Big Technology Podcast - The Next Gen AI Models: Reliable, Consistent, Trustworthy — With Aidan Gomez

Episode Date: October 30, 2024

Aidan Gomez is the co-author of the "Attention Is All You Need" paper that launched the AI revolution and CEO of Cohere, an enterprise AI company. Gomez joins Big Technology to discuss the myths, facts, and realities of today's AI landscape. Tune in to hear why the real value of AI isn't in flashy consumer apps but in automating crucial back-office processes that could save businesses billions. We also cover the truth about AI capabilities, the likelihood of AGI, synthetic data training, and whether an intelligence explosion is possible. Hit play for a refreshingly grounded discussion about where AI is actually making an impact, from one of the field's pioneering voices. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Want a discount for Big Technology on Substack? Here’s 40% off for the first year: https://tinyurl.com/bigtechnology Questions? Feedback? Write to: [e-mail]

An author of the original paper that launched the generative AI revolution joins us to sort out the technology's myths and facts. And when we'll see a return on all that investment? All that more is coming up right after this. Welcome to Big Technology Podcast, a show for cool-headed, nuanced conversation of the tech world and beyond. We have a great show for you today because we're joined by Aidan Gomez, the CEO of Cohere, an AI platform for enterprise,

and he's also the co-author of The Famous Attention is All You Need Paper, which invented the Transformer and started this whole AI thing. Aidan, great to see you. Welcome to the show. Thanks, Alex. Thanks for having me. Great to have you here. I want to begin with some myths and facts about AI.

We have debates all the time on the show. Where's the technology going? Is it worth the investment? And no better person to ask than someone who is there at the beginning and now currently an entrepreneur in the space. So earlier this month, Open AI raised $6.6 billion. But the reporting says, they might be losing $5 billion per year.

And, you know, in some ways, okay, you need the investment to build the models. But in another way, it's like, okay, you know, where does this end? Because the compute, the data, the energy to train these models keeps getting bigger. The requirements for that keep getting bigger. And so does the money. And, you know, does this ever become sustainable? So what do you think? I certainly understand the urge for people to see the numbers being spent on training.

and be concerned that it's not going to recoup in value. But I think that those numbers are actually small relative to the long-term value that the technology will deliver. I think it is like now time to prove that. So last year was very much the year of the proof of concept. People were getting familiar with the technology. It was their first time working with it. And so there were a lot of small tests and experiments.

But this year is very much one of going to production. and getting these models into the hands of people at scale. Of course, we're already seeing a high degree of ROI in the sense that there's now hundreds of millions of people who are using the technology. It's actually in their hands. It's part of their day to day. And so that certainly is ROI. And with what Cohere focuses on, the enterprise side, we're starting to see this technology

get into the hands of employees and get into enterprises. It's a much slower process. It's a bigger lift. You have to integrate with existing systems within enterprises. You need to train employees on how to use this technology. But that's well underway now. And we're seeing quite dramatic growth in adoption. So I think we will find ROI, and it's coming soon.

And we'll talk more about the specifics of coherence and ROI in the second half. But let's keep on this line because it goes to another one of these missed and facts, which is that the next set of models are going to be this godlike set of models. And, you know, you talked about how there's going to be like a lot of cost at the beginning, right? And that's necessary cost to train these massive models. And the sense that I've gotten from my reporting, one of the things I've heard is companies have been willing to make those investments because they think that this next 12 or 18 months in model development is crucial.

And the capabilities will advance significantly. as they put more compute data and energy into the process. So let's go to this myth. In fact, number two, which is, does the next set of models give us that, you know, Godlike AI model and so many people are. I don't know about Godlike. I don't think I'd ever use that term to describe what's coming. I think we're going to have some really powerful and useful tools emerge.

I think that's what's coming. The idea that we're building AGI or something that's just going to like solve all our problems for us, I think we need to set that aside. I don't actually think that's... We'll get to that. But let me ask you more pointedly on this next generation of models. Okay. So you say we're going to see some new tools. What does the next generation?

