AI Predictions for 2025: Geopolitics, Agents, and Data Scaling — With Alexandr Wang

Alexandr Wang is the CEO and co-founder of Scale AI. He joins Big Technology Podcast to share his predictions for AI in 2025, including insights about emerging geopolitical drama in the AI field, AI agents for consumers, why data may matter more than computing power, and how militaries worldwide are preparing to deploy AI in warfare. We also cover quantum computing and why Wang believes we're approaching the current limits of what massive GPU clusters can achieve. Hit play for a mind-expanding conversation about where artificial intelligence is headed and how it will transform our world in the coming year. --- 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: [email protected] Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Alex Kantrowitz?

  Scale AI founder and CEO Alexander Wang joins us to predict where AI is heading in 2025, looking at everything from geopolitics to AI agents. That's coming up right after this. This episode is brought to you by Genesis. What does it actually take to put agentic AI to work across an entire enterprise? I recently spoke with Genesis chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, head of enterprise business solutions at Voya Financial, to go inside a large-scale agentic CX transformation, including the business decisions, trade-offs, and lessons along the way. You can watch both conversations

• 0:45Alex Kantrowitz?

  now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay? Akash is an open cloud that provides easy access, flexibility, and peace of mind. That's how companies like Razer were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation.

• 1:15Alex Kantrowitz?

  Welcome to Big Technology Podcast, a show for cool, nuanced conversation of the tech world and beyond. So thrilled about the show that we're bringing to you today because we have Alexander Wang here. He's the founder and CEO of Scale AI. That company, it's worth $14 billion. It raised $1 billion this year. It creates data that powers LLMs from OpenAI Meta and other big companies. And it also provides technical solutions to businesses and the US government, which helps them build and deploy AI. So Alex is working with all the big companies, really there in the heart of what they're doing, and including, you know, not just companies, but the US government. And we're definitely going to touch on that. So Alex, great to have you here. Thanks so much for coming on the show.

• 1:57Speaker 01

  Thanks for having me. Super excited to be chatting today.

• 2:01Alex Kantrowitz?

  Yes, and we're going to get into plenty of your predictions. And I just want to kick off the one that I find the most interesting, which is that you see some geopolitical shifts coming up in the next year in the world of AI. Why don't you lead with that one?

• 2:13Speaker 01

  So I think that one of the big questions of AI has for the past decade has always been the US versus China arms race. And I think the question that's often asked is which of the US or China is going to come out ahead on AI technology? And certainly, it's been a pretty tight race at various points over the past decade as we look at technology from to technology, like with autonomous vehicles, it was very close. And then now with with military use cases of AI was very close. And then now with generative AI and large language models, it's once again quite close. I do expect that the new admin will come in and help accelerate things to enable the US to compete more aggressively with China and ultimately come out ahead on the technology.

• 2:58Speaker 01

  But my prediction really is that we're going to be talking a lot more about not only which of the two superpower wins, but which one has AI systems that are going to be adaptable, sorry, adoptable and exportable worldwide. So which country is going to is going to have the AI technology that becomes sort of the infrastructure and the foundation of the world's AI systems? And, you know, there's a lot of countries that are kind of caught in the middle. Most of the globe is sort of caught in the middle between US and China. And there's always these questions where I think both the US and China ask them, hey, you have to pick a side when it comes to which technology you're going to rely on. And so, you know, we we like to call these geopolitical swing states

• 3:43Speaker 01

  or, you know, many, many countries which are sort of, you know, they could go either way. They could go to Western and US technologies, or they could go to to Chinese technologies. I think one of the best examples of this was in the past year, the Biden admin posed to the UAE, hey, which way are are you going to go in terms of AI technology? You could either go into the sort of Huawei China stack, or you could go into the Microsoft United States technology stack for AI and they ultimately pick the US stack. But I I think this is going to be one of the under the line battles that really defines the course of the next a few decades of geopolitics.

• 4:25Speaker 01

  I I don't think we can really afford another Chinese expansion expansionary expedition like the Belt and Road Initiative or Huawei's technology being exported very broadly. We need to ensure that Western AI technology is dominant globally.

• 4:40Alex Kantrowitz?

  So basically what you're positing is that there's a series of AI models that US companies like OpenAI, Google, Amazon, Meta are building. And then there's a series of models that Chinese companies like Huawei are building. And they're going to be in competition with each other in the globe. And it's important that the US wins or the the Western version wins because we also have Miss Raul in France. Why is that important?

• 5:06Speaker 01

  There's two sides of this. I think first there's the tactical question of, okay, which one is more powerful? US AI versus Chinese AI. And this is very relevant for national security. I mean, I think that like, if you believe that there's some potential of some kind of conflict over Taiwan or other some kind of other like hot conflict between the US and China. Then we really, the United States needs to ensure that we have the best possible AI technology to ensure that we would prevail in any kind of hot conflict, that that democracy would prevail. And that ultimately that we're able to sort of continue ensuring our way of life.

• 5:44Alex Kantrowitz?

  But having the better, having the better chat GPT isn't going to make you victorious in a conflict over Taiwan.

• 5:50Speaker 01

  Certainly, it will not be the only factor. But the history of war is a history of military technology. And time and time again, you know, you see when there's new technologies and new technological paradigms that come to warfare, it has the ability to fundamentally shift the tides. You know, we saw that most recently in Ukraine with drone warfare becoming all of a sudden the major paradigm. By the way, that I think that the drone warfare in Ukraine is becoming more and more enhanced. By generative AI and more advanced autonomy. So that's definitely one thread that is continuing.

• 6:27Alex Kantrowitz?

  Fascinating. Before you move on, before you move on, where would you say the US and China are in terms of competitiveness on AI technology? And especially not even broader, but like especially about the way that they apply it in war.

• 6:42Speaker 01

  So if you look at just the raw technology, the US is is ahead, but China is is is moving is fast following. You know, and we like to break it down across three dimensions. So AI really boils down to three pillars. It boils down to algorithms, computational power and data. So algorithms are the kinds that, you know, folks at OpenAI or Google or other companies build. Computational power comes down to chips and GPUs, you know, the the kind that Nvidia produces out of TSMC's factories or TSMC's fabs in Taiwan. And then lastly is data, which is maybe the the least focused on of the three pillars, but certainly just as important for the performance of these AI systems.

