CoreWeave, Supermicro Lead Tech Stocks Higher on Results — Bloomberg Tech 8/12/2026

[00:00:00] Speaker 1: Bloomberg Tech is live from the heart of Silicon Valley with Ed Ludlow in San Francisco. [00:00:14] Speaker 2: This is Bloomberg Tech. Coming up, tech stocks higher after positive results from CoreWeave and Supermicro showing the booming AI market continues to bolster their sales. Plus, Silicon Data is out with new funding, putting it to work on building independent benchmarks for AI Compute. The CEO joins us this hour. And investors are betting AI music startup Suno can be the next Spotify. But it's also got concerns of theft and AI slop to contend with. We'll discuss. It is neoclouds and new AI infrastructure demand that's driving markets. Look at CoreWeave right now up 17%. Actually, it's its only biggest jump or on track for its biggest jump in about 10 days. One point in the session up for its on track for its best day since June of 2025. It's all in the outlook for the current period, which is the third quarter of Bloomberg's Brody Ford is with us covering the print. Basically, they said this is what sales are going to be. And that was beyond street expectations. And CoreWeave seems to be executing, meeting the demand that's there with new neocloud supply. [00:01:16] Speaker 3: Right. CoreWeave is the poster child for the neoclouds, which is kind of the most controversial category of company out there right now. A lot of haters on the Internet will say that, you know, the margins will never inflect. It'll never really be a good return on investment. But an important piece last night was that margins are going up. Operating income is going up. And part of that is that the chips are still so constrained. We heard on the call last night that they're able to really extract good pricing out of these chips. And even older chips, even NVIDIA chips from 2020, they're able to sell through, you know, 2029. And it's a really positive sign that what they have has sustained demand. [00:01:58] Speaker 2: High utilization of older generation chips and pricing. We're going to get to you really deep later in the program with silicon data. They're not the only neocloud to report. So over in Europe, Nebius is another. Neocloud just simply data center for AI workloads, right? Or cloud capacity for AI workloads. What's the Nebius story? How is it different? The same to CoreWeave? [00:02:18] Speaker 3: Yeah, the macro story of higher AI demand is across the board. What's interesting with Nebius is a very important part of their story is a big contract with Microsoft in New Jersey that had been seeing some of the nimbyism, some of the anti data center sentiment we've all been hearing so much about. And Nebius effectively said that we're going to be able to deliver on schedule and don't worry about that. And so investors are pretty happy. [00:02:43] Speaker 2: Bluebird, Brady Ford on all things neocloud. Thank you very much. The other big AI earning story is Supermicro. And in that case, shares are surging as well. The AI server maker forecast as much as $15.5 billion in revenue for the current quarter. That was above even the most bullish estimates. Demand for AI infrastructure through the lens of Supermicro continues to surge. Joining us now is Sajal Dogra, Rosenblatt Securities Managing Director. A buy rating on the stock, but a new price target of $51 up from $45. Sajal, great to have you on the show. How much is this just Supermicro executing? Right? We know about the backlog for AI servers. They seem to be able to move more smoothly now. [00:03:26] Speaker 4: You know, so they've, I mean, if you take a step back, Supermicro is a name that's actually grown revenue at a 60% CAGR over the past five years and over 75% in the, you know, in, in, over the past couple of years. So they've, they've underperformed. If you look at it from a stock performance, because they've underperformed on gross margins, essentially. And now the thing is before, so we, we, we, we're going through a very weird period. We've had tariff costs. We've had expedite fees. We've had supply constraints. And so the net result of all of that is gross margins, which were averaging 15% before are now below 10%. And so, you know, I think over the past six months, the margins have actually been over 10%. And I expect, you know, we, we kind of going through this period where I think margins go higher from here. And, and that's the key debate on Supermicro. The debate's never been on the revenue grow. They've always had, you know, a 60, 70% CAGR revenue. And, you know, so I think, and, you know, that's, and the stock is in the perform given everything else that's been happening with the name, but that's the reason it's, you know, but finally they've, this is a breakout quarter for them. They've missed the last six, seven quarters, but I think finally they've raised the guide well above street. The blue pass consensus, but we're 25%. We were actually the high on the street. Like, so for example, consensus for next year was like $3.20. We were at like four, four 30 and they've, they're, you know, now we're modeling over five bucks in next year's earnings. So, you know, I think everyone else on the street will also reset numbers higher after this, this strong results from them. [00:05:17] Speaker 2: I would note that NVIDIA is one of the biggest points drivers on the NASDAQ 100. NVIDIA is pushing higher. Supermicro servers fitted with NVIDIA gear. So the, the, the read through is good there. You, you took us there. You said what's been going on with the, the, the stock, the name, uh, what's been going