For most of the internet era, the bargain for experts and creators was relatively straightforward: publish your knowledge, build an audience and hope that distribution eventually leads people back to you.
AI has fundamentally changed that equation.
Today, an expert’s work can become part of an AI-generated answer without necessarily identifying the source, compensating the creator or accurately reflecting that person’s point of view.
Onix wants to build a different model.
On this episode of The Media Copilot podcast, Pete Pachal speaks with Onix co-founders David Bennahum and Nicholas Nadeau about their vision for expert-owned AI: specialized models built around an individual expert’s knowledge, voice, judgment and perspective rather than a massive model trained to know a little about everything.
Bennahum and Nadeau argue that expertise is more than factual information. It includes the judgment and perspective an expert develops over a lifetime, including knowledge that may never have appeared online. Onix combines publicly available work with what the company calls “dark data,” including private notes, manuscripts and other material that can capture how an expert actually thinks.
“𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗶𝘀 𝗳𝗮𝗰𝘁𝘂𝗮𝗹 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗽𝗹𝘂𝘀 𝗮 𝗽𝗼𝗶𝗻𝘁 𝗼𝗳 𝘃𝗶𝗲𝘄, 𝗮 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗱 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁.”
— Nicholas Nadeau, Ph.D., P.Eng., Co-Founder & CTO, Onix
The conversation goes well beyond the technology.
Pete digs into authorship and intellectual property in the AI era, data sovereignty and privacy, the economics of expert knowledge, small language models versus frontier models, hallucinations and whether creators can build sustainable businesses around AI versions of their own expertise.
Bennahum describes the opportunity as creating an “authorized version” of an expert in AI, rather than relying on what he calls the “bootleg” version assembled by general-purpose models from information found across the internet.
“𝗧𝗮𝗸𝗲 𝗯𝗮𝗰𝗸 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗴𝗲𝗻𝗶𝘂𝘀.”
— David S. Bennahum, Co-Founder & CEO, Onix
The implications could extend far beyond Onix’s initial focus on health and wellness. The company envisions eventually expanding into areas including personal finance, self-improvement, relationships and lifestyle expertise, with a model that could ultimately allow more creators to build, privately share and monetize their own Onix.
Pete also asks the larger question hanging over the creator economy:
If AI is going to generate enormous value from human knowledge, who should own that value?
For Onix, success would mean that the economic benefits of AI are distributed among the experts whose knowledge makes these systems valuable, rather than concentrated among a handful of technology companies.
It’s a conversation about AI, but ultimately it’s about authorship, ownership and what human expertise is worth in a world where knowledge can suddenly scale.
In this episode
Pete, David and Nicholas discuss:
- Why Onix is betting on small, domain-specific AI models
- The difference between factual knowledge and true expert judgment
- Why AI’s current approach to authorship and intellectual property may be unsustainable
- Giving experts ownership and control over their AI models
- The role of private or unpublished “dark data” in creating higher-fidelity AI
- Why privacy and data sovereignty are central to the Onix model
- Whether specialized models can reduce hallucinations
- Turning an expert’s knowledge into a subscription business
- The potential for journalists, creators and other professionals to build their own AI products
- What a healthier “author economy” could look like five years from now
Onix is currently in early access. Media Copilot listeners can visit Onix.life and use the access code COPILOT to try the platform.
🔗 About the 👤 Guests
David S. Bennahum | Co-Founder & CEO, Onix. | https://www.linkedin.com/in/dbennahum
Nicholas Nadeau, Ph.D., P.Eng. | Co-Founder & CTO, Onix.| https://ca.linkedin.com/in/engnadeau
About the show: To explore more conversations like this and see what’s new, visit the Media Copilot website at mediacopilot.ai. You’ll find new episodes, expanded resources, and tools designed for journalists, communicators, and media leaders navigating the fast-changing world of AI. It’s the home base for everything Media Copilot and it’s just getting started.
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Subscribe to The Media Copilot on Substack, Apple Podcasts, Spotify, or your favorite app. On YouTube? Tap the Like button and Subscribe to the YouTube channel. For more AI tools and resources built for media professionals, visit mediacopilot.ai.
Produced by Pete Pachal and Executive Producer Michele Musso
Edited by the Musso Media Team
Music: “Favorite” by Alexander Nakarada, licensed under CC BY 4.0
All rights reserved. © AnyWho Media 2026
TRANSCRIPT
Guest interview with David S. Bennahum and Nicholas Nadeau, Ph.D., P.Eng. | Onix
[00:40] Pete Pachal
Hi, welcome to the Media Copilot. It’s a podcast about how AI is changing media, news, and communication. I’m your host, Pete Paschel. I covered tech for a long time as a journalist, and now I talk with the media leaders, the builders, and the creators, all trying to answer the question: how will we get information in the future? And how will that transform journalism and the business of media?
