Alex Triplett of You.com, chief operating officer, discusses the mixture-of-experts approach to web search application programming interface, API, and enterprise artificial intelligence, AI, during theCUBE + NYSE Wired at the New York Stock Exchange. Triplett draws on a background in investment banking private equity and operating leadership to explain AI infrastructure web-search API strategy and the mixture-of-experts approach. They outline the influence of founder Richard Socher and the company's pivot from consumer chat to enterprise web-search API offerings.
Key takeaways include You.com's emphasis on zero data retention and an unbundled web-search layer to enable multi-model flexibility and token-cost optimization. Triplett describes enterprise go-to-market tactics such as technical sales engagement, executive sponsorship and tunable API endpoints. They note market dynamics that include a near-term race for speed and messaging and a longer-term advantage for firms that build durable intellectual property and pursue consolidation.
This segment appears as part of theCUBE Research series and is hosted by Gemma Allen of theCUBE + NYSE Wired.
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Alex Triplett, You.com
Alex Triplett of You.com, chief operating officer, discusses the mixture-of-experts approach to web search application programming interface, API, and enterprise artificial intelligence, AI, during theCUBE + NYSE Wired at the New York Stock Exchange. Triplett draws on a background in investment banking private equity and operating leadership to explain AI infrastructure web-search API strategy and the mixture-of-experts approach. They outline the influence of founder Richard Socher and the company's pivot from consumer chat to enterprise web-search API offerings.
Key takeaways include You.com's emphasis on zero data retention and an unbundled web-search layer to enable multi-model flexibility and token-cost optimization. Triplett describes enterprise go-to-market tactics such as technical sales engagement, executive sponsorship and tunable API endpoints. They note market dynamics that include a near-term race for speed and messaging and a longer-term advantage for firms that build durable intellectual property and pursue consolidation.
This segment appears as part of theCUBE Research series and is hosted by Gemma Allen of theCUBE + NYSE Wired.
>> Palo Alto Studio, connecting Silicon Valley and Wall Street.
Gemma Allen
>> I'm John Furrier, the host of theCUBE here with Gabe Olave, my co-host. Welcome to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired. And today we are talking about mixture of experts. Joining me now is Alex Triplett, CEO of You.com. Alex, great to have you.
Alex Triplett
>> Gemma, thank you so much for having me.
Gemma Allen
>> So I want to get into You.com and the business and everything that's happening in the industry. But first, I want to talk a little bit about you. You've had an interesting career. You have been an investment banker, a long-term M&A scout, I guess I could say, right, at a couple of firms. Now you're in this very interesting point in the world of AI infrastructure. Tell me about, first of all, the decision to join You.com. What part of your M&A brain thought, yep, this is a great move for me?
Alex Triplett
>> Oh, it's a great question. Yeah, I did have an interesting journey. So starting in banking, went to private equity, had this realization in 2010, I can be a better investor if I get a little bit of operating experience. 16 years later, that's all I've been doing. And so when I first went to ION Group, fintech company, part of my job was M&A, raising capital, integrating acquisitions. Part of my job was operating business lines that once we acquired them, needed somebody to go run them. And so I did both of those jobs and did that for 12 years. We grew the business quite a bit, $150 million in revenue to $3 billion, 600 people to 12,000 in 12 years. So it was quite a run. Saw a lot of phases of growth of a company. I spent 3 years as CFO, COO at Appfire, so small application provider around the software development lifecycle. And that was fantastic. Grew that from $100 million of revenue to $300 million. When I was thinking last summer about what to do next, and so to your question, why You.com, what part of my brain was stimulated? The first thing I was thinking about was, given my experience, where can I make an outsized impact on a business that is ripe for change. Growth needs a catalyst. So that was first. Second, I wanted to participate in AI. How do you do that? Can you do that as an investor or can you do that as an operator? And I felt like being an operator within AI, you get closer to everything. You get closer to your suppliers, your customers. And for me, the decision on You.com was really centered around two things: our founder and our position. So founder first, Richard Socher. He has been doing this for 15, 16, 17 years, when I say this, it's been— he was writing papers in 2009, 2010, 2011 at Stanford contemplating hooking up neural networks with natural language processing, thinking about some of the things that ultimately became large language models as we know them today. Heavily cited researcher, started a business, chief scientist at Salesforce, did all these things. When he spun out of Salesforce in 2021 to start You.com, he was the first to take an LLM and hook it to the web because LLMs' training data ends. It needs accurate, fresh information from the web to make answers for anybody that's prompting it. And when I looked at his track record and his tenure and the fact that he's been doing this, he's got a North Star, he's a visionary, I thought aligning myself with somebody like him was probably a good move. And then the second thing to the point of what do we do? We're in this interesting little niche, which I can talk about later. Where we supplement and complement the LLM and we sit below the agent layer. And I felt like that was a really good space to gravitate towards because it's underappreciated. It's very, very important and less competitive.
