OpenAI’s Multi-Billion Ad Strategy, Modal Labs CEO on AI Infrastructure, Twilio’s Voice AI Surge

0:13Anita Ramaswamy:Welcome, everyone, to The Information's TI TV. My name is Akash Pasricha. It is Tuesday, May 26th. Hope you had a great long weekend. Before we get into today's show, tickets are now on sale for the Information's annual AI Agenda Live conference. It's taking place in San Francisco on September 23rd at SFMOMA, the Museum of Modern Art. You can now log in early bird tickets. We have more details on our events page at theinformations.com slash events. I encourage you to check it out. First up on the show today, the information has exclusive reporting about how OpenAI is looking to scale its advertising business ahead of its eagerly anticipated IPO.

0:54Anita Ramaswamy:Our e-commerce reporter will join the show to share with us what she knows. I'm also speaking with the CEO of Modal Labs about the company's roadmap after its recent funding round. It was valued at$4.65 billion. We'll get to that shortly. And we're also zeroing in on one software stock that has defied the broader declines in the sector, Twilio. But our financial analysis columnist says that despite the recent AI-driven surge, eager investors might be missing the bigger picture. It's going to be a fun show, so let's get right on into it. OpenAI is quickly trying to scale its advertising business as it looks ahead to its IPO.

1:37Anita Ramaswamy:My colleague Anne Guillen published exclusive reporting today on the current state of that effort and some changes that the company has been making. I want to bring on Anne to tell more with us about what she knows. Anne, welcome to the show. It's great to have you here. Hey, Akash. Okay, so OpenAI is on this ads push. We know it has big expectations for what it's going to do for the business this year. you went out into your orbit of advertising and e-commerce and you found out how it's all going. What did you find? Yeah, I mean, so OpenAI launched advertising earlier this year. There have been a lot of developments.

2:17Basically, every time I talk to my sources, you know, even day-to-day, week-to-week, things are changing really quickly. So I was trying to figure out, you know, what all these changes and launches from OpenAI all add up to. So, I mean, you see that OpenAI is really taking steps to build a more mature advertising business basically as quickly as it can. They're really trying to compete closely with Meta and Google, who are the two kind of dominant players as far as digital ads go. And just I think the pace that OpenAI is rolling out these new features. They're basically launching things as quickly as they can.

3:00And so this week, they're launching the first test of conversion-oriented ads, which measure success and build advertisers based on whether or not users actually take an action, whether that's making a purchase, creating an account, downloading an app, things like that. So you really see OpenAI moving from just general kind of brand awareness with the first kinds of ads that they rolled out a couple of months ago to kind of some more concrete, more measurable outcomes, which is something that advertisers have really been wanting and really been pushing OpenAI to add.

3:41Anita Ramaswamy:So this is kind of interesting because I remember last time when you were on the show, we've talked a lot about how OpenAI is positioning its product as a premium advertising product. And that certainly came through with the pricing, I believe, that it was offering advertisers. And so it sounds like they are very much leaning then towards an action-oriented result from the users, essentially. I mean, it's not enough just to have impressions on the screen. They're charging based on an outcome. Right. Yes, that's correct. I think you see with kind of the original ads that they launched, which you're right, were these very high priced, you know, just more kind of general awareness oriented kind of ads.

4:29I think, you know, there's obviously a lot of advertiser interest in running ads on ChatGPT. But I think despite all of that demand and all that hype, I think very quickly advertisers said, OK, this is great. You know, ChatGPT has 900 million weekly active users, but, you know, we want to see results. We want to know what we're getting for this premium price that we're spending. So I think that a little bit of that pressure is already kind of starting to build from some of the advertisers. And I think that is why you see OpenAI moving to roll out, you know, first these click-oriented campaigns a couple of weeks ago, and now the more action-oriented campaigns as well.

5:09Anita Ramaswamy:So another interesting part of your story that fascinated me is you said that OpenAI is also focusing on smaller businesses. You talked about, like, dry cleaners as one example. Yeah, it's interesting. I think if you take a step back and look at kind of the sum of all of the tools that they're building and that they're rolling out, they are really oriented more towards small businesses that, you know, want a very concrete return on their advertising spending. They want very measurable outcomes as opposed to, you know, a bigger name brand or a bigger name advertiser like, you know, Adobe or Target or Ford.

5:49Those were some of the very first advertisers that OpenAI worked with. They're more interested in, again, just kind of that general brand awareness, you know, general kind of brand spending, whereas, yeah, a smaller local business or a really small e-commerce brand, they want very measurable results from their spending. So I think that thinking and, you know, catering, I think the tools that OpenAI is launching now definitely cater more to that second group of smaller businesses.

