SoftBank’s Purchase of Ampere, Future of Ed-Tech
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From the heart of where innovation, money, and power collide. In Silicon Valley and beyond. This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. ♪♪

Live from San Francisco, I'm Tim Stenevek and this is Bloomberg Technology. Coming up, today is Quantum Day at NVIDIA GTC and Ed Ludlow is there. We'll have the main takeaways from the conference so far and discuss what to expect from today. Plus, SoftBank is acquiring the chip designer Ampere in an all-cash transaction valued at $6.5 billion. We'll sit down with Ampere's CEO to learn more about the deal.

And campus, a for-profit community college has raised $46 million from investors, including Sam Altman, Peter Thiel's founder fund, and Joe Lonsdale's 8VC. The founder and Joe Lonsdale join us on the future of edtech. First, though, a check of the markets, and we're seeing some green across the screen. Stocks opening higher after yesterday's Fed-fueled rally, or I should say lower, but then it was the best Fed day going back to July, then about a half-hour.

half hour into the trading session, we got some surprisingly strong housing data, which turned around the trade. At last check, about 65 stocks in the Nasdaq 100 moving higher right now, up about four tenths of one percent off its best levels of the day so far, but still in the green.

One of the stocks that's helping to push the Nasdaq 100 higher is Meta Platforms. Investors cheering the news that Meta will roll out Meta AI across 41 European countries this week. It's up right now by about 4%. Meta's intelligent chat function will also be rolled out across 21 overseas territories and available in six European languages, the company said in a statement. It's going to be free, too, across its apps, including Facebook, Instagram, WhatsApp, and Messenger.

Also watching shares of Intel down about seven tenths of one percent. It did fall by nearly seven percent yesterday after a TSMC board member dismissed a report that the company has pitched to major U.S. chip makers about taking stakes in a JV to operate Intel's factories. Shares were bouncing back earlier, but lower now.

And NVIDIA is higher today as the company continues its GTCAI conference in San Jose. Today, of course, Quantum Day, NVIDIA shares higher by 1.3%. And that's exactly where we go now, live from the GCC event. Our own Ed Ludlow joining us. Ed, I want you to set the scene for us, but I also want you to clear something up for us because there's lots of chatter this morning about NVIDIA in the context of money being spent in the U.S. What's going on?

Yeah, it's based on an interview that Jensen Wang, the CEO, gave the Financial Times. And what he's quoted on saying is that NVIDIA will procure half a trillion dollars worth of electronics in the next four or five years. But what he's not saying is that that is capital expenditures, right? This is a company that has 70% market share in the market for AI accelerators, high performance GPUs that go into data centers.

He spent a lot of time yesterday. We were with him for an hour explaining the mechanics of that, right? When you build a data center from scratch or you upgrade its technology, that is a tens of billions of dollars or hundreds of billions of dollars project. If you have 70% market share for the brain that goes into it, inevitably you're going to have to pay TSMC to manufacture the chips. You're going to work with Dell and HPE on the server rack.

assembly and packaging. That's what he's referencing, essentially, the cost of doing business. It's interesting because this is what Jensen Wang wants us to be talking about. He sees NVIDIA as foundational to all AI companies. In other words, companies are being created because of what NVIDIA is doing in the context of it calling itself an AI infrastructure company, an AI factory company.

Okay, AI infrastructure and AI factory. Today, though, is quantum day. And NVIDIA doesn't actually make quantum computers. What's going on? Correct. Yeah, so exactly the same point as with

AI data centers, NVIDIA does not make quantum computers. What it does is sell its existing technology to the quantum computing industry to help them make their own machines better. You can use an AI supercomputer for error correction and calibration of a quantum computer, but they're essentially two distinct fields. Of course, quantum computers follow quantum mechanics and are coded in qubits, not in bits, ones and zeros. But we're all here today because of what happened in January, right?

Jensen Wang was asked basically at point blank, what do you think of quantum computers? And he said they're more than a decade away from being useful. The net result that day, January 8th, was that the publicly traded quantum computing stocks all sank 30, 40%. And so we're all assembling today. And Jensen's going to be on stage with all of the quantum computing experts

CEOs who are basically his customers. He just sells the existing gear to them. And maybe we'll get an update on how Jensen feels on quantum computing. But from Nvidia's perspective, it's an arm of research and it's where they sell existing tech, GPUs principally, to that industry. They do not make quantum computers.