• 7:27Speaker 01

  If we're at a rack and stack versus China, we're ahead on algorithms, we're ahead on computational power. Thankfully, due to a lot of the export controls that the Commerce Department has put in place. And then on data, it's a little bit of a jump ball. You know, the the conventional wisdom is that China is actually probably going to be ahead on data in the long run, because they don't care as much about sort of personal liberties and and, you know, protecting personal data in the same way that we do in the West. And so so right now, the US is ahead. That being said, the sort of deployment of AI to military, you know, it's hard to track exactly. The PLA doesn't tell us exactly what they're doing. People have Liberations Army out of out of China.

• 8:09Speaker 01

  They don't tell us exactly what they're up to, but I certainly am worried that they're moving faster than we are in the US. And this has been the sort of pre pre-existing precedent when it comes to China's use of AI technology for national security or military use cases. So the best example of this is in the past decade, they rolled out facial recognition technology widespread. Across the whole country for things like we are suppression or global surveillance of their citizen base. And they did that incredibly quickly, much faster than any comparable technology scale up in the United States. So my expectation is that they will actually deploy AI to their military faster than the US, even though the US is ahead on the tech core technology.

• 8:52Alex Kantrowitz?

  Okay, so that that's the military point. So basically, you're going to want the Western countries to be stronger than China. And AI makes a big difference there. So it's important for the AI industries to be stronger because if you're not stronger than you're, there's a liability, especially as this stuff gets put into production on the battlefield with things like drones and computer vision, I guess, applied on top of satellite imagery to figure out where people are stationed in the middle of hot conflicts.

• 9:18Speaker 01

  And there's a more subtle point, which is, which is that it actually, not only does it matter for hot conflict, for war, etc., it also matters just in terms of, okay, which technology becomes the commercially or economically speaking, the global standard.

• 9:36Alex Kantrowitz?

  Right. And this is your second point here.

• 9:38Speaker 01

  Yeah, exactly. And and because in the US, you know, we benefit as a country from being the global standard in a number of areas. You know, we are the global standard for currency. That is something that's incredibly beneficial to our economy and to everything that we do. You know, certainly our, our, our search.

• 10:01Speaker 01

  So Google and a lot of our technology companies are the global standards. So for search and for for social media, many of these are the sort of like global standards. We benefit a lot from these being the global standards. And I think when it comes to AI, you know, it's a very interesting technology because not only is it a sort of technological utility, but it's also a cultural technology. Ultimately, if you, if a lot of people within, on the globe are talking to AIs to, you know, understand what to think or or how to feel about certain things, then ensuring that the AI substrate that gets exported around the world is one that is democratic. In nature, that is sort of believes in the ideas of of sort of free speech and and sort of,

• 10:47Speaker 01

  you know, open conversation about whatever topic is necessary. You know, that's a that's a really powerful cultural export that we can have from the United States that will, over time, I think, fulfill a lot of America's vision of ensuring that we have, you know, freedom and liberty for all. So I think it's one of these things that is unbelievably important, even beyond the sort of hot military implications. It's one that's important just for culturally ensuring that the United States is able to export our ideals.

• 11:19Alex Kantrowitz?

  So you're saying there's a soft power issue here as well?

• 11:22Speaker 01

  Yes, exactly.

• 11:23Alex Kantrowitz?

  I want to ask you about China's development of AI, because I always hear two contradictory things about how China's progressing with AI. The first is that they have the government that's willing to put all the resources that they can into building the compute power to train and run models. And they don't care about data privacy, so they have all the data that they need, right? And then the algorithms are, you know, they're basically all published in that Google paper. You know, you can tweak them a little bit, but basically they have the algorithms. So they should be the lead. And then you look at what's actually going on on the ground, which is that, and you correct me if I'm wrong, right now China is using a lot of American models, open source models.

• 12:08Alex Kantrowitz?

  In fact, Meta's model, the Llama model, which is an open source model that they have developed and released, we know for a fact has been used in applications by the Chinese military. So explain this one to me. How has China been able to effectively, you know, put all these resources toward the problem, but still has to rely on American open source technology to build the things that they want to build?

• 12:35Speaker 01

  Well, there's probably two major things. I mean, one undeniable trend over the past, let's call it five years, has been the sort of the collapse of the Chinese startup sector. And this is really driven by policies from the CCP to significantly, you know, they killed certain startup industries, they really like hampered the entire innovation ecosystem. And you see it in the numbers, the sort of amount of capital flowing into the Chinese innovation ecosystem has fallen off a cliff pretty precipitously. So.

• 13:12Alex Kantrowitz?

  Yeah, why did they do that? Before you move on, why did they do that? I know they also somewhat disappeared Jack Ma, right? Like they had Chinese tech icons that have sort of gone away. Was it that their tech industry was growing so large it threatened the government or what? It could be the possible logic there.

• 13:29Speaker 01

  Yeah, I do think that was, that's the sort of fundamental risk. I mean, I think that if, if the government, if the CCP has a desire to ensure that they consolidate all the power, either they have to nationalize the tech firms, or they have to ensure that they stay weak. And so, and there were some other, yeah.

• 13:49Alex Kantrowitz?

  Shoot yourself in the foot stuff.

• 13:50Speaker 01

  Totally. And I think it's, a lot of this hinges on, I think, they do really see the world differently from the way that we do. I think we, you know, in the West, it seems totally insane, but I think in, in certain doctrines or in certain, with certain ideals, I think it can make, can make total sense, right? But, but there is a death of the Chinese innovation ecosystem. So, so a lot of what they have to do in AI is, is just catch up and copy what, what we've been up to, which they have been pretty successful at. So, for example, the, the, you know, OpenAI released O1 and released the O1 preview a number of months ago.

• 14:30Alex Kantrowitz?

  This is such a reasoning model.

• 14:31Speaker 01

  Yeah, this is OpenAI's advanced reasoning model, which is great at sort of scientific reasoning and mathematical reasoning and reasoning in code, et cetera. And the very first replication of that model and of that.

