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Behind the Numbers: an EMARKETER Podcast
Behind The Numbers: DeepSeek: What It Is and How It’s Shaking Up the AI Universe | Feb 03, 2025
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- [00:00]
Gaja Sevilla
Foreign.
- [00:04]
Marcus
Hey, gang. It's Monday, February 3rd. Grace Garjo and listeners, welcome to behind the Numbers, an E marketer video podcast made possible by Zeta Global. I'm Marcus, and today we're discussing Deepseek, what it is and how it has completely shaken up the entire AI landscape. Today I'm joined by two folks. We have with us our technology and AI analyst who lives out in California. It's Grace Harmon.
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Grace Harmon
Hi, nice to be here.
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Marcus
Hello there. And we have with us as well our senior analyst based on the other coast in New York, coming to us from our studio, it's Gaja Sevilla.
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Gaja Sevilla
Grace, Marcus, nice to be here.
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Marcus
We start with the fact of the day. Which land animal has the longest migration? As Kayla Zhu of Visual Capitalist notes, land animals migrate to move between feeding grounds, safer habitats with fewer predators and their home ranges. According to a 2019 study published in the science journal Nature. Caribou, as they're called in North America, or simply reindeer, as they're known in other parts of the world, travel 840 miles round trip. So people are like, what the hell does that mean? So for folks on the east coast, so for Gaj and Victoria, who edits the show, who's coming to us from the studio as well, and Stuart, who runs the team, everyone's there. It's like walking from New York to Cleveland or Toronto and then back again, people on the West Coast. So graves, that's like walking from LA to San Francisco and back. And for my European audience, it's like walking from London to Switzerland and then back. What's that good for the caribou? So basically, you'd have to walk. It's like half a marathon's distance each day with an hour for lunch. And it would take you about two months to do that. So by the time you get there, you'd immediately have to turn around. That's so far. I want to say waste of time because I'm sure it's necessary, but it seems like it. Anyway, today's real topic, what is deep Seek and how has it slash, will it change the AI world? All right, so let's tuck into it, folks. Lots to talk about. ChatGPT maker, OpenAI tech giant Oracle, Japan's SoftBank, and MGX, a tech investment arm of the UAE government, just announced that they're planning on building $500 billion of AI infrastructure in the U.S. bBC article noted that the new company, called the Stargate project, was announced at the White House by President Donald Trump, who billed it as the largest AI infrastructure project in history and said it would help keep the future of tech in the US Less than a week later, Chinese startup Deepseek had other plans. They released a free AI powered chatbot called R1 that is reportedly at least as powerful as OpenAI's latest O1 model at maths, coding and natural language reasoning, but developed for a fraction of the price, allegedly of its US rival. Its sudden debut wiped $1 trillion off the value of US tech stocks. AI chipmaker Nvidia took about half of that hit, falling 17%, the single biggest single day stock decline in history due to concerns that future AI models could be developed more efficiently, possibly reducing the demand for Nvidia's powerful GPUs. As Kylie Robinson Elizabeth Lapato of the Verge put it, it took about a month for the finance world to start freaking out about Deep Seq, but when it did, it took more than half a trillion dollars or one entire Stargate project off Nvidia's market cap. So where is Deep Seat come from real quickly? A short history A company was founded in 20232023 how most normal people say the southeastern Chinese city of Hangzhou is where it was founded by liang Wenfeng. He's 40 years old, information electronic engineer graduate founded one of China's largest quantitative hedge funds that backs Deepseek. This past Christmas, Deep Seat released a reasoning model called V3. Its second model, R1 was released last week, called one of the most amazing and impressive breakthroughs he's ever seen, according to Marco Dreesen, a VC and advisor to the to the President. And it quickly became the most downloaded free app in the US just a week after it was launched. Okay Gajo, what's your initial reaction to the launch?
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Gaja Sevilla
So two things came to mind. First, it's free, it's a free tool, anyone can use it. And the second is it's built on open source AI frameworks, meaning a lot of the work that's gone into it is available for other companies and developers to use, alter and share if they want to. So I think those two things make it a compelling release, especially on a global scale, given that they've shown that it's been trained on a lot less energy using maybe older GPUs. So it's sort of leveled the playing field, especially for startups or companies that are looking at adapting gen AI solutions that are pretty much cutting edge at this point.
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Marcus
Yeah, that's a really interesting part of this. Because it's open source, people can kind of pick it up, adapt it for what they want to adapt it for. And it does open the door for these cheaper AI alternatives. The French government immediately said this message is that we can compete now. That's what the message is. We don't have to rely on raw computing power to determine who wins the AI race. However, that also means that smaller startups, similar to the French startup Mistral AI, now have a new competitor in Deep Seq, but also probably a bunch of other competitors who are going to look at this and say, oh, we can now get into the market. So it's kind of like almost like with, you know, Spotify for example, like before Spotify or YouTube, you know, it was hard to get recognized, but when you created Spotify, YouTube meant everyone could get there, which meant it was easy to get recognized because of the power of the platform, but it also meant that everyone was there. And so that in turn made it harder. So I'm interested to see whether this makes it easier or more difficult for, for some of the smaller startups to compete.