Bloomberg's Ed Ludlow, Ed at NVIDIA GTC. Ed, good to see you. We'll see you a little later, too. Thanks so much. Stay with us, though, because Bloomberg this afternoon has a special edition of Bloomberg Technology on quantum computing. Ed Ludlow will return live from NVIDIA's GTC event starting at 4.30 p.m. Eastern time.

For on the broader tech market and Nvidia, let's bring in Sylvia Jablonski, Defiance ETF's CEO. She joins us now. Sylvia, I'm wondering how you're watching everything happening at Nvidia GTC. You do argue that Nvidia is a buy right now off of its highs. How are you watching everything come out of San Jose this week?

Yes, good morning. Thanks for having me here. I think it's all very exciting. You know, what we've been seeing over the last year or two is just, you know, so much news around the growth of AI, the potential for quantum computing, the build out of 6G. And, you know, who's the star of that show? It's NVIDIA. And you're talking about a stock that, you know, was trading up over above 140 pretty recently before this pullback that we see here. At these levels, I mean, I

love the stock. I love the company. You're talking about potentially a fourth industrial revolution, compounded annual growth rate, themes of AI and quantum and things like this of 35% to 40% per year. As an investor, I'm patient with technology. It takes time for things to play out. But I want to get in early. And this is still first innings. A lot of people are saying that. But we're seeing that it's true as we get more news from this conference. So do you think that

Nvidia investors right now have it wrong. Does the market have it wrong? To what extent do you think this stock is undervalued right now?

Well, we can always say the market is a little bit emotional, right? So I think that there are a lot of reactions in the market. And when the market becomes emotional and people panic, usually they sell the Mag 7 or they sell kind of like the high-flying names that have done well for that year. We've seen it happen with Tesla. We saw it happen with Apple during COVID. You know, all these different sorts of things. And NVIDIA is kind of the poster child of the market this year. So when we have fear and panics about tariffs and

and things like this, people tend to run for the hills and sell the most profitable stock. So I just think that there's a lot of liquidity on the sidelines. There's still this consumer sentiment that is uneasy. But eventually, when some of the tariff things shake out and then we get the tailwinds of lower regulation and tax policy, things that are favorable to the market, these are the names that also rally first, right? So it's kind of like the buy the dips and sell the rips or hold on to the rip scenario here.

Well, we're still down on the NASDAQ 110%, so officially in correction territory. You said when we get the regulatory clarity, when we get tax cuts, when the tariff stuff shakes out. How are you so certain that stuff is going to shake out?

Well, I think all of these things take time, right? And the only information we have is the information that we actually get from Washington. And that information seems to be that the tariff policy is enacted because of these fentanyl issues, immigration issues, cybersecurity, these other types of things that have to be sorted out. We have information that the president plans to cut taxes to support deregulation of businesses. So I think, to your point, it's a fair question, right? We actually have to see these things pan out. But

even if nothing else happens, right? You have an economy that is still growing, positive GDP. It's a little bit lower, but we're still above that 2% range. Jobs are fine. Wages are fine. Corporate earnings are estimated to be in the high single digits, even up to 10% by some analysts' estimates.

The earnings season was very good. There are still strong balance sheets. And, you know, what we're hearing out of corporate America is that it's, you know, there's still a CapEx, right? I don't see a recession either way. So maybe you don't get hyperbolic growth. But when you have these themes like quantum computing and AI that are on sale, I think it's worth taking, potentially taking a risk for long-term returns, regardless of what happens in the next year or so with policy. Yeah.

Hey, Sylvia, before we let you go, just 20 seconds on Broadcom, another stock you're bullish on, but down 20% from all-time highs. Yeah, I mean, I think Broadcom is going to be one of the leaders in AI. NVIDIA is the poster child there, but Broadcom should be a second winner there. You know, they're in software, they're in VM sales. They've had over 57% growth in AI revenue per the last earnings call. I just think that this is a name that did sell off a little bit. It might be good to get in for the long run.

Sylvia Jablonski of Defiance ETFs, always good to see you, Sylvia. Thanks so much for joining us. Well, coming up, we're going to talk about SoftBank's $6.5 billion acquisition of chip designer Ampere Computing. Ampere CEO Renee James joins us next. This is Bloomberg. ♪

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SoftBank's $6.5 billion acquisition of chip designer Ampere Computing is highlighting how the increasing demand for compute is crashing up against infrastructure constraints. Renee James, founder and CEO of Ampere, joins us to talk more about this deal. Renee, good to see you. Congratulations on this. Thank you. I just want to know, how are you feeling this morning?