Gemma Allen
>> Let's talk about that niche for a second. The decision to really hone in on a niche, which has been the You.com journey, right? You mentioned that this started as a— I guess you could compare it to maybe a Perplexity type of model, but it was too early for the market at that time. Now we have a lot of companies coming on the show and they're saying, we're told we're a little bit early. We don't want to be a fast follower. Now we want to be a very niche player. How would you describe You.com's journey and You.com's market message at this moment? Because it has shifted somewhat, right? Yep. Yeah.
Alex Triplett
>> So we are Google for AI. We are agentic web search. We provide web search APIs for anybody to search the web. That's an LLM, that's an agent, that's a large consumer business, that's a portal, whatever. So we do that, right? That's the market message. That's all we do today. But you're right, we had an interesting journey to get here. When Richard started the business in '21, you're absolutely right. We started life as basically a chatbot, like Perplexity, like OpenAI was when it started. We had an interface, we had LLMs in the background, we had web search hooked up with that LLM, and we could provide great answers for people, typically consumers. Two things happen in the consumer space. One, you need an incredible scale through marketing dollars or incredible scale through compute dollars. And others got there first. And obviously OpenAI has been phenomenal in that space and so has Perplexity. And so what we had was we had this amazing search index. The search engine was underpinning the chat and all of a sudden enterprises started to pull us and say, wait a minute, do you have an API? I don't want the interface. I don't want any of that stuff. I just want the API because I want to ground my agents or my model to the web. And so we get pulled into the enterprise space, pulled into the Web Search API space. And this is kind of late 2024, early 2025. And eureka, we discover this is an amazing market. There's this huge need. Enterprise is a great space to be in. It's underserved. Most people right now cannot be served by Google and Bing, the traditional search engines. We are next. We are it. And so that's what we've been doing ever since.
Gemma Allen
>> Let's talk about that need for a second because this is essentially tailored endpoints, right? It is about making sure that you get the best possible answer that's available to you on the web. You have an interesting profile of customer though, because on one end, I'm guessing here, it's an assumption, but you are, I'm sure, serving many enterprise customers. You're also serving some of the frontier models. Break that down. Are you a competitor in one space and a collaborator in another? profile the customer base for me here.
Alex Triplett
>> Yeah, absolutely. Astute observation. Enterprise customers and frontier models. So I think about our customer set as basically being 4 categories. So category number 1, agent-native businesses. This could be Harvey, which is a great customer for us, right? The ubiquitous legal agent. Could be Factory, coding Droids, amazing company. Could be somebody like— I would put Salesforce in that category with Agentforce Slackbot. We serve them as well. So kind of agent-native businesses. Second would be the Frontier Labs. So there we could serve them at inference because every Frontier model needs web search. All of them have it. Sometimes they use us, sometimes they use other people, but all of them need web search at inference when they have a prompt or make a call. So we can serve frontier models. We also can help them with pre or post-training as well. The third would be any consumer type of business. Typically it's an agent application. So think Alibaba.com. They have an AI mode. that's summarizing specs around a product. We serve them, we serve Amazon to discern trends out in the marketplace that then will inform how they position product in their marketplace. So that can be consumer-based businesses. And then the last is developers. Anybody that's a developer that wants to build, that goes on LangChain or Replit or OpenClaw or Hermes or whatever it might be, MindStudio, whatever the application is, and they want to just build and they want to take an API and plug it into the agent that they're building, that could be us.
Gemma Allen
>> I mean, GitHub is certainly a minefield, so I can certainly see the value in that. I want to talk about enterprise, though, because we talk to a lot of folks who come on the show and talk about nailing enterprise, right? The enterprise win is the big bet of this next era. Yeah. You're not a RAG, though, right? You guys are very much about ensuring that what is broadly available is returned in the most succinct and I guess accurate fashion possible.
Alex Triplett
>> Yeah.
Gemma Allen
>> What sorts of tie-ins do you have within enterprise? Are you working with HR departments, finance departments? And I want to talk a little bit about the competitive side of that. But first, I want to understand, tell me how the product is being used. what niche are you building on?