on is that in March, U S prosecutors charged Supermicro and one of its founders were illegally diverting NVIDIA gear into China. Is that overhang gone? [00:05:43] Speaker 4: So that's still ongoing, unfortunately. So I'm not really like, uh, you know, there is. So the, the, the thing I'll say is they've, they've, they haven't, so on the financial restatements, they've actually, they filed the K, they filed the last three 10 Qs. They've, they have an auditor. They've said they don't need restatement. They actually did a capital raise a couple of months ago. And so all these big banks did their underwriting on the name, but the Chinese investigation is still ongoing. [00:06:15] Speaker 2: You said the margins. Now they're going to go higher. What's the lever that Supermicro is [00:06:19] Speaker 4: pulling its pricing. What, what can they control? So I think what's happening with them is they've, they have a couple of things going on. They've actually growing the enterprise sales channel. So they're really investing in that. They have this one-stop, uh, building block that helps customers with time to market. That's really nice. The margin of creative that used to be less than 5% of profits should be over 20%, uh, as we exit the year. So they've, you know, all these like incremental drivers, the lower expedite fees, they don't have tariff costs anymore. And so I think, you know, I think as that keeps going on, you will see, you know, further margin expansion from here. We were actually modeling 11% for next year, uh, versus street at around 10%, below 10%, actually. [00:07:09] Speaker 2: Shadal Dogra of Rosenblatt Securities on all things Supermicro, the stock pushing higher on a strong revenue outlook for the current period. Nvidia partner Honhai, also known as Foxconn, reported a better than expected jumping quarterly profit as global spending on AI infrastructure continues to surge. How many times have I said that in the first seven minutes of this show? Net income rose 35% to about $1.9 billion, topping estimates. The company says AI demand remains a key growth driver and that it's on track to begin mass production of Nvidia's next generation Vera Rubin platform this quarter, with shipments expected to start in the fourth quarter. Okay, coming up, the AI frenzy isn't going anywhere anytime soon, and powering it will come with new bottlenecks. Michelle Weaver, Morgan Stanley, US thematic research strategist, joins us next. This is Bloomberg Tech. Let's take a look at today's big number, $250 billion. That's how much Bank of America is pledging to develop critical infrastructure across the US over the next year. That includes data centers and compute power aimed to help the nation keep up with the power demands critical to the AI craze. Bloomberg's Catherine Doherty joins us now. It's a big number from Bank of America. How real is the big number? You know, this is them saying we're committed to this. We're pulling this capital from others. We're [00:08:40] Speaker 5: a channel for the capital. That's right. They're basically saying that they want to be at the center of capital formation. And there's a number of ways that they can go about this. Lending, capital markets, advisory. So it's not just the bank putting its own balance sheet in terms of the $250 billion number. They're going to be deploying this across the entire corporation. And that means bringing on some of their own clients and working on deals in the future that they can help arrange but aren't necessarily putting their own money to work. It's their resources that also are providing value. And it's a commitment overall to just moving the US's infrastructure forward. [00:09:22] Speaker 2: So the overall commitment is interesting. We've had a lot of nice big round numbers recently just this week. The Nvidia $500 billion with the six Wall Street firms. It wasn't that long ago that Bank of America basically said we will back as a bank $1.5 trillion of sort of what they would call sustainable initiatives but some of them related to power AI data centers. Is this new number on top of that? It's [00:09:47] Speaker 5: inclusive of it? It is inclusive. They're keeping that $1.5 trillion. They're not getting rid of it. They're not changing that. And that is a longer term commitment. This $250 billion is through the end of or through July of next year. And we've seen some of their competitors JP Morgan, Morgan Stanley have come forth with their own initiatives in a very similar vein. But those are over 10 year at a $1.5 trillion commitment. So Bank of America is talking about an 18 month and not 10 year time period. But the premise and the activity that all of these banks are getting behind and pledging to showing their support, all very much the same. [00:10:30] Speaker 2: Thank you very much, Karen Doherty on one of the most read stories today. Thank you very much indeed. As AI infrastructure grows, some of the biggest questions around the trade are only getting harder. How much AI will companies consume? Do cheaper open models change the economics? And even with high demand, is there enough labor and power to even run data centers? Michelle Weaver, Morgan Stanley, US systematic research strategist and executive director, has the job of passing these questions on a daily basis. We're going to talk about the AI economy. And for me, at the end of the day, it is just simply trying to measure demand as it is now against supply in the future states of both. [00:11:08] Speaker 6: Where are you netting out at the moment? Currently, we are still very much undersupplied. We are still seeing compute be