For most of the internet’s history, experts had a fairly simple deal. You published your knowledge. Platforms help distribute it, and hopefully some of the audience found its way back to you. AI has changed that arrangement. An expert’s work can now become raw material for an answer that may or may not identify the source, compensate them, or even accurately represent the work in the first place.
A company called Onix is proposing a different model. It builds a dedicated AI around one expert’s body of work, including material that may never have appeared online. Users can subscribe to that system, ask questions, and receive personalized guidance based on that expert’s knowledge and their stated point of view.
My guests this week are the co-founders of Onix, David Benham and Nicholas Nadeau. David started his career as one of the original writers at Wired and has spent a lot of time working at the intersection of media and technology. Nicholas is a PhD in human robot interaction, previously served as the CTO of Humanoid Robotics Company One X. And has worked on the data infrastructure behind major AI systems.
What I find particularly interesting about Onix is what its model could mean beyond the health and wellness category that they’re starting with. Could a journalist, an analyst, a creator, or even a comms expert turn their knowledge into an AI product they can control? And then who owns that system when the expertise was might have been developed inside a company? And maybe most importantly, When an AI speaks with a person’s name and authority, who is responsible for what it says?
I’m excited to get into all that. but first, quick note. If you’re listening on Apple or Spotify, please leave a five star review and maybe a nice comment. and if you’re watching on YouTube, please like the video and subscribe to the channel. Those things really do help more people find the show.
[02:53] Pete Pachal
All right, let’s get started. David and Nick, welcome to the media copilot.
[02:58] Nicholas Nadeau
Thank you so much for having us.
[02:58] David S. Bennahum
Thanks for having us, Pete.
[03:00] Pete Pachal
Awesome. So let’s get started on Onix a little bit. so David, for for someone just hearing about Onix for the first time, what exactly is it? And as I sort of mentioned in the intro there, like what is the subscriber paying to receive? Give us a little more texture on that.
[03:18] David S. Bennahum
Sure. So think of it as a platform where people with deep knowledge, expertise they’ve built over a lifetime, can take that knowledge, pair it to their own small language model that we provide them with. That model is then really fit to their tone, their voice, the depth of their expertise. And then the consumer side can summon those models, essentially summon that expertise to help solve some of life’s most challenging problems. And in our case, we’ve started with health and wellness because we think that’s an area where unique expert opinion and experience matters, we think it’s an area where trust and privacy are really paramount and that’s another key piece of this equation. But ultimately think of it as a marketplace where individuals that have acquired a lifetime of knowledge can make that knowledge available for subscription to anyone for whom that could make a big difference in their lives.
[04:13] Pete Pachal
Nice. I like I like the word summon. It sort of implies almost a sort of a magical quality to this that this sort of expertise on demand. Nick, why don’t you tell me a little bit about the the model you have? You have like a one expert, one system model. walk me through sort of what happens between an expert sort of handing you their work, you know, and a and a functioning Onix version of them appearing in the app. yeah.
[04:16] David S. Bennahum
Yeah.
[04:35] Nicholas Nadeau
Yeah. so, yeah, so it’s actually, it’s not that difficult. It’s quite easy in a lot of ways. A lot of the experts we work with that are already published, they have books, have podcasts, have Substacks, blog posts, you name it. already have a lot of internet presence. Now, this is a lot of the work that has been stolen and scraped by big AI in a lot of ways. But we’ve published some technical white papers basically showing that if you build these models with the intent and the actual intention to create high fidelity to that expert and their point of view, even just off that first layer of public data, we can already outperform. And this is where you get into the domain focus, you know. big god models. They are trained on all of the internet from the history of Japan to the French language to how to code. That’s not our focus at all. We’re domain constrained to the specific expertise of that expert. And then you bring a multimodal architecture. We have nutritionist experts, have sports medicine experts, have longevity functional health. Each one doesn’t need to know all of health and wellness, but you bring them in, as David mentioned, summon them in to your health and wellness journey. And they all take part in this approach. And so the architecture starts to look a little more like coding agents in a lot of ways. We’re working together on a workspace together. I come in as a user with, know, for using a terminology world model of me and my humanity with these experts and expertise working together towards some end goal, whether it be navigating perimenopause or fertility or looking at even like I myself using it for my marathon training, bringing in a nutritionist and a sports medicine recovery expert. to progress on that journey. And so we might start with the public data, and then we could go deeper working with our experts to unlock some of that dark data, the manuscripts and memos and notes that have never gone onto the internet, but that really show the nuance in the way they think, their judgment, and their persona in a lot of ways. And then we work with them, human-in-the-loop through an active learning system, where we can bring in the things that have never been written down, their real nuance.
