China Halts Nvidia H200 Chips, Discord's Confidential IPO File, AI Developer Platform | Jan 7, 2025

0:13Welcome, everyone, to the information's TI TV. My name is Akash Pasricha. It is Wednesday, January 7th. We have got a busy show ahead of us. First up, the information has exclusive reporting that China this week asked tech companies there to halt orders of NVIDIA's H200 chip. We will bring you all the details. Then we are talking to Sarah Guo, founder of the venture firm Conviction. Sarah opened for Jensen Huang at CES this week and published a sharp essay on how companies can stay competitive in the AI boom. And it is Wednesday, which means it is time to dive deep into this week's finance newsletter with our finance editor.

0:53And we are also then going to talk about a Bloomberg report that Discord has filed confidentially for an IPO. And we will wrap with a conversation with OutSystems, an AI platform for developers to talk about ROI in the age of AI and the intensifying race for the best coding models. Got a lot going on, so let's get right on into things. The Information's Asia Bureau published exclusive reporting this morning that China this week told tech companies to halt orders for NVIDIA's H200 chip. Joining me now to discuss the news is our NVIDIA reporter, Wayne Ma. Wayne, welcome back to the show. It's great to have you here.

1:32Hey, thanks for having me, Akas. So this was a big story that our Asia Bureau published. What did we learn about the regulations of China, what the government is saying, and how the situation is evolving? Well, I think the key thing here is that this is like a temporary halt, right? And the reason why it's temporary and not permanent halt is that Chinese companies still need NVIDIA chips to train their models. And China's ambitions to become a leader in AI still requires, I think, NVIDIA chips, if only because the Chinese equivalent still aren't as efficient or as capable in this sector. So temporary meaning, the way I write it was, I mean, it's basically, while we figure things out, hold off on placing your orders right now.

2:17That was my read on it. Yeah, that's right. And I guess the big question is, how long is that going to be? Is it going to be a month? Is it going to be six months? Is it going to be a year? I don't think it'll be that long. But at the same time, it's going to have a material impact on NVIDIA's earnings, depending on how long this is. Well, I want to get to the size of the opportunity in a second here, but this strikes me as a bit surprising because we just heard Jensen talk to reporters earlier this week talking about how demand is very high for H200. And, you know, from his end, seemed like it was all hands on deck.

2:52Yeah, I mean, I think the reason why there's a temporary halt is that the Chinese government also doesn't want to rely too much on NVIDIA, right? They don't want to rely too much on Western technology. And so they've been kind of trying to develop their homegrown AI chip companies to try to compete with NVIDIA. And I think there's a worry that if they just let Chinese companies buy as many H200s as possible, there's no incentive to foster that local homegrown kind of technology. And China Wall was just lagged behind and never be able to compete. Can you talk a little bit more about that? I mean, how much do customers need these chips in China?

3:28Oh, they definitely need them. They need them badly. Right now, China's AI chip industry is nowhere near at the level of NVIDIA's. And so it's going to take a long time, I think, for them to catch up if they ever will. And you talked about earnings from NVIDIA's end. How big of an opportunity or a missed opportunity, if they can't sell these chips eventually, we don't know what's going to happen in the long run, but how big a size of market is this for them? So NVIDIA has said in the past, as recently as late last year, that their sales to China could range from anywhere from$2 billion to$5 billion a quarter.

4:04So that's almost like a$20 billion a year opportunity for them. They took steps last year to stop reporting or factoring in China sales into their earnings just to give investors a better idea of what they will make. So there's that. But again, if they can unlock the China market again or include that in their earnings, it'll be a great opportunity for them and for investors. And let me just ask you a forward-looking question here. We just saw all of the announcements from Jensen about the Rubin family of chips. And, you know, I can't help but think about how long this issue sort of persists in different variations with different chips.

4:46I mean, we're talking about the H200 today. We were talking about the H20 a couple months ago. When the Rubin family of chips comes out, I mean, like, do you foresee this cat and mouse issue persisting for the next couple of years even? Yeah, I mean, I think at the end of the day, there needs to be a balance, right? The U.S. government doesn't want China to surpass, you know, the U.S. in AI. But at the same time, they don't want China to foster its domestic chip AI industry so that they don't need NVIDIA chips anymore as well. Like Jensen Huang, the CEO of NVIDIA, has always said that it's better that China and the world relies on U.S.