Alex Triplett
>> Yeah, it's a great point on how to crack the enterprise. So typically the conversation, if you're in one of those AI-native businesses, there's an engineering team that probably understands search or has somebody focused on search. And so we will cater to them directly. And there the buying motion is highly technical. it's very specific, they know exactly what they want. And so our positioning is, we call ourselves a search lab. You mentioned lots of endpoints. We have a variety of endpoints that operate across the Pareto frontier. You can be fast with a little less accuracy, what you sacrifice when you get fast. You can be a little slower with higher degrees of accuracy. There's lots of parameters and enrichments in the API. So we have basically 10 or 15 different varieties of our APIs. And so when we work with these AI natives, we're trying to tune the parameters of the API to exactly what they want. When we go into the frontier labs, it's typically somebody that's solely focused on data quality and search. And so that would be the buyer there. In the consumer space, it's typically a CTO, CIO, that type of person within the organization that's thinking about broader strategy. And then individual developers, it's just exactly that. But I think with the enterprise, the first thing that people often forget is human interaction matters. So we've tuned our team to have a sales organization that interfaces directly with the customer, that can work through their process, that understands how they want to buy, what their biggest pain points are, what type of commercials they're interested in, how their process works in general, just kind of quarterbacks everything. We've got a technical team of engineers that can interface directly with their engineers, hold their hand, help them with evaluations, help them tune and parameterize the endpoint. they're all technically capable. And in theory, we can just hand them the endpoint and go away. But that's not an enterprise relationship that you're trying to build. You're trying to solve business outcomes for somebody. So it's really those two teams. And then the last is we have executive sponsorship. I want to make sure that any enterprise has our commitment on reliability, uptime, zero data retention, these things that really matter. It's part of the reason why they come and choose us. They know we process 1 billion queries a month. They know we have zero data retention. They know we have a host of enterprise customers already. We understand how to solve their needs, service them. But you also want that executive commitment as well from the entire organization. So it's really those three facets of engagement.
Gemma Allen
>> Let's stay on zero data retention for a second because that's interesting, right? Especially in the world of enterprise, we hear a lot about context. About really getting inside the mind of your user, your agent. Is that a competitive advantage, do you think, over Claude for Enterprise? what are you seeing and hearing in the market, especially in the world of enterprise where there is a lot of concern about what could be leaking out there, right? About you and your company and how that information is protected. People don't want models training on their data, for example. What are you seeing and hearing? Give me the competitive sense on that.
Alex Triplett
>> Yes, it's a big, big issue. And zero data retention has gotten a lot of traction for us. And so the first thing is on zero data retention, when you send us a prompt, we won't store it. When we send you an answer back, we won't store it. And that's important because, as you say, people do not want their data being used to train a model. They don't want somebody like us storing the data, to train our index or something like that. Right. So it's very, very important, particularly the more regulated you are in healthcare and banks, it's vital that you have this. The second, when you mentioned somebody like Claude or other model companies, a lot of enterprises we see are starting to just unbundle the stack. So 2 years ago or even last year, you'd say, doesn't the LLM have web search? I'll just use that. And then you realize, wait a minute, they're going to retain my data and they're going to make their models smarter on my data. And I don't know what is being sent, what my employees or my agents are prompting. And so I don't really want that. if I unbundle the stack, at least I can have ZDR on web search. So that's point number one. Point number two, when you unbundle the stack, is this really fun thing called choice. You can be multi-model. All of a sudden, you don't have to use one single provider or one single model, and you have all sorts of flexibility. And I think the OpenRouter announcement with Stripe is just a manifestation of that in the last 6 months, where people want that choice. It's highly important. And so when you unbundle the stack, you get ZDR across your web search layer, you get multi-model, and then all of a sudden you cut your TCO dramatically. And so I think the next era that we're going into is in this token optimization era. And token optimization could be I'm multi-model and I use a bunch of open weight models because guess what? They're just more efficient. Or I use the most efficient version of Claude or OpenAI because it saves me tokens, but also if I isolate web search and isolate the LLM, I can then start to squeeze costs on both. So we run all sorts of TCO calculations for our customers saying, look, here's the benefit with unbundling. A, ZDR. B, full control over the web search layer, parameterize it any way that you want. C, multi-model. And D, lots of TCO compression.
Gemma Allen
>> I mean, TCO, it's certainly very compelling, right? On the enterprise question, though, especially as it relates to open weight models. Yeah, it seems as though the mood on that is changing again, like the Irish weather, right? We hear all sorts of skepticism and enthusiasm day by day on what's happening in terms of whether or not folks want Kimi in enterprise, right? Yeah. But from your perspective, from the position that You.com is coming at this from, are you in some respects the kind of one throat to choke in that scenario? are you offering a level of protection and security, whether or not it's even true that there is this additional security need around open weight models or not is TBD, right? We have no real proof of that, but it's certainly a narrative that lives strong. Talk me through that a little bit.