Because they are being trained on much more resources than they have been previously. So what tangible step forwards are you expecting to see from the next generation of models? I think the reliability and trust factor is huge and also just the competency, right? The accuracy with which it gives you answers. And so all of those are going to increase. I don't see a step change coming, but I see a steady, continuous course towards very high accuracy, very high reliability AI. There's been so much hype in the industry. This is one of the things that sort of comes along in this discussion.

which is like everybody I speak to who's on the ground says, yeah, we're not going to see a step change with like, let's say, GPT5, but exactly as you describe, more reliability, more consistency. Is there, I mean, is there a worry that some of the air is going to come out of the, you know, this AI moment if, you know, because again, like I, when I say Godlike, I'm not, I don't believe that's going to happen, but I'm reflecting what a lot of the hype is starting to expect. And so if it's just steady, you know, steady improving. and reliability, which is actually like, you know, we both agree pretty big. But do you think that that sort of takes some of the steam out of this moment for AI? Because people will look at

the step change as as a failure given where the hype is. Well, listen, I'm not one of the people who's saying we're going to be building. Oh, yeah. Godlike AI. You're doing it. Yeah, I don't, I don't have much to say towards those claims. What I would say is that even if, you know, Like, just as a hypothetical, even if the technology froze and what we have today is all we get, there's so much good to be done. There is so much work to go to, to implement this technology across the economy, really boosts productivity, drive better outcomes, build tools. So the technology does not have to move in order for incredible value to be realized. we just need to go do it.

And it takes a lot of effort and time and work to go realize that value. Okay. And again, more of that is coming in the second half where we go a little bit more tangible. But let's stay with the theoretical or at least like the industry stuff. What do you make with the fact that the GPUs? So we talk about like the ingredients, again, this is coming from your paper, right? The ingredients that are required for these models to get better. They need data.

They need compute. the energy and the compute right now is starting to go like through the roof in terms of the amount of compute that's being used to train models. So just for some context, so meta's Lama 3 model, which was like state of the art like 10 minutes ago, it used 16,000 GPUs to train that one. Now we're hearing that Elon Musk is building this super cluster. I think it's called Project Memphis that has 100,000 GPUs. So multiple. of what the cutting edge is being used to train on. So I'm curious if what you think that increase in GPUs are going to get us first and

foremost, and then we'll talk about whether the right way to scale these models is with just throwing more compute, because I know you have a new honest take on that. But like first and foremost, like if you go from 16,000 GPUs at the state of the art to 100,000, what do you think that delivers? It definitely delivers a bigger and better model. You have more compute. We know that scaling up improves things. There's questions around saturation and whether continuing to scale up is justified,

whether there's going to be enough gains from that strategy to justify the increasing cost. My personal perspective is that building a massive model, it's not actually useful for the world if it's too big to be consumed, if it's too expensive to actually deploy. And so for cohere, we've been very focused on building the right size of model. But if your question is, what is more compute on lock? It will be a better model. Objectively, we know that scaling leads to more capability, a smarter model that's more reliable.

And so that's the output. And does that ever end? I mean, that's one of the big debates here is that, you know, basically you could add compute and data basically to infinity. and it will continue to improve, or is there a tipping, you know, sort of a saturation point? I don't think within any achievable scaling up for humanity that will reach that tipping point. It just saturates. The gains become much, much smaller.

And so you're much less willing to want to pay double the price for a minute difference. But it is pretty consistent that bigger is better. and that just continues, but it tapers off over time. I mean, Open AI has talked about how like their goal is to build AGI. A lot of people in the industry talk about AGI's North Star. I know cohere is more like let's make this practical for businesses. But I want to get your sense because because that's not your North Star, I think you can speak a little bit more about like more honestly about what it means

and whether it's achievable. So do you think that let's just use this definition of AGI as intelligence that's as capable as humans in the tasks that humans do. Do you think that that is something that we should even be thinking about or is it a marketing tool and is it achievable? I mean, with that definition of H-EI, I think it's both achievable and a fairly reasonable target. So we can measure how good humans are in any particular task.