6:20Anita Ramaswamy:But help me understand, I mean, if we take the aggregate advertising spend of small businesses and we compare that to what it means to get, you know, a large advertising deal from Target or someone like that. I mean, I would have thought that you really would have wanted to focus on the large businesses and the small businesses. I mean, it's a much more fragmented segment of customers. Like, why is this like a strategic imperative for them to focus on the dry cleaners and the mom and pop shops? Yeah. And it's not just the mom and pops of the world. I think, again, those smaller e-commerce brands, you know, kind of small and medium-sized businesses, that's definitely a super, super lucrative, you know, group of advertisers that there definitely is very fierce competition to own.

7:12And, I mean, Meta and Google have dominated the advertising business for small and medium-sized advertisers for years. They make hundreds of billions of dollars in ad revenue from those advertisers spending every year. And those businesses are really dependent on Meta and Google. You know, there's a lot of kind of complaints about how beholden some of these advertisers are to Meta and Google. and any kind of new ad or new place to show ads that they can incorporate is really appealing. There's a big opportunity there. There's a lot of demand. I think if you look at TikTok's advertising growth over the past couple of years, that's a great example of just how much demand there is for new kinds of ads that aren't on Meta and Google.

8:03So I think OpenAI is really seeking to kind of capitalize on some of that demand. And yes, you know, these advertisers definitely do have the smaller budgets than the targets of the world. But I think when you when you look at it, it ends up being a huge, huge component of their marketing spend.

8:19Anita Ramaswamy:I mean, this is, you know, I remember you coming on and talking to us about how I mean, this is their marketing spend. And you can actually build a business based on the calculation that if I spend this much in, you know, Facebook ads or in open AI ads, for example, I can expect this ROI. And it kind of becomes a very solvable equation in some cases. Right. And I mean, and Target's advertising, you know, all different, you know, dozens of different channels. And these small businesses, they're really only spending on a handful of advertising platforms. And so I think any dollars that OpenAI feels like they can win that would have been spent on Meta or Google, you know, those are hugely important to them, especially right now, you know, as they look towards an IPO.

9:09I think being able to show that ad revenue, you know, let alone the fact that they're potentially taking dollars away from Meta and Google, I think that's really important to their business right now.

9:21Anita Ramaswamy:So overall, Anne, I mean, you came away from these discussions. I think it was, what,$2.4 billion was the advertising revenue goal for OpenAI in 2026. I think we had reporting earlier this year that they were at like$100 million, which meant they still had a ways to go. I'm not sure if we have figures to update that, but what is your overall conclusion here? Is it going well? Is it going not so well? Well, OpenAI definitely, they have very big goals for growing their ad revenue. We've reported that they've told investors that, you know, they expect over$100 billion in advertising revenue by 2030, which would be more than 35 % of the company's total revenue.

10:10Which, you know, so advertising is clearly a very significant piece of their business. But I think, you know, where for that to happen, I think OpenAI will continue to, you know, they'll have to continue to build out the advertising tools. I think you see just in the pace at which they're releasing new offerings, they definitely are trying to scale this up as quickly as possible and just kind of get, you know, even if it's very early versions of something, get it released as quickly as possible and start charging advertisers for it, basically. But I think just in talking to advertisers and talking to agencies that are helping advertisers set up these ads, it's definitely they view it more as an experiment still.

10:56It's, you know, no one has a dedicated budget for chat GPT ads yet. I think a lot of advertisers are still just trying to figure out what works. You know, OpenAI has been even, you know, releasing and thinking about new kinds of ad formats. So I think there's so much change that, you know, and it's so new that no one really has a reliable strategy for this yet. So I think advertisers are still mainly treating this as an experiment. And we're not seeing any kind of long-term shifts of spending move from Meta or Google to ChatGPT just yet. But I think OpenAI is trying to do everything they can to prove to advertisers that, you know, these ads are valuable and that they are getting, they're able to provide the results that advertisers are looking for.

11:47Anita Ramaswamy:Great. Well, Anne, I want to thank you for coming on. That is Anne Guillen, our e-commerce reporter here at The Information. motivation. Modal Labs raised$355 million at a valuation of$4.65 billion. This was a funding round that my colleague Stephanie Palazzolo first published details about earlier this month. Today, we have Modal Labs CEO Eric Bernardson on the show to share with us more about his company's roadmap. Eric, welcome to TITV. It's great to have you here. Thanks. It's great to be Sure. Tell me, in the simplest possible terms, can you explain to us what it is that Modal Labs does? Oh, boy.

