As the artificial intelligence landscape becomes increasingly dominated by massive, cloud-based models, some innovators are looking in the opposite direction. David Stout, founder of Web AI, is a leading voice in the movement to bring advanced AI directly onto everyday devices. In a recent conversation, Stout details his journey from a farm in Michigan to pioneering the infrastructure for offline AI, a technology that prioritizes privacy, efficiency, and user ownership. Valued at over $700 million, Web AI is challenging the status quo with its vision of a decentralized “web of models”—millions of specialized AI systems working together across phones, laptops, and other hardware. This approach not only keeps sensitive data secure but also unlocks real-time AI capabilities in environments where cloud connectivity is impossible or impractical, from aircraft maintenance to personal health monitoring. Stout’s perspective offers a compelling look at a more distributed, accessible, and secure future for artificial intelligence.
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Nataraj: To set the context, could you give us a brief overview of your journey in the field of AI? When did you start working in AI or machine learning, and what was your journey like before founding Web AI?
David Stout: My background, as you mentioned, I grew up on a farm. When I was studying it, AI was very much vapor; machine learning was the actual field of study. NLP was progressing, but it was very early, even in regards to convolutional neural nets. I think this is important because my research started in a very much yet-to-be-defined space that was incredibly esoteric. There was no LLM to help you research; there were no AI tools. This was very much first-principles design.
We were looking at ways to bring convolutional networks like Darknet and YOLO to low-energy devices. At the time, these object detection or computer vision models were some of the most sophisticated and heaviest in terms of compute. They showed the most promise, in my opinion, of being truly disruptive. Having visual intelligence in spaces was going to be incredibly powerful. My research started there, and I was able to bring some of the best computer vision, object detection, and masking models to devices like iPhones and their Bionic chips.
Nataraj: And this was through your research at Stanford or at Ghost AI?
David Stout: This was through Ghost AI at the time, right around when I dropped out of school and started pursuing this full-time. We were bringing Darknet models to an iPhone. This got the attention of a lot of outside investors and technologists because it was the first of its kind. There was no TensorFlow Lite or PyTorch Lite tools that were bringing AI frameworks to devices. We wrote the whole thing from scratch, talking directly to shaders and primitives using the MPS framework on these devices. What we found, as in any moonshot, is you discover other things along the way. We realized that to bring these models to devices, we were discovering incredible compression and architecture techniques. This ultimately led to WebFrame today, which is our own in-house AI library and framework. Those early days mattered because it shaped what we ended up building. We had this desire to run models at the edge because, in computer vision specifically, if you didn’t have real-time AI processing, it was a null use case. Computer vision in the cloud is not super interesting. That’s where we started to really understand the value of AI at the edge.
Nataraj: What applications that we see today in the wild are a result of these efforts?
David Stout: The research of getting models to devices is continuing to play out; it’s not done by any means. A lot of these examples are still referencing a cloud model. Not a lot is happening on the device still. But yes, you are seeing basic object detection. A good example would be Photos on an iPhone. That’s running on-device, and you’re able to search and query basic object states or titles or names and index things. There are also modes on the iPhone in the magnifier that let you detect objects you’re looking at, and the audio kit will turn on. If you have a vision impairment, the object detector in real time will talk to you and tell you what’s in front of you. I think those are examples of some of that early work in the industry, but there’s still been a tremendous amount of focus on cloud AI. We’re seeing a lot more now in the private sector with people we’re working with where it’s multimodal, which we think is the ultimate paradigm.
Nataraj: You were working on compressing models onto devices, and in 2019 you started Web AI. What was the thesis for Web AI, and how has it changed over time?
David Stout: I started the company on three pillars. I’m a simple thinker when it comes to business and strategy; I wanted to know the utility value. I thought this cloud arbitrage is not going to work. This idea of big data and cloud compute is going to flip the whole cost structure upside down and is not super promising for AI in regards to individual ownership. It felt like we were going to copy the internet era and reproduce all the mistakes we made there. The thesis for Web AI, when we founded it in January of 2020, was: if we could bring AI to devices and run it privately in a way that a user or enterprise owns it, would people pay for that? Would people want that? If you could serve AI on a device and bring world-class intelligence and put it in someone’s pocket, is that valuable? The simple answer is yes, it’s worth pursuing.