[06:20] Pete Pachal
Well let’s
[06:45] Nicholas Nadeau
point of views. And so the core belief for us is that expertise is factual knowledge plus a point of view, a perspective and judgment. And together, that’s what true expertise is, not just, you know, statistically average.
[07:00] Pete Pachal
Yeah, I I think what you’re saying like is the sort of a more articulate way of talking about what I think is probably an overused term in AI, which is like personalization or hyper personalization is kind of the the cool term. And yeah, I I the seeing sort of the well, I would like you to articulate for me for like like the advantage that this has over big generic systems, because I think that’s what most people are trained to do now, right? Like it’s like
[07:25] Nicholas Nadeau
Yep.
[07:29] Pete Pachal
just ask ChatGPT, just ask Claude for your advice on what supplements to take or what have you. And for a lot of people, like maybe that’s enough. But the idea that, like, you know, you want to go deeper, but you also, you know, don’t necessarily have the budget or the time to go to a a a human and make make appointments and calls and all this stuff, like it feels like this is fulfilling that that gap, but in a much
[07:52] Nicholas Nadeau
Yes. Yeah. David, do want to take this one? Yeah.
[07:58] Pete Pachal
sort of more efficient way.
[08:01] David S. Bennahum
Yeah, there’s a well, I mean, just there’s a a huge latent demand for expert opinion. And that latent demand is really unfulfilled because people with it only have so much time in the day, and we don’t live near them, and they’re even hard to see physically. And when you think about all the domains of human knowledge that are out there, which are just basically limitless, what does it mean to unblock that expert in being able to give that knowledge to really anybody? And the the key is Would the fidelity or the accuracy of the model be such that the expert would feel really good that this would maintain the integrity of their own brand and their own commitment to their knowledge, right? This is a key piece. So this doesn’t work if experts felt it was misrepresenting them or falling short. But if they do, wow, the implications are tremendous because the volume of latent demand around service knowledge. Literally in the trillions of dollars. I mean, it’s actually huge. It’s one of the biggest drivers of inflation in the world today is actually the increase in prices for services. And that’s because there’s essentially a kind of wage inflation that occurs there. That’s a function of the scarcity. It doesn’t have any efficiency in it. You know, a world-class doctor can only see so many hundreds of people a year. End of story. And that’s not going to change. It was the same in 1850, it’s the same in 2026.
[09:05] Pete Pachal
Mm.
[09:28] David S. Bennahum
What does it mean when all of a sudden that latent demand can go from a few hundred people a year to a few million people a year? Again, presuming the doctor or the expert just felt that that AI faithfully represented their wisdom. If the answer is yes, then holy moly, this is one of the biggest contributions to human flourishing we can think of, because it suddenly democratizes access to all this wisdom that otherwise would have not been available.
[09:55] Pete Pachal
Well it also seems like you’re coming at this like at a at a at a good time, like with everything you were talking about, Nick, with regard to smaller models, right? And they’re like the the biggest thing that everyone’s talking about right now in AI is like how much it costs to d just operate some of these sort of frontier models and how do how do we prevent our token costs from getting out of control. And so getting like a more efficient model in there and also the edge computing, right? There’s that that’s a part of that equation.
[10:10] Nicholas Nadeau
Yeah. Yeah.
[10:24] Pete Pachal
And it seems like your your system like takes care, like it is just sort of inherently coming at this time when, hey, you can actually not only run more efficiently, but get better results by sort of narrowing the focus. This whole small model approach, which I think was not but not theoretical, but I think the the sort of mass application of it was probably a little a blurry, but now you’re sort of giving it a little more a little more concrete in terms of application.
[10:38] Nicholas Nadeau
Yeah. Yeah. Yeah, exactly. We’re really at a perfect point right now where like the crest of the wave, where the models are becoming smaller and more efficient and the phones and like the supercomputers we carry in our pockets every day are becoming more more powerful. And so if you have like an iPhone 17 Pro, you can already start running these models in your pocket. And so our end goal from a data sovereignty and privacy and trust point of view is let’s run everything local to you in your data vault and your safe place basically where your wearable data lives, where your Apple HealthKit lives, where your deepest, darkest secrets live, giving you that space to explore your wellness journey. Because honestly, we don’t need that data. And that’s the entire premise of Onix at the end of the day is we’re at a point where you don’t And you no longer need to trade your privacy and your trust for utility. That was the Web 2.0 era where you wanted Facebook, you wanted to hear, I’m great, it’s free, but give me all your data. That’s no longer the case and doesn’t need to be.
[11:46] Pete Pachal
Mm-hmm. Yeah, let’s talk about that. Let’s talk about the ownership, the data sovereignty aspect of this. So I I I understand sort of like your model is that the experts own their model, right? And they can they can leave anytime. And the all of the conversations that any user might have with the the AI version or the wh what is the term? Is it is it an Onix? What do you what do call it? Yeah, so for their Onix.