a very constrained resources. And I think what you really have to look to is the AI adopters. What is the everyday business consuming AI seeing? Are they starting to see ROI? Are they starting to see benefits from AI adoption? And the answer there is yes. We're still very early on the S curve for AI adoption. But our tech team CIO survey shows that by the end of this year, the majority of companies will have at least one AI project live and in the field. And we've seen that when we look at S&P 500 companies, 25 percent of those companies are now able to quantify the benefits that they're getting from AI adoption. That's up from 14 percent a year ago. So companies are incrementally seeing positive signs around what they're getting out of AI adoption and voicing that to investors. [00:12:01] Speaker 2: So you've gone straight to this through the lens of basically corporate America. It's attitude towards AI and the action it is or isn't taken. And that's measured on token spend. The debate around token maxing. Yes. What is the current in aggregate attitude towards token spend? [00:12:18] Speaker 6: So we're certainly seeing a decline in the culture of token maxing or trying to consume as much compute as possible. So I think you have to separate what's going on with the engineer and with the power users. So power users, yes, have been consuming a ton of tokens. And enterprises are now thinking about, how can we rationalize that? How can we make sure our employees are using an appropriate budget of tokens? But the typical user, the median user, spends less than $11 per month on tokens. So for the vast majority of people, token spend is still very much under control. The big story this week has been NVIDIA and the [00:12:56] Speaker 2: $500 billion it is channeling through those six Wall Street firms, right? The idea is it greases the wheels. It takes away the excuse of not being able to finance data center. But it seems like capital just isn't enough, right? There are labor bottlenecks. There are other supply chain bottlenecks. How severe are they? [00:13:14] Speaker 6: Yeah, still significant bottlenecks to getting data centers built. And that does make the value of compute even more strong. But I would say the number one bottleneck is labor. There is a huge shortage of electricians and specialized labor to actually put up these data centers. And then the second biggest bottleneck is power. We estimate around a 40 gigawatt shortfall in power needed through 2028. When we start thinking of some of these innovative time to power solutions like Bitcoin site conversion, innovative fuel cells and turbine projects, you still have around a 10 to 20% shortfall in that power needed. So [00:13:51] Speaker 2: still a huge bottleneck there as well. We have the midterms fast approaching and the NIMBY movement, anti data center movement seems very real. Have you modeled for that in your forecast for the market [00:14:05] Speaker 6: and growth in industry? Yeah, so I would say they fall roughly into three different buckets of concerns. And there are ways for these concerns to be addressed. There's just a lot of nuance here. So first concern, power bills. Communities are very concerned that if a big data center comes to town, starts consuming a bunch of power, ultimately that could end up on consumer power bills. And that really speaks to how worried consumers are about affordability. But being off the grid helps to solve for this. You're not touching the grid. You can't possibly impact power bills there. Second bucket, environmental concerns, water and air. This really depends on the type of cooling you're used. Closed loop cooling, much less water intensive than evaporative cooling. And then the last bucket is local quality of life concerns. A lot of concern around land use, traffic, noise, dust. Here I think you'll start to see more brownfield sites where you're saying, okay, we're coming in, we're cleaning up this site, we're able to provide jobs to the community and provide a productive use to this piece of land. So a lot of nuance here, but I think this is going to be a very big topic as we head into midterms. [00:15:08] Speaker 2: Michelle, we started this conversation with you saying we're still supply constrained, demand vastly outpacing supply. So this is strange to ask, but like, how real is the risk that we we get to a place of oversupply? Too much capacity gets built. I think we're still very far off of that. I [00:15:27] Speaker 6: don't see any signs that we're seeing supply overbill. I think those bottlenecks, we were just talking about the power bottlenecks, the political bottlenecks, the labor bottlenecks, that's really going to keep a check on supply for the next few years. Supply, I wouldn't start to worry about that within the next few years, just given how short we are on power through the next few years. [00:15:46] Speaker 2: So this is interesting. Later in the program, we're going to talk to silicon data, the benchmarking for compute. Everything I'm hearing from you is that all the factors are in place to keep compute prices high. We talked about token maxing or token spend. There's a difference between quality of token and bringing the cost of tokens down. None of that seems to be the direction of travel. [00:16:08] Speaker 6: Correct. I think the big debate right now is the open model versus closed model. Who is ultimately going to process those tokens? And I think there's a different picture for when we look at the US versus global. Globally, for the majority of firms, I think they're just looking for something that is good enough. They just