• 14:44Speaker 01

  Paradigm of model actually came out of China from a lab called DeepSeek. The DeepSeek R1 model. So they certainly are extremely good at catching up. Now, there is a very real hamper in a lot of their progress, too, which is the chip export controls. And this has been an incredible effort, I think, from the US Department of Commerce and the Biden administration in general to sort of hamper the ability of the Chinese AI ecosystem to build foundation models of the similar size, scale and magnitude as the ones we have in the US because, you know, they have not been able to get access to the cutting-edge Nvidia GPUs

• 15:27Speaker 01

  that we have in the States. And so whether or not you think that's good or bad policy, it has hampered the progress of Chinese AI development, which enables us to stay ahead.

• 15:40Alex Kantrowitz?

  So let's circle back to your prediction that you talked about how US and China will be head to head, trying to get their vision for AI adopted across the globe. So that's your prediction of like, what's going to happen? Who do you think is going to win there?

• 15:53Speaker 01

  I think that the trend right now is currently very positive in the direction of the United States or of the West, broadly speaking. We have the most powerful models. We also have, I think, the most compelling value proposition in terms of our models are going to keep getting better. And yes, maybe the Chinese ones catch up over time, but we are the innovation ecosystem. We are going to be the ones who innovate far ahead of the adversaries. That being said, I think that there's, you know, on the flip side, you have to look at what's the total package that the CCP or China might be able to offer. You know, in the Belt and Road Initiative, it was through this sort of like total package of technology plus infrastructure build outs plus debt

• 16:36Speaker 01

  that sort of managed to move a lot of folks over to their side. And so I think we need to watch it closely to make sure that we always have a compelling total value proposition. I do think, you know, one sort of sub-prediction that I have too, which is important to mention here, is that, you know, the technology is moving so quickly that I do think that 2025 will be the year where we start to see several militaries around the world start utilizing AI agents in active warfighting environments to great effect. I think you're going to start seeing this in some of the hot wars that we have going, as well as some sort of militaries, advanced militaries who aren't at war, start utilizing AI agents.

• 17:21Speaker 01

  And so I think that the the temperature, so to speak, on on AI deployment to military is going to is going to go up pretty dramatically over the course of the next year.

• 17:30Alex Kantrowitz?

  Yeah, I just wrote a post on big technology about how AI is going to be an enterprise thing for a while, right? Like companies, B two B software companies, not exactly the most exciting stuff in the tech world. Is it going to be where this stuff is adopted because it solves a problem for them? Where they have loads of information, they can't organize it, they can't share it, they can't act on it. And generative AI in particular is quite good at handling that. And then you think about, well, where else could this be of use if it's not going to be for regular people? Right? Like we're not, we don't have an AI phone right now, but we have like plenty of companies working in AI software and the military is just like the perfect example of where it could apply because of all of the information and the logistics issues.

• 18:10Speaker 01

  Yeah, exactly. And I think that this is, you're hitting on the core point, which I think is some is is often glossed over. I think when people think about the military and think about a war, they often think about the the literal battlefield and the sort of actions on top of the battlefield. But, you know, eighty percent of the of the effort that goes into any warfighting effort or any military is all of logistical coordination that goes into, you know, the manufacturing of weapons or the manufacturing of various supplies, the logistics and sort of delivery of all the supplies to to a battlefield, the decision making process, the the sort of data processing of all the information that's coming in.

• 18:53Speaker 01

  And so most of what happens actually looks, to your point, a lot like in enterprise. The stakes are just dramatically higher.

• 19:00Alex Kantrowitz?

  Yes. Yeah, military today is all about logistics. It's like the firing of the guns is like the last thing that happens, but it's a logistics game. And so just to, you know, drill down a little bit on one of those sub predictions that you made. So how do AI agents help in that case?

• 19:16Speaker 01

  So you know, there's, there's probably two core areas where I think AI agents are gonna have immediate value. One is in, is in, you know, kind of to reference your point on enterprises, it's in processing huge amounts of data. Right now, most militaries already have, you know, more information coming in the door than they have the ability to process. There's terabytes and terabytes of data that, that come in, whether it's data from the battlefield, data from their partners and allies, data from satellite networks, data from other data collection formats, and they need to process that into insight that actually can help them, you know, make real decisions about, you know, what they should be doing differently.

• 19:57Speaker 01

  So, so the first is just sort of this like huge problem of massive data ingest into real decision making. That's, and that, that sort of general problem set fits a lot of sub areas, whether it's in logistics, or intelligence, or military operation planning, or whatever it might be. The second area where I see it having very, very real impact is just in, in fundamentally coordination and optimization of, of complex systems. And this is, this is really where the, I think the logistics or the manufacturing cases are very clear, where these are incredibly complex processes with lots and lots of moving

• 20:39Speaker 01

  parts, and it's hard for humans to get your hands around those processes and really optimize them effectively, whereas AI systems can ingest far more information about the processes than otherwise, can run simulations on their own around what are various configurations that might operate better, and they can sort of self-optimize those processes to, to perform better. And then there's, I think, the sort of third area, which are more sort of speculative or sci-fi, which is the use of AI agents more actively in drone autonomy, or a lot of the autonomous missions that are being run right now. And, you know, I think this is an area of active experimentation for a lot of militaries, but I think if you start to see that happen, then you're gonna, you're, you will have more

• 21:24Speaker 01

  autonomous drones that are able to be more and more lethal, more and more effective, and that's gonna be a cat and mouse game in and of itself, a real race.

• 21:35Alex Kantrowitz?

  That scares the shit out of me. Are you comfortable with that?

• 21:39Speaker 01

  I think it's no, I think I think ultimately we're going to need to have global conversations and global coordination around to what degree we actually want a lot of this, a lot of AI agents to be used actively on the battlefield. That being said, there are, there are hot wars going on right now where militaries and countries are desperate and I think they'll do whatever they need to in the near term to get the, to get the leg up.

• 22:07Alex Kantrowitz?

  Yeah, it's one of those things that I feel like once it leaves the station, it ain't coming back. And when we talk about agents, it's basically like AI applications that make decisions on their own. If we end up having that, you know, deployed in war, it's just gonna, once somebody does it, it's just everyone is gonna do it. It's like the opposite of mutually assured destruction with nukes, I think, where that's like, oh, like, you know, if we do this, then the world is over. Whereas with like agents deciding what to bomb, where to bomb, how to attack, as long as they don't have access to nukes, it's really tough for that to go back in the barn because if you don't use it, you're going to be destroyed.