[12:14] David S. Bennahum
Yes. Anonymous.
[12:15] Nicholas Nadeau
Yeah, analytics.
[12:17] Pete Pachal
are encrypted. And so so tell me a little bit about like like how you see data privacy and also the data sovereignty of the experts and what safeguards specifically that you have in place to ensure that well that the data doesn’t get used in ways people might not might not like.
[12:38] David S. Bennahum
Well, maybe maybe we just begin with a basic North Star. And let’s say instead of expert, let’s say authorship, right? That you as a human being, Pete, create knowledge. You do that for for a living. And many people do that in different domains. And just think of that as authorship. It it’s fundamentally part of the success of our civilization has been giving people the property right to own their intellectual work.
[12:52] Nicholas Nadeau
That’s one.
[13:06] David S. Bennahum
Within certain guardrails, and then from there to allow other people to build on that in ways that are called fair use, et cetera. But ultimately there’s a regime that protects people’s intellectual property. And guess what? It’s been part of why we’ve thrived as a society since at least the eighteenth century. Now, what is it about AI that raises its hands and says, geez, you know, authorship doesn’t matter?
[13:19] Nicholas Nadeau
So,
[13:32] David S. Bennahum
Property around owning knowledge doesn’t matter. We somehow have this ability to go and take it all without compensation. Well, that’s something they just said. And that partly they got away with it because it was first mover advantage, you know, in 2017 when the first general purpose transformer models really started to work. A lot of people took a lot of stuff from the internet and nobody really noticed until 2022 when GPT, ChatGPT came out. And then all of a sudden I was like, ooh, look at this. It’s so cool. Then people wondered how did they get all this data? And it was a little bit like the cat.
[13:48] Nicholas Nadeau
So.
[14:01] David S. Bennahum
Was out of the bag. Okay. Having said that, authorship remains paramount. And the reality is the frontiers of human knowledge, what you do today, Pete, what other people are doing, those things deserve to be protected. And people won’t have much incentive to continue pioneering the frontiers of knowledge. If all that happens is their knowledge gets extracted without permission and used by somebody else for their profit. It’s it’s just completely laughable. And
[14:16] Nicholas Nadeau
So I’m just going to take a moment to our Dr. David
[14:27] David S. Bennahum
One of the many reasons there’s a huge backlash against AI is in part this perception is just taking everyone’s stuff willy-nilly and doesn’t seem to care. That is coming to an end. Now, when you enforce authorship and what I would say is ownership adjacent to it, right? Your ownership of what you’ve created, Pete, then you create a system in AI that allows for the frontiers of knowledge to flourish and even more interestingly, allows for orchestration to occur. Where Pete’s knowledge can potentially connect with, say, Malcolm Gladwell’s knowledge, not that the two things get synthesized in the same model, not at all. Presuming Malcolm had his own, shall we say, Onix as well. Those two things could be summoned by a human being to discuss something around, say, innovation and company formation, where they wanted Pete’s point of view and Malcolm’s point of view in the discussion. Currently completely impossible with a frontier model today. That wouldn’t really mean anything to do what I just said, because their knowledge has been blended together, right? pureed and munged together into the same model. So authorship is at a point now where it’s going to become ascendant in the domain of AI for the reasons Nick also pointed out, which is small models are are having their moment. They can be really fit tested to a person’s knowledge. And from there they can be orchestrated, as we say, or summoned collectively together to solve problems in ways that a blended model can’t actually have the discernment to solve. Because it doesn’t have a differentiation of judgment. It just keeps looking for the average between, say, Pete and Malcolm Gladwell, hypothetically. Like what is the average between Pete and Malcolm? Well, on some level, who the heck cares what the average is? That’s not the point. The point is, what’s the debate, maybe, between Pete and Malcolm? that’s much more interesting. How do you get that debate on a frontier model? you can’t. That’s interesting. So when enforcing authorship, enforcing ownership of that authorship back to you, leads to a better form of AI that can solve problems in ways a frontier model.
[16:01] Pete Pachal
Hmm.
[16:25] David S. Bennahum
cannot, that is a rare moment where actually the moral, the moral architecture happens to be the more effective architecture in terms of performance. When that happens, we all ought to seize it vigorously. And that is where we are today, in a very exciting moment where I think a lot of the assumptions around AI are about to be challenged. And guess what? Always happens in our industry. This is nothing new, guys. It’s happened to, you know, from inception Different topic. We could talk about it, but there’s nothing new about big tech incumbents getting blindsided by a change and how things can be done.
[17:02] Pete Pachal
So the the whole aspect of creators owning their work and you know the the the everything you just sort of touched on there where where you know there’s been what a lot of people see as sort of this grand theft of of knowledge on the internet. you may have heard there’s a bit there it’s sort of led, I think, in some ways to a bit of a backlash against AI, particularly around like IP owners. And so I’m curious as you talk to people to come into your network, how much of a process of, I guess, education or convincing them that, you know, you’re you’re the good guys in this, you know, they like as opposed to the people who’ve been strip mining the internet for data. and you know, pope I’m both curious about the general sort of cultural
[17:46] Nicholas Nadeau
So.