want to be able to process the requests that they need. In the US, there is still a lot of concern around security. With using non-US models, there is a lot of concern around just business continuity risk. If the government comes in and regulates and you spent all this money fine tuning and open weights model, how do you deal with that? So I think in the US, you likely end up in a hybrid state of the world where you have both closed and open models being used in parallel. Currently, around 63% of companies use both. So I'd expect that to continue. [00:16:55] Speaker 2: Michelle, it's your job at Morgan Stanley, as much as is possible, to have a crystal ball to tell us what happens next. What are the outcomes you're expecting here, particularly on the open-closed [00:17:08] Speaker 6: debate that we are having? That hybrid end state of the world, I think, is most likely. I think when we think about companies are already adopting AI. Yes, they're early on in that adoption curve, but adoption is sticky. A lot of this is changing employee behavior. This is changing human behavior. And so given that a lot of companies are already using closed models, employees are already building skills on these models. They're already changing their workflows. I do think there will be a pretty strong degree of stickiness there. But maybe you have smaller use cases where you really want to fine tune a model. You want something really specialized. That would argue for an open weights model. If you have new use cases that you just want to serve it as cheaply as possible. That would argue for an open weights model. But there's a lot that's already been built on top of those closed models. And I think it will be very challenging to come and rip that out. [00:17:59] Speaker 2: Michelle, real quick, regulation a wild card here in the U.S.? [00:18:02] Speaker 6: Yes, very much, very much a wild card in that open versus closed debate. I think that is something companies are thinking about. If they were considering implementing an open weights model from overseas, what if those companies are no longer allowed to operate in the U.S.? And so I think there's a lot of hesitation to go fully into some of those open weights models given that geopolitical concern. The other big issue, too, is like we're talking about the community pushback, the state-level moratoriums. New York State was the first to introduce a state-level moratorium that could be spreading to other states. So that's the other political wild card to watch here. Michelle Weaver of Morgan Stanley, [00:18:42] Speaker 2: U.S. Thematic Research. It's great to have you on the show. Thank you very much indeed. Some news. AI startup Cognition is in early talks with investors for a new funding round that could boost the firm's valuation to $40 billion. Sources shared that investors are eyeing Cognition less than three months after the firm raised a billion dollars at a $26 billion valuation. Cognition is expected to raise over a billion dollars in this next round. Coming up, Google unveils its new Pixel 11 lineup. We'll break down the biggest announcements from its Made by Google event. This is Bloomberg Tech. It's time for Talking Tech, and it's all about breakups. First up, Chinese AI startup Manus will resume independent operations as it part ways with Meta. The Facebook parent had planned a landmark $2 billion takeover of the AI agent developer, but Beijing forced the pair to unwind the deal over concerns it would lose control of valuable tech to a geopolitical rival. Plus, OpenAI executive Brad Lightcap is leaving the company to start something new. Lightcap joined OpenAI in 2018 and helped build its enterprise business. A few months ago, he shifted out of his role as chief operating officer and began leading special projects. And Uber has divested from Serv Robotics after clashing over the deployment of delivery robots. The move underscores growing challenges in Uber's bid to become a platform for autonomous vehicles. Last month, Uber said its exclusive arrangement with RoboTaxi firm Waymo would end in early 2028. Okay, Google is holding its Made by Google event today, unveiling a slew of new hardware, including its latest generation of Pixel smartphones. Bloomberg Senior Tech Editor, Dana Warman, is with us. What I see is prices going up and memory allocation coming down. Is that fair? [00:20:32] Speaker 7: That's true. And at least Google was honest about it. Really cited in its statement a "severe memory shortage," which of course has been afflicting so many consumer tech companies. And the company pledged to out-engineer, essentially, the memory shortage. But that's where we are right now. Higher prices and less [00:20:49] Speaker 2: memory with the starting models. Okay, so we're showing the 11 lineup here. What is different, you know, from prior generation? What has incrementally changed? [00:20:58] Speaker 7: So, things like performance, the durability. I think the most standout feature is something called Highlight on one of the higher-end phones, which is essentially repurposing the camera flash as a notification indicator. So that could potentially be neat and useful for users. But a lot of the improvements are otherwise sort of quality-of-life things. This is not a flashy new design. The company is now in its third year of what is essentially the same design. And you might compare that with other companies. Apple, for instance, the new iPhone 18 series would be in its second year, according to our reporting. And other companies like Samsung have tried bolder designs with their foldables and other [00:21:35] Speaker 