Alex Triplett
>> Yeah, great, great questions. So firstly, definitely when you unbundle the stack and use us as a web search layer, if you want ZDR and protection on your web search queries, absolutely. We're the one throat to choke and happily so. I think we also can start to promote to the enterprise and we partner with a lot of the open weight firms, with the American open weights, Reflection's an amazing company. They're going to come on strong. Thinking Machines, also great company. They're going to come on strong.
Gemma Allen
>> Mira
Alex Triplett
>> Murati.Mira
Gemma Allen
>> Murati.Yeah, she's doing
Alex Triplett
>> it.She's making it happen. So I think those two come to mind immediately. I know NVIDIA is working on the Nemotron Coalition. That's going to have some interesting side effects. We're close to the Poolside folks, right? There's that relationship with NVIDIA that just happened earlier in the last couple of days. So there's a lot of the US firms that are thinking about this open weight. Initiative, but also with the Chinese models. You mentioned KIMI, Alibaba Qwen, MiniMax is another one. ByteDance with Seedance and others. We are completely happy to facilitate the ability for people to be multi-model with those open weights. Our belief is over time it's not really going to matter where the model is created. May the best model win. You're sending your data somewhere if you send a prompt to a model. That's for sure. And I'm not entirely sure that it's a negative thing to be sending it to Kimi K2 versus sending it to Claude Sonnet. I'm just not entirely sure. We internally, we're users of Kimi, we're users of Qwen, we're happy, we're users of Claude, users of OpenAI. We're super happy with all those. So we like the choice. We like the differentiation between different tasks. We certainly like the price of the open weights, but we do encourage our enterprises, how can we facilitate A, your web search layer is secure, so that's nice. And then B, how can we facilitate, you know, TCO compression? And open weights is the way to do that. But there's also different ways you can do this. let's say your core jobs, some core data is on a closed weight model where it's more segregated. And then your adjacent jobs, your tertiary jobs, your long tail jobs could be on the open weights where you're a little bit more relaxed on where that data goes. There's all sorts of ways within an enterprise to figure this out.
Gemma Allen
>> I want to talk a little bit about You.com, the commercial side, right? The kind of growth journey you guys are on. Break it down for me. I know it's valued over $1 billion plus. Maybe you have— give me the exact numbers and talk a little bit about, how you guys are thinking about things, especially from the perspective of M&A. You're an M&A guy by trade, right? that is your wheelhouse. How are you thinking about this point in the market? It's an interesting time. Do you think we're going to see a lot of convergence Where is your head at with this, Alex, considering your background?
Alex Triplett
>> You're leading the witness here and it's appropriate. So we were valued at $1.5 billion in October of last year and it felt a little early because we had some peers raise at higher valuations recently. So kudos. But it just shows that this is a really, really interesting space in the market to be in. For sure, we need consolidation. I think in any space and we've seen this in software as a service and every— whether you're horizontal, whether you're vertical, I saw it in fintech in spades, particularly in capital markets where, equities and FX and fixed income and all these places we land grab and we consolidate. At ION, we were consolidators at my last company, Appfire. And certainly part of the reason I came to You.com was thinking about the inevitability of where all of these markets end up going, which is after you get the understanding and the adoption of a market and after players are established, ultimately you will go into a consolidation phase. And I think we're going to be a really good home eventually to consolidate many, many of the businesses that are out there in our space. And I think about either core providers that are doing something very similar to us. And so there it's just maybe reinforcing best of breed, maybe it's reinforcing bigger customer base. And so in scale, because scale matters and scale wins, but then also adjacent to core web search, what are other things that can be consolidated? Interesting structured data assets. People that are doing something that is a little bit on the edge of maybe core web search, but is ultimately delivering information or an answer from generally publicly available sources could be a consolidation target. So we have our list, we have active discussions, and we'll see.
Gemma Allen
>> Last question. In this market, it is a speed market, right? It is act now, think later in some respects. At least that's how it certainly feels watching it every day unfold. What do you think people are truly scouting for here? Is it product? Is it enterprise? Tom, where do you think the real acquisition-heavy conversations are truly honing in on?
Alex Triplett
>> Hmm. Where people could be most successful?
Gemma Allen
>> Hmm.
Alex Triplett
>> At this juncture of the market, in this phase, and I see this in our space, I see this in the agent layer, I see this in the inference layer. So just above us, and below us, it is about messaging and speed to market right now. It is not necessarily about the absolute nth degree of product quality. Most of the providers in those spaces I named are generally pretty good, and all of us benchmark against each other. All the inference providers benchmark against each other, all the web search providers benchmark against each other. All the agent layer providers are trying to come up with benchmarks to benchmark against each other, which is refreshing. Unlike in software where you kind of sort of think somebody's better, we can all benchmark and we can see quite literally who is better. And so that's useful. But people aren't necessarily optimizing for end-state IP and product quality. They're optimizing for marketing message and speed to market, which I think is appropriate. Right. We are in a race. It is invigorating every single day to wake up and know that there's heavy competition. You have to move fast. It's really exciting. But I think that's the short term. And so you've got to win that short-term battle. Long term, though, great IP always wins. And so I do see some providers out there that if they aren't systematically building real IP under the hood, they may short term win a marketing race, but long term they will lose the ultimate battle because the IP needs to follow. And so we're trying to do both at pace. But I think this phase is like the marketing and land grab. Phase.