12:27I always try to explain to my kids. But Modal basically helps companies run AI. So, if you think about all these cool companies out there building a lot of cool AI applications, whether that's Suno, which does AI-generated music, or Lovable, which powers a lot of live Vib coding, they all need infrastructure. So we help them with the underlying infrastructure layer. And part of why that's needed is a lot of traditional infrastructure. If you go to the clouds, the hyperscale, etc., they're just not built for AI. It's quite hard to work with these large models, iterate quickly, work with GPU capacity, whether that's training or inference or sandboxes or a bunch of other stuff.

13:05So we built this underlying layer that helps engineers move much faster and helps you kind of abstract away in a way you don't have to think about that underlying capacity, routing, load balancing, all that stuff.

13:19Anita Ramaswamy:So when you say that the cloud providers, I'm thinking about the hyperscalers and then I'm also thinking about the NeoCloud companies who we call them NeoClouds. They call themselves AI specialized clouds. But I mean, their pitch, their whole pitch is that we make it easy for you to run your applications on our cloud. So I hear you saying that they're not actually well-suited then to run the applications. So help me understand why you think it is that your layer needs to exist and that an application can't just go straight to a cloud. Yeah, there's a couple of reasons. Like the NeoClouds are great, by the way.

13:57We use a lot of NeoClouds. We work with a lot of hyperscalers. We consider them partners, a lot of them. What they give you is raw GPUs or raw hardware. So you have to still set it up. And typically the way you get access is you rent, you know, for a year or three years or something like that, like kind of fixed number of capacity. So you still have to work with like sort of fixed capacity. A big problem is when you have a lot of these applications in production, your demand is actually very variable. You have this like, you know, peaks and troughs and, you know, that depends on your users. So a big part of the challenge that a lot of people face when they deploy things into production running on GPUs is this variable demand.

14:34So we help them basically get very flexible capacity. We have a fully usage-based model. You only pay for the time the GPU is actually running. You can scale up to thousands of GPUs. You can scale down to zero. You only pay for that time. And we offer that flexibility because we pull together capacity from a lot of different neoclouts and hundreds of regions all over the world. The other thing is the developer experience. So when you get the hardware from these clouds, you still have to deploy the apps, set up maybe Kubernetes and Docker and manage all of that stuff. So you need an infrastructure team.

15:07Whereas with Modal, we offer a much better developer experience. But with a few lines of code, you can just deploy something that audit scales completely, asset demand comes in, scales up, scales down. You can do it very quickly. You can have research teams that can deploy things to production themselves. You can build very complex applications. Right.

15:27Anita Ramaswamy:So you compete with the Fireworks, the Base 10s, the Together AI, and now Modal Labs. I mean, this is what we call the infrastructure provider. That's sort of the segment of the market that you're competing in right now. Roughly, yeah. I would say all of them and modal, what we have in common is we think of ourselves probably, or at least like we think of ourselves, I don't know if they think of ourselves, but as a kind of a second cloud layer. So you have the underlying cloud with the capacity, and then we sort of manage the layer above, making that easy to access, making possible for engineers it very quickly.

15:59we offer LM and friends as a service, but we also do a lot of other stuff. So I think we're a little bit more general purpose than many other players in the space.

16:07Anita Ramaswamy:So long-term, I guess my question is, why does the layer that you live in right now, this second cloud layer, why does it need to be an independent layer? In other words, long-term, I mean, this is a hypothesis I have. I mean, I could kind of see the cloud companies looking to you or to together or to Fireworks, you know, even the NeoCloud companies, they're all building out their own software platforms as well. I mean, I sort of see the long-term game here being a company like you getting acquired by a cloud provider. Do you not see that to be the case in the long run? I don't think that's true.

16:46I look at, you know, historically, when you look at companies like Snowflake versus Redshift, you know, Redshift was AWS's product, Snowflake was a standalone database. Snowflake is doing exceptionally well, even though they're running on a lot of hyperscalers. And we think it's something similar here. We're building a service higher up. The NeoClouds and the hyperscalers, they're very good, and we work with a lot of them. And they're very good at offering capacity all over the world at a decent price. But it's quite hard to manage that capacity. But what they're very good at is putting that behind an API and making it possible to, you know, they're very good at running data centers, physical capacity.

