Scaling AI, agent-led future, and race to AGI
Will we see a one million GPU cluster opening up sometime in the next three years? Kate Soule is a director of technical product management at Granite. Kate, welcome back to the show. What do you think? No, I really don't think so. Anthony Annunziata is director of AI Open Innovation. Anthony, welcome to the show for the first time.
What's your take? I don't think so either. And then we've got a very special guest, Naveen Rao is VP of AI at Databricks. I think our first external guest on Mixture of Experts. Naveen, what do you think? Unlikely. Um, I think there will be a reset in terms of, uh, expectations, ROIs, and that's probably going to drive a little more rationality into building this out.
All right. All that and more on today's Mixture of Experts. I'm Tim Hwang welcome to Mixture of Experts.
Each week MOE brings you the insights you need to navigate the ever changing, ever unpredictable world of artificial intelligence. Today we're going to be talking about 2025, what the future holds for agents, what the future holds for AGI, but first let's talk about the future of scale. AI companies have basically been chasing scale.
Unless you've been living under a rock, that won't be something that's unfamiliar to you. And kind of where that has been most prominent has been in data centers and power. McKinsey just came out with a report that estimated that global demand for data centers could triple by 2030 with generative AI driving huge increases in energy consumption. And, you know, their estimate, which is mind boggling, right, is that spend will be 250 billion.
Um, for this infrastructure by 2030, um, and so I guess maybe Kate, maybe I'll kick it to you first. Can you give our listeners a little bit of intuition for like why all these companies are chasing scale and why that's been important to the history of AI so far? Yeah, sure thing, Tim. So if you think of how these models have trained and evolved over time, it's basically been a really simple formula of taking as much data as you can get.
Adding as much compute as you have access to and training a model for as long as you can afford it in order to maximize performance and send that out. So, you know, to date, the recipe for scale has been a mixture of getting more data and getting more compute. And obviously that's going to continue to drive, uh, costs and potentially drive demand for data centers. I think there's going to be some interesting things that start to emerge, though, that are going to maybe break some of the trends that we've seen. For one, we're just running out of data.
Uh, we're seeing all the data is being, you know, used no matter the model size, it's no longer scaling proportional to size. And there's only so much, uh, data out there that's worth training. We're also seeing a lot more compute being starting to spend at inference time instead of just training time. So as we continue to max out what we can bake, pre bake into the model as it starts to train, we're starting to see are there other places, like when the model runs inference, that we could spend some extra compute to try and boost performance. So that also might start to break some of those trends. That's great.
Well, Naveen, maybe I'll turn to you because I know in your opening comment when we were talking a little bit before the show, you were saying that, hey, look, you know, maybe scale is not all you need, right? And that we're going to have to kind of really evaluate how we do machine learning. And I guess maybe to kind of take Kate's comment there, you know, like, why shouldn't we believe like it's been working so far? Like, why shouldn't it keep working? Basically, like, it feels like, you know, we had these huge successes, just kind of doing the dumb thing, which is add more data and add more compute. Um, why is, why is now different? Right? Like, you know, well, I think you also got to look at the motivations. I mean, I was a scale maximalist for a long time. I mean, I, I started the first AI chip company, uh, back in 2014, and we built it for scale from day one. It was designed to be a scale out sort of a thing.
And, uh, I'll offer a different explanation. I mean, yes, everything Kate said is correct, but there's also another motivation. As an engineer, it's a really freaking cool problem to scale something bigger and bigger and bigger. It's just cool. And I've been seduced by that myself.
Like, oh, this is cool. I want to build that, you know? And like, there's, there's interesting challenges that get presented each time. Like the, you know, the, the, the latency starts to matter. How do I deal with that? Can I come up with new strategies? So.
It's one of these things that's like, it's like an intellectual pursuit. I'm like, I'm going to keep going bigger and bigger and bigger. And you know, it, it is a cool problem. It is a fun problem, but at some point you've got to solve problems, not just for their own sake.
And, uh, and I think that's what we've come to come to now. Like Kate said, we have run out of data, but also the paradigm is simply trying to train on more data isn't going to yield more results. And I'm happy to go into why, um, because. These, these things are essentially conditional probability estimators, and you can never uncover every conditional probability in the data you have. You will, you will, I've said it many times, you will get to the eventual heat death of the universe before you will uncover all of those.