• 22:48Speaker 01

  Yeah. Yeah, I think the good news is that if you take nukes as as an example, what has happened with nukes is like we've, we've built incredibly advanced technology, technology that has the ability to frankly be world ending, but that has actually led to more peace than without it because you know, you have this deterrent threat of of the utilization of nukes. And so my hope certainly is that while AI's application into military is something that is is very concerning and potentially extremely powerful, it is the sort of same overall effect, which is to ultimately deter more conflict than than create it.

• 23:26Alex Kantrowitz?

  I hope you're right and I'm wrong. And we did have Paul Merlucky on the show a couple of months ago, and he talked about countries don't start wars that they believe they're going to lose. And so maybe that adds to that. I mean, that's certainly been the case with nuclear. All right, I want to get into your second prediction. We already have brought up AI agents, but I think we should go a little bit deeper because, you know, I think people hear about AI agents and they say, is that supposed to be something on my computer that's gonna like book me travel, book me tables at restaurants, look things up for me, do my expense reports if they, if I need them to do that, or, you know, basically, agents that act on behalf of the individual. We haven't really seen those yet. We've seen

• 24:06Alex Kantrowitz?

  some examples of companies and militaries using these things and the average person doesn't get a chance to touch that. But you think it's gonna change?

• 24:15Speaker 01

  Yeah, I do think that 20, yeah, I think that 2020, 2025 is really gonna be the year where we start to see some kind of very basic primordial AI agents really start working in the, in the consumer realm and creating sort of real consumer adoption. You know, another way that I think about this is, is, you know, we'll see something like a chat GPT moment in 2022, five for AI agents, which is, you know, you'll see a product that starts resonating, even though to technologists it may not seem like all that, or it may not seem like that big of a leap relative to what we had before. And I think a lot of that is gonna come from

• 24:57Speaker 01

  probably two main threads. First, obviously, the models continuing to improve and getting more reliable and sort of, you know, getting down that curve. And the second is really evolving in the UI and experience of what an agent does. I mean, right now, we're so stuck as a, I think, tech industry still on this sort of like chat paradigm and, you know, having everything be a chat with one of these models. And I think that's a constrictive paradigm to enable agents to actually really start working. And, and to me, what it, what it really means for an agent to start working is, you know,

• 25:37Speaker 01

  me as a user or consumers in general, start actually outsourcing some real workflows to the agent that they would have had to do otherwise. And so we're start, we'll start to just sort of like fully trust the agent to do full end-to-end workflows, you know, maybe it'll be something around travel, maybe it'll be something around calendaring, maybe. Be something even around just like, you know, producing presentations or managing your workflow. But we'll start to really offload some of the the meaningful chunks of our work to the agents. And there will be something that that really starts to take off. You know, I don't know if it's going to be one of the big labs.

• 26:18Speaker 01

  It'll be a new startup that comes up with it because I think so much of it will come from kind of like

• 26:25Speaker 01

  experimenting and the natural innovation ecosystem working out. But, you know, what we see is that the models and their capabilities are certainly strong enough to enable a pretty, a pretty incredible experience. You know, there's no, there's all this talk about whether or not we're, you know, we're hitting a wall or whatnot. But the models are really, really powerful and and we should see something big here.

• 26:51Alex Kantrowitz?

  Okay, so just walk me through like what that experience might look like. You don't, you know, we don't have to stick with this. Like, it doesn't have to necessarily be the use case. But since you've imagined the idea that AI agents could end up helping us in 2025, like, what are some experiences that are in the realm of feasible for someone?

• 27:10Speaker 01

  So let's first, let's let's walk through what what's an ideal AI agent? An ideal AI agent is one that that I think is observing and and naturally in all the sort of like core flows of information and core flows of of context that you are in digitally. So, you know, it's it's in all your Slack threads, it's on your email threads. It like, you know, it's see, it reads your your Jira or all of your tools to understand everything that's going on in your in your work life. And then it helps to sort of organize all that information to start taking certain actions. And so like one agent that I think Will would be super beneficial and one that I think is in the realm of feasible is,

• 27:54Speaker 01

  you know, something that starts to um uh take a hand at responding to a lot of your emails. Um, you know, flagging when it needs you for like additional context or information to to be able to to address your emails, um can sort of summarize a lot of your emails for you naturally. And so something that just turns the the experience of doing email from, hey, I'm like having to respond piece by piece to every single email to leveling you up to being, hey, this is like all of the overall work streams and workflows and how do you want to engage um at a high level on top of those workflows?

• 28:33Alex Kantrowitz?

  But this is, this is a business use case and I'm curious if you think that like how everyday people might end up using AI agents or is that just still a ways off? Like maybe not in 2025?

• 28:45Speaker 01

  Uh, everyone works, you know, so.

• 28:47Alex Kantrowitz?

  Uh, give me an example outside of the work context.

• 28:51Speaker 01

  Yeah, I thi- I think one that's more personal, I mean, I think similarly, um, I think in everyone's personal lives, you're also juggling and navigating a whole set of of various priorities, you know? I'm planning a trip with my friends over here and I'm, I need to, you know, get gifts for my for my family and figure out what they, what they want for Christmas and then I need to, I have all of these sort of personal projects which are still sort of like sitting there. And so I think in the same way, helping you sort of like level up on top of all of the projects that you're navigating and sort of like help you sort of coordinate between all of them more naturally. I think that's something that uh that we're going to start seeing.

• 29:34Speaker 01

  Now, I don't know the perfect way that that happens, right? I don't, I think that the product experience is so.

• 29:43Speaker 01

  So important as a part of this and having a product experience which where you don't expect it to be perfect, but you expect it to be pretty good. I think that's like 99% of the challenge and that's why we haven't seen it yet despite the fact that the models already can do a lot of this stuff pretty well.

• 30:03Alex Kantrowitz?

  My 2025 prediction is that guys use AI agents to use dating apps for them and some get found out and some don't. And we're going to see some stories about how like some guy like set it on autopilot and ended up, you know, lining up more dates than he could ever hope for.

