route where we're getting more intelligent because it has more benefits for us. Why didn't we go all the way on that route? If intelligence is so powerful why aren't all humans as smart as we know humans can be? If intelligence is so powerful, why hasn't there been stronger selective pressure? I understand hip size, you can't give birth to a really big headed baby or whatever. But you would think evolution would figure out some way to offset that if intelligence has such big power and is so useful. Yeah, if you actually look at it quantitatively that's not true and even in recent history it looks like a pretty close balance between the costs and the benefits of having more cognitive abilities. You say, who needs to worry about the metabolic costs? Humans put 20 percent of our metabolic energy into the brain and it's higher for young children. And then there's like breathing and digestion and the immune system. For
most of history people have been dying left and right. A very large proportion of people will die of infectious disease and if you put more resources into your immune system you survive. It's life or death pretty directly via that mechanism. People die more of disease during famine and so there's boom or bust. If you have 20% less metabolic requirements [unclear] you're much more likely to survive that famine. So these are pretty big. And then there's a trade-off about just cleaning mutational load. So every generation new mutations and errors happen in the process of reproduction. We know there are many genetic abnormalities that
occur through new mutations each generation and in fact Down syndrome is the chromosomal abnormality that you can survive. All the others just kill the embryo so we never see them. But down syndrome occurs a lot and there are many other lethal mutations and there are enormous numbers of less damaging mutations that are degrading every system in the body. Evolution each generation has to pull away at some of this mutational load and the priority with which that mutational load is pulled out scales in proportion to how much the traits it is affecting impact fitness. So you get new mutations that impact your resistance to malaria, you got new mutations that damage brain function and then those mutations are purged each generation. If malaria is a bigger difference in mortality than the incremental effectiveness as a hunter-gatherer you get from being slightly more intelligent, then you'll purge that mutational load first. Similarly humans have been vigorously adapting to new circumstances. Since agriculture people have been developing things like the
ability to have amylase to digest breads and milk. If you're evolving for all of these things and if some of the things that give an advantage for that incidentally carry along nearby them some negative effect on another trait then that other trait can be damaged. So it really matters how important to survival and reproduction cognitive abilities were compared to everything else the organism has to do. In particular, surviving famine, having the physical abilities to do hunting and gathering
and even if you're very good at planning your hunting, being able to throw a spear harder can be a big difference and that needs energy to build those muscles and then to sustain them. Given all these factors it's not a slam dunk to invest at the margin. And today, having bigger brains is associated with greater cognitive ability but it's modest.
Large-scale pre-registered studies with MRI data. The correlation is in a range of 0.25 - 0.3 and the standard deviation of brain size is like 10%. So if you double the size of the brain, the existing brain costs like 20 of metabolic energy go up to 40%, okay, that's like eight standard deviations of brain size if the correlation is 0.25 then yeah, you get a gain from that eight standard deviations of brain size, two standard
deviations of cognitive ability. In our modern society, where cognitive ability is very rewarded and finishing school and becoming an engineer or a doctor or whatever can pay off a lot financially, the average observed return in income is still only one or two percent proportional increase. There's more effects at the tail, there's more effect in professions like STEM but on the whole it's not a lot. If it was like a five percent increase or a 10 percent increase then you could tell a story where yeah, this is hugely increasing the amount of food you could have, you could support more children, but it's a modest effect and the metabolic costs will be large and then throw in these other these other aspects. Else we can just see there was not
very strong rapid directional selection on the thing which would be there if by solving a math puzzle you could defeat malaria, then there would be more evolutionary pressure. That is so interesting. Not to mention of course that if you had 2x the brain size, without c-section you or your mother or both would die. This is a question I've actually been curious about for over a year and I’ve briefly tried to look up an answer. I know this was off topic and my apologies to the audience, but I was super interested and that was the most comprehensive and interesting answer I could have hoped for. So yeah, we have a good explanation or good first principles evolution or reason for thinking that intelligence scaling up to humans is not implausible just by throwing more scale at it. I would also add that we also have the brain
right here with us available for neuroscience to reverse engineer its properties. This was something that would have mattered to me more in the 2000s. Back then when I said, yeah, I expect this by the middle of the century-ish, that was a backstop if we found it absurdly difficult to get to the algorithms and then we would learn from neuroscience. But in actual history, it's really not like that. We develop things in AI and then also we can say oh, yeah, this is like this thing in neuroscience or maybe this is a good explanation. It's not as though neuroscience
Is driving AI progress. It turns out not to be that necessary. I guess that is similar to how planes were inspired by the existence proof of birds but jet engines don't flap. All right, good reason to think scaling might work. So we spent a hundred billion dollars and we have something that is like human level or can help significantly with AI research. I mean that that might be on the earlier end but I definitely would not rule that out given the rates of change we've seen with the last few scale ups. At this point somebody might be skeptical. We already have a bunch of human researchers, how profitable is the incremental researcher? And then you might say no, this is thousands of researchers. I don’t know how to express this skepticism exactly. But skeptical
of just generally the effect of scaling up the number of people working on the problem to rapid-rapid progress on that problem. Somebody might think that with humans the reason the amount of population working on a problem is such a good proxy for progress on the problem is that there's already so much variation that is accounted for. When you say there's a million people working on a problem, there's hundreds of super geniuses working on it, thousands of people who are very smart working on it. Whereas with an AI all the copies are the same level of intelligence and if it's not super genius intelligence the total quantity might not matter as much.