So I think there, there is always going to be some, um, return from getting bigger and more data, but like it's diminishing, uh, for real world applications. So you need a new paradigm. Yeah, for sure. And do you want to talk a little bit about what you think that new paradigm is? I mean, in some ways, it's like the multi billion dollar question. But, you know, while we're speculating around 2025, kind of curious if like you've got intuitions on, okay, if not this, like it actually turns out the data is failing us, which is kind of a crazy thing to say, but it's like, well, what, what comes after data, right? Data is kind of what we know in some sense, if you think about like trying to train these models.
Yeah, I think there's several facets to it. Okay. So I'll say on the algorithmic side, like It's intuitive.
If anyone's been around, you know, a child learning or even trying to train an animal, um, you don't train it through exhaustive, um, observation. You don't put a kid in front of every observation of how to do a task and then expect them to learn it. That's exactly, that's what we're asking right now of an AI model.
So, we actually do it through a trial and error of actually performing something, getting a reward or an anti reward for, uh, for, for performance. You're talking reinforcement learning. Yeah, I mean, that's, that's a big part of it.
We do some form of reinforcement learning with neural networks. Uh, to be clear, uh, it's kind of a weak version, but it is, it is there. So this concept does exist, but it's also predicated on this huge, you know, set of distributions that has been trained upon. And what animals tend to do is actually be much more efficient. They, they observe some, they build some, some baseline distributions and then they act and update these distributions kind of all at once.
So I, I think something towards that end is going to be the answer. There's no doubt in my mind it has to work that way, because this way we can be much more compact with our representations. We can actually discern causality. Causality may or may not exist from a physics standpoint, but the reality is, it's a more compact way to describe how the world tends to work, right? And so I think this is something that has to be uncovered, uh, in our models.
We can't make it just hugely observational. It's not going to work. Yeah, that's super fascinating and it's actually kind of funny to think that like the recent history of deep learning is kind of out of order, right? Like I remember in the AlphaGo era, it was like everything's going to be reinforcement learning and then that kind of just like sort of petered out as all of these other approaches kind of had success. But almost you're saying like we kind of got to get back to that, like that's actually true. I actually think AlphaFold and AlphaZero were very much on the right track.
I think we didn't have the scale part figured out yet. Um, but honestly, I think it was the right approach. I'd present also a complementary perspective, which is maybe a simple one. So research is hard. Research in AI is hard.
When you find something that works, uh, people jump on it and they run, right? So what happened a couple years ago is that, Um, and when that happens, there's kind of irrational exuberance almost, right, in the research community. Sometimes we think decisions are made, uh, more deeply than that, but sometimes you just find something that works, and you push it as hard as you can until it stops working as well, or until other things catch up, including, you know, costs and ROI. 100%.
Yeah, for sure. And I think what's interesting, I mean, your title is, uh, looking at specifically open innovation. And I did, do think that is one thing to talk a little bit about is, you know, traditionally, traditionally, and by traditionally, I mean, like last 36 months, right? Like a lot of the breakthroughs have been, you have access to this really big computer that no one else has access to.
And I guess, I don't know, Anthony, if your predictions about how these dynamics change, right? Like if scale is no longer the thing that really gets the breakthrough, are there just more opportunities elsewhere now? Like there's going to be more people who can do you know, kind of advance the state of the art here without necessarily having to have access to a million GPU cluster. Yeah, I think so. Uh, just taking a little bit of what Naveen was saying, um, innovation at the architectural level, innovation at the feedback level, innovation in how AI systems are built, like there's a huge opportunity for that. Um, In the open community and universities in players that I think have been left on the sidelines, but have struggled to catch up with the scale story, right, just because of the centricity of compute.
I think we're going to see even more of that. I think it's really important. I think the other side of it is the product of a couple years of just pushing ahead really hard. Is that we have great, um, open models out there, uh, that are very capable. And you've already seen a flourishing of innovation with them. But, um, there's a lot more to go just with what we've built already.
And, and, you know, what's going to continue to come out. For sure. I want to force the panel to make some concrete predictions here, right? I think one of the interesting things about scale is there's always the dream. If you like flip over a few more cards, you know, maybe the model is just going to get that much better. And so like, it feels like this kind of like the gas could run out of the scale car Before we realize that kind of scale is broken, but I'm kind of curious, like is 2025 the year where scale sort of breaks like we're just like, actually, it turns out that this is not going to work anymore. I think it already broke.