I'm feeling, I think it hasn't sunk in. I'm, of course, thrilled with this outcome for my employees, my investors, and most importantly, we're a group of inventors and innovators who are very excited about the vision that Masa has for AI and our ability to continue innovation as part of SoftBank.

What is that vision? Because Ampere will operate as this wholly owned subsidiary of SoftBank. Of course, it is a majority owner of Arm. How do you fit in to that vision? What is that vision?

You know, Masa's talked a lot, even the Stargate announcement that was recently done in the White House about AI and the role AI will play in everybody's lives and building super chips. And he's talked a lot about that. So I think our role is to make that come to life. We are the leading supplier of high performance, very power efficient technology.

processors for data centers on the ARM architecture. So it's a very synergistic for us to join into the SoftBank family and continue working on the silicon roadmap that we have, which includes AI acceleration. And now we'll have a broader mandate. So I'm very excited about that. What happens to your existing customers and your existing product line when this deal does close in the second half of the year?

Yeah, we continue as is. We continue with the product line that we've worked on for the last eight years, our very low power, high efficiency microprocessors. And now we've announced that we have AI acceleration in our products. So I think that's the future of where we're going in the data center. We're going to see compute and AI start to come together, especially as inference becomes the larger part of the market.

And so our customers continue with us and hopefully will be excited about a broader set of products from us. Well, Renee, you recognized something really early on that there's this need and there's going to be this need. And indeed, we're seeing it right now for super high performance that required lower power. When you look across the landscape right now and where we are in AI, what do you see that perhaps other people don't see right now?

Well, as you know, Tim, I've been doing this a long time. I've been in semis a long time. And power has always been a variable in semiconductors for how you get performance or a limiter to performance. And so one of the things that I didn't get to work on in my long career at Intel was

working on how to do the highest performance possible in the most constrained power envelope. That was a portion of the spectrum of computing that we didn't really work on. And the reason, the thesis was we knew that power would be the biggest limiter to growth long term. There wouldn't be enough of it. You need increasing amounts of compute. We've talked a lot about AI, especially with GTC this week.

Taking nothing away from that, we're also having a massive growth in compute that's going alongside this massive growth in AI compute. So those two things are just taking a tremendous amount of growth and power. And AI is a 10x, if you will, a function.

growth in power required. And I think we knew that. You could know that from the workloads. And we decided that one of the things we should go pioneer is this efficiency. ARM architecture is very efficient, and we preserved that efficiency, but we used our experience in high performance and building high performance microprocessors to really get us to this level.

You know, we've heard a lot from Jensen this week about physical AI. And I'm wondering from your perspective, when you think about the compute that will be needed in the years to come, what does that world actually look like for the normal person? What are the products and services and tools that we are all going to be using that will require this compute?

You know, I used to think this. This is funny. You know, every wave of computing, whether it was the wave of PCs, the waves of mobile phones and laptops, we thought this is it. We're going to have computers. You know, you're going to have a computer in your pocket. You're going to have a computer here, a computer...

I think that in this next phase, as was discussed at GTC, we begin to really crest over into integrated computing and everything. And it really is transparent. It's a background activity that happens in your life that's assistive in different ways. Whether it's robotic or not, all of your appliances are smart now.

All of your homes have become smart. Your car is smart. So the experience of computing that used to be isolated to your computer or your phone or what have you is now integrated into your life. And you have, I think, you know, what we'll see, this is why I'm very positive on semiconductors. Semiconductors have fueled every single one of these waves of growth. And the base technology to go into any kind of AI is,

is basic computational semiconductors. That's why despite semis are always cyclical, we do see this, we see these downturns. I'm very confident that we have another growth cycle ahead of us in semis. - And I'm here with founder and CEO, Renee James. Renee, thanks so much for joining us on what is certainly a really big day. Really appreciate it.

DeepSeek's innovation made ripples across the AI industry when it announced that its models performed as well or better than its American counterparts and at a cheaper price. That was back in January. Since then, China and many other companies have been racing to integrate that model throughout the country. Bloomberg's David Inglis sat down with O1 AI CEO Kai-Fu Lee to discuss their adoption of DeepSeek.