2: flagship phones this year. Just real quick, there's some news in wearables where we get insulin resistance features. But let's be clear, this is not continuous glucose monitoring. What is it? [00:21:46] Speaker 7: So this is insulin resistance is a precursor to things like prediabetes and type 2 diabetes. And what Google is doing is using a combination of AI and other kinds of biometric sensing. And it's basically tracking physiological markers that it believes are indicators of insulin resistance. So it's using AI and these other sensors like skin temperature, heart-related sensors, to paint a picture, an estimate of where your insulin resistance is. And if it senses shifts in that pattern, it sends the user notification, at which point they can follow up with a doctor. But the company is really clear that it's not trying to offer medical diagnoses or advice. [00:22:29] Speaker 2: Bloomberg's consumer tech editor Dana Warman. That was awesome. Thanks. Now coming up, the CEO of Silicon Data, Carmen Lee, joins us to talk about the firm's latest funding round. It's working to build independent benchmarks for AI compute. It's halftime. And from San Francisco, this is Bloomberg Tech. Welcome back to Bloomberg Tech. Neo clouds and AI infrastructure is really what's driving the market higher. Look at the NASDAQ 100. And the biggest percentage gain is our CoreWeave and Nebius or elsewhere Supermicro up 12%, 13%. All about strong earnings where it was basically the guide for the current period that came in so significantly in both cases above the streets' expectations. Let's get a digest with Bloomberg Equities reporter Ryan Vlaselica. I also point out, and I did earlier in the show, right, you know, Nvidia is pushing higher. Nvidia highly analogous to those two names at the new cloud level and the server level. What else are you seeing in the markets? And how big a driver are these two earnings prints today? [00:23:36] Speaker 8: Well, I think these two earnings reports are really reinforcing how the AI trade, especially the AI infrastructure trade, remains very strong. And that is helping to kind of improve sentiment across a lot of the tech space right now. CoreWeave gave a very strong forecast, as did Nebius earlier this morning, really highlighting how much demand there is for AI-related cloud computing services. And then on the hardware side, you had an extremely robust forecast from Supermicro, really underlining in a different way how much demand there is for servers, for chips, all the different kind of physical infrastructure. And I'd also flag Lumentum, the optical company, that's been a real area of focus for us lately. Really, we are seeing strength across the board for AI infrastructure. [00:24:19] Speaker 2: Thank you very much indeed. I want to go to private markets. Silicon Data, which provides pricing and performance benchmarks for AI compute, has raised a $30.5 million Series A to help grow its tools and capabilities. Joining us is Silicon Data CEO Carmen Lee. She's also the former head of strategic communications for enterprise data at Bloomberg. Congrats on the round. Really interesting group of investors that have backed you. You've grown very fast. And I think that the best place to start is how are you going to use the funds? What is it you're trying to get going on? [00:24:56] Speaker 9: Thank you. Thank you. Thanks for having me on the show. So it's a quite exciting week for Silicon Data. As the video story rolled out, as CME announcement came out yesterday, our goal really become the independent referee for the whole compute stack. So as independent referee, we need to keep developing indices as more chips, there's more tokens, there's more LIM models coming online. We need to design next generation of benchmarking services. This is all going to be used to design products and software applications. [00:25:33] Speaker 2: Carmen, very quickly, the valuation wasn't disclosed. Can I just ask you what the approximate valuation of Silicon Data was in this round? [00:25:41] Speaker 9: It is a very healthy range. Are we at unicorn status yet? [00:25:48] Speaker 2: Not quite yet, but I will definitely ping you. We'll get that out of the way. Really interesting. Gavin Baker with Valor through the Valor Atreides AI Fund led this round. What should we take from that? You know, beyond just the check and the capital, what are you able to get from their sort of expertise [00:26:07] Speaker 9: and background here? So Gavin and the team, they are the pioneer in the whole compute industry. They're the one backed a lot of new clouds from very early stage. And we're also backed by CME, F Prime, which is part of the Fidelity ecosystem, Vanak, obviously DRW, geometry trading. So we, as independent referee, we want to make sure our product is very well aligned and it's useful for the market participants who are the natural long and natural shorts. So that's why we're excited about the whole suite of investors that's going to be heavily our clients at the same time. [00:26:54] Speaker 2: Right, right. The big thing that's to come is CME planning GPU futures that are settled against your benchmark. What's the progress there and what is it that actually will be traded? You know, just explain to the Bloomberg tech audience that might not be familiar with this story, the basics. [00:27:13] Speaker 9: So it's quite exciting. So the news came out, CME, the plan date is October 5th. The first suite of indices is going to be cash settled against our H100 and B200 new cloud on demand indices. So what is interesting, there's two actually volatility the market