[17:47] David S. Bennahum
Yeah.
[17:59] Pete Pachal
stuff you’re seeing, but also like what is the the thing in the value prop that that they can look at the most that that is like this is different.
[18:11] David S. Bennahum
Well, maybe let’s begin with an assumption, which is if you’re somebody who’s created a large body of knowledge proprietary to you, right? Your knowledge, and a fair amount of that has been put on the internet, right, which is pretty typical for a person on the frontier of knowledge, you’re pretty well educated in being upset about AI. Right? Because one of the things you did is you ego tested yourself on on Anthropic, on Claude, and on GPT, and one of the things you probably discovered pretty quickly was it took all your stuff and you’re just like outraged.
[18:21] Nicholas Nadeau
Okay.
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[18:40] David S. Bennahum
You’re not e even necessarily a technically proficient person. It’s not even related to tech savvy. But maybe you’re flattered initially. Let’s these are the stages of grief, right? And the very, very first moment may in fact pe be flattery, like when you googled yourself for the very, very first time and were like, I exist, I’m on the internet. fine. I exist, I’m in GPT, fine. And then after a little while, you’re like, What the frick is going on, man? How what
[18:41] Nicholas Nadeau
Sorry.
[18:43] Pete Pachal
So some people might have been flattered, but anyway, go on.
[18:50] Nicholas Nadeau
So.
[19:07] David S. Bennahum
This isn’t right. And then there’s a sense of resignation that kicks in. They’re so big, I can’t do anything about it. Life is not fair. Or maybe I’ll try to sue them and they’re so big, they don’t care. Life is not fair. at which point you become very open to taking that headwind, so-called headwind, and making it into a tailwind, which is like, how do I get out of this box? And so for us at Onix, one of the preconditions we see with every expert we talk to is they’ve been through this journey of sadness and rage. And then when they talk to us, they’re pretty educated about their sadness and rage, and they ask us pretty smart questions right away. And we’re prepared for them. And so really one of the big messages here is take back control of your genius. what does that mean? It means take all the stuff that’s been put online that they stole without your permission, the bootleg version, put it into the proprietary model we’ll give you. And then since it’s the authorized version, not the bootleg. You have an incentive to put in there the stuff that they couldn’t have stolen, because you didn’t put it on the internet, but it’s germane to your depth of wisdom. So it’s the stuff Nick had mentioned, your personal notes, maybe even audio recordings of your thinking that you make exclusively for your AI to train it further. And when you do that, the fidelity gap between what I would literally call the bootleg, which is what you get on GPT, and the authorized, which is what you get on Onix, is sufficiently large that then you can look to your audience and say, Do you value me enough to pay for me?
[20:34] Pete Pachal
Hmm.
[20:34] David S. Bennahum
If the answer is no, then use the bootleg on GPT. If the answer is yes, because what you’re working on is so challenging, and you actually do respect my expert opinion, then the ten dollars, twenty dollars, thirty dollars a month to use my wisdom is a no-brainer. And welcome to the authorized version. And the good news, Pete, is we saw this happen in music, right? 2001, Napster steals everything, LimeWire has it, music business says it’s over, we’ll never make money again as a recording artist.
[20:35] Nicholas Nadeau
Okay.
[21:04] David S. Bennahum
An Apple computer said, What about this thing called iTunes? What if we create a way for music to be elegantly downloaded and synchronized, pay ninety nine cents a song, high quality streams, ease of search, ease of curation. Optional. You can still go on The Pirate Bay, steal the music. And the reality is music piracy is no longer an issue today, even though it’s just as available as it was in 2001. We think the same phenomenon is going to happen with AI.
[21:28] Pete Pachal
Mm.
[21:31] David S. Bennahum
Sure, it’s really easy to go on Claude and ask for the bootleg version of so and so, but when the authorized version is out there and is demonstrably superior to the bootleg, welcome to the new world of AI.
[21:31] Pete Pachal
Right.
[21:42] Pete Pachal
Sweet. So I’d love to talk a little bit about the first area you want you went into, but also like where it could go further. Because obviously we already talked about health and wellness. That’s where you’re starting. but you know, when I first talked to you guys, I was thought about the model that you have and just thinking about it sort of writ large, scalable, so that people with domain expertise, people with a body of work, might want to just simply create one of these and you know, obviously thinking selfishly of my own profession, journalism and media creators, but of course that’s what I cover. give me some thoughts on like how scalable this is, because I also know part of your value prop, at least currently, is that you don’t you’re not exactly flooding the zone with like experts and have you know, tons of people in every field, it’s it’s it’s more curated than that. So as you expand, how are you going to balance that curation with regard to like making it scale? And also like areas like creators, like, like independent journalists, like you know, even even comms in independent comms people, like that sort of thing. Like, how are you thinking about that?