Well, I think China had its chachapiti moment when Deep Seek came out. We can call it Deep Seek moment. Everyone's aware of it over the Chinese holidays. Everyone's talking about it. And the CEOs came back to work saying, put Deep Seek to work in my company.

And what they found out was DeepSeek is a fantastic model, amazing AI, but it doesn't have the middleware and the user interface that it takes to connect to corporate databases, to build applications, to make it useful for HR, finance, and customer service. So what Zero1.ai did was we saw DeepSeek has been making great momentum, and we decided to really bet on DeepSeek and build that missing middleware

and UI so that DeepSeek can be made useful in corporations. That's the product we announced this Monday and we're getting fantastic reception in China and also in Hong Kong. Tell us more about that launch. What we talked about was many of you have DeepSeek now, you love to use it. In fact, one CEO friend of mine asked his employees, "What do you use it for?"

and the answer was fortune telling. By the way, that's a great thing to try for you. But it's not very deep into the industry, the company. You know, every company has ERP and CRM databases. They have employee records. They have their internal information. And they want the model to be more a generalist. They want it to be knowledgeable. Bloomberg would want a finance knowledgeable model.

Ping An would want an insurance knowledgeable model. So our job is to really build that layer for that purpose. It's sort of like if I gave you a Windows kernel that is the core operating system, you wouldn't know what to do with it. You need all the Windows layer, the application interfaces, so that

the Windows kernel can be useful and we like to think that 01.ai is providing that layer for DeepSeq, which is the underlying model and technology. That was Kai-Fu Lee, CEO of Cinovation Ventures and also 01.ai.

Meanwhile, with the advent of the AI boom, many of the manufacturers involved are becoming monopolistic and only ever-growing. Companies such as NVIDIA and its partners who make the semiconductors just keep getting richer every time you use your favorite chatbot. That's the story in today's Quick Take, and Bloomberg's Peter Elstrom joins us now. Peter, the team over at Quick Take are writing that every time we use ChatGPT or CLAW,

or Lama. We're making a handful of companies wealthier. Take us through it. That's right. Or even DeepSeek, for that matter. They also use this supply chain. So what we took a look at is this really unusual dominance that we've seen in the supply chain of AI technologies. It begins with NVIDIA, which is probably the highest profile player here in the supply chain. But it's also TSMC, the company that makes the chips,

SK Hynix, which makes the high bandwidth memory that is paired with NVIDIA's chips. And then ASML, the maker of these lithography machines that are really necessary to be able to make the highest end chips in the business. So what we've seen is this really consolidation of power in the AI supply chain with these four companies where they wield tremendous power over how companies are able to get these technologies and then deploy them. That's true for all the hyperscalers, meta,

Microsoft, OpenAI, etc. But also the companies in China have been trying to buy these. Now there are limitations on which chips Chinese companies are able to buy, including DeepSeq and even Kai-Fu Lee's ZeroOne.ai, but they want to be able to get those NVIDIA chips and the rest of the technologies from these companies to be able to build the AI models that are now going to be marketed to companies and to individuals.

What is the moat that these companies have, Peter? Because typically when we think about innovation and technology such as this, we think about it from the perspective of, okay, if these companies are making money, a rush of competitors are going to come in and they're going to try to do the same thing. What's the moat that these companies have?

Yeah, that's a very important question. And just to take a step back, I'd say that when you look at monopolies over time, especially monopolies in tech, they tend to last for quite a while. We saw it with Windows and Intel in the PC era. Before that, we saw it with IBM, which got sued three different times for antitrust.

allegations, but they tend to last for a very long period of time and they tend to fade not just because of competition, but because of government intervention too. Now these AI dominating players, let's call them monopolies for now, for players that are effectively monopolies in their categories, they've only been in place for a very short period of time at this point. When it comes to Nvidia, they have lots of competition. TSMC and ASML have quite a bit less.

Bloomberg's Peter Ahlstrom joining us from London today. Welcome back to Bloomberg Technology. I'm Tim Stenevek in San Francisco. Let's get a quick check of the markets. We do see stocks off their best levels of the day. We did see a lower open, but then we got some surprisingly strong housing data, which turned around the trade. At last check, just about 65 stocks in the NASDAQ 100 were moving higher.