5:27technology for everything. And so they have to, so I think this is always going to be an issue with whenever NVIDIA comes out with a new chip is how much can you sell to China just enough so that China does, Chinese companies don't try to compete and build their own chips, but not so much that China surpasses the U.S. in AI. Right. Well, it is a fast-moving story. And Wayne, I want to thank you for coming on to discuss it. That is Wayne Ma, our NVIDIA reporter here at The Information. Thanks for having me. Okay. AI adoption is being closely watched in 2026, and there have been a lot of theories thrown around as to why it has been slower than many tech companies would have hoped.

6:08Sarah Guo from Conviction wrote a good essay on X about that earlier this week. She also published a piece about how VCs can remain relevant in a hyper-competitive landscape. And she was at CES opening for Jensen Huang earlier this week. I want to bring her on to talk about all of that. Sarah, welcome back to the show. It's great to have you here. Thanks. Great to see you. So quiet week for you to start off the year, eh? Yeah. Well, I think we are just running on full cylinders in the industry. Good stuff. Well, look, I want to talk to you about some of the stuff you wrote about. I also want to talk to you about the panel that you led at CES.

6:46You know, right after you had your panel, Jensen Huang unveiled some new details about the Rubin chip. And the broad question I have for you is what net impact you think that family of chips and those innovations from NVIDIA is going to have on AI adoption broadly? Yeah, I think the two basic takeaways for the broader industry versus chip experts would be, one, the NVIDIA pace of innovation is really punishing for competitors. This is a 5x more powerful chip on a flops basis from an inference perspective. It is a huge, like it's a family of chips, but the systems are huge. And so there's continued scaling of memory and memory bandwidth and a lot of the bottlenecks that people have been concerned about in the NVIDIA ecosystem.

7:42And, you know, late 2026 is a very near term timeline to be delivering such a big upgrade. Right. And so, you know, we'll see we'll see what happens later this year. But I think if those chips actually get widespread in the industry on time, application developers and then end consumers should expect that we can use a lot more tokens for much less cost, right? Which is sort of the overall goal for NVIDIA here. And when you say if those chips sort of get in the market on time, I mean, I guess the assumption that I'm hearing you say is that they eventually will get there and they eventually will get bought.

8:22it's just a question of when is that the idea uh absolutely so um you know it's been stated already that they're in full production yeah and i i i'm only nodding to the fact that these are extremely large scale like right it's the whole system it's the whole system right and so you can end up with all sorts of constraints to to get to you know final delivery at the massive scale we're talking about but i i do think this idea of inference efficiency that's starting with in video chips, but it's also in all the software layers, like these cloud platforms like Base 10, the impact for the industry is that a lot of things that look like much more advanced applications of AI, like long horizon agents, just smarter applications overall, are going to be widespread.

9:08And a lot easier to roll out in some cases. It leads me to an essay that you, one of two essays you wrote on X, which is articles on X are now people's favorite way to get their message across. You wrote about AI adoption. And I want to read a quick passage from what you wrote. You said, there's a mismatch between where AI value is showing up and where organizations are looking for it. Enterprises complain about missing ROI, even as demand for models and inference grows rapidly. Someone is getting the value. It just isn't always visible on a dashboard yet. I want to ask you about that. Who is getting the value then?

9:51If we can't see it on the dashboard, where is it coming up in companies? So I think there's like a mix of who's getting that value today. If you do better journalism, right, using AI, you know, you accrue some of the value. Maybe you get to go home earlier maybe the information gets better readership and engagement right but i'm you know not being glib it's really like productivity gains get reaped in different ways um part of the point of the essay is you know there are a lot of reasons why we're like still very early in sort of business deployment of ai but one of them is a product question right things are very, very early in getting frontier capabilities exploited from a product perspective, like deeply in the workflows.