And then, yeah, I mean, it's a reasonable goal to want to create technology that can perform that well in that task. So I think based on that definition, I think when we start to think about, you know, you described it as like godlike models, these are being described as well beyond human capabilities. My definition is probably artificial general intelligence. And I think that this godlike is the super intelligence thing that a lot of, and I think a lot of people will use AGI as a synonym for a superintelligence, which seems wrong to me. But there is this belief that once we hit A. AGI, we've already reached super intelligence because if it can do everything that humans can do and doesn't get tired, doesn't need to sleep, doesn't need to get paid necessarily, you're already at super intelligence. But sorry, go ahead. Yeah. But no, I think that definition of AGI is a reasonable one. I think it is exciting. I think that that's definitely the target. What we want to do is we want to

create machines that have this unique property that humans have of intelligence. And we want to be able to deploy them in the places to take work off of the shoulders of people and put it onto these machines to make work better and easier. And in order for you to do that, in order for you to trust the machine enough to shift that work over, it better be as good as the human. Otherwise, you're paying some price. You're reducing inaccuracy. Things get worse, not better. and so that that's a very reasonable objective. And when do you think we might reach it? In many respects, we're already there in many fields.

The models are as good, right? We're still like, I don't think those two things are in conflict. I don't think that. Really? Why not? Well, because I think that we will never see mass unemployment. of humans. I think that this technology is going to unlock more opportunities. It will let us do more as opposed to scaling back what we do. Humanity is very supply side constrained, not demand

side. We always, we want more, we want better, we want to be healthier, we want to do more, we want to have things be cheaper. And so we have all this demand. And we're trying to keep up with our own society's demand. And this technology, it's true promise is in bringing productivity and letting us do more. Now you can zoom in and you can pick a specific field and you can say this field might be automated by AI. And I think that's true. And we should be thinking about retraining and shifting certain skill sets over to other new domains,

like retraining people. But in general, at the macro scale, I think this technology will. create much more opportunity than it will take away. I mean, if we have AI technology that can basically do work for us, whether it's knowledge work or whatever, right, we already have a lot of technology that can automate, you know, factory work. Why are we continuing to work? It brings purpose and meaning to a lot of lives, and we enjoy it. I think that the right form of work is something that fulfills you and that is enjoyable, intellectually interesting, compelling. And that's really what I want to spend my time

doing, is that as opposed to number crunching or, and maybe someone else enjoys number crunching, but for me, I'd rather outsource that to my Excel spreadsheet. So yeah, I think that work in its best form is incredibly fulfilling. And that's never, that's something that humans will never give up. We'll always want to do that. But if we can hand off and if we can have an assistant that is, you know, on 24-7 and has access to all the information and tools that I have access to and I can ask it to do things for me, that's a very compelling value proposition. It changes work in a way that is extremely positive, I think, for almost everyone.

How far away do you think we are from having reliable assistance? Like a lot of people looked at Open AI's 01 reasoning model and they're like, oh, this is just kind of like a step toward assistive AI. What do you think? I think the notion of using reasoning or letting the model have an inner monologue to work through problems, think through them, make mistakes, but then realize that, catch mistakes and correct them. I think that's a crucial piece in improving not only the accuracy or robustness or usefulness of the model. But also the trust in the model. Because you're able to inspect how it arrived at its conclusions, how it decided to do what it did, you actually trust it much more.