A couple of individual equities I do want to check in on. Check out what's going on with PDD, higher by about 2% right now. These are shares listed in the U.S. They erased that earlier decline, this coming after the company reported its fourth quarter results. Sales did miss estimates for a third consecutive quarter, but earnings were better than expected.

And look at Tesla, down about 0.8% of 1%. The company is recalling all Cybertrucks produced and sold in the US over the past 15 months, this due to a safety issue, with steel trim pieces that can detach from the vehicle

The company's recalling them all, but it estimates that only about 1% of the recalled vehicles have the defect. It can actually create a road hazard and increase the risk of injury or collision. Now let's head back to NVIDIA GTC where Bloomberg's Ed Ludlow is standing by. Hey, Ed. Yeah, there's just such a large volume of news and data about NVIDIA out of GTC. If you look at the stock over the first four days of this week, there's also skepticism in the market about the

the understanding for demand long-term. That's all many care about. And I've got a brilliant guest to unpick it with me. Vlad Galabov is research director at Omdia. The statistic that caught my eye is that compute demand, particularly from agentic and reasoning, is 100x today. 100x.

what it was one year ago. And to many people that doesn't actually mean anything, but the way that it was explained to me by NVIDIA is that they just counted all the tokens, right, in a tokenization context.

You basically take a token, three bytes of data, and you say, "Okay, what are companies beyond the hyperscalers doing right now? Today it's 100x more than it was a year ago." How do you model that? I mean, it's a very, very difficult forward-looking metric. So there's two ways to do that. And by the way, there is a misunderstanding because it's complex, right? That's why people struggle. So one part of my team tests models.

So, you know, what they tried to find out is how good a model can behave. And they found that the reasoning models behave better. The reason why they behave better is that extra tokens, is the extra computing. By being able to, in essence, think, they actually end up getting a better result. So my team got very excellent results from that.

But I have a different part of my team that actually tries to understand exactly how many GPUs the different companies bought. So my team in China was able to... You're going to bring us to DeepSeek, aren't you? Yes, I'll bring you to DeepSeek. Because my team in China found out that DeepSeek bought a huge amount of GPUs.

So imagine then they release, we have given that information to our clients, they release their paper and in the paper they say, "We don't use many GPUs for training." So my clients immediately came in, they said, "Vlad, why did you tell us they bought so many GPUs?" And I said, "Well, they did. I have the receipts."

And we know now that they bought them because their inference is so compute intensive, because their inference uses, as you said, 100x more tokens than a traditional knowledge model. But that's a good thing for us. It's actually better for us to train quickly and simply and have a better output through more tokens, through more reasoning. People are, especially environmentally conscious people, which we all should be, are very concerned about that extra...

tokenization during the inference stage. But actually, if you can get the right answer once with one question, that stops you from having to prompt many times. You might have got a good answer from ChatGPT, but you would have needed to ask it 100 questions. So you're doing the reasoning for it. Now, if you use a reasoning model,

with what they call a Genting AI, you end up having the right answer straight away. This is the core of Jensen Huang's argument, right? If you were at GTC in 2022, 2023, 2024 maybe, the fixation was on H100 and training, the next frontier model. But the world's very different now. I think NVIDIA's really focused on its enterprise customers. What Jensen Huang did outline was a roadmap, four years and four generations worth of hardware.

Electronics companies, consumer electronics, technology companies, they don't do that. They don't say, "Here's what I'm doing this year all the way through to 2027." What did you make of that?

So I disagree that electronics companies don't share the roadmap. I'll be honest, because I think if you look at AMD, they share the roadmap pretty broadly. I think Lisa is very transparent. You know, I come from Intel. At Intel, we've always shared a roadmap pretty broadly. Well, maybe not. In the last few years, I've been a bit shaken. Well, there's a difference between sharing a roadmap and executing on it, isn't there? It is a very big difference. I think what's amazing about NVIDIA is an extreme laser focus.

and this incredible culture, and they understand. They understand the hardware stack, they understand the software stack, they understand the services, they have a strategy, and they understand that the world is getting tokenized. So they're focused on that. They're laser focused on how do we make the most efficient token processing engine. The analogy that Jensen Wong gave was Louis Vuitton bagged. So for what it's worth, he argued, Louis Vuitton comes out with its 2025 bagged,

but at the same day, it doesn't tell you what it's going to be doing in 2028 or 2029. Whether you agree with that analogy or not remains to be seen. What is different is you get a sense that NVIDIA has moved beyond the hyperscalers. The demand side of that equation, what do you see?