[23:01] David S. Bennahum
Yeah. But we thought about it initially as we had to demonstrate quality over quantity. Like we have to demonstrate that the quality of the AI, meaning the experts you’re summoning, can outperform the frontier. And at the same time, we felt a dense network of affiliated experts with high quality would be something to really demonstrate fully for all the reasons around orchestration of knowledge. So when you just take that thesis.
[23:08] Nicholas Nadeau
So, thank
[23:31] David S. Bennahum
You’re gonna sacrifice the other verticals and you’re gonna potentially just go to one. And the one we went to with went to was health and wellness. And again, we did that because when we really looked at the domain opportunities, health and wellness scored highest on where expert opinion really matters. Like I wanna have the perspective of this cancer doctor, not that cancer doctor, or this nutritionist because I’m vegan, or that nutritionist because I’m paleocarnivore, right? These are just Fundamentally reasonable discernments around different types of expertise, what we might call expert opinion. Really matters. Number two, privacy really matters. The health and wellness domain tends to be a very intimate, personal one. It’s different than say, what should I watch on Netflix tonight? That’s a different domain. It’s also private, by the way, but it’s not the same level of privacy as I’m having maybe IVF treatments and going through fertility.
[24:07] Nicholas Nadeau
So, you.
[24:25] David S. Bennahum
And I want to talk about that with an expert AI, that’s a different level of privacy than maybe which sitcom should we watch tonight, right? And then number three, trust being paramount. And so if you don’t trust that the expert has a high quality AI that really represents their knowledge, and if you don’t trust this conversation is going to be private and my data is private, then the whole thing falls apart. So health and wellness is a really high trust domain. So if you look at those boxes. goes right to the top. And then there’s other verticals adjacent and beyond that we believe we can go to over time. So things like personal finance is a high trust area too. Not quite the level we think is health and wellness, close. There’s things like relationship and self-improvement. So that’s all the domain of maybe I want to be an entrepreneur or I’m struggling with something in my marriage or give me advice on something pertaining to how to be the best, most optimized version of me. It’s very close to wellness, right? You can see that adjacency. And then we can go further from there. There’s lifestyle expertise. But again, that’s a little further out. So I’m thinking of traveling somewhere, and I want a really great expert to advise me on where to go in Sri Lanka, because I’m gonna do Sri Lanka. Okay.
[25:38] Pete Pachal
Yeah.
[25:40] David S. Bennahum
Again, an interesting area. And so you could just go on here, at which point I think there’s a second part to your question, which is to what degree do you kind of open this up so that people can just become creators and come on the platform? And the answer is yes. And think of it happening in three phases. Phase one, where we are today, invitation only. We work together to build your Onix. Once you, meaning the expert, and we are aligned, it’s fit for production, it goes live. Again, health and wellness being that initial domain. Phase two. You have the ability to create an Onix, share it privately. Right? Privately, which means it’s not in our discovery environment, but it’s available. That is happening soon. Like we’ve built the technology. We actually have some people testing it now. We think that’s very, very interesting. It will teach us a lot around the kinds of things people want to do. Phase three is take that private Onix and submit it to us for listing in the marketplace with a price.
[26:23] Nicholas Nadeau
So, thank
[26:24] Pete Pachal
Mm.
[26:39] David S. Bennahum
‘Cause you want discovery, you want revenue. Then we’re gonna have to judge it in some way. And that’s where we’re gonna have to start getting into like with any app store and a little bit of a discernment, right? Why this, why not that? That would be phase three of the process.
[26:53] Nicholas Nadeau
Yep. Yeah.
[26:53] Pete Pachal
And can every every creator or or person on your platform set their own price? Could be sort of whatever they want.
[27:00] David S. Bennahum
Yes. There’s a minimum price, obviously, because there’s a certain cost of goods sold. Yeah, there’s inference, there’s compute. So we think below about there’s no free well, there’s no free tier if you just want to pay for it directly. This is a whole other conversation. But let’s just say for now in the current framework, around ten dollars a month would be the logical minimum. There’s no ceiling. You know, if someone said, Listen, you know, I have a very limited edition Onix, I think it’s worth several thousand dollars a year.
[27:03] Pete Pachal
Sure. So there’s no there’s no free tier.
[27:13] Pete Pachal
Right.
[27:28] David S. Bennahum
And that’s on you. And if you feel like you can make that work, by golly, we’ll be delighted to be your partner and you should totally do it. Not on us to say.
[27:37] Pete Pachal
Nice. And as I understand it, you have this sort of seventy, thirty cut, right, in terms of just this how that’s how you guys make money.