Gemma Allen
>> So to summarize, I'm going to say folks need to remember that it is both a marathon and a sprint.
Alex Triplett
>> Absolutely.
Gemma Allen
>> Alex Triplett, thank you so much for joining us on NYSE Wired.
Alex Triplett
>> Absolutely. Pleasure. Thank you.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired, a Mixture of Experts. Thanks for watching.
>> Palo Alto Studio, connecting Silicon Valley and Wall Street.
Gemma Allen
>> I'm John Furrier, the host of theCUBE here with Gabe Olave, my co-host. Welcome to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired. And today we are talking about mixture of experts. Joining me now is Alex Triplett, CEO of You.com. Alex, great to have you.
Alex Triplett
>> Gemma, thank you so much for having me.
Gemma Allen
>> So I want to get into You.com and the business and everything that's happening in the industry. But first, I want to talk a little bit about you. You've had an interesting career. You have been an investment banker, a long-term M&A scout, I guess I could say, right, at a couple of firms. Now you're in this very interesting point in the world of AI infrastructure. Tell me about, first of all, the decision to join You.com. What part of your M&A brain thought, yep, this is a great move for me?
Alex Triplett
>> Oh, it's a great question. Yeah, I did have an interesting journey. So starting in banking, went to private equity, had this realization in 2010, I can be a better investor if I get a little bit of operating experience. 16 years later, that's all I've been doing. And so when I first went to ION Group, fintech company, part of my job was M&A, raising capital, integrating acquisitions. Part of my job was operating business lines that once we acquired them, needed somebody to go run them. And so I did both of those jobs and did that for 12 years. We grew the business quite a bit, $150 million in revenue to $3 billion, 600 people to 12,000 in 12 years. So it was quite a run. Saw a lot of phases of growth of a company. I spent 3 years as CFO, COO at Appfire, so small application provider around the software development lifecycle. And that was fantastic. Grew that from $100 million of revenue to $300 million. When I was thinking last summer about what to do next, and so to your question, why You.com, what part of my brain was stimulated? The first thing I was thinking about was, given my experience, where can I make an outsized impact on a business that is ripe for change. Growth needs a catalyst. So that was first. Second, I wanted to participate in AI. How do you do that? Can you do that as an investor or can you do that as an operator? And I felt like being an operator within AI, you get closer to everything. You get closer to your suppliers, your customers. And for me, the decision on You.com was really centered around two things: our founder and our position. So founder first, Richard Socher. He has been doing this for 15, 16, 17 years, when I say this, it's been— he was writing papers in 2009, 2010, 2011 at Stanford contemplating hooking up neural networks with natural language processing, thinking about some of the things that ultimately became large language models as we know them today. Heavily cited researcher, started a business, chief scientist at Salesforce, did all these things. When he spun out of Salesforce in 2021 to start You.com, he was the first to take an LLM and hook it to the web because LLMs' training data ends. It needs accurate, fresh information from the web to make answers for anybody that's prompting it. And when I looked at his track record and his tenure and the fact that he's been doing this, he's got a North Star, he's a visionary, I thought aligning myself with somebody like him was probably a good move. And then the second thing to the point of what do we do? We're in this interesting little niche, which I can talk about later. Where we supplement and complement the LLM and we sit below the agent layer. And I felt like that was a really good space to gravitate towards because it's underappreciated. It's very, very important and less competitive.
Gemma Allen
>> Let's talk about that niche for a second. The decision to really hone in on a niche, which has been the You.com journey, right? You mentioned that this started as a— I guess you could compare it to maybe a Perplexity type of model, but it was too early for the market at that time. Now we have a lot of companies coming on the show and they're saying, we're told we're a little bit early. We don't want to be a fast follower. Now we want to be a very niche player. How would you describe You.com's journey and You.com's market message at this moment? Because it has shifted somewhat, right? Yep. Yeah.