You think it already broke? Why? Why do you think that? Show me a bigger model than gbt for no one built one. And there's a good reason for it, right? They probably have built one, but it didn't do anything all that special. Right? Right. Uh, and I think that's been the issue, is I think it's already I asked for hot takes, and it feels like you're really delivering for us in the opinion. Yeah, there you go, right? Yeah.
Show me something bigger than 1. 6 trillion parameters. I mean, not to say that there won't be a way that that does yield advantages, but there's got to be more to it.
It's not the only ingredient, scale is not the only ingredient, you need that plus something else and maybe then you'll get some super intelligence or whatever you want to call it, but we haven't cracked that yet. Is, uh, Kate, Anthony, I don't know if you'd agree, is, is scale already failed? Right? Like, are we, we're already living in a kind of post scale world, basically? I mean, I think there's an important part of the story that we haven't covered yet, which is part of the advantage of scale right now is being able to then boost the performance of smaller models. So maybe the performance of how far we can push the top of the spectrum has been maxed out to some degree, just on pure size alone, but I think there's still a lot more to talk about in terms of.
how to scale the performance and the amount of performance you can pack into fewer and fewer parameters on the smaller model sizes, using those large models as teacher models, as synthetic data generators, as, uh, you know, using them in our AIF workflows in order to better improve smaller models. So we've seen a trend, right? If you look at what, you know, a model could do last year, you could do that if it took 70 billion parameters or a hundred billion or a trillion parameters last year, you can do many of those same tasks in fewer than 8 billion parameters today. I don't think we've maxed out that curve of downsizing and packing more and more performance into smaller and smaller models.
Yeah, the commercial dynamics of that are really interesting because you know the rhetoric has often been we're gonna train this massive model And then we're gonna sell an API against it right like basically that it's gonna be an external phenomenon Okay, you're almost presaging a world where like the each of the big labs will have their gigantic model But it'll kind of be for internal purposes almost it's like for minting things The smaller models that really are absolutely the commercial action. I think there's like this huge competitive advantage that model providers have simply by having their own in house large model to boost and create the smaller models that everyone's actually going to use. No one wants to run a trillion parameter model for real tasks that inference done as cool as it is.
It's cool. Everyone wants to say they have it, but no one wants to actually use it in real world applications, right? It's the smaller models that will be much more cost effective. I'll offer another set of data.
So I'm a neuroscientist, uh, um, from grad school. And, you know, I think I, I like to look at biology as a, as a blueprint for many of these things, because over 4 billion years of evolution, you know, some, some interesting things came about. And, uh, if you look at brains, scale was not all you needed.
Uh, humans do not have the largest brains in the animal kingdom. You know, brains do scale with body size, so blue whales have the largest brain by mass. Um, it's actually very likely also more neurons. Dolphins have very large brains as well. So, uh, there are, and elephants. So there are lots of mammals that have larger brains than us, but clearly haven't had the same impact on the world as we've had.
So, I mean, there are several reasons for that, but I actually argue there's some architectural differences. in their brains that, that lead to this. And we came up with the right, um, mix of scale, architecture, and environment to actually, you know, build human intelligence. Yeah, I like that. It's almost like the adage like super intelligence is not all you need, right? It's basically like yeah, like you might have a huge brain, but actually its impact may be actually quite limited in some ways Yeah, yeah, that's a whole other topic I'd love to love to dive into if you want, but like I don't even know what the hell super intelligence is, right? Like how do we even define this? I mean have some definitions, but I think everyone's like talking about, oh, it's a foregone conclusion. It's happening in two years.
Like guys, we haven't even solved regular intelligence. You can't even define it. So we will, we will definitely get to that. I guess, Anthony, maybe I'll turn to you on predictions and we'll close out this segment is so, I mean, I would just observe, right? Like the contracts to build these massive data centers are happening now, right? Like regardless of what's happening in scale lands.
Hardware is data centers are certainly scaling. So is it kind of like we're going to see in 36 years or 36 months basically just like These huge facilities just kind of mothballs like we're gonna have big empty data centers is kind of the future No, I don't think that's going to happen. I think there'll be some correction, but I think it'll be a smooth correction Also, I think what's really important is we you know, we focused a bit on the training part of scaling, right? So the scaling of deployment, whether they're medium sized models, small models, APIs to big models or whatnot, will absolutely depend on the availability of cloud data centers. So I think the trend is reasonable, maybe it's inflated a bit, but I don't think it's going to, uh, I would agree with that as well.
