SuperTruth Co-Founder Jason Snyder discusses AI infrastructure, predictive bias, and the future of human-centered technology.
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Show Notes:
In this Mission Matters episode, Adam Torres interviews Jason Snyder, Co-Founder of SuperTruth, who explains how AI is shifting from a productivity tool to a core decision-making infrastructure. Jason shares his vision for designing AI systems that respect authorship, consent, and human values—especially in healthcare, hiring, and marketing. A must-watch for anyone navigating the evolving tech landscape.
About Supertruth
SuperTruth is a health data management company with a people-centric approach. We build data solutions to maximize value for AI, ingest and understand health data, remediate data decay, and ensure the highest quality and most accurate data.
Full Unedited Transcript
Hey, I’d like to welcome you to another episode of Mission Matters. My name is Adam Torres, and if you’d like to apply to be a guest in the show, just head on over to mission matters.com and click on BR Guest to apply. All right, so today I have Jason Snyder on the line and he’s a co-founder over at Super Truth.
Jason, welcome to the show. Thank you so much, Adam. Thanks for having me. All right. Great to have you here. And for everybody listening, just so you know, this is part of our Milken Global Conference coverage series where we bring on speakers, panelists, attendees and guests of the Milken Global Conference here in Beverly Hills.
And Jason, correct me if I’m off on this, you were one of the speakers at the conference this year. Is that right? That’s right Adam, at Milken, I, spoke on a panel called AI and the Economy Transformation and Disruption. The focus of what we, we spoke about and what I spoke about was on how AI isn’t just changing what we do, it’s reshaping how we decide what’s worth doing in the first place.
Mm-hmm. So, I. Challenge the room to think about, you know, what happens beyond automation and efficiency to consider authorship consent and, and the long-term value of human judgment and increasingly predict predictive world. Wow. Have you been to before, I want to go further into this topic, but I’m just curious on the conference overall.
Have you been to the conference before? Is this the first time attending? Like , what’s been your experience? It was my first time attending, but I’ve worked with Milken in the past. I’ve participated , in a number of kind of their surveys and, research and, have worked with them.
And then they invited me to speak this year. Ah, that’s wonderful. And I always like to hear the, you know, the opinion of a, first time attendee. I know, you know, the end, the conference, but wait, or the institute as a whole, you’ve been working with them, but what’d you think about the conference?
Man, it’s kind of spectacular in my opinion. I don’t think there’s anything quite like it , in the world. A, it’s a really unique experience to connect with people you know, kind of at all levels you know, across all verticals that are, are working towards a common goal of making the world a better, more creative, more positive place.
So, yeah, it was it was a pretty special experience. Yeah. I, have to agree with you on this one. And let’s let’s switch up the topic a bit here. I do wanna go a little bit further into your work but just I guess to, to open up the conversation. Everyone’s talking about AI and jobs. Talk to me about that and what’s your stance on that, AI and jobs, and is that even asking the right question by the way?
Or is there a deeper threat? Yeah, so I, think that that is maybe not necessarily the best question because I think really most today, like most businesses are treating AI like you know, some kind of plugin and mm-hmm people don’t really understand the impact that it’s.
It’s going to be having on their business. AI is not really staying in a tool belt. It’s becoming the, the substrat. It’s becoming the business itself. It’s becoming the infrastructure it’s the operating system of how decisions are being made, how products evolved, and how trust is being brokered.
And when AI becomes the infrastructure truth becomes a design decision. And if we don’t get. That design. Right. You know, we’re losing the ability , to tell signal from simulation and , it’s really much bigger than jobs. It’s about the impact that it’s having , on culture and the fidelity of reality.
I. Hmm, that’s interesting. Can you give an example of what that looks like? And it could be a real use case, , a fictional use case, whatever. But like when, when you talk about being infrastructure, like what could go wrong? And I, and I’m completely okay with the opposite end of that question too, which is what could go right.
Sure. I mean, , , the reality is that, you know, you have to imagine, I guess in a real world example, like a nurse applying for a new role in a major hospital system. Mm-hmm. And the hiring platform uses AI to screen resumes and flag profiles and recommend compensation bans. But the model was trained on historical data that’s rid with gender bias and outdated performance.
Proxies, right? So she’s qualified, but she’s flagged as a lone match, and the system says that she’s like too much of a job Hopper, which it doesn’t know is, you know, she moved often because she followed a spouse who may have been in the military, right? So she never gets the interview, right? So the impact on reality is that the hiring manager never even sees her resume.
So as far as the dashboard is concerned. She didn’t exist. And reality, you know, has been quietly overwritten by prediction. And that’s where we are because that’s the kicker. You know, no one can audit how that decision was made. The model was pre-trained, the data was third party, and the pipeline has no providence layer, so you can’t really fix what you can’t trace.
So that’s not just bias. Infrastructure failure. Wow. And when AI become becomes an operating system for reality, then there’s no authorship, there’s no consent, there’s no traceability. And what you end up with isn’t just bad outcomes. You get synthetic fairness built on broken ground. Hmm. So what are some of the, what are some knowing that this is a, like, first off, great example, like, I think that’s one I, I definitely understand.
I’m sure everybody can, anybody can understand you like that, you know, that you may argue, somebody may argue that, okay, but this is, you know, a certain subset or certain percentage of people. But like, if you’re part of that group, right, like that’s, that’s kind of messed up, right? Like, he’s not getting that job.
She’s not part of the system by structure. Nobody’s ever gonna even pay attention. She’s off the grid, right? Like just period. Correct, correct. So, so taking it, I guess a step further, understanding that that’s gonna be, you know, I shouldn’t, I don’t know if I should say possibility or a reality, whichever we want.