10:40But the reason my essay focuses on is like, what's the incentive structure, right? If I, you know, leverage all this productivity and like, let's say 900 million plus chat GPT users as an example, and everybody else consuming these tokens is actually leveraging these AI capabilities, well, I can take those gains or I can share them with my business. And, you know, some of those gains are personal, obviously, in terms of new capability. But it depends on the incentive structure and, like, whether or not organizations really are aligned with their workers as sort of owners, right? Right. Now, the other thing you talked a little bit about is the change management angle to this story.

11:23And that was something that you also covered in your panel before Jensen Huang came up on stage. Another part, and I do want to read it because I thought it was a good point in the essay, is you said, using AI privately to improve your own output is generally safe. Proposing automation in a visible, coordinated way is not. And then you go on to say, this often gets misread as fear of technology. It is better understood as caution about how institutions handle change. So, you know, I talked about this a little bit with a couple of months ago with Vinod Khosla, and he was talking about who the people are on the ground trying to use these tools.

12:02And your comments here kind of reminded me of a question that I've been having is that, is it that we don't have the right people on the ground at these companies trying to implement AI or just that those people are resistant to it? I mean, where do you see the problem coming up? You know, I actually wouldn't say, you know, in some cases, maybe it'll be a question of the people and the adaptability of people. But I'd say for all humans, like, am I or are you using AI to the maximum effectiveness today? I am not. I don't know about you yet. But I really want to be. I don't think that's because I'm resistant to change.

12:39I think change is hard. And this change has happened really, really rapidly. Right? And so that's just like a fundamental recognition we should have, which is change and technology adoption is hard for every human being. And if organizations want to happen, they really have to invest in that. I think a lot of great organizations already understand that. But sometimes we're in this vortex of misunderstanding the timescales of how quickly this is all happening. It's been happening for three years. And so I think that's one recognition. But the fear, I think actually fear is really rational, right?

13:17If I think that I leverage AI and I figure out how to automate parts of my job and I am smart and informed enough to know that AI capabilities are progressing, it's a natural question to ask, well, like, does my role change, right? And that causes some nervousness around, do I want to attach my name to this initiative, to this P &L? Absolutely. And I think if that change affects other people, too, when you grow up in the management structure, it depends on the incentives within organizations, right? Okay, well, do I want to change Akasha's role? Do I want to change Sally's role? And what does that do for me within the politics of an organization?

13:55I don't mean that simply. It's just like these organizations have a bunch. Every large organization has a bunch of mixed incentives. And the winners in this transition to AI-native companies are going to have to figure out how to align that with the success of the organization. Now, you talked in your panel about how healthcare has been sort of a sleeper sector that has really adopted AI faster than some would have otherwise expected. And Open Evidence is a portfolio company of yours that has seen as a lot of success. Can you talk about why healthcare has been sort of this oddball case study? And, you know, what is it about Open Evidence as a product that has really gotten traction?

14:36I think there is a business model alignment here that is really special in the case of Open Evidence. And then there's a technical structural reason. So really quickly, Open Evidence is an app that any provider can use as a consumer, right? You download it in an app store, and you just start using it, and it's free. And it helps you with your day-to-day work against a really difficult challenge, which is understand medical research and, you know, be the best provider, the most informed provider you can. And expanding capabilities from there into sort of helping with all sorts of interactions. But like, there's no misalignment.

15:13It just makes me better at my job. And Open Evidence has an ad based business model that supports that, right? It doesn't have to do change management until half the doctors in the country are already using it. And that's a lot easier, right? You know, know, the organization is convinced by its users that it's useful. And so I think this is just a really interesting case study for the industry, which is like consumer and prosumer can be a really powerful adoption mechanism. The technical reason why AI could be really interesting for healthcare is that a lot of the data and a lot of the interactions are unstructured, right?

15:49And so, So, you know, large language models and agents like in these fragmented systems, working on free text, medical research, doctor's notes in phone calls. It's really good at that. And I mean, they've been trying to find ways to make that a little bit more structured for a while now. I'm curious, you know, I know you got to run here. You know, you put out this predictions episode on your podcast a couple weeks ago, and one of the predictions you made was that you are forecasting some kind of a change in the sentiment around robotics, or at least parts of robotics. And I thought that was interesting because the conversation around physical AI and robotics is becoming so much more – it's much more of a focus for everyone, really, even in the first few weeks of the year here.

