Mark Suman: Building Privacy-First AI in an Age of Surveillance
Transcript — TFTC
Article: https://www.tftc.io/mark-suman-privacy-first-ai-surveillance
Transcript page: https://www.tftc.io/mark-suman-privacy-first-ai-surveillance-transcript
Published: 2026-09-13
Machine transcription, lightly cleaned; may contain errors.
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[0:07] Intro clip: You've had a dynamic where money's become freer than free. If you talk about a Fed just gone nuts, all the central banks going nuts. So it's all acting like safe haven. I believe that in a world where central bankers are tripping over themselves to devalue their currency, Bitcoin wins. In the world of fiat currencies, Bitcoin is the victor. I mean, that's part of the bull case for Bitco.

[0:31] Marty Bent: If you're not paying attention, you probably should be.

[0:34] Mark Suman: Probably should be.

[0:35] Marty Bent: Probably should be unique New York. You have to do the voice quick. Brown Fox, the voice. Practicing before you start. People are making fun of my vocal fry. Oh, yeah, you got a Vocal Fry on YouTube. I just talk slow. Maybe I need to work on breathing exercises.

[0:55] Mark Suman: But somebody told me they watched a clip of me and said, I'm getting real Michael Keaton vibes from you. And I asked them, is that a compliment? And they didn't respond.

[1:04] Marty Bent: So I would take it as a compliment.

[1:06] Mark Suman: I like Michael Keaton. He's great.

[1:07] Marty Bent: He had a great. He had like a Batman, was living on cloud nine. Had a bit of a valley there in his career.

[1:16] Mark Suman: Then he came back with Birdman, got his Oscar.

[1:20] Marty Bent: Got his Oscar. I would take it as a compliment. All right.

[1:24] Mark Suman: And then he had a Beetlejuice 2, which I could only get halfway through and had to turn it off, even though the original is one of my favorites, is a great one.

[1:34] Marty Bent: We're not here to talk about movies and vocal fry, though. We're here to talk about AI. It's been a while when we catch up April this year.

[1:43] Mark Suman: Is that when it was?

[1:43] Marty Bent: Yeah, right before I left Austin.

[1:46] Mark Suman: Yep. And that's when we were more focused on Open Secret. Now we're like, all in on Maple.

[1:53] Marty Bent: And that's why I wanted to bring out. I was texting with you yesterday about this. We were on a call, and it's becoming clear we're at this inflection point in many different levels. Economically, societally, socially, and obviously technologically, which you're on the cutting edge of with this AI revolution. I think one thing you said yesterday when we were talking was it's a foregone conclusion. AI is here, and I think this is a critical moment because we have to decide what the AI future is going to look like. And you were just joking before we hit record about all the AI agents and all the different softwares that say, hey, do you want to have me record notes of this meeting?

[3:09] Mark Suman: Yeah, yeah. And to piggyback on those truths. Right. That first truth I think is that AI is here to stay. The second one is it needs your personal data, like that's its lifeblood, that's its fuel. So you can't have successful AI without it getting very intimately involved in all of your personal information. And so the third one is how are we going to secure that? Or are we just going to give it all over to closed systems? So that's the third piece that we're working on.

[3:41] Marty Bent: And you were highlighting yesterday when we were talking. I mean there's plenty of examples out there and I don't think the public is aware of these. I wasn't aware of this one example that I'll pull up on screen now, which was shared on Schneider Schneier. Excuse me. On security abusing Notion's AI agent for data theft. It looks like somebody was able to upload a PDF to Notion's AI agent and sort of injected malware, I guess that enabled the attacker to get access to all Notion users private data. So the lethal trifecta of capabilities is access to your private data. One of the most common purposes of tools in the first place, exposure to untrusted content.

[5:47] Mark Suman: Yeah. And this was, thankfully, it was just a researcher, it was a white hacker who did this. And so they showed that it was possible. But it's a new version of the old SQL injection where you would put into a web form on an old website and you would add instructions for the database layer at the end of your name or whatever, and it would go into the database and look up all this information and then execute a command to send it out to a third party. So that's what they're doing here. They're just hiding all these instructions in the PDF and uploading it. And then it's telling Notion, go grab all this information.

[7:37] Marty Bent: Yeah. And Schneier says here, this kind of thing should make Everybody stop and really think before deploying any, any AI agents. We simply don't know how to defend against these attacks. We have zero agentic AI systems that are secure against these attacks. So we're learning on the go. And it's funny, you have this tension where everybody's excited, these things can do things and you want to utilize the productivity that they can bring to your personal life or your business. But I think people are walking blind into many privacy and security traps that they're really not aware of. And this is just one of many examples.