So if we look at, again, where there is opportunities to add something that's not scale into the equation to try and improve and boost performance, I think we're starting to see there's a lot more innovation that we can do at a single model, regardless of its scale at runtimes, allowing it to run multiple times, generate multiple answers is a very basic example, um, in order to boost performance for any, any given inference. And if that trend continues, then we have a whole much larger population that's going to be driving up inferencing costs. And they only have to pay for their small fraction or part of it versus training, right? You have to get these big model providers to dump tons and billions of dollars into building these compute centers. But if everyone can start to see that lift and, you know, have their own, uh, ROIs that they can take advantage of, I think that's going to continue to drive the investment at inference time compute. Even more so than what we have today, you know, any given API call could, you know, cost 10 times what it does today just because it could be worth it from a performance gain perspective.
Yeah, for sure. I think that'll be really sort of interesting to see as you build these big data centers being like, we're going to do the mother of all training runs. And it's like actually like we need it for inference. So, yeah, I think that's a whole scale AI practically that, you know, really just started and I think, uh, yeah, I fully agree with Kate. There's some parallels to, uh, the internet build out as well.
Like, I think a lot of, there was a lot of talk around, around 2000 timeframe when the stock market crashed, that, oh my God, did we overbuild, you know, a bunch of network infrastructure, blah, blah, blah. And, you know, in the fullness of time, none of that was true. It was underbuilt, if anything.
Uh, but it, you know, it took a few years. There was an overbuild for a short period, like maybe two or three years. Until all the demand caught up. And I think you're absolutely right. That's where we're going to end up, probably, is like, it's not going to be like these data centers are way, way fallow, but Everyone's gonna, there's gonna be a bunch of articles that say like everything was overbilled, the bubbles burst, and then in two years it'll all make sense. Wouldn't that be a nice change? High availability of GPU compute at reasonable prices.
I think it's already true, honestly. It's true, it's true. Dream the impossible dream.
I'm going to move us on to our second segment. Um, so if there is one word that has characterized enterprise and AI in 2024, it has been, uh, agents. Agents, agents, agents. Uh, even on this show, it's become a little bit of an in joke that like, agents need to come up at least once during the course of the episode. And there is news out, that Salesforce, uh, is planning on hiring one thousand salespeople to support its push into the agents market. Um, as we kind of get into November here and start thinking about 2025, I just want to ask, like, is the future really agents? Like, are we going to continue to live? Like, I'm going to have to hear more about agents in 2025.
Um, I guess maybe Naveen, I'll kick it over to you first, is like, how do you think this market's going to evolve? And are we, are we about to like, is hiring a thousand salespeople justified here? Well, honestly, no, um, because I know the state of the art of where agents are, but, you know, it's a great headline and that's what they do. I mean, Salesforce is great at this and it's fine. You know, it's going to make them try to appear as more of a big AI player and that's what they're going for, uh, with that statement. So, uh, I think it'll serve their needs. I don't think it's actually necessary because when an agent's really good, when an agent really works, you're not going to have to do much to sell it.
Honestly, it'll just automate things, but we're not there yet. And I think that's where. The hype is a little bit ahead.
And again, it's going to be one of these things. It'll be a big disillusionment in the next two years. And then it'll come back slowly and it'll actually be super useful in three or four years. That's kind of how this is all going to work. So you have to join us November 2025.
And then maybe then Naveen will be like, eh, maybe. Yeah, exactly. But I, but I think, you know, it's not, you're not going to need a thousand people just to focus on agents.
It's just going to, it's going to be something that's going to be amazing for the products and people will use it and their, and their, their sales infrastructure should be able to handle such a thing. I mean, at Databricks, we have similar problems. You know, we've actually decided, should we hire, like we, we gone through this, we hire a whole bunch of people to sell AI, or should we try to like layer it into the product? We've actually done a mix. We haven't hired a thousand, but we have hired some people. And, uh, you know, it comes with mixed success because What you need to do is really integrate it into how people use the tools and make it somewhat invisible. And then it will sell itself.
Yep. For sure. Anthony, I see you nodding vigorously.
I mean, maybe I can just ask you to go a little bit more into almost like what Naveen said is like, almost like the promises right now are not necessarily matching up with where we are. I'm curious if you've got thoughts on like where the gaps are at the moment. Yeah. A few thoughts. So look, first, you know, what is an AI agent? What is an agent in general? Like there's a large spectrum of what that means.