• 30:21Speaker 01

  Yeah, yeah, yeah, that well, hopefully.

• 30:24Alex Kantrowitz?

  Maybe that's already happening.

• 30:26Speaker 01

  Hopefully there'll be good dates. Yeah, I don't know. What are you seeing? What are what? You know, you? I know you had Benioff on the the podcast a few a little bit ago. What are you seeing as the things that that seem to seem to make sense from an AI agent's perspective?

• 30:39Alex Kantrowitz?

  Well, I think that Mark Benioff, the Salesforce CEO, when he came on, talked like pretty convincingly that like we'll have AI agents at work. And again, this is like the work or the enterprise use case because work has all this data and there are all these tasks that we do all throughout the day at work that are just arduous and really quite, you know, quite annoying, preparing reports, making dashboards, going to meetings we don't need to be in, pulling out highlights from those meetings, sending them to our bosses, telling our bosses, you know, in the Salesforce instance, for instance, like how each conversation went and what our expected pipeline is to close that quarter and all this stuff can be used for AI. I think it can be used with AI. I think it's really

• 31:20Alex Kantrowitz?

  interesting in the medical use case. I was just speaking with GE Healthcare about how they've now put in dashboards for doctors, sort of summaries of cancer patients, medical histories, which run thousands of pages and and the doctors never had a chance to read the whole history. And now the generative AI is summarizing it and going out and finding available treatments for them and notifying them when they miss tests. And I think this is also an example that Benioff gave about the healthcare example. Where that can actually be proactive in scaling medical advice and medical treatment in a way that you'd never hear from like your doctor after you showed up to an appointment.

• 32:03Alex Kantrowitz?

  And now can they create an agent that just kind of keeps you on your plan? You know, in terms of like follow up stuff that you need to do. On the consumer side, like for everybody else, that's kind of where I wonder. Because all of our internet has been designed to effectively combat bots. But if we have agents that work on our behalf on the internet, like travel sites, dating sites, social media sites. I'm very curious like whether they're going to come up against these bot protection systems. Like, are they going to do CAPTCHAs on our behalf? Are they going to get the text messages and fill in those numbers? So they're able to log into different systems. Because again, the whole internet has been built to defend against these things. So I'm curious what you think.

• 32:45Alex Kantrowitz?

  I mean, is this vision of, you know, personal agents that act on our behalf to do things like book travel, keep up with our health, take action on internet services for us? Is it even a feasible thing to do, given all of the protections to sort of guard against them up until this moment?

• 33:01Speaker 01

  We will have to sort of fundamentally reformat how the internet works to be able to support it. And I think that like the, you know, we're going to need in some, in some senses, like there will be like two webs. There will be the web that, that, that, that humans use when they need to navigate stuff on their own. And then there will be the web that agents use, which is sort of under the surface and something that humans will never see, but allows them to sort of, you know, conduct actions on our behalf more, more efficiently and easily. And that I think will be, in the long run, what, what ends up happening. And my, my honest take is I think that to the degree that most of us, you know,

• 33:44Speaker 01

  there's, there's sort of like two kinds of usages of the internet today. There's, there's a sort of consumption, which is, which is where we're seeking out content. And, you know, we're curious about things. And then there's utility based usage. And I think the, the sort of addressable market, so to speak, for the agents is all the utility work. Like everything where I'm using the internet just to like get something done. I want that to happen faster, easier, better. I would rather have to not have to do that actively at all. Let's say it's like booking a appointment and looking up a particular piece of information or, um, you know, uh, figuring out how to like, you know, you fill out my tax return or whatever it might be.

• 34:29Speaker 01

  Like that stuff should all be handled by the agents and we're still gonna have to, you know, do a lot of consumption of, of content just to sort of like, you know, as part of our, as part of what we like to do. And so, um, uh, yeah, I think, I think it's a, it's a really good point. I mean, I think ultimately, I think agents are gonna start in an area that they'll feel pretty, um, uh, it'll feel like a toy, just like with any technology. So maybe, um, you know, we'll all start with like a language learning agent or we'll start with a cooking aid agent. Or it'll just be something that feels pretty innocuous, but then we'll start to realize we can really rely on it. And then, and also relying on it for a lot more.

• 35:12Speaker 01

  And that's kind of what happened, I think, with ChatGPT. Initially, it was sort of, we realized, you know, it was kind of a toy. And then people started using, doing a lot of homework with it, people started to code with it. And then now people do all sorts of stuff with ChatGPT and other chatbots. Um, that'll be the, the, the thread.

• 35:30Alex Kantrowitz?

  Let me ask you this question before we move off of agents. Do you think it's ethical for me to like have my AI agent, which can type and talk, uh, go out and email and call a bunch of humans on our behalf, people working, you know, let's say in customer service or, uh, I don't know if I'm applying to schools and they're trying to find out like information about like whether I qualify and what I need to submit. I mean, these processes, I, maybe they've been designed as arduous to sort of filter out the people who aren't willing to do the work to sort of get in or pass that application threshold. So it's, in some way, it's combating these guardrails that companies and institutions have set up for us. On the other hand, it could end up wasting a lot of people's time.

• 36:15Alex Kantrowitz?

  Uh, like I'm, I have really anticipating like no agent policies from like certain schools or institutions being like, if you're gonna reach out to us, it has to be a person versus an agent. What do you think?

• 36:27Speaker 01

  You know, I I saw this uh this thing on Reddit. There was this post of of how um a uh a an admissions officer, she sort of um created all these uh all these ways in which they could they could track whether or not an essay was AI-generated or not. And there were very detailed things. It was very specific. There were a list of maybe twenty or so criteria that um that they that they looked for. And um and I think that, you know, to your point, it's it's uh it was kind of heartbreaking to see because that means that, you know, let yeah for if a student used an AI to generate an essay, you know, they have to spend way more time just figuring out whether

• 37:11Speaker 01

  or not it was AI-generated to like sift through all the noise. Um and so, yeah, I think I think you're totally right. I think we're gonna need, there will um almost in the same way that there will be like an internet for humans and an internet for agents. There will be processes for humans, processes for agents. And and a lot of um uh a lot of things that are high intent or very expensive or otherwise special in some way are going to be reserved for humans um only. And and it'll sort of be the the sort of like more transactional stuff that uh that can be handed off to agents in in mass.