Alex Triplett
>> So we are Google for AI. We are agentic web search. We provide web search APIs for anybody to search the web. That's an LLM, that's an agent, that's a large consumer business, that's a portal, whatever. So we do that, right? That's the market message. That's all we do today. But you're right, we had an interesting journey to get here. When Richard started the business in '21, you're absolutely right. We started life as basically a chatbot, like Perplexity, like OpenAI was when it started. We had an interface, we had LLMs in the background, we had web search hooked up with that LLM, and we could provide great answers for people, typically consumers. Two things happen in the consumer space. One, you need an incredible scale through marketing dollars or incredible scale through compute dollars. And others got there first. And obviously OpenAI has been phenomenal in that space and so has Perplexity. And so what we had was we had this amazing search index. The search engine was underpinning the chat and all of a sudden enterprises started to pull us and say, wait a minute, do you have an API? I don't want the interface. I don't want any of that stuff. I just want the API because I want to ground my agents or my model to the web. And so we get pulled into the enterprise space, pulled into the Web Search API space. And this is kind of late 2024, early 2025. And eureka, we discover this is an amazing market. There's this huge need. Enterprise is a great space to be in. It's underserved. Most people right now cannot be served by Google and Bing, the traditional search engines. We are next. We are it. And so that's what we've been doing ever since.
Gemma Allen
>> Let's talk about that need for a second because this is essentially tailored endpoints, right? It is about making sure that you get the best possible answer that's available to you on the web. You have an interesting profile of customer though, because on one end, I'm guessing here, it's an assumption, but you are, I'm sure, serving many enterprise customers. You're also serving some of the frontier models. Break that down. Are you a competitor in one space and a collaborator in another? profile the customer base for me here.
Alex Triplett
>> Yeah, absolutely. Astute observation. Enterprise customers and frontier models. So I think about our customer set as basically being 4 categories. So category number 1, agent-native businesses. This could be Harvey, which is a great customer for us, right? The ubiquitous legal agent. Could be Factory, coding Droids, amazing company. Could be somebody like— I would put Salesforce in that category with Agentforce Slackbot. We serve them as well. So kind of agent-native businesses. Second would be the Frontier Labs. So there we could serve them at inference because every Frontier model needs web search. All of them have it. Sometimes they use us, sometimes they use other people, but all of them need web search at inference when they have a prompt or make a call. So we can serve frontier models. We also can help them with pre or post-training as well. The third would be any consumer type of business. Typically it's an agent application. So think Alibaba.com. They have an AI mode. that's summarizing specs around a product. We serve them, we serve Amazon to discern trends out in the marketplace that then will inform how they position product in their marketplace. So that can be consumer-based businesses. And then the last is developers. Anybody that's a developer that wants to build, that goes on LangChain or Replit or OpenClaw or Hermes or whatever it might be, MindStudio, whatever the application is, and they want to just build and they want to take an API and plug it into the agent that they're building, that could be us.
Gemma Allen
>> I mean, GitHub is certainly a minefield, so I can certainly see the value in that. I want to talk about enterprise, though, because we talk to a lot of folks who come on the show and talk about nailing enterprise, right? The enterprise win is the big bet of this next era. Yeah. You're not a RAG, though, right? You guys are very much about ensuring that what is broadly available is returned in the most succinct and I guess accurate fashion possible.
Alex Triplett
>> Yeah.
Gemma Allen
>> What sorts of tie-ins do you have within enterprise? Are you working with HR departments, finance departments? And I want to talk a little bit about the competitive side of that. But first, I want to understand, tell me how the product is being used. what niche are you building on?
Alex Triplett
>> Yeah, it's a great point on how to crack the enterprise. So typically the conversation, if you're in one of those AI-native businesses, there's an engineering team that probably understands search or has somebody focused on search. And so we will cater to them directly. And there the buying motion is highly technical. it's very specific, they know exactly what they want. And so our positioning is, we call ourselves a search lab. You mentioned lots of endpoints. We have a variety of endpoints that operate across the Pareto frontier. You can be fast with a little less accuracy, what you sacrifice when you get fast. You can be a little slower with higher degrees of accuracy. There's lots of parameters and enrichments in the API. So we have basically 10 or 15 different varieties of our APIs. And so when we work with these AI natives, we're trying to tune the parameters of the API to exactly what they want. When we go into the frontier labs, it's typically somebody that's solely focused on data quality and search. And so that would be the buyer there. In the consumer space, it's typically a CTO, CIO, that type of person within the organization that's thinking about broader strategy. And then individual developers, it's just exactly that. But I think with the enterprise, the first thing that people often forget is human interaction matters. So we've tuned our team to have a sales organization that interfaces directly with the customer, that can work through their process, that understands how they want to buy, what their biggest pain points are, what type of commercials they're interested in, how their process works in general, just kind of quarterbacks everything. We've got a technical team of engineers that can interface directly with their engineers, hold their hand, help them with evaluations, help them tune and parameterize the endpoint. they're all technically capable. And in theory, we can just hand them the endpoint and go away. But that's not an enterprise relationship that you're trying to build. You're trying to solve business outcomes for somebody. So it's really those two teams. And then the last is we have executive sponsorship. I want to make sure that any enterprise has our commitment on reliability, uptime, zero data retention, these things that really matter. It's part of the reason why they come and choose us. They know we process 1 billion queries a month. They know we have zero data retention. They know we have a host of enterprise customers already. We understand how to solve their needs, service them. But you also want that executive commitment as well from the entire organization. So it's really those three facets of engagement.