I'm not sure what your model is here. Is the model that the diminishing returns kickoff, suddenly has a cliff right where we are? There were results in the past from throwing more people at problems and this has been useful in historical prediction, this idea of experience curves and [unclear] law measuring cumulative production in a field, which is also going to be a measure of the scale of effort and investment, and people have used this correctly to argue that renewable energy technology, like solar, would be falling rapidly in price because it was going from a low base of very small production runs, not much investment in doing it efficiently, and climate advocates correctly called out, people like David Roberts, the futurist [unclear] actually has some interesting writing on this. They correctly called out that there would be a really drastic fall in prices of solar and batteries because of the increasing investment going into that. The human genome project would be another. So I’d say there's real evidence. These observed correlations, from ideas getting harder to find, have held over a fair range of data and over quite a lot of time. So I'm wondering what‘s the nature of the deviation you're thinking of? Maybe this is a good way to describe what happens when more humans enter a field but does it even make sense to say that a greater population of AIs is doing AI research if there's like more GPUs running a copy of GPT-6 doing AI research. How applicable are these economic models of the
quantity of humans working on a problem to the magnitude of AIs working on a problem? If you have AIs that are directly automating particular jobs that humans were doing before then we say, well with additional compute we can run more copies of them to do more of those tasks simultaneously. We can also run them at greater speed. Some people have an intuition that what matters is time, that it's not how many people working on a problem at a given point. I think that doesn't bear out super well but AI can also run faster than humans. If you have a set of AIs that can do the work of the individual human researchers and run at 10 times or 100 times the speed. And we ask well, could the human research community have solved these algorithm problems, do
things like invent transformers over 100 years, if we have AIs with a population effective population similar to the humans but running 100 times as fast and so. You have to tell a story where no, the AI can't really do the same things as the humans and we're talking about what happens when the AIs are more capable of in fact doing that. Although they become more capable as lesser capable versions of themselves help us make themselves more capable, right? You have to kickstart that at some point. Is there an example in analogous situations? Is intelligence unique in the sense that you have a feedback loop of — with a learning curve or something else, a system’s outputs are feeding into its own inputs. Because if we're talking about something like Moore's law
or the cost of solar, you do have this way where we're throwing more people with the problem and we're making a lot of progress, but we don't have this additional part of the model where Moore's law leads to more humans somehow and more humans are becoming researchers. You do actually have a version of that in the case of solar. You have a small infant industry that's doing things like providing solar panels for space satellites and then getting increasing amounts of subsidized government demand because of worries about fossil fuel depletion and then climate change. You can have the dynamic where visible successes with solar and lowering prices then open up new markets. There's a particularly huge transition where renewables become cheap
enough to replace large chunks of the electric grid. Earlier you were dealing with very niche situations like satellites, it’s very difficult to refuel a satellite in place and in remote areas. And then moving to the sunniest areas in the world with the biggest solar subsidies. There was an element of that where more and more investment has been thrown into the field and the market has rapidly expanded as the technology improved. But I think the closest analogy is actually the long run growth of human civilization itself and I know you had Holden Karnofsky from the open philanthropy project on earlier and discuss some of this research about the long run acceleration of human population and economic growth. Developing new technologies allowed the human population to expand and humans to occupy new habitats and new areas and then to invent agriculture to support the larger populations and then even more advanced agriculture in the modern industrial society. So there, the total technology and output allowed you
to support more humans who then would discover more technology and continue the process. Now that was boosted because on top of expanding the population the share of human activity that was going into invention and innovation went up and that was a key part of the industrial revolution. There was no such thing as a corporate research lab or an engineering university prior to that. So you're both increasing the total human population and the share of it going in. But this population dynamic is pretty analogous. Humans invent farming,
they can have more humans, they can invent industry and so on. Maybe somebody would be skeptical that with AI progress specifically, it’s not just a matter of some farmer figuring out crop rotation or some blacksmith figuring out how to do metallurgy better. In fact even to make the 50% improvement in productivity you basically need something on the IQ that's close to Ilya Sutskever. There's like a discontinuous line. You’re contributing
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