The second question is what kind of use cases could you unlock that the alternative would be unable to do? A simple way to look at this is you have companies with IP-centric data that they can’t share with a foundational model. You have companies with regulated data they can’t share. And you have use cases that require real-time, no-latency decision-making that can’t go up to the cloud. These problems require an AI solution that lives in the environment, that they can directly engage with, and that’s state-of-the-art. That’s really the problem we were solving.
Nataraj: General audiences often think in terms of large models, especially post-ChatGPT. But before that era, it was all specialized models. When you identified these factors, what was your initial approach to productizing this? How did you focus, because the field is so wide?
David Stout: It is very wide. Actually, our strategy was the inverse. We said we need to be as horizontal as possible. We need to own the tooling, the methodologies, the frameworks, the communications. We don’t need to own the model. We want to be the pickax and shovel of an industry rather than be the best medical model company, and that’s all we do. The reason for that, and I think it’s played out quite like we thought it would, is that so many VCs told me, ‘You guys have great technology, you should just focus on one industry.’ We disagreed for the fundamental reason that we’re seeing now: if you’re not horizontal as a tech stack, you’ll get steamrolled by these incredibly smart, powerful foundational model companies. If you’re building an app focused on coding, I still think you’re at great risk of just getting steamrolled. I just don’t see how those companies have long-term staying power when the model that they rely so heavily on is not theirs. We decided to focus on the tools that made the models great, the way to retool these models so they could run anywhere, the connective tissue that lets the model talk to another model, to a device, to a person. And we will enable our customers to interact with their data with these models and make them better. That’s our staying power. We support everything from vision models to multimodal models across the ecosystem, with the idea that the platform is designed to be horizontal and not a point solution.
Nataraj: What type of customers use your product today? Can you give a couple of use cases to crystallize where Web AI plays a role?
David Stout: We work in industries where there’s highly contextual data that is not on the internet. It’s not on Reddit, whether it’s working on an airplane engine or with individual personal health data. It’s data that does not exist on the web that needs to be navigated, trained on, and personalized for each of these users to drive real results. We work with the Oura Ring, if you’re familiar with them. Additionally, we are working with major airline companies and aircraft manufacturers to improve maintenance as well as assembly. And outside of that, we are working with the public sector on all sorts of use cases that require AI to work anywhere, not just in a data center stateside. The ubiquitous connective tissue in all of our customers is they have data that no one else has. They operate in a privacy-mission-critical environment where data cannot go somewhere else, it needs to be highly accurate, highly performative, and it needs to operate at the edge.
Nataraj: I haven’t seen a lightweight personal model that exists on my machine yet. Is that model not possible, or why haven’t we seen that kind of experiment from any company?
David Stout: I think we haven’t seen it because the models that are easy to ship to devices are bad. People have become accustomed to a certain performance and intelligence capability. Web AI is actually in October releasing what you’re describing. We’re releasing our first-ever B2C solution, which is that: download it, run it on your machine, run it on your phone. Why it’s taken us time is we had to make some architecture changes so we have a great model that’s performative, that’s not disappointing, and that runs and lives on your phone. That’s a hard problem to solve. It’s always been easier to just have the cloud do it. I think a lot of companies are hitting the easy button on this one and just using the cloud. It works from a functioning perspective, it absolutely works; it’s just astronomically expensive and inefficient. The AI companies that are popular today are really focused on trying to solve the super-intelligence problem rather than solving the actual unit economics, monetization, and privacy problems. These tools will be valuable for users because now they have something that’s private that they own. It lives on their device, it’s personalized, and it’s ultimately safe.
Nataraj: There’s so much spending going on in data centers, rationalized by the argument that this will lead to something that looks like AGI. What are your thoughts on the trajectory of these foundational model companies and AGI?