Gemma Allen
>> Let's stay on zero data retention for a second because that's interesting, right? Especially in the world of enterprise, we hear a lot about context. About really getting inside the mind of your user, your agent. Is that a competitive advantage, do you think, over Claude for Enterprise? what are you seeing and hearing in the market, especially in the world of enterprise where there is a lot of concern about what could be leaking out there, right? About you and your company and how that information is protected. People don't want models training on their data, for example. What are you seeing and hearing? Give me the competitive sense on that.
Alex Triplett
>> Yes, it's a big, big issue. And zero data retention has gotten a lot of traction for us. And so the first thing is on zero data retention, when you send us a prompt, we won't store it. When we send you an answer back, we won't store it. And that's important because, as you say, people do not want their data being used to train a model. They don't want somebody like us storing the data, to train our index or something like that. Right. So it's very, very important, particularly the more regulated you are in healthcare and banks, it's vital that you have this. The second, when you mentioned somebody like Claude or other model companies, a lot of enterprises we see are starting to just unbundle the stack. So 2 years ago or even last year, you'd say, doesn't the LLM have web search? I'll just use that. And then you realize, wait a minute, they're going to retain my data and they're going to make their models smarter on my data. And I don't know what is being sent, what my employees or my agents are prompting. And so I don't really want that. if I unbundle the stack, at least I can have ZDR on web search. So that's point number one. Point number two, when you unbundle the stack, is this really fun thing called choice. You can be multi-model. All of a sudden, you don't have to use one single provider or one single model, and you have all sorts of flexibility. And I think the OpenRouter announcement with Stripe is just a manifestation of that in the last 6 months, where people want that choice. It's highly important. And so when you unbundle the stack, you get ZDR across your web search layer, you get multi-model, and then all of a sudden you cut your TCO dramatically. And so I think the next era that we're going into is in this token optimization era. And token optimization could be I'm multi-model and I use a bunch of open weight models because guess what? They're just more efficient. Or I use the most efficient version of Claude or OpenAI because it saves me tokens, but also if I isolate web search and isolate the LLM, I can then start to squeeze costs on both. So we run all sorts of TCO calculations for our customers saying, look, here's the benefit with unbundling. A, ZDR. B, full control over the web search layer, parameterize it any way that you want. C, multi-model. And D, lots of TCO compression.
Gemma Allen
>> I mean, TCO, it's certainly very compelling, right? On the enterprise question, though, especially as it relates to open weight models. Yeah, it seems as though the mood on that is changing again, like the Irish weather, right? We hear all sorts of skepticism and enthusiasm day by day on what's happening in terms of whether or not folks want Kimi in enterprise, right? Yeah. But from your perspective, from the position that You.com is coming at this from, are you in some respects the kind of one throat to choke in that scenario? are you offering a level of protection and security, whether or not it's even true that there is this additional security need around open weight models or not is TBD, right? We have no real proof of that, but it's certainly a narrative that lives strong. Talk me through that a little bit.
Alex Triplett
>> Yeah, great, great questions. So firstly, definitely when you unbundle the stack and use us as a web search layer, if you want ZDR and protection on your web search queries, absolutely. We're the one throat to choke and happily so. I think we also can start to promote to the enterprise and we partner with a lot of the open weight firms, with the American open weights, Reflection's an amazing company. They're going to come on strong. Thinking Machines, also great company. They're going to come on strong.