16:43I mean, talk to me a little bit about why you see the sentiment around robotics changing and what you're expecting there. This is a very high-risk year for the robotics industry, but that's why I'm excited, right? I think that there are a number of companies, including Sunday Robotics in our portfolio, that are meeting reality this year. They intend to ship real products to consumer homes for testing. And these are like their sub function different than the products that have existed before, because they are generally capable, right? They're not designed for a single task, much like the LLMs were different from the pre-foundation model task specific models.

17:23And so I think there's sentiment improving or, you know, there's like all this heightened interest for two reasons. One is like all the researchers believe that there can be a huge amount of progress on both the, you know, models, data and deployment side. And then, of course, if that is real, there's a lot of investment and a lot of big tests, real-world evaluations that are going to happen this year. And it was a huge part of Jensen's CES keynote as well. He's been one of the earliest believers in the physical AI world, in part because that multiplies the size of the opportunity for AI. Yeah, I know.

18:02Well, I mean, the business case is certainly there. Yeah, but, you know, it's a really high conviction bet to be investing in it for as long as he has. And I'm a big believer, but we'll sort of meet reality this year. But where does that sort of collapse in sentiment happen then? I mean, where do you forecast it sort of breaking down? um so um uh none of this works yet in production right if you ask me is there a generalist robot uh in any real world environment like me and other people from this industry are going to say no right so it's a really big open question um uh they're like amongst robots are really hard products to deliver right like you know think of autonomous vehicles as an example of it yeah yeah The safety case is really important.

18:52And the real world is always more varied than one would hope, right? Like, I think it's easy to imagine, like, in the home, but also, you know, in factories or in data centers. And so, you know, these robots have to be really robust to those environments. And the products are really hard to deliver to begin with. And so we are going to see more failures than successes of these companies. and they're very expensive to ship, right? I think if they work, they can be, they're hardware companies. They can be, and perhaps general ones with really expansive applications, you could have hardware like Tesla, Apple, you know, GE, Siemens type companies that are really impactful to the manufacturing world, which is another area of interest for us.

19:39But there's going to be a lot of failure as well. And it's expensive. I think it kind of leads to an interesting question for me, which is that, you know, where does that cycle sort of start? Because, you know, I think what you're pointing to is that investors will wake up to the reality that, hey, this stuff is not working as well. We shouldn't be affording it, the valuations that we've seen sort of start to creep up. On the other hand, you have big public tech companies, you know, continuing to talk about it as an application. And I guess in my mind, I'm sort of wondering where the, you know, what's the chicken, what's the egg?

20:12You know, what comes first in terms of if NVIDIA continues to keep talking about it, I don't know, does it take longer for investors to wake up to that reality? I wonder what you think. Oh, I don't think so at all. I would push back and say, I don't know that the prices are wrong. Like some of those people, I think some of those people are going to be right at massive prices, and some of them are going to be wrong. Like in any other really high value market, right? It's about picking winners. And NVIDIA has been a huge contributor to this ecosystem, delivering models, open source data, frameworks, the hardware itself.

20:52And so I think they're definitely supporting the success case here, as you would expect. I would just say, I think it's a question of like, does it work or not? And we can certainly have waves of sentiment as an investor. If a specific bet doesn't work, your reaction can be that bet didn't work or the sector didn't work. And so I think that could be the change in sentiment. Great. Well, Sarah, I want to thank you for coming on the show again. It's always a great conversation. That is Sarah Guo, founder and investor at Conviction here on TI TV. Okay. In some ways, it's never been easier to invest in AI with every public company jumping at the opportunity to overhaul its business in the face of the new technology.

21:41And on the other hand, given how much of the stock market is reacting to AI news, it's becoming a whole lot harder to beat the market with a novel AI trade, I should say. That is a phenomenon that Ken Brown, our finance editor, wrote about in his weekly finance column out today. And I want to bring him on to tell us more about his piece. Ken, welcome back to the show. It's great to have you here. Hi, Akash. So what made you want to write this column on this day? The first week of the year is people are looking ahead to their investment portfolios. I mean, this is not something we typically write about the information.