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Gaja Sevilla
Yeah, I think you can contrast that with Project Stargate, because what is Project Stargate now? They're building massive data centers. These are power hungry, water hungry data centers. They're building that for one company. That's for OpenAI. It's not for all the AI companies in the US or even in North America. And that tells you it's a closed system. They're looking at really heavily resourced, heavily funded investments to sort of get to the next level, which is AGI. And on the other hand, you have, you know, deepseek, who's, who's taking a completely different approach. You don't hear about, you know, their plans to get investors to get, get massive data centers. And they're really just focusing on the building of the AI, you know, through their models, through refining what's already existing, whether or not they've taken, you know, bits and pieces from companies like OpenAI or other open source, you know, AI companies. The point is, it looks like Deep Seq has become what OpenAI was when it first started. Meaning, you know, it's a project, it's about building the best AI possible with what you have. And so that really shows you the dichotomy between those two events that have happened in a relatively short time.
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Marcus
It does seem like data centers are the investment project du jour. There were some numbers from McKinsey saying global demand for data center capacity would more than triple by 2030, growing between 19 and 27% annually. So lots of appetite for that. I'm also wondering two things I guess Grace one is the Stargate project dead on arrival because Ben Berkowitz of the Axios was saying that if China can do AI better and faster at 1/1 of the cost, kind of cast a shadow on the rationale for the Stargate project investing $500 billion in AI infrastructure. And then kind of alongside that, how are shareholders of US tech giants kind of looking at this? Because they're looking and saying, hey, why are you spending billions and billions of dollars on AI? Is that an efficient use of money when this other company's done it for a lot cheaper? Meta is planning to spend over $60 billion in capital investment. That's 50% more than it did last year. Microsoft's planning to earmark, or has earmarks, 80 billion on AI data centers in its current fiscal year. So how do you think this announcement has disrupted those larger, more established, at least in the West, AI players?
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Grace Harmon
Sure. Well, I think that one thing that Deepseak is doing that could impact the project is it's kind of upending the idea of scaling law, which is the more data and the more computing power that you put into an AI model, the smarter it gets. And like Gajo said, Deepseek is operating with older hardware and slightly weaker GPUs and being able to produce these really high quality models with investors. There's been concerns for a while, but whether there's an investment bubble growing because of the sheer amount of money going in, and investors are becoming a little less patient. You have CEOs of these big tech companies saying that the risk of over investing is greater. Excuse me, that the risk of underinvesting is greater than the risk of overinvesting. But that's not necessarily a sentiment that's shared by investors. I think that one thing that deepsea could have done that might shoot itself in the foot a bit is that OpenAI and Microsoft are already looking into whether Deepseek used OpenAI's APIs to train its models. So if that's something that's true, I think that kind of undercuts the idea that they were able to do so much with so little. If that's what's true. I'm not saying it necessarily is. I mean, Microsoft's jumping in on that investigation because they are the exclusive licenser of OpenAI's APIs. So I think that if that's accurate, that's something that could be a really big issue. I think that what Deepseek is doing also just alters the standards for AI development and there's pros and cons for whether that'll let more people get into the market. Like you said, like Mistral. But for investors, I think that that is why this had such a big impact on the stock market, is that investors don't want to be paying as much as they are paying for AI, considering how long the timelines are for AGI, for AI to become so good that you can really product it and really ask consumers to pay a lot for it. So I think that's why there was this scaling back on the stocks.
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Marcus
The U.S. tech stocks, I mean, we talked about. Nvidia dropped 17% in a single day, wiped half a trillion dollars out of it. But others also took a significant hit. Oracle found 14%. Super microcomputer, which makes servers used for Gen AI, fell 13%. Chipmaker Broadcom fell 17%. So a lot of folks were affected by this. One of my questions, Gaja, is what does this do to AI spending? Does it supercharge it? Does it make people kind of reconsider all of this money that it's being pumped into it? Because Ion Stoicker, co founder and executive chair of AI software company Databricks, said the lower cost of deep seat could spur more companies to adopt AI into their business. As opposed to people saying spend less because it shouldn't cost as much. People might spend more because look at what we can do with the advent of this R1 model. It could also help with the kind of huge energy hurdle facing AI. However, because it uses less energy, it could mean more investment, which means that inevitably it will use more energy. The energy problem basically is the 2024 Energy Department reporting the AI will account for 7 to 12% of U.S. electricity by 2028. It's up from 4% in 2023. So where does AI spending go from here?