So, I do think that, let me just kind of just touch on enterprises in the world for a second. Enterprises need predictability and they like it. So, and actually they've been looking ever since Intel stopped delivering, they've been looking for more partners to be honest, to give them a roadmap to explain things and to then deliver. So, I think it's actually the best way to address the enterprise. Does it protect?

the enterprises ability to commit spending if they know what in four years time the technology bar will be. It does. In my discussion with Jensen that's exactly what we got into. This protection of enterprise spend, this guarantee because the investments these days are huge but it also helps to create an ecosystem. So what you need to

make it in the enterprise is you need an ecosystem. So over 70% of IT is purchased through partners, through channel partners, but in the enterprise, if you zoom into just the enterprise, it's virtually every transaction. So you really need to have trust in partners. You need to have trust that there'll be people who will help you to have a hardware ecosystem. NVIDIA

may make a GPU, but they work with the cooling vendors on this exact specification of how the cold plate that will cool it will look like. That's very impressive, right? So then when you go to the software layer, you want to get the developers behind you. And on top of developers, you want also users that might not be experts. So by having both a language, by having a platform, by having models, that means that the different level of skills

You know, people can work with you. And then you need to have a services layer. Enterprises, some enterprises like to do stuff in-house. Other enterprises like to have a partner. And... Vlad, we were short on time and I've got to mention Quantum Day. That's why we're here in this room. NVIDIA does not make quantum computers. Yes.

We're having Quantum Day. How do you approach it? So I think, you know, big speculation on quantum computing. When is it coming? So I'll just tell you one very quick story. ARM CPUs, right? ARM CPUs are now a really big part of the ecosystem. We use it. NVIDIA has them. Amazon has them. Many people have them.

But when the first kind of data center ARM CPU was launched in about 2011, 2013, I was at Intel and we were very worried about it. But at the time it lacked performance. So it took another five years for performance to happen.

but then it lacked software ecosystem, it lacked programmability, it lacked libraries, and it lacked being able to use enterprise software out of the box. So it took another five years, huge investment from Amazon, actually, if we're honest, for codes to be rewritten to work.

So we're now in the place where ARM was in 2011. So I think that we need at least another five years for the hardware to get to a place where it's highly reliable, but then

the programmability of it, how easy it can be popularized, that's the difficulty. So in many ways there might be truth behind everyone's sayings. Pat Gelsinger thinks it's going to take five years. For the hardware, yes. But I think Jensen thinks about it very practically. It takes longer for the programmability. For the ecosystem. And NVIDIA's argument would be their AI supercomputer, separate technology, can help in the development. Vlad Galabov of Omnia, really great to catch up here in San Jose. Tim, back to you and SF.

Great stuff. A big thank you to Ed Ludlow out there in San Jose. Tune in at 4.30 p.m. Eastern today for a special edition of Bloomberg Technology hosted by our very own Ed Ludlow live from NVIDIA's GTC event. Now coming up, investor Joe Lonsdale and campus CEO Tadeo Rende are going to join us to discuss the startup Series B funding round and changes to higher ed. This is Bloomberg. ♪

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Terms and conditions and other fees may apply. Product and services are provided by Moomoo Financial, Inc., members of FINRA and CIPIC. Please visit Moomoo.com for full disclosures. EdTech Startup Campus has just raised $43 million in a Series B funding round. The company aims to give students a more affordable path to a college degree. Bloomberg Beta, the venture capital arm of Bloomberg LP, is one of campus' five largest shareholders, we should note. Joining us now is campus CEO,

Tadio Yerinde and one of its investors, Joe Lonsdale. Tadio, I want to start with you because you studied aerospace engineering in the UK and at Embry-Riddle in Florida. Was it your experience with education that led you to start this company? Hey, Tim. Thanks for having me. Hey, Joe.

Definitely. I mean, before college, I was homeschooled until high school. My paternal grandfather was a college dean. My maternal grandfather was a high school principal. My mother is a college dean. My older sister is a professor. So probably I was brainwashed from birth to get really excited about education.