[9:12] Mark Suman: Metas was one more step farther than this in that they had to hit the share button themselves. And it was, it was basically like a suggestion to you at the end of chatting with the AI chat bot on Meta, it said, hey, do you want to share this to your timeline? So users were just hitting that button thinking it was like it was kind of a dark pattern, thinking it was the the end discussion button or continue on to the next phase, not realizing it was do you want to post this on your public timeline to all your friends and family? So they were taking this chat where they discussed really difficult problems with AI and then just shared it on their timeline.

[10:24] Marty Bent: Yeah. And it seems this is driven by multiple points of tension at this point in time where you have this arms race amongst the large language models who's going to be, who's going to commoditize the LLM layer of this and win the race of at least perceived race, if you think it's winner take all and the LLMs are going to be commoditized and there's going to be one to a few large language models that people heavily depend on. They're in this race to make sure they win that game. And part of winning that game is collecting as much data as possible to train the models to improve them. And then you have on the other side, individuals and businesses who want to be more productive at a way cheaper cost. And this is leading to what I deem to be lapses in judgment, particularly on behalf of the companies building these models of cutting corners and really doing things that are ethically dubious in terms of privacy and security.

[11:29] Mark Suman: Yeah, well, and to go back to that first article we talked about where he says this should be a stop and think moment. I think for the listeners right now, this is truth for the commoners. So, you know, everybody listening. This is your stop and think moment where we have perplexity that came out with their AI web browser called Comet and then ChatGPT just announced their AI web browser. Also seems like all the big AI companies are coming out with browsers and that vulnerability we just discussed, it's very possible something like that lies within the browser and there could be plenty more because you're basically given access to all of your web traffic that you search every website you go to.

[12:34] Marty Bent: whether it's ChatGPT, Anthropic Meta, Google, to a certain extent they all feign, sort of, they feign that they actually care about privacy and they'll sort of hand wave about their different protocols and processes for ensuring that users privacy is protected. We describe what their policies, what their stated policies are and whether or not they actually have any teeth in terms of protecting user privacy.

[13:07] Mark Suman: Yeah, I think it was Google. I was looking up some stuff about Gemini and Gemma and they have a whole page about privacy and it looks very strong. When you start reading it it's like, oh, we care about the user, things are private. But then on the same page it talks about how they utilize your data to improve your experience and basically discuss like targeted advertising and other things. And so basically they are using your data and sharing it with third part. And so that right there breaks the privacy paradigm like privacy should be. It's just me and an AI agent talking to each other, nobody else in the room, nobody listening and that's it.

[15:32] Marty Bent: Sup freaks? This RIP, a TFT was brought to you by our good friends at BitKey. BitKey makes Bitcoin easy to use and hard to lose. It is a hardware wallet that natively embeds into a two or three multisig. You have one key on the hardware wallet, one key on your mobile device, and block stores a key in the cloud for you. This is an incredible hardware device for your friends and family or maybe yourself, who have Bitcoin on exchanges and have for a long time, but haven't taken a step to self custody because they're worried about the complications of setting up a private public key pair, securing that seed phrase, setting up a pin, setting up a passphrase.

[18:06] Mark Suman: Well, I think at the least it should be opt in. If you think about our own brains and the training process that we go through. We consume public information constantly and then we train off of that. We train off of all the input data around us every day. And then we have our own internal thought process that categorizes things. So I think as a first step we should have models that are trained off of publicly available information. And then the next step would be, well, how do we get that thought process that's going on someone's head? How do we get that into the model and see how they work?

[20:12] Marty Bent: I think that's, that should be table stakes. It's like, hey, let me hit the opt in button. Maybe get paid for it too. Maybe there's some economics incentive thrown in to help train this data. But maybe they would argue, well, we are paying you by giving these tokens for free because that's another, that's maybe a whole nother rabbit hole is like the economics of the top tier LLMs right now. They're highly subsidized.

[20:41] Mark Suman: Yeah, yeah. Your $20 a month subscription to ChatGPT. They're losing money on you. They're not making money off of all those. You can go, go look at their financials that they post publicly and they're definitely not making money off of the average user.

[20:55] Marty Bent: ChatGPT OpenAI is about to IPO at a trillion dollar valuation though. It's going to hop right up there.

[21:00] Mark Suman: That's a massive nonprofit right there.