22:17Right. So there's two reasons. One is this is going to be a year where we're going to probably see some AI IPOs. There's talk that Anthropic will go public this year and OpenAI will follow maybe next year. and investors are eager to get into this stuff. The other thing is I got a question from a relative of mine, a couple of relatives, because I deal with AI all the time and I deal with money and how should we invest in AI? And so I did a little research and looked at what was out there and decided the timing was right to write a column. Okay. So the column was great at diving into some of the more creative ways that people are, that companies rather, are offering exposure to.

23:03And by companies, I should say, you know, companies whose business it is finding retail investors, ways for them to make money. Talk a little bit about, you know, some of the deals that we've seen in the market and how that's shaped up. Yeah, so, you know, most of these big AI companies are still private, right? I mean, you know, there are public ones like Google and all that, but the pure plays, the startups are private. And so both Schwab and Robinhood and other brokers are trying to figure out ways to help investors get into these private companies. And, you know, my research shows it's generally not a very good idea for individuals to do this.

23:41The fees are high. They get in at the wrong time. The prices are high. And the reality is they're kind of last in line to get this stuff. I mean, all the big, powerful investors, the VCs, the private equity firms, the sovereign wealth funds, they're all in there first. And so, you know, us little guys, we are not a priority and we don't get the best deals. And so I just wanted to, if people got excited about it, I wanted to sort of at least sober them up a little. And just to be clear, this is individual investors finding ways to get exposure to these private companies through these newer platforms, I guess, that have become more popular.

24:22That's sort of where your caution is coming into play here. Well, so my caution is two things. So one is, yes, there is a push by brokers and investors are asking for it to get access to these companies, Anthropic, OpenAI, all that stuff. I want to invest in AI. I want to get into this. How do I do it? And so that's happening. The other thing is people are making bets in all kinds of novel ways on the stock market with the companies that exist publicly and other ways that they feel like they can get AI exposure. I also warn people about that. I mean, so this is a little bit of a personal finance column.

25:04it's a finance call it's just a finance call of ken they can be whatever you want and we don't and we don't tend to do that but it is a confluence of events and so the track record of these kind of funds that people are buying now is terrible the funds perform badly uh they they can do great for a while and then when everyone puts the money into it they they do terribly a lot of them shut down most of them trail the market um i mean when you look at the data like 80 percent of them shut down or lag the market after three years. And so I just want people to be aware that this is exciting. We spend all day writing and talking about it.

25:41But for an individual investor to get into it, you just got to be smart about it and careful. The other problem is everyone owns this stuff already. Google, Microsoft, Meta, all these companies are public. They make up around 25 to 30 % of the stock market. So everyone's exposed to this. So why do you need any more? Wow. Because more is more, Ken. I mean, that's the whole point. No, exactly. And it's a very exciting area and there's going to be monster companies and lots of money to be made. And so, and, you know, as I end the column, I say, I've given all this advice and next year, everyone's going to come back to me and say, we made all this money ignoring your advice.

26:23And so - Right. Yeah. That's typically how holiday dinners end up going. Yeah. Yeah. Yeah. And so, you know, you too can come back to me. No, no, no. Look, I don't invest in any of this stuff. We just write. That's all we do. Exactly. But I do want to talk to you about another pocket that you touched on briefly, which is the energy sector. And the energy sector is another area that people have been talking about as an opportunity. But then as I thought about that more and I read your column, then I got to thinking, again, if you just buy the S &P, I mean, the energy companies are all moving according to these deals anyway.

27:02Right. And so the beauty of index funds, which is a lot what a lot of people own, is you get exposure to everything. And so you never miss a big, big run up in something, which is fantastic. Yeah, I know the energy companies. I mean, you know, they and it's a volatile sector and people are pouring a lot of money into AI. But, you know, people with longer memories, memories longer than a year, remember that the hottest energy trade two years ago was the energy transition to solar and wind and batteries and all that stuff. And that trade has turned out to be a terrible trade for investors. They lost a bunch of money.