17:20What they're often not that good at is, in my opinion, in delivering good developer experience and delivering a software product that makes it possible to iterate quickly. And I frankly think of it as like a great partnership. Like we're building that layer above and we're spending a lot of money on the underlying hyperscalers and neoclouts. And so in that sense, I think, we don't necessarily think of it as a competitive dynamic. We think of it as a partnership.

17:45Anita Ramaswamy:What are you planning to do with the money that you raised? Well, we're planning to grow very quickly. We are hiring a lot of people. We're quite acquisitive, so we might go out and buy some companies in order to move faster. What are you looking to buy? We like small teams that are extremely tactical and maybe haven't had the commercial success they're expecting. There's a lot of companies out there that are exceptionally strong engineers building very cool things. I'm thinking, I don't know, like AI coding, for example. That's a place where developers have been flocking to. you know maybe that's an area that you're trying to build more compatibility with i mean you know maybe there's more uh on the back end integrating with the cloud company like where where do you think you could see yourself actually making an acquisition i think there's a lot of interesting stuff in in complex ai stuff you know whether that's reinforcement learning or lm inference optimization things like that another bucket of things we're looking at is would be things like file systems and runtimes and like very sort of low level, you know, Linux and deep sort of, you know, management of infrastructure.

18:53A couple of different other buckets, but we're, you know, I think there's a lot of strong teams out there that we're very excited to potentially working with.

18:59Anita Ramaswamy:And so this is the other part that I have been interested about with your segment of companies is you are basically, you are renting cloud capacity out from the cloud companies. Do you own any chips yourself at all? No. Okay. Is that a plan? Do you plan to get into that business at all? I wouldn't rule out anything. I think everything is on the table. We are a software company and being pure software enables us to grow very quickly and move very fast and being very capital efficient, more like a marketplace business, frankly. I wouldn't rule anything out. It's possible we might own chips in the future, but it's not something we're particularly desiring to do.

19:41Right.

19:41Anita Ramaswamy:Now, the developers that are your core customer base here, I wonder if you think about just the last six months and the trends with respect to how they are using your platform, things that have surprised you maybe. We've seen AI coding be very top of mind. We've seen token maxing come in and out of the periphery in terms of what people are focused on. What has surprised you about what developers have changed in their expectations the last six months? Yeah, I mean, I think a big factor for us has been the massive explosion in sandboxes. So Modal started out five years ago thinking about this space as a very general purpose.

20:23Like we wanted to build a platform with a lot of different tools. And so we saw a very strong product market fit initially within Friends. We've been working on training as well, seen quite a lot of batch jobs. We put out a product called Sandboxes about three years ago. And the idea is you can basically run third-party code in an isolated, safe way, typically LM-generated code, because we saw that a lot of people were using LM to generate code. That exploded this summer. So we've seen tremendous growth and enormous traction with reinforcement learning, with Vibe coding, with background agents.

20:55And I think it sort of reflects a lot of the new world we're in, where engineers are running background agents and building a lot more code. And in many cases, under the hood is actually powered by modal when they're doing that. Or when some companies are training coding models, they're using modal for reinforcement learning for the rollouts.

21:14Anita Ramaswamy:How is the issues that GitHub has been having, and I'm talking about the outages here, has that affected anything to do with your business or any integrations at least? I mean, we were affected by it. We actually spent a lot of time dealing with, there was some merge issues. One of our engineers tweeted about it and it went viral. um what did he what did he say what did he say that that you know github is having some severe issues i i'm github is a product that hasn't been well maintained in my opinion for for quite some time so like i don't know like i think that the space would benefit from some more alternatives and some more development what what haven't they done well that you think they could have done better i think github is it has all the engineers it has an amazing sort of you know community and And the tools are quite good, but there's like constant small issues here and there.

22:06Like, I don't know. I love the product a lot. Like overall, it's gotten us to a very good place. I mean, I've used it for probably 10, 15 years at this point. But there's small bugs here and there that are sort of accumulating.

22:17Anita Ramaswamy:And I mean, for someone who, I don't use GitHub. So, you know, I'm asking for a bit of an explainer here. Like what is, like the product just hasn't innovated? Is that the idea? or like what exactly has been slow about it and how could they have been debated, I guess? I think there's many things that GitHub could have done in the past. I mean, they were very early to AI coding. I mean, they had the co-pilot very early. I think they, you know, could have actually been a more dominant player in that space. I think the product is just not caught up with all the development. I don't know. I mean, I mean, again, like I do like GitHub.

22:51I don't want to, you know. No, it's interesting, you know, because we, look,