David Stout: If we’re fairly pragmatic about what we’ve seen, there’s this common consensus that it will keep scaling, models will get better, and we’re going to steamroll everyone with the best model. My problem with that is the empirical evidence we have right now doesn’t say that. Pre-training, in all senses, is pretty much proven to be flattening. GPT-5 is an MoE, a model router. It’s a lot of post-model work. Most of the gains we’re seeing are post-training. For the last several iterations of these models, the majority of the advancement is happening post-training, which would indicate that we are hitting a plateau on this idea of training continuing to scale. I think we’re tremendously overbuilding. We have an energy problem, a water problem. It’s so early. I’m not a big believer in the long tail of the transformer architecture. To build all these data centers when we don’t even know the architecture… it’s questionable. For me, what makes the most sense is this idea that civilization is the only example of super-intelligence. You have groups of people with different contexts, talents, and abilities that build incredible things. We don’t have any example of singular super-intelligence. What I would say is much more likely is we see super-intelligence come out of millions and billions of contextual models that are living across the world as a compute dust that’s everywhere. That statement is far less risky than the one we’re talking about in parallel, which is, ‘I’m going to figure out a way to train this one model, it’s going to solve everything, and it’s going to be AGI.’ The civilization approach is not only theoretically accurate, but nature and science have demonstrated it to be true.
Nataraj: I was watching your talk where you had this very interesting line: ‘Prompts don’t pay bills.’ Can you elaborate on that?
David Stout: These companies have created bad habits. Prompting is horrible for their business model. They need to be proactive; they want to get prompting out of their business. Every question costs them money. It’s not the same model as internet companies, where a user coming to your website is a dollar sign. With OpenAI, when you log in and ask a question, you’re cutting into their profits. That’s a challenging business to be in. The philosophy of ‘prompts don’t pay the bills’ is about how we create AI interactions that are precognitive, working on behalf of the user so the user doesn’t have to ask another question. This supports the distributed model architecture as well. When you create an AI application on a foundational model, you use a system prompt to tell the model how to behave. Fundamentally, you’re telling the model to be smaller. You’re saying, ‘Be a doctor, answer this way, don’t talk about race cars.’ What Web AI would say is you just want a doctor model. And the doctor model is going to be far better than a system prompt model pretending to be a doctor. That’s how you get to super-intelligence: you have millions of models that are category-leading. They aren’t prompted to behave a certain way; they just *are* a certain way. This is why the internet beat mainframes.
Nataraj: What do you think about what XAI is doing? I feel like they are result-maxing for the leaderboards, but I don’t see XAI being used much in real applications.
David Stout: I think everyone trains towards leaderboards. You’ve seen the party games where people wear a sign on their head and have to guess who they are. AI is doing the same thing with a benchmark. When you train around a benchmark, you eventually realize what the benchmark is. That’s all that’s happening. A really interesting example we saw personally: we trained on open-source F-18 data for the Navy and ran a retrieval task against it. We got about 85-90% accuracy on a really complex maintenance manual. We did the same exercise with GPT-5, and it was 15% less accurate than our Web AI system. What was interesting is on the open QA benchmark, OpenAI was only seven points lower than us. So on the leaderboard, it seemed like we were far closer in performance, but in practical application, the delta is always a little bit bigger. I think the leaderboard is a little irrelevant to what’s actually happening.
Nataraj: We are almost at the end of our conversation. Where can our audience find you and learn more about Web AI?
David Stout: I’m on Twitter, @DavidStout. We’ve got a lot of new announcements coming out. We just released two new, really significant papers. We’ll be sharing more in our fall release, with several new products that will be available for users the day of the announcement. You can get more information on our website and on social media. I’m really thankful for the opportunity to come on and talk and learn from you.
David Stout’s insights offer a compelling vision for a future where AI is not a monolithic entity in the cloud, but a distributed, personalized, and private tool running on our own devices. This conversation highlights the practical and philosophical shift towards an accessible and secure AI ecosystem.
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