I think if you look at some of the announcements, like the one you referenced, uh, the use of agent is kind of, uh, a relatively early version in terms of the level of automation and, you know, task automation and execution. So like. If by agent we mean, you know, a chat experience that has a bit more of a lookup and, you know, search capability and the ability to ask sort of questions to get the right data and things like that, right? A little bit more interactive, a little bit more, you know, kind of implicit reasoning. I think we've already seen that. I think that'll, you know, steadily and incrementally grow. If instead we're talking about an agent, like give it a goal and it'll go off and interact with a large variety of systems and execute without any supervision, like no way, right? There's so many steps with compounded error across that whole, that whole environment.
Like we can't even get high accuracy basic Q& A in many industry domains yet. Like no way we're going to get that level of automated agent execution. So I think like, like any story, there's a piece of it that's, that's valid and true and will grow.
I think there's a long tail of research that has to be done to get this kind of full fruition of uh, what an agent might mean. Yeah, for sure. And it's kind of funny.
I mean, one of the adage about AI is always like, we don't know what we're talking about. People just say AI to refer to everything, like, do you mean linear regression? That's not AI, you know? And I think that like, almost, it sounds like Anthony, you're kind of arguing that that's almost happening in the agent market where it's kind of like the word has become so broad that it's like, Yeah. You know, are you, are you just talking rag? Because like, if that's the case, then sure agents exist. That's right. I agree.
It's the, there's, there's a definite stretching of the definition here. Okay. Maybe I can ask you to jump in. I mean, so are you ultimately, I guess, you know, because Anthony had these two pictures of the world.
One is like, is it just a chat bot that looks things up for you? And the other one is like, you tell the agent to do something and it does the whole thing in the real world. Are you kind of an optimist? Like, do you think we're going to get there to that second vision? Or is that going to be like way, way off from your point of view? Yeah. So, I'm pretty skeptical of the broad definition of agent as it exists today, you know, an agent is really just a long prompt right now, it's like a multi page prompt where you're asking a model very nicely to do five different things and to always think in a specific order and to call APIs a specific way, and it works pretty well. Pretty well, but you know is not controllable.
There's no real thought yet in my mind on like what are the control points that need to be inserted along an agentic workflow in order to have any degree of robustness and reliability deployed out in the world. And You know, ultimately, I think that there's a lot of work to do to transition from here's a four page kind of word vomit of everything I want an agent to do and it goes off and does the thing to here is a very controllable program that I've executed that has very clear rules, some of which are at the system level, some of which are at the model level that can go out and execute a series of tasks within a certain degree of Freedom, um, not, uh, not unlimited. And I worry that right now everyone's just so amazed that if I give a model four pages worth of instructions, it can do a reasonable job.
It can do, I mean, I can't read four pages worth of instructions, remember everything I'm supposed to do. So like, well, yeah, it's really impressive. And I see a lot of excitement and hype being built around it. But if we're not careful, like we're just going to keep going down this road of how do I cram more and more instructions into this? prompt for what I want the model to do and not really focus on like what are the control points needed for AI enabled workflows to be automated out in the world and does chat even need to be a part of them? Agent also I think really connotes like having a conversation or a dialogue and I think a lot of the opportunities for AI and where we're going to be incentivized to build AI are not necessarily chat based and so there's I think just a lot of Evolution that's going to be needed for agents to really find their their application and actually get traction. Yeah, it's pretty interesting to hear that like I think like that did that the chat thing actually might be totally just this kind of Mistake of history and like the long term evolution of this stuff is like Actually, it's like kind of a bad interface for this stuff.
Well, I, I agree. If I'm writing an email, I don't want to, like, talk to somebody multiple times about what the email should have. I want to, like, have just a short little, you know, box I put some info in and an email comes out. I mean, chat has been an obsession in AI for decades, right? Like, it's like a life definition.
Yeah. And these kinds of things. Eliza. That's what I was thinking. That's right.
Yeah. Well, again, it's a little bit like Naveen was saying earlier. I mean, it feels really cool. That's actually a really strong motivator, for sure. It is. Um.