It's explicitly written out. And so I think we've all known these sorts of tools would need to emerge. And yeah, I think it is a big step towards dramatically more reliable assistance, ones that you can trust and work with and give feedback to. I think it's really exciting. Okay. And then where does that put you on the fear around AI? I mean, if AI can sort of go step by step, figure out these processes, realize where it went wrong, go back, take action, right? I think that's sort of where people get weirded out is when these things start to take action on their own. What can they do that we're not prepared for? So what do you think about that? Are you worried? AI might cause harm to people? I think it's really important to remember that we get to choose where we deploy models. It's not like they get to choose where they work or what they have access to. We have to plug them in and we have the opportunity to implement safeguards.

So to make sure that before these models are put in any very high-stakes situations, that there's oversight, that a human has to approve high-stake actions. It's not carte blanche and the model is now smart and we just, plug it into everything and say, go at it. It's very much intentional and we're going to need to be thoughtful and careful about that. So I'm not scared of like a doomsday like Terminator scenario. I think that media has certainly instilled that with lots of sci-fi stories and it's a very compelling story, which is why well before AI was remotely competent, we were coming up with stories about how this might happen. Yeah, but it's not just media, right? It's like also

AI leaders are saying, you know, how many people signed that statement that said we should be treating AI risk the same way we treat climate change and nuclear? Why do you think there's so many people in the industry that are stirring up the fear around this stuff? I think that's a great question to ask them. I did not sign that letter. And so it puzzles you as well. Yeah. Yeah. I mean, I'm empathetic to the fears because, yeah, like, this is a very salient story. That's why it's been so popular in sci-fi and all this sort of stuff. So I'm actually understanding of why people are so attached to those stories. But as more and more evidence emerges that these models are much more controllable,

then we may have thought that they're a little bit less capable than we may have thought. It's harder and harder to make that narrative. And I think you see the discourse shifting now. I think the discourse has begun to shift away from doom and existential risk. And now it's much more about practical concerns, which I'm really happy to see, stuff like, okay, this technology could be really useful for health care, but it could also cause harm if we don't do it the right way. And so specifically, how do we set up the safeguards to make sure that harm doesn't happen? Same thing with finance, right? I'm like distributing loans or something like that.

Or with people using them maliciously to pretend to be human and trick people. how do we prevent those things that discourse is super productive like that's very effective and so things are shifting in that direction now and i'm excited to see that change now some of this fear comes from this line of people saying oh there are emergent behaviors in the models right that basically that they've found them able to sort of come up with things that are outside of their training set and there've been some papers that say okay actually they don't really have any emergent properties or emergent behaviors? And as someone who wrote the paper that kicked this all off, what do you think about that? Can LLMs have any emergent behavior or discoveries

that they weren't trained on? I think that they can, what's the right word? I think they can interpolate between skills. And so if they've seen how to do A and they've seen how to do B, they can get kind of the average of A and B. But they don't just go completely beyond anything that they've seen. I've never seen a model behave in a totally unexplainable way. They're really good interpolators. If you show them different domains, they can blend domains quite well. But yeah, I've heard the same thing about emergent behaviors. And I think the research is really inconclusive there. There's not a lot of compelling evidence that says we're going to have some total step change or capability takeoff. Even in the like the latest state of the art research, a lot of

It's about synthetic data and models teaching themselves. And so self-improvement is this notion of can a model actually teach itself without human intervention? This is now a huge part of model building. It's a big part of how we create data at cohere. And before this started to become mainstream and actually part of the production process of creating these models, people were saying self-improvement, these things are just going to take off. They're going to become superhuman overnight. And we won't be able to control it.

Well, it turns out that doesn't actually happen. Right. So this intelligence explosion or intelligence takeoff is not something that happens. It's not happening. It's not happening. It improves for, it can self-improve for a while and it tapers off. And so, yeah, you get some good improvement out of it, which is why we use it. But then it plateaus.