• 37:49Alex Kantrowitz?

  That's right. I mean, I'm in some ways, I'm looking forward to this future. On the other hand, I do sort of think like the more we talk about it, how how much AI will take care of for us, I do sort of feel like we're cannonballing our way towards that Wally future where we're all fat and drinking big big sodas and having Roombas take us around the world.

• 38:10Speaker 01

  It's uh, yeah, I think I think uh uh ease and convenience, which definitely are the directions that technology has taken us, uh, you know. Clearly, there should be limits at some point, but if they exist, we don't know where they are.

• 38:27Alex Kantrowitz?

  Exactly. And this idea of removing friction, in some ways, it's made the world great. In other ways, it sort of changes the brain chemistry of people where we don't expect to go through hard things. And when we do, we lose our minds. And that's why you end up seeing the YouTube videos and the videos on X of people in the airport. Because we've removed so much friction and companies have competed on the base of customer experience to the point where now if something goes wrong, we're fragile and we think that we deserve better. And there is something to be said for friction. It toughens people up a little bit.

• 39:05Speaker 01

  Totally.

• 39:07Alex Kantrowitz?

  All right, we're here with Alexander Wang, CEO and co-founder of Scale AI, 14 billion dollar company that works with others to help generate AI data, AI data for them, and also help them scale their AI solutions. We're going to talk a little bit more about Alex's third prediction when we come back right after this. This episode is brought to you by Avpoint. Everyone's racing to roll out AI right now. Co-pilots, chatbots, agents doing real work. But here's the part nobody loves talking about. All that AI runs on your data and most teams have no single way to see it, secure it, and prove it's under control.

• 39:48Alex Kantrowitz?

  That's exactly what Avpoint does. For 25 years, they've been the trusted layer beneath the world's most demanding data, now extended across your entire AI estate, your data, your cloud, and the agents acting on your behalf. It's how more than 28,000 organizations deploy AI with confidence. So innovation scales without scaling risk. Avpoint, the unifying trust layer for AI. Head to avpoint.com to see how enterprises deploy AI with confidence. Learn more at avpt.co/bigtechnologypodcast. That's avpt.co/bigtechnologypodcast.

• 40:30Alex Kantrowitz?

  Violent threats against executives are growing at an alarming rate. When a company experiences backlash, executives are the first people blamed. When their address and other personal details are sitting online, this backlash can lead right to their front door. The team at Ironwell knows this better than anyone. They've protected some of the most targeted executives and individuals on the planet for almost two decades. As a wide-glove enterprise security service, they protect your people with continuous personal data removal, proactive prevention tools, and human-led emergency support. So when someone goes looking for your executives, Ironwell ensures they hit a dead end. So here's what to do. Go to ironwell.com/bigtechnology, fill in the quick form, and request your free risk assessment. Their team will show you just how exposed your executives are

• 41:16Alex Kantrowitz?

  and how to lock it down before a threat reaches their front door. That's ironwell.com/bigtechnology. Learn how to stop online threats before they become real-world attacks. The link's in the show notes.

• 41:31Alex Kantrowitz?

  And we're back here on Big Technology Podcast with Alexander Wang, the CEO and co-founder of Scale AI. So Alex, I want to ask you about this interesting shift that we're seeing, right? So up until this point, we've talked entirely about AI models on the basis of how many GPUs or chips they're trained on, right? It used to be that you could train a model on like 16 chips, right? By the way, they're not cheap, like $20,000 to $40,000 each. Then it went to $1,000. And now, towards the end of the year, we started hearing crazy numbers like 100,000, 200,000. I was just at Amazon's Reinvent Conference in Vegas, and Matt Garman, the CEO of AWS,

• 42:16Alex Kantrowitz?

  told me that they're going to train the next anthropic model on hundreds of thousands of GPUs, GPUs or GPU equivalents. And then I was like, oh, that's a lot. And as he's saying that, Elon Musk came out and was like, "Well, we are going to train the next XAI model in Memphis on a million GPUs." So I think we're really hitting, like, maybe we're hitting the limit? I don't know, of what you can do with chips. And so you believe that we're going to shift this conversation beyond chips in terms of what makes the most powerful model? So I will tee you up for prediction number three.

• 42:50Speaker 01

  Yeah, and so so so much of the dialogue, to your point, over the past few years has really been around GPUs and computational power. And I think what's going to happen in 2025 is we're going to we're going to only be focused on who can create newer, better chips or bigger data centers with more chips, but also who can create newer and better data. And one of the things that I think we're going to see is a focus of the focus shift from just computational power to computational power plus data being sort of considered nearly equally. You know, data really is at its core the raw material

• 43:31Speaker 01

  for intelligence. So the conversations around data are going to be really interesting. And one of the big topics that's been that's been bounced around for the past few months has been, you know, are we hitting a wall? Have we hit the data wall? And are we hitting a wall on progress overall? And I think the interesting thing that's been happening is, you know, this has come from an approach of scale up computational power at all costs. If we just scale up the number of GPUs and create huge, bigger and bigger, you know, data center of GPUs without creating more and more data to train these models on, then we're going to hit issues and we're going to hit walls and barriers where we we stop

• 44:16Speaker 01

  seeing the level of progress that we expect out of the models. So one of the big things that we see, especially in our work with a lot of the frontier labs is, you know, it is true, they're scaling up the GPU clusters, they're scaling up the number of chips. You know, that's still a very aggressive path for them. But the the in parallel conversation is, how do we scale up data? And that, you know, there's two sides of that. One is obviously scaling up the volumes, but also scaling up the complexity. So they're seeing the need to go towards more of what we call frontier data. So go towards advanced reasoning capabilities, agentic data to support the agents that we were just talking about, advanced multimodal

• 44:58Speaker 01

  data. We just saw today, for example, that OpenAI released Sora. And and so the the needs for, you know, video data and more complex combinations of video, text, audio, imagery, et cetera, all together is going to be really, really interesting going into the next year. And so I think one of the lessons that's really played out more recently with the models is that you can't just scale GPUs and expect to get the same levels of progress. You need to have a strategy by which you're going to scale up all three of the pillars. You need a strategy to scale up the compute, you need a strategy to scale up data, you need a strategy to continue improving the models. And it's only through the sort of

• 45:41Speaker 01

  concert of all three of those things that you're going to be able to get, keep pushing the boundaries and barriers on AI progress. But I'm curious what you think. I mean, you've talked to all these CEOs. What are they talking about?