want to hedge. Number one is if you are natural loans of use, meaning you have your own tons of servers, obviously your revenue is high with the rental rates. And for you to hedge out due to volatility, you really want to short futures. You do not care about physical delivery because you already have your servers. All you're looking to do is hedge out the price volatility in the future years. If you are a consumer of compute, consumer of GPU, obviously you're paying the rental prices. Ideally you will lock prices in for a duration of time so you can loan the futures and lock in a certain prices. So that's really the use cases for natural [00:28:18] Speaker 2: factors. That takes us to the bigger picture and you referenced it. The Nvidia $500 billion with all of those investment firms raised a lot of questions. For example, well, are these not assets that depreciate? How do you in your benchmark reflect computers collateral? Just go through some of those ideas. [00:28:41] Speaker 9: Yeah. So it's actually the same story. The Nvidia $500 billion story is about a financing layer. CME and silicon data story is about risk management layer. So you need both. You can't have a market about half a trillion size without way for the investor, the people with exposures, having a place for them to [00:29:05] Speaker 2: hatch and have prices coming. Let me ask you this as a quick follow-up and I'm sorry to interrupt. What does your data tell you about how quickly a GPU generation can depreciate? What I see in the news right now is A100s, very high utilization, very high pricing, but they're generations old. [00:29:28] Speaker 9: So great, great observation. So what's interesting is A100 and H100 prices have been pretty stable for the past 20 days. The price went up about 20% for both chips since January this year. The last 20 days has been pretty stable. The way I will read is, so there's a few points you raised before. Just because something depreciates, server is a machine, right? The machine depreciates just like aircraft carriers, just like ships, right? Everything depreciates in terms of its lifespan. Doesn't translate to just because something depreciates, meaning they have no economic value, right? So you look at residual value population for any asset class is looking at future cash flow of that particular assets. And for the A100 to your point, right? It's an older chip, but people can still use that for different workflows and for smaller model use cases. Then they can keep charging at whatever price that, you know, that's the utility. Exactly. And even L40s, right? Forget about A100. And L40s has machine learning use cases, which is a complete. Right. That takes us back many generations. [00:30:40] Speaker 2: To end, I've got a question from the audience quickly, very interesting. Jucan on X. What are three signals in silicon data's data over the next two to three years that would tell you AI infrastructure over supply has begun? Jucan on X. So, great question. [00:30:55] Speaker 9: Jucan on X. So there are three data points I can actually, really good to take a look. Number one, spot prices, right? Spot prices is where supply and demand meet. If spot prices keeps coming down for a period of time, you can argue either there's less demand or there's just outpacing supply, which is not really the case right now. Number two is forward curve, right? If the forward curve is contangled, which is kind of we have now, that translates to people are paying even higher price to locking a longer-term contracts versus short-term rental. So that doesn't say it's oversupply, right? However, if the forward curve became a very steep downward sloping curve, then the implied rate for the forward, for the future, meaning it will calms down quite a bit. The third indicator is residual value, right? If the secondary market transactions of any servers, those prices came down, that represents people's expectation of those future revenue of those servers will be coming down, right? So residual value from the [00:31:58] Speaker 2: secondary transactions is also a good point. Really quick, contango where future prices of an asset currently higher than their spot price. Silicon Data CEO, Carmen Lee, thank you so much. Just a note before we end, Silicon Data's index is based on historical data. It recalibrates based on the importance of each factor at the time. Bloomberg LP, which publishes the index, is the parent of Bloomberg News. Now coming up, we'll take a look at how private markets are shifting as AI gets more integrated into the workforce. Foundation Capital General Partner Joanne Chen joins us next. This is Bloomberg Tech. Global venture capital firm Excel just raised $3.5 billion to invest in early stage companies globally, raising four separate funds dedicated to backing younger startups. The firm behind AI giants like Anthropic Cursor Perplexity said one of the funds includes a $1.35 billion expansion fund targeting firms specifically in the US, Europe, Israel and India. There's an abundance of capital flowing into the latest AI startups. Our next guest comes from a firm that has a 15 year history of investing in AI and she says the opportunity has evolved to become more exciting and important than ever before. Foundation Capital General Partner Joanne Chen joins us now. Venture capital supply is there for these founders and for these startups. I guess what was more interesting at this juncture is the decision to not invest in something to be disciplined. How real is that struggle? Very, very real. Thanks for having me, [00:33:35] Speaker 1: by the way. It's great. We