It doesn't just keep going forever. And so I think there the evidence points firmly in the direction of a lot of those fears may have been misled. Now, talk a little bit about that. it's interesting you bring up the use of synthetic data and having the machine self-improve because one of the big questions about whether this plateaus is, you know, does the world run out of data to train the AI? And I was watching one of your recent interviews where you talked about how, you know, back in the day, you could run up to anybody and they can add knowledge

to a model, but as the model got smarter, the models got smarter and smarter became less easy for people to add supplemental knowledge to them, which points to like sort of, sort of running out of available data to make these AI models smarter. So how does like AI generated data actually solve that problem? And where is synthetic data being used to make these models better? Yeah. So I think the example you gave is a good one like where it's getting harder and harder to get the data that incrementally improves the model.

And it's important to note that that's because the model is getting so much better. And so before we could just grab anyone off the street and they could teach them all of something. And then that signal started to go away. And so we had to go to undergrad students in bio to teach the model about bio. And then we had to go to master's students, then PhDs. And we're kind of at that level where we're currently hiring PhDs to teach the model in their specific domain. But then after PhDs, where do you go, right? Like I guess professors. What about after that? So I think the models are catching up with the state of knowledge across a bunch of different fields. I would say that synthetic data probably doesn't get us out of that, that issue.

I actually, I don't know if synthetic data, outside of easily verifiable domains like math, it's hard to use synthetic data to drive outcomes. So we'll be able to do it in. So how is it being useful for you? Not, not for making our models fantastic philosophers or making them fantastic, fantastic social scientists or something like that. For that, we rely on humans. What we do use synthetic data for is for crafting how the model responds to stuff and in domains that are verifiable like math, like coding. In those places, it's actually quite effective. But that's still a huge domain of interest for people building and deploying these models. We want them to be good at math and computer science. And so more and more, synthetic data,

is becoming a huge chunk of the data that we train on. Okay, fascinating. One last part of this discussion is sort of what methods help get this AI to improve? And there's been a question of whether LLMs can take it like all the way or whether you need to combine LLMs with different forms of training, whether that's reinforcement learning. I guess that's part of it already. But the other side of it is do you have to like build world models with like, like robots going out in the real world and learning things like

things like gravity and what happens when you bump into things, which you just can't convey in text. So I'm curious if you think the current methods are able to get this field to the promised land or whether they need to be combined with others. There's definitely proof points out there, which suggest large language models or like the transformer architecture is capable of handling a bunch of different modalities. And so you can merge not just text,

but video and audio as well into the model. So you can give them a much more balanced experience of the world. You can show them the world. You can show them videos to demonstrate physics. You can let them see, hear, speak. And so as a platform, it does seem like this is a pretty good platform as far as they go. There's a more philosophical argument which has had among academics around is text enough or even is supervised learning enough.

Is it enough for the model just to observe the world, or does it need to take part in the world to really understand it? For instance, would you understand the world if you read all of the internet and you watched every video on YouTube? Would you really understand it? Or do you need to actually be embodied, be a little robot out there kicking a ball or running down the street? I actually take, I think, the less popular view, which is the internet is enough. And by observation, you can actually learn enough to be extremely, extremely compelling. I think that's, if we're talking about AGI and doing things as well as humans do, I think that's enough. All right.

I want to take a quick break, hear from our sponsor, come back, talk about ROI, and then just talk a little bit about your journey, Aiden, from being somebody who, wrote that paper to where we are today, I think it'll be interesting for listeners. So we'll be back right after this. Hey, everyone. Let me tell you about The Hustle Daily Show, a podcast filled with business, tech news, and original stories to keep you in the loop on what's trending. More than 2 million professionals read The Hustle's daily email for its irreverent and informative takes on business and tech news.