And I think part of this is also that, uh, we haven't built those models that do what I was saying, right? About actually trying to uncover, causality. You can't build something that has quote unquote agency unless it understands the, the, the intrinsic, uh, causal nature of the world, right? I do this and that happens. Like, these models don't have that. They, they basically can pick up on patterns and extract different sorts of patterns, but they actually don't understand this causal relationship. So, Naveen, I know you're on the show for the first time. I'm trying to, I'm starting to get a sense of your vibe, which is that you're, you're grumpy about AI.
Uh, I'm wondering if I can kind of push you in. I'm actually very hopeful. I think it's actually, I devoted my last 15 or 18 years of my life to this field. I'm not grumpy. I'm just a realist. Well, I think in the spirit of realism, can I push you on your predictions around agents in 2025? But like, what's the bull case? Like, what do you think the most impactful thing on agents is going to be in the next 12 months, if anything? Yeah, I think, uh, if you basically narrow the definition a bit, we actually get something that's, that's super useful, right? To be clear, an LLM, the thing that can summarize and do all the things that they do, is actually super useful.
It doesn't mean it's AGI or whatever. I, I kind of hate that term, uh, but it, it, it is something that is super useful. And I think, um, you know, as Kate said, the interface is not necessarily a chatbot. Like, what I want is something that when I'm in an Excel spreadsheet, I want it to, like, You know, impute values or describe things or, you know, there's so many ways that you can add value to those experiences.
That's what we can do now. And so, uh, being able to automate, okay, I want to like copy all these cells and then, you know, apply this formula across the rows. You know, there's all these kind of tasks that we do. If I could just say, hey, do this for me, that's an agentic workflow, if you will, but it's not thinking on its own. It's I'm telling it what to do.
It just has to carry it out within the framework of the app. So I think that's what we're going to, we're going to see more of. And you know, inside Databricks, we're seeing a lot of this now.
In fact, we've been using, um, LLMs and, you know, generative AI to improve the experience of Databricks itself, like actually finding bugs in your SQL code or You know, and actually be able to fix it for you or propose a fix for you. These kinds of things actually are big time savers. So I think that's what we're going to see in 2025 is more of that. It is going to drive demand for compute and everything, but it's not, you know, super intelligence.
That's what I'm grumpy about. Maybe if you want to put a point on it. I'm glad you said that, Naveen, because it's always a good sign when a panelist is like, I really dislike that term.
Moving us on to the third segment of today, let's talk a little bit about superintelligence and AGI. This is the last segment I kind of wanted to focus on, uh, just as we kind of look towards 2025. And of course, it's part of the narrative of where AI is going. Um, the information reported out, uh, that, uh, OpenAI is seeing sort of the rates of improvement in GPT kind of slowing over time. And, um, I thought, I think I caught earlier, there's an interview with Ilya, um, where he basically said, Hey, you know, maybe, maybe this is like actually progress is slowing down. Um, and I think I just kind of wanted to put.
Those kind of rumblings or concerns, uh, next to some of what we're hearing from leaders in the industry, right? So Sam Altman did a blog post where he predicted that superintelligence is potentially a thousand days away. Um, Anthropic recently warned that, you know, these systems are advancing so quickly, we need serious kind of targeted regulation in the next 18 months. Um, and so, You know, I guess maybe like Anthony, I'll kind of kick it to you first is what are we to make of this right like is is AGI on the way is this kind of like how do we kind of square a lot of what we've been talking about this episode, which is, you know, it's going to get harder with, I think, kind of like pretty some pretty strong claims that like, hey, we're about to have ultra powerful systems, you know, in the next thousand days. Look, we're talking about it. So the headlines work, right? Like, it's a compelling topic.
It, uh, attracts the public's attention. It's like the superhero obsession or whatever you want to call it. Um, I think a lot of it is that, right? Look, where are we today? I don't even know what a working definition of AGI is at this point. Um, I can propose my own, but I think what we're gonna start to see really matter is more and more ways that AI is integrated and embedded and helps in specific contexts, right? So Naveen mentioned some, certainly coding assistants, embedded coding assistants, um, have made a lot of progress. It's kind of an early set of use cases. We've seen lots of utility.
We'll see a lot more of that. Um, look, in terms of like, when does AI reach, Some level of general intelligence, uh, even if we take a definition of that being like, you know, equivalence to human capacity to not only, you know, know things, but to reason, to perceive, I mean, that's a very long way off, I'd say. Yeah, I mean, I don't know if it's all, I guess it depends on what you define as long way off. Um, I think we will get there. Uh, it's just going to take, it's going to be harder than we think.