Gemma Allen
>> Mira
Alex Triplett
>> Murati.Mira
Gemma Allen
>> Murati.Yeah, she's doing
Alex Triplett
>> it.She's making it happen. So I think those two come to mind immediately. I know NVIDIA is working on the Nemotron Coalition. That's going to have some interesting side effects. We're close to the Poolside folks, right? There's that relationship with NVIDIA that just happened earlier in the last couple of days. So there's a lot of the US firms that are thinking about this open weight. Initiative, but also with the Chinese models. You mentioned KIMI, Alibaba Qwen, MiniMax is another one. ByteDance with Seedance and others. We are completely happy to facilitate the ability for people to be multi-model with those open weights. Our belief is over time it's not really going to matter where the model is created. May the best model win. You're sending your data somewhere if you send a prompt to a model. That's for sure. And I'm not entirely sure that it's a negative thing to be sending it to Kimi K2 versus sending it to Claude Sonnet. I'm just not entirely sure. We internally, we're users of Kimi, we're users of Qwen, we're happy, we're users of Claude, users of OpenAI. We're super happy with all those. So we like the choice. We like the differentiation between different tasks. We certainly like the price of the open weights, but we do encourage our enterprises, how can we facilitate A, your web search layer is secure, so that's nice. And then B, how can we facilitate, you know, TCO compression? And open weights is the way to do that. But there's also different ways you can do this. let's say your core jobs, some core data is on a closed weight model where it's more segregated. And then your adjacent jobs, your tertiary jobs, your long tail jobs could be on the open weights where you're a little bit more relaxed on where that data goes. There's all sorts of ways within an enterprise to figure this out.
Gemma Allen
>> I want to talk a little bit about You.com, the commercial side, right? The kind of growth journey you guys are on. Break it down for me. I know it's valued over $1 billion plus. Maybe you have— give me the exact numbers and talk a little bit about, how you guys are thinking about things, especially from the perspective of M&A. You're an M&A guy by trade, right? that is your wheelhouse. How are you thinking about this point in the market? It's an interesting time. Do you think we're going to see a lot of convergence Where is your head at with this, Alex, considering your background?
Alex Triplett
>> You're leading the witness here and it's appropriate. So we were valued at $1.5 billion in October of last year and it felt a little early because we had some peers raise at higher valuations recently. So kudos. But it just shows that this is a really, really interesting space in the market to be in. For sure, we need consolidation. I think in any space and we've seen this in software as a service and every— whether you're horizontal, whether you're vertical, I saw it in fintech in spades, particularly in capital markets where, equities and FX and fixed income and all these places we land grab and we consolidate. At ION, we were consolidators at my last company, Appfire. And certainly part of the reason I came to You.com was thinking about the inevitability of where all of these markets end up going, which is after you get the understanding and the adoption of a market and after players are established, ultimately you will go into a consolidation phase. And I think we're going to be a really good home eventually to consolidate many, many of the businesses that are out there in our space. And I think about either core providers that are doing something very similar to us. And so there it's just maybe reinforcing best of breed, maybe it's reinforcing bigger customer base. And so in scale, because scale matters and scale wins, but then also adjacent to core web search, what are other things that can be consolidated? Interesting structured data assets. People that are doing something that is a little bit on the edge of maybe core web search, but is ultimately delivering information or an answer from generally publicly available sources could be a consolidation target. So we have our list, we have active discussions, and we'll see.
Gemma Allen
>> Last question. In this market, it is a speed market, right? It is act now, think later in some respects. At least that's how it certainly feels watching it every day unfold. What do you think people are truly scouting for here? Is it product? Is it enterprise? Tom, where do you think the real acquisition-heavy conversations are truly honing in on?
Alex Triplett
>> Hmm. Where people could be most successful?
Gemma Allen
>> Hmm.
Alex Triplett
>> At this juncture of the market, in this phase, and I see this in our space, I see this in the agent layer, I see this in the inference layer. So just above us, and below us, it is about messaging and speed to market right now. It is not necessarily about the absolute nth degree of product quality. Most of the providers in those spaces I named are generally pretty good, and all of us benchmark against each other. All the inference providers benchmark against each other, all the web search providers benchmark against each other. All the agent layer providers are trying to come up with benchmarks to benchmark against each other, which is refreshing. Unlike in software where you kind of sort of think somebody's better, we can all benchmark and we can see quite literally who is better. And so that's useful. But people aren't necessarily optimizing for end-state IP and product quality. They're optimizing for marketing message and speed to market, which I think is appropriate. Right. We are in a race. It is invigorating every single day to wake up and know that there's heavy competition. You have to move fast. It's really exciting. But I think that's the short term. And so you've got to win that short-term battle. Long term, though, great IP always wins. And so I do see some providers out there that if they aren't systematically building real IP under the hood, they may short term win a marketing race, but long term they will lose the ultimate battle because the IP needs to follow. And so we're trying to do both at pace. But I think this phase is like the marketing and land grab. Phase.
Gemma Allen
>> So to summarize, I'm going to say folks need to remember that it is both a marathon and a sprint.
Alex Triplett
>> Absolutely.
Gemma Allen
>> Alex Triplett, thank you so much for joining us on NYSE Wired.
Alex Triplett
>> Absolutely. Pleasure. Thank you.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired, a Mixture of Experts. Thanks for watching.
Alex Triplett
Gemma Allen