Now, they have a daily podcast called The Hustle Daily Show, where their team of writers break down the biggest business headlines in 15 minutes or less and explain why you should care about them. So, search for the Hustled Daily Show and your favorite podcast app, like the one you're using right now. And we're back here on Big Technology Podcast with Aiden Gomez. He's the CEO of Cohere. Also, the co-author of the attention is all you need. Paper that kicked this entire generative AI moment off, right, invented the transformer. Before we get deeply into ROI, Aiden, just a personal question for you. I mean, are you, what does it feel like having seeing, what does it feel like seeing your invention being taken in all these wild directions and sort of being

this key moment in a truly like step forward for the tech field. I mean, it's like beyond my wildest dreams. I think I don't take full credit for it at all. I assign the overwhelming majority of the credit to my co-authors on the Transformer paper. So it's hard for me to, accept the reality of what the Transformer has accomplished out in the world as my own. But it's so incredible. Like even if I step away from being one of the authors of the paper, the impact and what the architecture has been able to do for the field has been a huge shock, a colossal shock. Just the technology we have today, I thought we'd be here maybe in like half a century.

you know, not seven years. So it's really surreal and amazing. Has Google effectively capitalized on it, given that this came out of Google? I think Google has done super well. You know, they supported Google Brain in creating this technology, and it's been integrated all over Google.

All right, let's talk quickly about ROI, or maybe let's go deep into ROI. We'll see how we end up here. Again, we talked about all this, all this money being spent on, you know, upfront cost training the models. And you mentioned that even if the technology stopped today, there'd be so much work to do with it because there's a lot of benefit out there that isn't being realized yet. But talk a little bit about the places that you're seeing already getting a return on the investment in terms of implementing generative AI technology.

Because I think in the common conversation, people don't even think those places exist, but it seems like you're seeing it on the ground. Yeah, I think today we're starting to see it integrated into production. In enterprise, it's much slower than in consumer. There's a much higher lift to actually get it integrated, and there's a higher bar of trust necessary to drive adoption. Like I was mentioning earlier, you know, last year was very much the year of the proof of concept. But this year, we've started to see it go to prod. So there's some good examples of that with our partner Oracle, which they have this suite of applications, which basically power enterprise, HR, supply chain, all of these sorts of back office functions.

And we're powering over 50 different applications within those software tools. And so it's actually starting to get into the hands of employees and drive efficiencies. Wait, hold on. Talk about what that looks like for an employee on the ground there. How does the software that they were working in change when you put generative AI in it? Drew, go here. Yeah, so you're, you're automating parts of the job, little tasks within the application. You can now just push a button and the model will do that. You might need to provide a high level. A good example might be in writing job descriptions, right? So a manager, a hiring manager wants to hire for a specific job. What they want to do is just put in bullet points. I need someone who has this background, does this, etc. And then press go. and it will generate the full job description with everything that the company needs included there and in a way it's actually presentable to the people applying.

And that's a known use case. Yeah, so let's hear something else. Yeah, another good example might be in supply chain when you're looking for an alternative supplier to one of your products, doing that search and retrieval and being able to iterate with a model and not just do a single step search where you search over suppliers, But where you give feedback, you say, actually know that one that you just recommended doesn't work for this reason.

And you're able to refine iteratively with this assistant or agent. And these models basically touch every vertical. And so there's no particular vertical specialization. It's totally horizontal. So we were working with a legal tech startup that helps with reviewing contracts and building an assistant for a lawyer to help them review contracts more quickly flag, you know, concerning terms, that type of thing. We're working with a healthcare startup that tries to use news and social media to track pandemics and are people getting sick in a particular area

reporting specific symptoms. And so using models to screen for that, it really impacts every single vertical. Can I take the devil's advocate position on this? Let me see if I can channel an AI critic and see what you think about this. And basically what they would say is job descriptions, okay, we'll save you a tiny bit of time if you have the AI, right? The job descriptions, if you're looking for a supplier, chances are if you work in vendor management, you're going to have a good familiarity with those suppliers anyway. If you're a lawyer, like, yeah, it might be a little bit of time, but you can comb through

a contract and find out what's, you know, what might be concerning about it. This is your expertise. you're almost like a narrow neural net train for that one specific purpose and now we're giving it over to AI. And they'd look at just the billions being invested in this technology and say, well, what am I really getting for that if this is effectively doing some of these things that humans are quite good at to begin with? I would counter and say that risk to supply chains is many trillions of dollars. I would say that lawyers are extremely, extremely expensive and you don't want them combing through your documents, no matter how efficient you think they are.