[21:04] Marty Bent: But with all this being said, there are, I know I would argue, I imagine most users are unaware of this. Most people don't really think to care to understand how their privacy is being abused on these platforms. But there are others that do. And I'll just pull up this clip that you guys shared when McConaughey was on Joe Rogan a couple months ago. And it was actually extremely refreshing to see somebody like him. You would not expect to be. Or I would not. Maybe I'm, maybe I'm judging. But like he seems pretty, he seems pretty ahead of the curve in terms of understanding the, the nature of privacy. So we'll just play this clip real quick.

[21:45] Matthew McConaughey (clip): Have a little pride about not wanting to use an open ended AI to share my information so it can be part of the.

[21:50] Mark Suman: Yeah.

[21:51] Matthew McConaughey (clip): Worldwide AI vernacular. I am interested though in a private LLM where I can upload. Hey, here's three books I've written. Here's my other favorite book, right. Here's my favorite articles I've been cutting and pasting over the 10 years. And log all that in. And here's all my journals, whatever the people out and log all that in so I can ask it questions based on that.

[22:15] Mark Suman: Right.

[22:16] Matthew McConaughey (clip): And basically learn more about myself. Right?

[22:18] Mark Suman: You could actually ask it, hey, based on what you know about me, like what Books you think I would find interesting. Yeah.

[22:23] Matthew McConaughey (clip): Where do I stand on the political spectrum? Right. I'd like to know. That's. That's what I would like to do. Which is sort of a glorified word document, but it still would hold a lot more information than just, oh, can you find this term? I would be asking it and it would be responding to me on things that I've forgotten along the way. And I do have a little pride about.

[22:46] Mark Suman: For the zoomers out there, a word document is like Google Docs, by the way. It's a place you type stuff anyways.

[22:55] Marty Bent: But it's funny, he's describing what you guys are building. I think that's why you shared the clip. And there's people unaware that there are these private options on the market.

[23:04] Mark Suman: Yeah, well, and he kind of talks about right there, the training dilemma that you brought up a second ago, where he is someone who produces lots of content and gives it out to the world for free, effectively. Right. Yeah, he makes money off of it, but he's given it away. And information just wants to be distributed. So he makes films, he writes books, he goes on podcasts. So he's given all this information out there, and LLMs can totally train on those. It's. It's in the public domain as far as I'm concerned when that happens. But what they don't get is they don't get his thought process.

[24:56] Marty Bent: Yeah. No, I mean, you're talking about the opt in button. But what I love about Maple and why we're very excited to be supporting you guys at 1031 is that it's not even. You don't even have the option to opt in because you guys, the way you've constructed your product makes it impossible for you guys to see the data at all, I think. Why do you think there is this misunderstanding or not misunderstanding? Why do you think people are unaware that products like Maple actually exist? Is it becoming more popular? What have you seen over the course of this year as your user base has grown and you guys have been iterating on maple A.I.

[25:40] Mark Suman: yeah, part of it is we've only been around for nine months, so it hasn't been very long. Trying to get the word out there, coming on shows like this definitely helps. And so you've seen our user chart. Our user growth is up and to the right, so people are finding it. But you look at ChatGPT's user growth in their infancy, and it was just like. It wasn't up into the right. It was just like a straight line up. And so they grew crazy. They obviously had the network effects of Y combinator and all the other things that had gone before, so they had a lot of support that way, immediate visibility.

[29:46] Marty Bent: And this is all verifiable too, which I think is the most important part, because there are a ton of other quote unquote private AIs that exist out there. It's sort of, trust me, bro, privacy, right?

[29:58] Mark Suman: Yeah, yeah, exactly. There are some other great ones. People love to bring them up and say, how are you different than this one or that one? And they all operate on the like, read our website, here's what we say we do now. Just trust that we do it, which is the VPN kind of logic as well. A lot of VPNs, you just have to trust that they're not keeping track of your web traffic and then giving it over to law enforcement. And so when you use some of these other private LLM, private AI services, you simply just have to trust them because they don't have open source code.

[31:17] Marty Bent: And really digging into the juxtaposition what you guys are building fully encrypted on your device, in the cloud, encrypted in transit back to your device. And we've really been focusing on the privacy aspect, particularly the ethics around the privacy leaks that exist with closed source walled garden AI products. But it's really two sides of a coin there. You have the privacy leak and the ethics around that. But then the other side is they're taking all this data, they're training their models, and not only that, they have these system prompts that inject some sort of bias. And you wrote a manifesto recently, a free thought manifesto. And really highlighting the second, the other side of this coin, which is this sort of subconscious censorship, algorithmic persuasion that enters the equation. So let's dive into the.

[32:14] Mark Suman: Sure.

[32:14] Marty Bent: Dive into that why you wrote this and the sort of gentle nudging that these models can have on individuals and the profound effects that could have societally if they're successful.