Well, a school's reputation when it comes to academics is everything. How do you build that reputation, Tadej, from the ground up, especially in the early years and when for-profit schools have had such a checkered past? Look, it's about elite education for all. And so that's what we're doing at campus. We're sort of rethinking the first two years of college. We're building a

a new kind of two-year college where students get to learn from A, the best professors from the top schools in the country, Princeton, Stanford, UCLA, knock out the first years of college with us, and they're not just learning theoretical nonsense, they're learning really useful skills

and then they transfer to the four-year school of their dreams to complete their bachelor's with no debt. And I think that's the key, no debt. Student loan debt is about to pass $2 trillion in this country. We're hearing crazy stories, students taking out $100,000, $200,000 loans. They're graduating. They can't even get jobs. It makes no sense. It's got to stop. And now there's actually a better way.

Hey, well, speaking of that, I want to bring in Joe to this conversation. Joe Lonsdale, look, you've already invested in campus, but this isn't your first foray into education. You co-founded the University of Austin a few years ago. What, in your view, is wrong with higher education? You went to Stanford. You seem to be doing pretty well.

Well, of course, there's a lot of issues with the very top of our education, which is what the University of Austin's focused on. But, you know, I would argue that our community colleges, unfortunately, are even more troubled in this country. Many have very low graduation rates. A lot of them also are focused more on ideology than skills, sadly. And so what TAUDE and campus represents to me, it represents excellence, it represents merit. And, you know, our

Our economy is changing drastically. AI, as you guys are talking about on other segments today, is changing everything, how it's going to work. And we need to get the right skills and the right frameworks to millions of young adults. And I'm hoping, Todd, I can scale this to a million students, capture 10% of the community college market, and really help all of those live better lives and succeed more in the economy that's coming. Let's go.

Well, Joe, what's your input on the curriculum? Because you're hiring a lot of folks. Your portfolio companies are hiring a lot of folks who have diverse backgrounds, who have skills that are arguably not necessarily taught in some schools and universities. What's the input that you're giving him on the curriculum?

you know, my push from my side is let's do, let's add some more courses in that reflect what you need to know for AI. You know, there's people like Sam Altman involved as well who build OpenAI, of course, and others who are invested here. And the idea is how can we help hundreds of thousands, millions of young Americans, you know, obtain the skills necessary to work in an economy where AI is going to be involved in a lot more. So that's not the immediate focus today. The immediate focus today is on a lot of

basic skills needed in today's economy. But what's really fun is Todd is talking a lot and thinking a lot about what else can we add in here to really make sure we get people ready for the 2030s.

Well, Tata, you were talking about the cost of college getting out of control. You were sharing some pretty staggering statistics about the trillions of dollars of student loan debt that exists in this country. Nobody argues with that. How do you make the economics of campus work, though, when other colleges and other even junior colleges, community colleges, are more expensive?

Yeah, I think the key is like the completion rates actually have to go up for the economics to work. So the traditional community college has an average completion rate of about 27% graduation rate. And so when you lose students, when they drop out, you actually earn less tuition revenue per student.

So if you actually, it's sort of paradoxical, but if you actually keep students longer because you help them graduate, then guess what? You earn more tuition revenue, which makes the economics more healthy. And so that's like the sort of the beautiful symmetry in terms of what's best for the student, what's best for campus, and what's best for our country. Driving up graduation rates is actually how you make the economics work.

Joe, come on in here because I'm curious about your view of the federal government's involvement in education. The government has frozen, suspended or cut more than a billion dollars from universities in recent weeks. We're talking about reports from Columbia, Johns Hopkins, Penn and more. And I'm wondering as an entrepreneur, as somebody who's hired a lot of folks who founded successful companies, somebody who has founded successful companies, are you concerned about the American talent pipeline being cut off as a result of these cuts?

You know, I'm more concerned about making sure we spend money effectively and efficiently. And so I really like what Tadej is doing along those lines. A lot of the policy I'm pushing, you know, coming from my side of things is how we make this spend accountable. So, for example, if you're going to do vocational education, unfortunately, just like our community colleges, a lot of the vocational programs, low graduation rates, wrong skills, not helpful. If you can spend the money effectively, if you say this money is going to be tied

For example, when you tie the money to the salaries of students coming out of vocational schools, it doubles those results. Those are types of policies that I think should be popular on both the left and the right. And what I love about what Tata is doing is it's not really playing the ideological games. There's people of all backgrounds. There's people involved from the left, from the right, black, white, Hispanic, everything. And it's just about merit and excellence and getting good results for very small spend. So I think this is the sort of thing that is going to remain popular with everyone, regardless of some of the other fights going on.