So everybody. Our perception is very linear. It's like, okay, this thing has been going and every year it gets better and better and better. So then by two more years, we're going to have this other thing.
That's not actually how these technologies seem to evolve and that's never really been true. And so we always underestimate or overestimate the technology in the short term, but underestimate in the long term because they actually work on exponentials. So 10%, 5 percent improvement a year on year, um, of something. actually adds up a lot, you know, very fast once you get to like year seven.
So I think what we're going to see is in 10 years, we very well might have something that does reason and actually does understand causality. My prediction has been between, I think, by 30 years, it's a 95 percent chance we will solve that. By 10 years, I think it's like a 30 percent chance. So that's kind of my bounds are 10 to 30 years from now, but I think that's not that long, right? So like you're kind of saying, like, you know, there's, there's people alive today who will definitely see that.
Yeah, totally. Right. I mean, uh, which I think is very cool. Uh, but It's not something that's going to happen next year. I think that's just a hype train, to be honest with you.
We haven't solved fundamental problems yet. We will see around that precipice a year ahead of time pretty clearly. And right now, it's not super clear. So to me, it doesn't feel credible to say that. Well, I think we're also conflating a lot of things.
Like, Cause and effect and causal understanding versus super intelligence like there are Causal models that are out in the world today that can help break down and isolate cause and effect relationships and things particularly in like drug discovery That are are widely used, you know So, are we just talking about can we get the models to better understand causal reasoning or are you talking about sentience and in, like, every stretch of the world and having a model that has a personality and, you know, goes off and does, you know, things of its own, uh, own will, so to speak, and I think that In general, those aspirations are really more around marketing, and I don't think there's even necessarily the right economic incentives to develop that, versus developing, you know, cause and effect reasoning, but developing better tools for handling language and doing different tasks. Absolutely. Um, you know, I think in the next, three to ten plus years, um, is more realistic. Yeah, there's this adage in, um, kind of financial markets that like the market be, can be irrational longer than you can stay solvent. And I was joking with a friend recently, it's like, AGI can be imminent longer than you can stay solvent. It's like, it's just around the corner, everybody believe me, it's just around the corner.
Um, I guess, Anthony, maybe to go back to you, I mean, I, I want to kind of challenge the idea that it is potentially just all marketing, right? I think, I think one of the really interesting comments, uh, that came out of this, uh, kind of essay that Dario Amadei, who runs Anthropic wrote, we were talking about a few episodes ago, you know, he's writing about the future of AI and how it's going to change the world and everything. A lot of people say, ah, marketing. And, you know, some people kind of looked up, you know, like his writings from when he was like a grad student. And he's like still writing about this stuff, right? Um, and I do think that that is kind of an interesting thing that I would love to kind of get your thoughts on is like, it almost feels like in order to be able to look past all of the current problems with the technology, you almost kind of have to be a true believer in some sense. And like, in some ways, like, I actually don't know if it is marketing coming out of some of these companies. Like, I think they do genuinely believe that it is imminent.
I don't know how you think about that. I think AI is going to change the world. I think it's going to change it incrementally, practically, and pretty quickly.
And it already is. Um, in all the practical ways we've talked about specific applications, integrated with software, integrated with capabilities that we want assistance with. Um, no, I wouldn't say that people are like disingenuous. Uh, I just think that there's this cultural kind of continued obsession with, you know, intelligent, super anything, right? Uh, and it's interesting and it's fun. Another kind of more negative side of that is, you know, uh, the whole existential debates that have hopefully started to, to die back, I think. Uh, but you saw that like, you know, a year and a half ago, especially like really, really, uh, with a lot of heat.
Um, Yeah, I'd say that it's just kind of so natural and attractor, like it's hard not to bring it up, but, uh, look, I don't know, maybe I'm just too much of a pragmatist. I just try to focus on all the ways that AI is actually helping and will help, like every day, like on this podcast probably before too long. It's just to me, that's how the world changes, not with some super intelligence. Well, and I think what's interesting, uh, is, I agree with you.
I don't think there's this they're being disingenuous. I think people really believe it and that's fine. Um, but we want to pull back and contextualize a bit. Like, do you care that the airplane was invented in 1903 instead of 1910? Does it really matter? It doesn't, right? I mean, these were splitting hairs a little bit. Like, why am I right? And someone else is wrong.