• 45:56Alex Kantrowitz?

  I mean, this is exactly the thing that they're talking about. We had Aiden Gomez from Cohere in a couple of weeks ago, and he basically said that this has sort of been the path of training the models. Whereas in the early days, you could effectively bring anybody off the street, take down anything they had to say, and it would be new information for the models. And then you started to have to bring in grad students to talk about their because that general knowledge base was built. So then you bring in grad students to talk about their area of discipline. Then you go to the PhDs. And then he goes, where do we go next? Because we have all this general knowledge, and now we have all this specialized knowledge that we've used to train these models on. And by the way, it's just amazing the way that they've improved and been able to sort of handle some complexity. It's really

• 46:39Alex Kantrowitz?

  crazy. And so the question is like, where to go next? And I think that's what you guys are working on now. And I'd be curious to hear what the process is like on your end for, you know, generating more data for these models to train on.

• 46:53Speaker 01

  Yeah, so it's exactly what you just mentioned. Like, a lot of what we're focused on is how do we bring in expertise and really the sort of expertise from every field you might imagine, from, you know, medicine to law to math to physics to computer science to, you know, even knowing about really advanced systems of various kinds, or being a great accountant, or, you know, whatever field you might imagine, getting the sort of all, what are all the arcane knowledge? What is all of this sort of really specific? Deep knowledge that exists in each of these areas and pull that into, you know, large scale data sets that we can use to help train these models to keep improving

• 47:38Speaker 01

  in a lot of these areas. And a lot of the, a lot of the effort for us has been something that we call hybrid data. So how do we, so one of the things that we've seen over the past few, past year in particular is that synthetic data has not worked as well as I think everybody had hoped, you know, pure synthetic data, just using data generated from the models to try to train future models, that can sometimes cause real issues for the models. And so one of the things that we've been really pushing forward is this idea of hybrid data. So you have synthetic data, but you use human experts to mix in with the synthetic data to ensure that you're producing data that's really, really accurate

• 48:23Speaker 01

  and high quality and it won't cause issues, but also you're able to do it very efficiently and at large scale.

• 48:28Alex Kantrowitz?

  So you also have those PhDs that will sit down and kind of write what they know or dictate what they know, and then you feed that into the models?

• 48:35Speaker 01

  Yeah, exactly. And a lot of times it's even more targeted than that. You know, you run the model until you realize the model's making mistakes over and over again. And then you know you've hit sort of a, a limit of its knowledge or limits of its capability, and you have a PhD sort of come in and help, you know, set the model up on the right track, so to speak.

• 48:58Alex Kantrowitz?

  What's the limit then, in terms of where are we going to get to? Because if we, let's say we have all these specialized fields input their knowledge, does that eventually make like AI complete if it just kind of knows everything about every subject? Or does it have to hit like a new benchmark to really show that it's, has this like next level intelligence? Like, does it have to start making discoveries of its own? What do you, what do you think the benchmark should be?

• 49:23Speaker 01

  Yeah, I think, well, to me, I think there's like clearly many more levels of improvement. So now it's it's sort of testing, okay, can it can it do each of these things right once? Or or how are there's sort of the first track was just reliability. So getting these models from doing something right once in five times to right ninety nine point nine nine percent of the time. And that requires a lot of development just to get to that, you know, increase the level of reliability of systems. And then to your point, it's really about how can the model start taking more and more actions in a row? You know, one of the things that really is true in all the models today is that they're not that good at, you know,

• 50:08Speaker 01

  at taking multi-step actions. Whenever it has to take a few hops, whenever it has to take chain a few things together, it'll invariably make mistakes along the way. And so the the next level of improving reliability is really enabling the models to do more and more multi-turn, more and more multi-step reasoning, to be able to enable them to to sort of do more and more complex tasks. And then the last piece as we go is to, and and this is the the key to where you're going is like, eventually it'll be able to start be able to start making its own hypotheses, running those tests on its own and sort of ultimately making its own sort of discoveries or realizations

• 50:52Speaker 01

  or or sort of conduct its own research. And even then, it's still going to get stuck sometimes and still going to need a human PhD to come in and sort of help it just in the same way that like, you know, a PhD student these days, you know, still needs an advisor to sort of still give it the right nudge. And so and so I don't think the sort of like the symbiosis, so to speak, between the humans and the AI will ever go away. Like, I think we'll always be able to sort of Will always be very important in helping the models, you know, get on the right track and ensure that they always are continuing to improve. But we're going to see the model sort of level up in terms of what is the what is the degree to which they're able to be autonomous and the degree to which they're able to operate on their own.

• 51:38Alex Kantrowitz?

  And on the multi-step thing, right, taking a bunch of different steps. I heard something interesting from Moody's last week, and I want to run it by you where they said, basically, they've created 35 individual agents. So let's say they want to evaluate something for their portfolio, like a company for their portfolio. They'll have one that will look at, one agent will look at the financial data. Another agent will look at the, let's say, weather risks. Another agent will look at the location that they're based in. Another one will look at the industry. They have 35 different variables or whatever it is. And then they have, they, all of them come back and they deliver their results to this compiler agent, which evaluates all of it and then runs the results by voting agents, which ask, okay, is this reliable or not?

• 52:23Alex Kantrowitz?

  I walked away from that impressed by the idea, but also like kind of my reporter brain went off and was like, I don't know if this is real or not. So I'm curious what you think. Is that a possible solution and how feasible is that in terms of a way to get into these multi-step processes?

• 52:39Speaker 01

  So that's a very, in my work, it's a very sort of regimented way to try to, to try to enable the systems to do multi-step reasoning. Because ideally, what you want the model to do is to, to, just like how a human does, be able to sort of go through and figure out what are the bits and pieces it needs to know as it goes along and be able to do so on its own dynamically without having to sort of like predetermine and preset this entire regimen for the models to need to go through.