Well, Tadej, how do you watch what's happening in the federal government? Because President Trump today is expected to sign an executive action that formally asks officials to take steps to dismantle the Department of Education, according to our reporting. What happens to your business then? Because 40% of your students qualify for Pell Grants, and those are administered by the

Department of Education? Yeah, look, the vast majority of our students use Pell Grants to cover their tuition, and so they don't have to pay anything out of pocket for tuition expenses. Pell Grant is not going away, even if the Department of Education is dismantled. Some of these key programs that are mandated by Congress are going to be split across maybe Treasury or the IRS or other organizations. The way I look at what's happening in Washington right now is

Hey look, obviously everyone's looking at this and saying we need to be more accountable as Joe talked about. We definitely need to be more efficient with taxpayer dollars. It's really early days. Secretary McMahon's been in there for less than three weeks. I think we're watching it closely, but we're going to have to let this one play out.

Hey, Joe, last one to you. Speaking of efficiencies in the federal government, you've been supportive of Doge. This week, though, a federal judge ruled that Elon Musk's actions to shut down USAID violated, likely violated the Constitution in multiple ways. Are you concerned that the courts are going to prevent Elon from being able to do the cuts that you want to see him do?

You know, that particular ruling, I'm glad you mentioned it because it was so ridiculous. Actually, the ruling was so misguided that it thought Congress had created USAID, which is not correct. It was actually created by executive action. USAID is just such a great example of just complete waste, right? There's just all sorts of scams and fraud that we've uncovered. I think no matter what your party background, if you look at the details, you'll agree this should have been turned off. And there are actually...

So, have you spoken to Elon about this? Have you spoken to Elon since he's been at Doge?

He's a friend, and I am in touch, and he's working really hard with a lot of smart people. They're being very aggressive. A lot of my friends are involved in Doge. And listen, I don't think everything they're doing is going to always be perfect, but there's so many crazy things that have to be turned off and have to be confronted that overall I'm very, very happy for the work they're doing. And I think they're kind of shocked about some of the ridiculous things they're finding as well that they're publishing.

All right. Well, really appreciate both of you guys joining us. That's Joe Lonsdale from 8VC. Also, campus CEO Tadej Oyereende. Thanks so much for joining us.

If you wanted to grind the world to a complete halt, you could achieve that by removing magnets. They're crucial to basically all tech, including EVs, and the next nuclear breakthrough: fusion energy. Primer, the latest Bloomberg original series, takes a deep dive into all of this. It takes a lot of work to build something big. You also, it gets very expensive. Like, the amount of money you need to spend on something to get just the first one can get very, very expensive.

That's exactly what's happening with ITER, a giant fusion reactor currently under construction in France that uses superconducting magnets. Look at this thing, it's huge! And as a result, it's projected to cost as much as 65 billion dollars. So, to make fusion smaller, cheaper and more practical, Commonwealth's founders needed a whole new kind of magnet. The question was like, would that material ever happen?

And it wasn't until the early 2000s that we could really see that that material was gonna happen. That there would be a new type of superconductor. And it's a weird-- It's not a wire. It's a weird thing. It's a film. And it won the Nobel Prize, like, months after it was discovered.

So this is a material called HTS, or high temperature superconductor. It is literally, it comes out in a tape. It's probably kind of hard to see on the camera. It's very thin. It's actually mostly copper and steel, but there's a very, very, very thin layer inside of this that is high temperature superconducting material.

High temperature, in this case, is still wildly cold, but not quite outer space cold. And that was the breakthrough that Commonwealth needed. Magnets made with this material can create a stronger magnetic field so they don't have to be so massive. You can shrink the size of the device by a factor of 10. Basically allows us to make things smaller, which makes things cheaper and makes things faster. To get fusion to a spot where we can make energy from it.

And that was the voice of Caroline Hyde. Tune in to the first episode of Primer tonight on Bloomberg TV at 6 p.m. Eastern time. That is going to do it for Bloomberg Technology. Tune in later today at 4.30 p.m. Eastern, 1.30 p.m. Pacific for a special edition of Bloomberg Technology live from NVIDIA's GTC event. Also, check out our podcast. You can do that on the terminal as well as online at Apple, Spotify, and iHeart. This is Bloomberg. ♪

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