And same thing with doctors. We really want them spending time with patients, not combing through hundreds of notes and filling out forms afterwards. I would say those are, maybe this stuff feels banal. Maybe productivity feels boring compared to some of the hype of AI. But it is the value. This is what we're trying to build for. And so I would push back quite firmly on that.

I'll react to this. I think this is a thread from Benedict Evans, tech analyst. There's an interesting difference between people outside tech sneering at generative AI as chatbots that get things wrong and make crappy, you know, quote unquote stolen images and people inside tech who are mostly working on using it to automate a huge number of boring back office processes inside giant corporations for billions of dollars. I think that's a great observation. I think that there are some very superficial critiques of generative AI that have become very popular. I think the substance is in actually doing the work and getting this technology to be productive for humanity. And a lot of people are working on that right now. It's going to take time, like I've said,

but the opportunity is immense. It's the biggest in a generation. Yeah, I think that's kind of the misconception. That's the interesting point about what this technology can do. So I was speaking with flex port again supply chain management and i think about writing about how the fact that like supply chain is actually like ground zero for where this technology is being applied and useful but they're basically they're like getting faxed things they're getting pdfs you know to try to log that and comb through that you know the volume is crazy and they're using generative ai to read through the documents and give them actionable insights on it and they're like look like it's not going to be like the most exciting use case but this is saving us a tremendous amount of time

Yeah, I was about to say, I'm like, it's so boring, but it is so valid. Like, people don't understand the actual scale of impact of some of these crucial banal things. And if we can scale them up, make them more accurate, more reliable. Yeah, it really is world-changing. Isn't it kind of crazy that, like, the picture of AI again, is this just like, you know, I guess maybe because chat GPT was the thing that started the hype cycle. But the popular picture of AI is like this masterful, again, like, godlike technology that, you know, can do all these things and be this friend for you, like the character AI type startups and people talk about AI girlfriends. But then the value is really being realized in like the back office.

I mean, it's pretty crazy sort of divergence there. I don't think I've ever seen a technology with that type of divergence. Yeah. I mean, I think like the internet is a good, or like computing in general, like these general plus. platforms for supporting new types of products and tools. Yeah, sometimes they have biases in certain ways, but it's all about diffusion, like diffusing into an economy, diffusing into our daily lives. And it takes time for that to happen.

And we should remember that we're like 18 months in to that journey. And so it's still, it's really so early. But yeah, I think the intranet has had huge impact. both on the commercial side, on the enterprise side, as well as with us as consumers and people and AI will be the same. There will be products that are pure play AI products, targeting consumers that bring tons of joy and value to consumers. And then there will be platforms like Cohere that enable huge value within the enterprise world. Yeah. And again, talking a little bit about how impactful this is in enterprise. I think this is from Reuters.

Generative AI business, which helps companies automate operations to save costs and boost productivity, recorded about a 50% jump in new bookings quarter over quarter. This has outpaced growth in Accenture's other core businesses as a go-to consultant and outsourcing service provider for companies migrating their operations to the cloud. Analyst expects slow demand for such service as enterprise spending plateaus. So basically, this is like finding ways to automate as like giving life to the consulting industry. What do you think about that? I think, you know, Accenture is a really good partner and there's just so much work to be done implementing this technology that that makes perfect sense.