I don’t know if I’m, I have , a clear opinion on that yet, but understanding , at the very least a threat. Like what are some things that, you know, we should be considering? Well, I mean the biggest thing we need to consider , is to make sure that we put friction into everything we do because AI likes to smooth out.
Reality, right? Mm-hmm. So, you know, the, things that we need to consider about a i right now specifically is, you know, really who’s writing the rules that the models follow and who’s benefiting. Mm-hmm. Because models don’t just learn data. They inherit values. Right? And whose values are they inheriting?
And, if you are in a business, then you, can, you have to think, can your business trace where its AI decisions come from? Because if there’s no providence. Then there’s no accountability and there’s no defense when things go wrong. So, you know, you have to ask yourself, are you optimizing for outcomes or are you abdicating decision making?
Mm-hmm. ’cause , if you are using AI to get to speed, speed without scrutiny leads to systems with huge blind spots inside of them. So what happens then when your data changes? But the model keeps acting like it didn’t because stale data, you know, like you could change your address, you could change your phone number, your job, , your email address.
That data is invisible until it becomes a problem. And in a really scary model. What if it becomes a misdiagnosis? Wow. For you or a family member, right? Or what if a market collapses as a result of that? So the question is, are you building AI into your business or are you building your business on ai?
Mm-hmm. And the difference determines if you control the machine or if the machine is controlling you. Hmm. So for this, for the I guess in looking at this, boy, this is fun talking to you about this, by the way, Jason. ’cause you got my head going. I’m like, yeah, that’s true. But then you know, me as a small business owner, like, I mean, we, a little over 30 of us a year.
We’re not. iHeart Radio is 4,000 plus, you know, employees. I’m still, we’re still at the, I would argue we, I mean we have. We have ai, we have many, many tools that allow us to do what we do at this point. And we’re, I don’t wanna say we’re the most advanced, but we’re pretty advanced. We stay on the cutting edge of a lot of different things in our space, I should say, in podcasting and otherwise.
Sure. But like, what’s the, what’s the small business owner to do when they’re still trying to catch up and think about and just stay ahead of what they’re, you know, the present day, like whether it’s in their business. Directly, or whether it’s voting, whether it’s just the education piece of how they should be looking at these things.
Like what? What is a small business owner to do? Look, having tools is great and for small business owners like you, Adam, like AI can absolutely unlock leverage. It can write copy, it can automate, automate workflows. It can do things that used to require teams of people. Yeah, but here’s the thing. Tools aren’t strategy.
And speed isn’t value. So if your tools aren’t connected, if your data isn’t structured, if your AI has no memory of why it’s doing what it’s doing, then you’ve just built like a high speed treadmill. Mm-hmm. And that’s not really a business advantage. Yeah. So the, the todo isn’t to use more ai, it’s to know what your AI is doing and make sure it’s doing it for you and not the other way around.
Mm-hmm. Right. That’s the difference between being advanced or being automated. Hmm. Yeah. That’s great. What industries are you following right now within, I mean, AI obviously is a given, like that’s your, specialty, but what in industries are you following that you work in primarily? Are you agnostic?
Like talk a little bit more about your work. I. Sure. You know, like healthcare and wellness you know. Mm-hmm. Because I’m working on driving transformation in precision diagnostics, biomarker testing, personalized health, you know, infrastructure. And I’m doing that through, you know, the companies that, that we run and own, which are both I’m aware and supert truth, the super truth being the parent company.
Mm-hmm. And so our focus is on making health data actionable, sovereign, and AI ready. While respecting consent and context. So as part of that, , I look a lot at BIOCOMPUTING and Life Sciences. You know, , we have a what we call the bios, signal compute Core at super truth. So we’re helping to shape the future of biological computing for storage in, for in and medical modeling.
And, you know, what we want to do is expand the lifespan and fidelity of biological systems to compute more like nature. So they’re more fluid. More adaptive and more resilient. And another area that we’re really, you know, focused on and interested in is, is actually in commerce enablement and experiential marketing.
, My background has been working in the marketing technology stack for a number of years, and, you know, I’ve been helping to redefine how AI powered brand experience in retail intelligence and commerce. Commerce infrastructure work, and that’s really part of that, that whole flywheel, I mean , that’s the go to market strategy.
So building systems where human attention, data, and trust drive value, not just impressions or transactions. I. That’s fantastic. Jason, how do people how do people follow your work? How do they connect? How do they keep the dialogue going and, and follow what you’re up to? Sure. Our, our website is super truth, S-U-P-E-R.
TRUT h.ai. And you can, you can reach out to us there and, and see what we’re doing. You know, of course we’re on LinkedIn. And and, and that is the best way to kind of get, get in touch, you know, is, is through LinkedIn. That’s where personally I share most of my writing, thinking and provocations.
I also write, you know for Forbes, they publish several times a month on Forbes. So, so you can find me there. Perfect. And for everybody listening, just so you know, we’ll definitely put some links in the show notes. So you can just click on the link and head right on over and check out some of Jason’s work.
And speaking of the audience, if this is your first time with Mission Matters and you haven’t done it yet, hit that subscribe or follow button. This is a daily show. Each and every day are bringing you new content, new ideas, and hopefully new inspiration to help you along the way in your journey as well.
So again, hit that subscribe or follow button. And Jason, this has really been, has really been a pleasure. I love the way you break down these ideas. I like the, I like your use case examples that make it so that you know everybody can, can understand what’s going on and are better educated. Again, thank you for coming on the show.
Thanks so much for having that. It was great fun.