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Gaja Sevilla
I think there's going to be a lot of. There's going to be reassessment along AI spending. Investors are probably not going to want to consolidate into big tech players that much as Deepseek has shown. It's worth looking at startups, it's worth looking at that spirit of innovation over kind of a spirit of profit. And I think we're going to see that going a lot deeper. So if there have been stalled investment cycles in startups, I think that might spin up again the same time. I mean, companies like Nvidia might see, you know, less investment because we've now seen a world where maybe you don't need, you know, the greatest, biggest, most expensive, hardest to get hardware that's, you know, decided on and supplied by one company.
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Marcus
Right, yeah.
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Gaja Sevilla
And so that, that changes the game a little bit. And if you, if you had money to invest, you'd probably want to spread it out and bet on maybe longer term. Sure. Bets, right?
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Grace Harmon
Yeah. It's a chance to hedge your bets.
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Gaja Sevilla
Yeah, absolutely.
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Marcus
So let's talk a bit about this model. How much more efficient is R1? Well, Deepsea claims its model can be trained on 2,000 specialized chips versus an estimated 16,000 for leading models. And it also says that it costs $6 million to train, a fraction of the over 100 million alluded to by OpenAI boss Sam Altman when discussing GPT4. There was, I saw one ranking. I don't know if you say Imsys or Imsis, but it's a crowdsource ranking of chatbots. They put R1 7th higher than any other open source model and the highest produced by any company other than Google or OpenAI. But I mean, how much of an impact do you think this should be making to the market? Given, Given, yes, how relatively cheaply it was developed and with less processing power. But what's your assessment of how good this model is as it stacks up against some of the ones out there in the market?
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Gaja Sevilla
Okay, I think. Well, I'll start with this. A lot of the original chatbots are one solution for a number of things. With Deep seq, it came out of a quant lab, so basically it was tuned for very specific types of logic. Right. Which means it doesn't need a whole amount of data. It knows to make the most with what it has. And that speeds up the processes and the training. And it also gives you, I think, more accurate solutions. The one thing that stood out to me was in terms of coding, they compared it with a number of tools and deepsea consistently provided code that was good to run without any debugging needed. Whereas other solutions like the latest GPT still had a bit of a clunkiness to the code they produced. Deepsea kind of leapfrogged them in that respect. And code is actually a good test to see. It's that it's good in math, it's good with that sort of logic. So it's not going to be a one size fits all. But for the things that matter to businesses, I think it's, it's spot on for now. Right?
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Marcus
Yeah, yeah, there's, I mean there's. This has come out. A lot's happened in a very short space of time. So we had, we had 01, which came out the end of last year. That was kind of the first reasoning model, quote unquote, that hit the market. And that basically just means something that has to stop and think a bit more about, you know, what the answer could be. One of the examples I was reading was if someone said to a human, what's the capital of France? Capital city. You would say Paris pretty quickly. But if someone said, you know, what is the second biggest city in France? You might be like, oh, you have to stop and think. You might be like, Lyon might be up there, Nice, Marseille. And you'd have to kind of think through what the answer might be. And then you eventually be like, through kind of logical reasoning, you might figure out what the answer is. Google came out with a reasoning model called Gemini Flash thinking in December. OpenAI then came out with O3. A few days later, China's version of Amazon Alibaba. They released a new version of QEN Chatbot. Qwq with the same reasoning capabilities as well. So, I mean, this R1 projects hit the market, but there are a lot of others out there. Grace, I wonder what you think of this, because I thought when this came out I was like, okay, this is probably going to be a rallying cry for the President. He's already called it a wake up call. And big tech using it to kind of make folks a bit more nervous about what China's doing and letting them charge ahead with AI investment kind of shooting. Also shooting down any legislation that might get in the way. Deep Seek, their privacy policy makes it pretty clear that the company stores the information that they collect in secure servers located in the People's Republic of China. That was the fear over what TikTok was doing collecting a ton of American data and with the Chinese government collecting a ton of Americans data, which Chinese government could potentially allegedly have access to and is the reason that their future in the US is in limbo. What do you make of how successful R1 might be allowed to be in this country given America's reaction to TikTok?