[27:43] Nicholas Nadeau
Yeah.
[27:44] David S. Bennahum
Correct. So it’s essentially like an app store commission, right? That we take thirty percent of the revenue. If you don’t make any money, we don’t make money. So we are we often say to the experts, we’re business partners. You’re we’re not a you’re not like we’re not like a vendor and you’re like a client. You know, we don’t charge you to come on the platform. There’s no setup fee. There’s no charge to come on. What we’re really doing here is saying let’s work together to build a million flourishing businesses on this platform. So having said that, if you don’t succeed, we don’t succeed. We have an incentive to promote and market you. And you obviously have an incentive to promote and market yourself. Similar to Substack in that way. but ultimately if you don’t if you don’t make money, we don’t make money.
[28:24] Pete Pachal
I like I liked how you described almost I don’t know if this is how a stand actually I’ve that’s my question. Is it kind of a standard? ‘Cause you said like, is what’s coming out of your Onix better than what you get out of like a generic AI as kind of a bar, right? Like it’s kinda like, Well, that’s that’s I think it’s a good sort of measurement. I’m just curious, like how do you measure it? Like it’s got sounds a little more qualitative than quantitative, or is that how you think about it or
[28:47] Nicholas Nadeau
Yep.
[28:49] David S. Bennahum
Well, I’m gonna let I think Nick should actually talk about how we do the evaluations because there is an element of of quantitative science here, which is in really important. And yeah, there’s a qualitative element too, which is does the expert ultimately feel like their Onix is awesome? That’s a qualitative call. But I’ll let Nick talk talk about the quantitative piece maybe a bit.
[28:55] Nicholas Nadeau
So I’d actually start with the qualitative. It’s something when the true expert, the person who knows themself and their expertise the most, they battle test their Onixes. The fact that we have 25, 30 experts live public on the platform, they have
[28:57] Pete Pachal
Nice.
[29:24] Nicholas Nadeau
battle tests of these Onixes with them, their teams around them, the people they know best, even sometimes our clients and patients have battle tests of these Onixes before they go in public. And they say, wow, this is exactly me and my expertise, but now at scale, at mass personalization. On the quantitative side, you know, this is where evals come in. So evaluations, what you typically see in across all the different benchmarks, you know, we work with a lot of the common evaluation frameworks and whatnot. We also started publishing, we said we have two papers out on how we do our evaluations. And there’s really four pillars. There’s accuracy, groundedness, persona, fidelity, and judgment. And so this, we come together and call it the expert fidelity score in a lot of ways, where is it actually representative of the expert? Is it grounded in their work? Is it accurate to their work? Is it reflecting their persona, how they approach a problem, their style, their tone? you know, a happy-go-lucky doctor versus somebody who’s a little more straightforward and blunt, and then also their judgment. And so this is where I get into not only just, you know, one-off analysis, but what is the multi-turn approach and nuances? And so we had one of our second paper that we published, one of our awesome experts, Dr. Dave Rabin, psychiatrist and neuroscientist, he takes a very different perspective than the statistically average view on neuroscience. he looks at things like, and so it came up that he doesn’t subscribe to what is the psychology 101 polyvagal theory. He actually explicitly rejects that theory as part of his practice, but nowhere has it been written down that he doesn’t subscribe to that. And so if you’re asked, know, ChatGPT or Claude or whatever, say, pretend you’re Dr. Dave Rabin and tell me your thoughts on this, it will reach out towards what is the Wikipedia definition of stress response in the body, polyvagal theory, not knowing that he personally rejects that. But working with us, working human-in-the-loop to build the model together, these are the types of dark data that come into making his model not only a higher fidelity, but his. Not some other big AI companies. This is his model that is verified, validated, stamp of approval based on his work and his approach and his judgment and nuance. And that’s what expertise is.
[31:43] Pete Pachal
And speaking of like measuring things and quality, one of the things we haven’t really touched on too much is hallucinations, even though, you know, I know sort of by implication you have smaller models and there’s probably a lower rate. But I mean, it’s all generative, so like it can’t be zero. So how do you warn against that, think about that, and and measure it and and sort of mitigate it as a, you know, because all AI systems have to deal with this at some point?