are a very early stage venture capital firm and early stage means different things for different people. It does. For us, it means investing in founders that have an idea, that have a lot of ambition. They don't necessarily have customers or a product. You don't want to see the business plan necessarily. We're investing in people at the very end of the day. And we have done this over and over again over the last 31 years of the firm's history. One of the examples is Cerebras, which went public about three months ago, which we actually incubated in our offices back in Mellow Park. And at the time, Cerebras had just a few co-founders, an idea and a ambition and a prediction that the AI [00:34:18] Speaker 2: market was going to explode. Maybe it's not the same field of technology, but like an interesting example that I find is humans. And so let's call it a neo lab or something like that. It was just four founders able to raise hundreds of millions of dollars at multi-billion valuation out the gate. [00:34:35] Speaker 1: Right. And that's an interesting phenomenon because now we have examples of very large outcomes, right? As AI has changed and become much more powerful as a technology, there is an impact to organizations that could create a lot more revenues eventually. And so investors are looking at OpenAI and Anthropik as examples of, hey, maybe we can invest at these valuations and have them become this large. And therefore the entry valuations or the appetite for these entry valuations have gone way up for some of the types of companies. I think what's dangerous though is the neo labs don't have commercial traction for and possibly not for a long time or for even forever. And I suspect we're going to see that play out over the years. Some are going to have a very large scalability and others may end up not being able to [00:35:25] Speaker 2: raise capital in the future. So how does one do due diligence on an individual or a few individuals [00:35:31] Speaker 1: who just have an idea? Yeah, it's not dissimilar from how do you recruit someone awesome, right? Okay. You look for exceptional people, right? If you think about the Bay Area or even San Francisco, there are fewer than a million people in San Francisco. And yes, so many companies are started here. We look for exceptional people, exceptional founders with big dreams. Oftentimes they're technical. Oftentimes they know how to sell. Oftentimes they have a very strong growth mindset because as the companies grow, the job of the CEO changes and the founder has to be able to become a CEO and also evolve as a CEO. So we look for exceptional people at the end of the day with the understanding that they will become someone different. It's hard to look for exceptionalism. There's lots of ways to do that. And for each of us, we have different ideas what that looks like for us. And I think this diversity actually gets us a really interesting portfolio of different types of backgrounds. [00:36:23] Speaker 2: I'm really interested in how you're investing for the world you foresee. You published this in March. I'll get shouted out for doing that. But the great reorg, a human's guide. And the conclusion of the 16 pages is that AI won't simply replace people, but it will reorganize companies. And as part of that, you paint a picture of a world where there are smaller teams, fewer specialists, AI agents doing a lot more work. That seems a little contrarian to me because it's still net-net means less people. [00:36:56] Speaker 1: I would disagree with that because I think what AI is doing today is removing bottlenecks and organizations. And in order to remove bottlenecks, we do need possibly fewer teams to start with. However, if you're an ambitious entrepreneur and you're thinking, "Hey, I can build a billion dollar company with 100 people. Why not hire 500 people and build a much larger company?" It doesn't stop there. I think the misconception is that AI is just about job automation or job loss. In fact, I think what's going to happen is AI is going to create a lot of use cases that we haven't yet thought about. Think about the early days of mobile. I think the first mobile app that I used was this beer drinking app where I could tell my iPhone, the beer would come this way. And yet five years later, Uber came about and it really changed our words. And I suspect we're going to see the same things with AI use cases that are going to be [00:37:54] Speaker 2: transformative to us. So you talk about high agency, basically being able to take an idea and actually do something with it, build, execute it rather than debate the idea. Do you have some sort of evidence of that in the portfolio since March or otherwise? One of that is that we're seeing [00:38:13] Speaker 1: younger and younger founders build big companies. And I think that's possible because AI can teach someone who knows nothing about a subject and get them up to speed very quickly. It can do things like guide how you're going to fundraise, come up with your marketing launch plan, help you with interview questions, help you prep for Bloomberg TV interviews. So this is interesting because away from the [00:38:36] Speaker 2: investor side, the operator side of being a venture capitalist, you would pitch those services to a [00:38:41] Speaker 1: founder as well. We do. And we help with founders with very specific things to help them go through the zero to one journey, right? You can imagine having no customers getting your first is a big deal or not knowing how to fundraise and doing that for the first time. And we're also leveraging AI within [00:38:55] Speaker 