Like there's a huge technological shift happening and the technology has unlocked a whole new set of applications. And so now we need to go out and do the work to realize it. Yeah, and what type of partnerships are you having with Accenture? Is it like going into companies and again automating back office or like what is what's going on there? on there? Yeah, so there are a solution integrator partner. And so yeah, it's about taking on projects inside of enterprises to help them accomplish something. Like maybe it's implementing for their finance team. There's some function that they're stuck on and it takes a huge amount of their time, but it's totally non-strategic. They shouldn't be spending time on it. And so can

we automate that or a big part of it using these models? It's about these strategic projects to try and unblock and automate parts of usually back office functions. It's amazing how like, I wrote about this a little bit in my book, but we're like living in the knowledge economy. And even still, like we've gone from industrial economy, which is like literally like pulling levers and pushing buttons to make stuff to knowledge economy, which is all about knowledge. But even in the knowledge economy, so much of our time is like legitimately on like straight up, you know, repetitive kind of text that we wish we could automate to make room for us to do more knowledge stuff.

I hope that that goes away to a large extent, but I don't think it will. Like, I think there will always be on the margin, these sorts of not good uses over time that we spend time on. And we'll continue to push that margin back and back and back and try to automate as much of that as we can. But it's a huge, huge project. What we're focused on is kind of building from the foundation, start by automating the biggest of those, the ones.

that you're wasting the most time on and then gradually get into more niche targeted specific automations or applications. Is anybody using your technology to replace full-time employees? I am not aware of it. I don't think I have any example of that happening. It's very assistive, actually. So it's less about replacement. It's more about augmentation. Like at the moment, What everyone's building are tools to augment their workforce to make them more productive. I can't think of a single example of displacing people. Okay, I know we're running out of time. One more thing I want to ask you about is sort of like the role of cloud providers

versus like the role of like people buying direct and like how this is helping or what type of pressure this is putting on cloud. This is again, we talked about this recently. So Anthropic, they just broke down, CNBC just broke down Anthropics revenue. and third-party APIs like Amazon and I think Microsoft, Azure, if they're available there, let's just say Amazon, 60 to 75% of their revenue. So how important are these cloud providers like Amazon, like Azure, in driving this forward? The cloud providers are great partners to Co-Year.

That's where the majority of compute workloads are happening, but not all of the workloads. So Co-Yeer has had a long-time focus on on-prem as well, because for a lot of regulated industries like finance and health care. A lot of that data doesn't actually go on the cloud. But certainly for many industries that are cloud first, that's the place that their AI workloads are going to happen. And so I think it makes sense for revenue to be coming from those sources. But for go here, we support both. And so it's perhaps a little bit more balanced. And so your technology is basically going to work, your company will basically work to integrate your technology into existing systems or you have your own software?

So we build our own models from scratch and we we build a platform that lets people plug in their data sources, the tools that their employees use into the models by a system called RAG retrieval augmented generation. And that's something that we're specialized in. The guy who created RAG when he was at META is Patrick Lewis and he leads our RAG efforts. but it's basically the dominant architecture or system that enterprises are looking for right now. They want to customize these models with their proprietary data. And the best way to do that is with Rack.

So that's something that we provide out of the box in like a super simple plug-and-play way. Let's end with this. Can you give us your prediction for what the AI field looks like in the next two years and five years? Yeah, in the next two years. I think we're going to start to see really compelling assistance. It won't just be little convenience functions or small features. It'll look a lot like a partner that you do work with, someone that you interact with every single day and you view as a collaborator. Over the next five years, I think it's not a major shift, but it's an increasing incompetency.

The scope of those assistants will expand. They'll be trusted with doing much more. And they'll be integrated into many more systems. So they'll be dramatically more capable. So I view it as like a continuous change over time towards much more compelling independent agents that we can collaborate with. Well, Aidan, thank you so much for coming on. Great to see you. And thank you so much for sharing everything about the industry in general and where, you know, companies are finding the ROI.

I do think that this idea that, listen, like, it may be quote unquote boring. But, hey, if it's saving billions of dollars, then don't tell me that that's. a boring application of technology. That's kind of my main takeaway today, and I think it's pretty fascinating stuff that you're working on. Yeah, thanks for having me on. It was great seeing you, Alex. You too.

All right, everybody, thanks so much for listening. We'll be back on Friday breaking down the news, and we'll see you next time on Big Technology Podcast.