[32:09] Nicholas Nadeau
Yeah, so as you hit it out, generative AI as a whole is a probabilistic process. There is literally no way to prevent to a 0 % chance hallucinations. But as you also mentioned, small language models, domain constrained, really isolate to anthropomorphize a bit. These models think in their domain and their domain only. And when you start to cross domains, it’s outside of their trained body of knowledge. So they have nothing to really reach towards. Whereas if you take a, you know, a God model, a giant model, it’s been trained on everything under the sun from 4chan conversations to Wikipedia to, like I said before, like the history of Japan and what else. And so it’s always going to try to reach for things to fulfill the requests of the user, because not only are the things trained on, you know, all the internet, but they’re trained to be pleasing bots that always have an answer for you to maintain engagement and keep the conversation going. And so think about the incentive structure underneath this is that You have a system where engagement is the metric. Well, everything feeds that metric to keep on answering you. We have a system where wellness and empowerment and the expert themselves, the human version, the in-real-life version is the metric that we’re optimizing towards. And it’s a very different line of thought, which leads to even how you build the system from the ground up. What is the architecture going into this from a business model, from a technical perspective that goes into that. And then. You know, like all other tech companies, have guardrails, have evaluations, we are nonstop spending our time comparing, contrasting ourselves to what is frontier, how do we do it? Have we done, have we thought of this, how we thought of that? Corner case is getting feedback from users, feedback from experts. And that’s why it’s a bit of a community approach with experts in the loop that we’re always getting this feedback, tweaking, fine tuning, and making sure that’s best representative of them and best representative of what the community needs.
[34:03] Pete Pachal
corner cases where two edge cases meet. I don’t often I don’t often hear about that term, but yes, I’ll I take a point. listen.
[34:06] Nicholas Nadeau
Hahaha! Yeah, well, hang out with my developers and you’ll hear it a lot more.
[34:11] David S. Bennahum
Tonight. May I add one thing, Pete, which is the AIs will on on our side will actually say I don’t I don’t know. And it’s awesome.
[34:21] Nicholas Nadeau
Don’t know. But that’s what’s fun is you talk to a nutritionist model and you start talking about sports medicine recovery. And so I’m like, well, I don’t know that, but I know somebody who does. Let me summon Dr. Jordan Metzl into this conversation. that’s like one, it reaches for its, that’s to be discussed of how we.
[34:22] Pete Pachal
Nice. Very smart.
[34:37] Pete Pachal
Very nice. Just just another twenty nine ninety nine. First answer’s free. Awesome, guys.
[34:41] David S. Bennahum
Yeah, but that’s about right. Yes, let’s support let’s support authors, you know, economically.
[34:49] Pete Pachal
We’re almost out of time. I just wanted to hit you with sort of a variant on my normal final question. so this is for both of you, but like if you look out, say five years from now, and what what would be the outcome that would convince you that your view, your basically your point your your I don’t know if it’s a model or what you would call it, but basically what you’ve put forward in the realm of expert owned AI, the the author. economy you might call it. How how will you know it’s healthier? Or what is the outcome that would make you think it something had gone wrong? go ahead.
[35:27] David S. Bennahum
Yeah. So I think one of the big signals of health is that the value being created by AI has distributed outward to more and more people economically, where you can look to many, many people having flourishing economic experiences thanks to AI as experts, that would be a real signal of success. And a signal if something went really, really wrong is at the end of the day, a handful of companies controlled.
[35:28] Nicholas Nadeau
So, have a lot of things to do. I think we lot of to do. I think to do. I we have of things I lot to I we have a lot things to lot to
[35:57] David S. Bennahum
all this knowledge and reaped all the economic benefits to the detriment of everybody who wasn’t in that inner circle. It’s a very simple arithmetic that’ll point to a source of truth.
[36:11] Nicholas Nadeau
I’m going to go with, I’m going to extend David’s a bit where in, I think you said like five years, a few years, we’re no longer talking about IP theft or data scraping because there is a better model, just like David’s analogy to the iTunes model. We don’t talk about Napster LimeWire, even though they technically still exist in a lot of ways. The other side is like the socioeconomic access to expertise. If we achieve our mission, expertise is fairly democratized where, you know, what does it look like when the whole world has access to a nutritionist or nutritional support? What does it look like when people have this expertise in their pocket to help them flourish? What kind of, you know, what kind of society looks like now when it goes wrong is, you know, what happens if it only supports one cohort and doesn’t get democratized, know, that’s the worst case.
[37:05] Pete Pachal
Awesome. Great stuff. We’ll just go for it.
[37:05] David S. Bennahum
May I add one small thing, Pete, if I could. I would like to let you know that we have an access code for all the copilot listeners. We’re currently in early access. If you want to try Onix, you can go to Onix.life, which is our website, Onix.life install for iPhone. And then when asked for an access code, just type in copilot. And once you type in copilot, you’ll go in and you’ll enjoy experience the product.
[37:07] Nicholas Nadeau
Thanks.
[37:23] Nicholas Nadeau
Stuff.
[37:34] Pete Pachal
Good stuff. Thank you guys. I really appreciate you coming by, giving me your the four one one on the author economy and what you’re doing to augment it.
[37:44] Nicholas Nadeau
Thank you, Pete. Thank you for having us.
[37:45] David S. Bennahum
Thanks, Pete, for having us. Yeah, great to see you.