It actually doesn't matter. Like, I think if it's It's three years, like as Dario says, or it's 10 years. If you look back at 50 years, it doesn't matter, right? Right. Because of exponentials, you know? So I think it's okay. It's okay that we're exuberant and we believe. I also think some of the anthropics, uh, warnings, so to speak, uh, and the need for safety and better understanding of these problems aren't necessarily just predicated by the arrival of super intelligence.
Dumb intelligence can be pretty dangerous. And if it's out in the world, right, uh, if we're starting to give LLMs, all these API calls and ability to impact the world and pull real data into their decision making. So, you know, I, as we talk about it being genuine, I think from that perspective. It absolutely is true, uh, and something that everyone should be aware of regardless of is this quote AGI or superintelligence or not. Yeah, for sure.
Yeah, I think that these last few comments are really interesting because I think all three of you kind of would picture yourself as like realists in the world of AI. Um, but kind of where we're almost landing is, look, we're all agreed this technology is going to be a huge deal. We're just hair splitting over whether or not it's going to be 10 years or 20 years You know, two, two months from now, um, which I think is, is a pretty interesting outcome. Yeah.
Um, maybe the final comment I'll, I'll kind of throw in, because I'm just kind of curious to get all of your thoughts on this is, you know, to talk about realism. Like all three of you are talking to customers that are in the market. People that need to just basically wake up in the morning and be like, is this technology going to be better than what I currently use in my stack? And should I implement it? Like, do you hear from customers? They're like, And by the way, Anthony, should I be worried that this technology is going to destroy the world? Like, I'm kind of curious about how much of this is kind of, sort of, chin stroking media discussion, or how much of it actually is, like, influencing actual enterprise decisions and discussions happening on the ground, or if those two are basically, like, completely separate worlds in some sense. Certainly. Lots of customers are concerned and ask questions about accuracy, about trust in systems, about how to implement specific use cases, right, with a high quality of output that they can trust in deployment, that they can trust. Save money or make money on and not have a big liability with and there's lots of challenges across the board In all sorts of domains right in health and finance and in legal and all many many areas I hear very little if any questions about Their you know helping AI, you know, destroy the world, right? These big existential kind of, if I deploy AI, am I going to, you know, contribute to the robot army that takes over humanity? Like, none of that stuff, right? It's very practical.
It's business focused as it should be. Right? That's what I hear. That's so interesting to me is kind of like, we think about the AI discussion as being like kind of one block, but I think in practice it's actually these like pretty distinct, you know, kind of like fora in which these discussions are happening. And okay, Naveen, if you've got thoughts on this, on like what you're hearing from customers, and whether or not this AGI stuff even kind of like registers at all. Yeah, I mean, I think Anthony nailed it. It's very practically grounded.
That being said, I think the motivations are such that I don't want to be the one who didn't jump on the train and made the company get left behind, whatever that company is, right? And so, uh, there's a lot of tops down, uh, push for, for getting AI coming even from the boards. I've spoken to multiple boards of very large public companies and, you know, that this is a discussion front and center there. And it's really about this is the next technology transition, we have to be part of it.
No one's really talking about like, is it going to take over the world or whatever. It's just like, how do we, how do we craft a strategy such that we are, we are part of this new world? Yeah, I'd echo both of those statements and I think Overall, what I'm really optimistic about, honestly, is that a lot of the conversations with enterprise is about how do I take advantage, but how do I also make sure I've got the right protocols, the right control points, the right safety measures in place, because, I mean, their bottom line is ultimately at risk with the deployment, and I think that provides a lot of really helpful and healthy pressure on model providers to develop the solutions that are needed for a more responsible and governed approach versus a, uh, just build as far and fast as you can. And so I think that is going to ultimately help us create a lot of the, the tooling that's needed so that it isn't necessarily the, the concern that, you know, AGI is going to take over the world. We will have hopefully built the right controls and processes in place to be able to have a very well governed world.
AI system. Yeah, I can't think of a better note to end on than that, Kate. So, thank you. Um, so I'm going to wrap us up for today.
Uh, Kate, as always, thanks for coming on the show. Really appreciate it. And, uh, Anthony and Naveen, we hope to have you on the show in the future, hopefully.
Thanks for joining us. If you enjoyed what you heard, you can get us on Apple Podcasts, Spotify, and podcast platforms everywhere. And we'll see you next week on Mixture of Experts.
2024-11-16 17:17