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Grace Harmon
I think we're seeing really mirrored issues between TikTok and Deep Seek. Deep Seek is storing all user data on servers in China, which doesn't necessarily mean that the Chinese government, it doesn't mean that the Chinese government is getting instant access to everything that's going through, but it does mean that the Chinese government would have a much easier time requesting and accessing that user data from a company. We're getting some issues reported from users with censorship in terms of being able to get answers to questions about territorial disputes with the South China Sea, questions about the Chinese government. I think that one thing that could be a block is being able to access the same level of success that OpenAI has and some AI companies have had with large enterprise clients in the US and with the US government. OpenAI has contracts with the Department of Defense. The US Navy has already banned use of Deep Seq. It's already been pulled from App stores in Italy, App Store and Google Play Store. They're already launching a probe into it with concerns over data privacy. So I think that that is going to be very easily a big block. I think in terms of seeing a ban with the new administration, I think there would be a newer motivation with the Biden administration. I think that a clampdown would come more from the angle of wanting guardrails to protect user privacy. With the Trump administration, I think it would come more from the angle of wanting to protect U.S. innovation. I think it's. It's likely that we will see some motions to control or maybe ban deepseek. I don't think they will move that fast. I mean, we've seen how long the process has been with TikTok. It's been a long, long time coming. It might be a lot longer, but it's already causing issues and it's been mere days.
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Marcus
I was reading Dr. Richard Whittle from the University of Salford in England was saying that he had various concerns about data and privacy with the app with Deep Seq, but said that there were plenty of concerns with the models used in the US as well, because they are both hoovering up an incredible amount of data about. Folks, Gajo, let's close out with this. How does this change things moving forward? Given everything we've talked about, everything that's happened, what are you looking for, what you're paying most attention to in terms of how you expect the landscape to shift as a result of this R1 model.
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Gaja Sevilla
Okay, so we know that Nvidia took a hit, huge hit, But I think in the long term, it's going to be OpenAI that will have to be on the defensive. They are the leading AI company and now they have a rival that's more affordable, open source, easier to adopt and use. And so, you know, they've just started to charge, I think, $200 for their pro plan. And performance wise, you know, people might just say, do I really have to pay that much? You know, so they've spent the past year convincing us that you have to pay for access to AI and They're losing money and they're losing money. And now we have a situation where, you know, we might just have a new model to play with every month, seeing us how level the playing field is. So it's going to be difficult for them to, you know, even if they're say the next GPT is really good, do we really need really good or just good enough? Right.
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Marcus
Yeah.
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Gaja Sevilla
And Deepseek has shown us that it's really good. So that bar has kind of been changed and I think other AI companies will have to, to adjust as well whether to lower the cost or cost of admission to their products and services. Since a lot of them are shifting towards AI agents, I think that area is safe, especially since these are focused on enterprise and security and privacy is addressed because all that information is kept in the US or within those company servers. But for general use, I could see Deepseek becoming basically what OpenAI was two years ago, something everybody wants a piece of.
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Marcus
Yeah. Grace, how about for you, how does R1 change things moving forwards?
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Grace Harmon
Well, we've already gotten a response from OpenAI. Sam Altman said on X, I think it was yesterday, that they will deliver much better models, that it's legitimately invigorating to have a new competitor. Whether or not that's a genuine statement. Yeah, I think it's a really big pressure to step things up. Like Ajo said, I mean, they're losing money on their Pro subscriptions, they're $200 a month. OpenAI has its own issues with whether or not it's using users data for model training and whether or not users can consent or not consent to that. But yeah, I think it really shakes up the idea that if you want a better model, you have to pay more money for it or we have to pay more money for it.
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Marcus
I thought it was quite ironic that. So the question being, did the restriction of AI chip exports to China backfire? Because Mark Cslik of the BBC was pointing out that all of this has been achieved using lower end technology, at least reportedly from them from, from deep sea because of chip export restrictions. Those restrictions may have initially helped to cause this AI bubble and. But they are also what led to the structural integrity of the AI bubble in the US being called into question.
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Grace Harmon
And China has its own successful companies, whether they have big funding or little funding. I mean, they have pretty powerful AI companies with backing from Alibaba or Baidu. Their AI chatbot has the biggest market share for search engines, bigger than Bing. There's a lot of pure play startups there. So even if you're just looking within China, whether or not they're relying on US chips, there's a really good market there already for us or for, excuse me, for AI developers.
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Marcus
Yeah. The Economist writing the competition nipping at American AI heels may yet spur it to greater things. We shall see. That's all we have time for for this episode. Thank you so much to my guests for hanging out with me today and explaining what the hell is going on with R1. Thank you. First to Gajo.
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Gaja Sevilla
Thanks. This was fun.
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Marcus
Yes, sir. Thank you to Grace.
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Grace Harmon
Thanks guys. Nice to talk with you.
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Marcus
Yes, indeed. And thank you to the whole editing crew, Victoria, John Lance and Danny Stuart who runs the team and Sophie who does our social media. Thanks to everyone for listening in to an E Marketer video podcast made possible by Zeta Global. Tune in Wednesday for the Reimagining retail show where the gang will be discussing TikTok Shop.