2: our organization. Foundation Capital General Partner, Joanne Chen, it's been great to have you here with us in the studio. Thank you so much. Thank you so much. Now coming up, investors are betting that the AI music startup Suno will be the next Spotify, but it's got accusations of theft and AI slop to overcome. We have that story next. This is Bloomberg Tech. Blackmail. That's what California Attorney General Rob Bonta is calling reports that Paramount Skydance plans to leave the state if he doesn't drop a lawsuit challenging its merger with Warner Bros. A source says Paramount's board has approved a move from California as early as October and CEO David Ellison told executives that he's putting together a five year plan that would shift most of the film and TV studios jobs to the new home. AI music startup Suno is setting out to change entertainment with an app that lets anyone generate a song using simple text prompts. But the company valued at more than five billion dollars is facing headwinds, including lawsuits from two major music labels and concerns that it could flood the internet with AI slot. Bloomberg screen time editor Lucas Shaw wrote about this in the latest big take in Bloomberg Business Week magazine. And I find this really interesting because the tagline is that investors are betting Suno will be the next Spotify that that's worth an explanation. [00:40:25] Speaker 10: What do we mean by that? Well, you could pick Spotify, you could pick Instagram, TikTok, TikTok, any major sort of consumer internet app. That's what people see Suno as, right? They believe that there, you know, there will be a winner in AI by music creation consumption. And what Suno is pitching at least is it will change entertainment much as Instagram. You know, I guess the simplest way of putting it is, you know, not everyone is a professional photographer, but everyone uses Instagram either to post photos of their own or consume photos and increasingly videos that other people put up there. Right. And in the case of Suno, not everyone is going to be a professional musician, but maybe anyone can make a little song and share it with friends. [00:41:03] Speaker 2: So the audience is probably familiar with AI slop in the medium of video, you might see it on some of your social timelines. What is the concern or what is AI slop in the audio or music context? [00:41:16] Speaker 10: Well, the concern is fundamentally about dilution, right? So you already have, you know, so many songs available to stream at your fingertips on Spotify or YouTube or, you know, pick your preferred outlet. But what Suno and other apps like it do is make it so much easier to generate songs. And so the amount of songs being uploaded to Spotify and other service providers is soaring, right? There are thousands of songs all the time. And that's what people are worried about. [00:41:44] Speaker 2: You speak to so many people for this business week story. Spotify weighs in. I think we should get to the legal dispute. Universal Music Group and Sony Music sued Suno for copyright infringement. What do we need to know about that? And I guess what's the response to our reporting around it? [00:41:59] Speaker 10: Well, look, there used to be three of the big record labels suing them and included Warner, which has since settled just this morning BMG, which is one of the largest independents, did a deal with Suno. And so we're seeing lines kind of divided in the music business among those who are willing to do a deal. And those like Sony Universal, who so far are not, I would view it primarily as negotiating posture. You know, they have a lot of history of dealing with new technology. And if they are if the company is behaving in a way that they don't appreciate, that they think violates their copyrights, they take them to court to try to compel them to do a better deal. That is certainly what what I think Universal is doing. You know, you know, that even people who work there would acknowledge that this is all sort of part of a dance and a negotiation. And the question is, can they do how quickly can they do a deal? And the pressure on Suno would be what if Spotify and YouTube and some of the established players come up with similar products sooner? [00:42:52] Speaker 2: Lucas, you've published many stories in the last 24 hours. One is Call Her Daddy host raises money at a 500 million dollar valuation. The details very quickly. [00:43:02] Speaker 10: Well, Alex Hooper, host of one of the most popular podcasts in the world, has raised money from a firm led by kind of a former Hollywood super agent named Patrick Whitesell. It's a big number for a company that's mostly, you know, a podcast and some related videos. [00:43:18] Speaker 2: Bloomberg's Lucas Shaw with a must read big take on Suno, but also a lot going on in the world of media and entertainment. Thank you very much. That does it for this edition of Bloomberg Tech. It's an earnings driven market right now. The NeoCloud's AI infrastructure, particularly Supermicro on the server side, is seeing a translation to upside at the Nasdaq 100, like the index level. Earnings kind of continues. NVIDIA is much later in the month. NVIDIA is one of the names with the feel good today on the idea that the infrastructure build out for AI is intact. And you see the SOX, or Philadelphia Semiconductor Index, outperform as a result. The MAG7, more broadly, those biggest tech names, they're under pressure. Don't forget to check out the pod, recap all the great conversations from the program. You know where to find it. From San Francisco, this is Bloomberg Tech.