• 53:14Alex Kantrowitz?

  You're saying that might be something that a model can do entirely on its own. That's pretty cool.

• 53:20Speaker 01

  I think in the future, like we're gonna, the models will improve to be able to get there. You know, and I think the real, um, on the multi-step side and the multi-step reasoning point, I do think that the, um, there's a lot of blockers because, um, this is the kind of thing that humans learn how to do, kind of from a lot of trial and error and experimentation. Like, we'll try to, um, do a complex task and then we'll realize, we'll learn that, oh, we actually missed, you know, let's say you try to bake a cake for the first time, you know, a reasonably complex endeavor, and then you realize you missed A, B, C, and D. And then the next time around, you'll be like, okay, I'm definitely gonna remember.

• 54:01Alex Kantrowitz?

  I just had a pan of flour that came out of the oven. Where'd I go wrong?

• 54:06Speaker 01

  Um, and, uh, but yeah, exactly. I mean, like, we learn a lot through trial and error. And right now, the models are, the models are early in their process of doing the same thing, of going through and, and sort of, uh, um, and being able to do these sort of dynamic processes where they learn through trial and error and they are able to continually learn from their mistakes. That's where we need to get to.

• 54:29Alex Kantrowitz?

  Okay, great. I, I know we have just a couple of minutes left, so let me throw a couple, um, quick hits at you and then we can head out. First of all, I'm just curious, we talked a lot about how data is gonna matter a lot, but I can't get my mind off the fact that Elon's gonna try to build this million GPU, uh, super cluster. What's your prediction for what that spits out?

• 54:50Speaker 01

  I honestly think right now, at where we are in AI development today, um, we are more bottlenecked by data than we are compute. So

• 54:59Alex Kantrowitz?

  It will just have a incremental improvement then, with something like that?

• 55:02Speaker 01

  Yeah, I think that, I think the real step changes come from data.

• 55:06Alex Kantrowitz?

  Okay. Um, so just a quick follow-up to that. If we, um, if we end up, like, I just saw there was news from Google today about this breakthrough they had in quantum computing, which we'll probably cover more on the Friday show. Uh, if we have working quantum computers which can process data much faster, what do you think that does for AI?

• 55:27Speaker 01

  I really think, so I had the opportunity to tour Google's quantum facility earlier this year. It's very impressive. It's, I think quantum computing is on, kind of like the way AI was back in 2018, it's on a few scaling laws where you can definitely sort of squint and see that, you know, in five to 10 years, this is going to be a really, really impactful technology. And ultimately, what I think what it's going to enable is, it's going to speed up AI's ability to, to do scientific discovery. And so whether it's, you know,

• 56:08Speaker 01

  I think, I think a lot of the use cases that excite people are in biology, or chemistry, or fusion, or a lot of these very chaotic and difficult to understand, you know, natural sciences. I think that's where quantum computing has the ability to be pretty transformational fundamentally. And I think AI will be able to use it as a tool to be able to enable it to do incredible research in those fields.

• 56:31Alex Kantrowitz?

  That's crazy. Okay, so, all right, last one for you. We're in the middle of like this race where it seems like every week, the foundational model companies put out a new development. Whether that's OpenAI, whether that's Anthropic, or even XAI, Google. Amazon just released a set of new models last week. So who do you think is in the lead at the end of 2025?

• 56:55Speaker 01

  Oof, that's hard to say. I mean, I think that one thing that we see today with the models is that because all the benchmarks that were used today are what's called saturated, i.e. you know, in other words, like, all the models do really well at the benchmarks. It's really hard to discern actually which ones, which models are fully on top versus not on top. You know, there's a lot of argument, for example, on the internet, you know, At least in the Twitter feeds that I see in terms of whether Claude is better or a one is better. And there's a lot of these comparisons and between the two of them.

• 57:34Speaker 01

  So one of the things that I think we're going to need in 2025 are much, much harder benchmarks and much, much harder evaluations that are going to be able to help us figure out, you know, separate the wheat from the chaff a little bit. I don't know who's going to, who's going to be in the lead, but I do think that, I think that we need much better measurement to actually be able to discern between all of these incredible models that, that labs are pushing out right now.

• 58:02Alex Kantrowitz?

  Okay. All right. We'll take it. No, no prediction on who's going to be the best, but a definite interesting perspective on evaluations. Alex, great to meet you. Thank you for coming on the show. I think these predictions have been fascinating, definitely stretched my mind in areas that I wasn't thinking about. So thank you, and we hope to have you back sometime soon.

• 58:19Speaker 01

  Yeah, this was a lot of fun. Thanks for having me.

• 58:21Alex Kantrowitz?

  Thanks for being here. All right, everybody. Thank you so much for listening. We'll be back on Friday with Rajan breaking down the news. We will see you then on Big Technology Podcast.

• 58:31Speaker 02

  Now, more than ever, we need someone on our side. But Rob Whitman? Whitman's votes drove up costs, cut Medicaid and risked 350,000 Virginians healthcare to pay for tax breaks for himself and his donors. He's not on our side. Shannon Taylor will fight corruption, cut taxes and bring down costs. This November, vote for Shannon Taylor. She's for us. Paid for by HMP, the House Majority PAC dot com. Not authorized by any candidate or candidate's committee.

• 59:00Speaker 03

  In this life, you can't win.

• 59:02Speaker 04

  When the world gets you down.

• 59:03Speaker 03

  No!

• 59:04Speaker 04

  Escape to Pluto TV.

• 59:05Speaker 03

  This is objectively fantastic.

• 59:07Speaker 04

  Relax into the movies and shows you already know and love, all for free. Shwing! Even our news is uplifting.

• 59:13Speaker 01

  You stay classy, San Diego.

• 59:15Speaker 04

  Watch School of Rock, Crazy Ex Girlfriend, Anchorman: The Legend of Ron Burgundy, The X-Files, Wayne's World and so much more.

• 59:21Alex Kantrowitz?

  We're not worthy!

• 59:22Speaker 04

  The best things on TV really are free. You just have to know where to look. Escape to Pluto TV. Stream now, pay never.