[32:25] Mark Suman: Yeah, yeah, definitely. How do you want to jump into it? Do you want to bring it up on the screen and talk about it? Or you want me just kind of describe why I wrote it and what the thinking is behind it?

[32:34] Marty Bent: Yeah, why don't you begin with the why in the thinking while I get the link? I actually have notes in my Maple AI account. I have to find the link. I don't have the article itself up.

[32:47] Mark Suman: Okay. Yeah. The thought process here is that we do talk about data leaks and that's kind of like a today problem or maybe even a yesterday problem. We've been dealing with data leaks in cloud infrastructure ever since the cloud became a thing. So that's something that we kind of understand already. And it's just like, yeah, we'll put up some safeguards, yada, yada, yada. What we have never dealt with before is this idea that we have a system now that is effectively building a global mind control system. And what do I mean by that is that these LLMs are. They're right there with us as we are working through problems.

[35:28] Marty Bent: Yeah, and that's, I think that's one of the scariest aspects of this is obviously the privacy leaks are scary, giving up intimate details. But I think training, using these intimate details of your life, how you think, how you react and then creating a system of control that pushes you to act a certain way. I mean, this is the great reset World Economic Forum, whatever you want to call it, 2030 plan wet dream, where it's like, oh, we Trojan horse this productivity tool into society, everybody adopts it and then we use that as a command center to push people to believe certain things.

[36:15] Mark Suman: Yeah, well, and right now this is simply just inside of a chat window where you're talking to ChatGPT. But like imagine five years from now, ten years from now, we have robo taxis that are just pervasive around the city. And so maybe you are a person now who decides, I don't need to own a car anymore, I'm just going to take robo taxis everywhere. And you've got some earpiece in your ear and you're sitting on the couch, you're like, I want a burrito right now. And so you hit this earpiece and you say, hey, I want to go get food, get me a taxi. And you walk out and while you're waiting for your taxi, you know, three minute estimated arrival time, you start chatting with it about what you want to eat and you don't know the directives that's been given.

[37:48] Marty Bent: Well, not only that, and you explain this particularly well in Nashville last month at the Imagine if conference. And another scary aspect of this is sort of memory, Washington and gaslighting you into believing that you believe something in the past that you didn't. But it's speaking so authoritatively that you get gaslit into believing something that you didn't.

[38:16] Mark Suman: Yeah, yeah, definitely. It's. We have these memories that are building up inside ChatGPT and other systems. Maple wants to get a memory service. We don't have that yet. We do plan to introduce one, but we're going to do it in a way that is open and verifiable, so it won't have this vulnerability. But effectively what it's doing is. The way I like to describe it is that you're sitting down. You know, I'm in a chair right here, and maybe there's a person in this other chair next to me and they're interviewing me to write a biography about Mark. And they're just getting as much detailed information and writing this super detailed biography on me.

[41:03] Marty Bent: Yeah, it's incredibly dystopian.

[41:09] Mark Suman: Yeah, someone told me, and I think this is great. But you can. You can go into ChatGPT or any of these other products and you can ask it like, hey, if you wanted to lie to me, if you wanted to persuade me on something, how would you do it? And the results are really fascinating. I recommend everybody try this. If you are a chatgpt user, just go Try this and see what it says to you. It might take a few prompts to, like, really warm it up and get it to tell you, but it'll be like, oh, well, I've learned that you believe stuff if I feed it to you this way. So if I wanted to lie to you, and this is how I would do it. And it's quite fascinating that it has very quickly learned that you are a type of person that is gullible in this direction.

[41:50] Marty Bent: Sup, freaks? Have you noticed that governments have become more despotic? They want to surveil more, they want to take more of your data. They want to follow you around the Internet as much as possible so they can control your speeds, control what you do. It's imperative in times like this to make sure that you're running a VPN as you're surfing the web, as we used to say back in the 90s. And it's more imperative that you use the right VPN, a VPN that cannot log because of the way that it's designed. And that's why we have partnered with Obscura. That is our official VPN here at tftc, built by bitcoiner Carl Dung for bitcoiners, focused on privacy.

[46:11] Mark Suman: Yeah, now. And you bring in that element of positivity there. Right. Because we've been, we've been kind of dark and doomer most of this episode. But like, the reason we go down this path is that there's so much cool stuff that can be done. There's so much, so many productivity gains that can be done. Humanity has the potential to really, like, do a massive upgrade in our standard of living. And I know that there are stories that are in the news right now, this week and last week of massive layoffs and they're blaming AI. I think that that is being shallow on the. You know, and looking for a scapegoat.

[47:50] Marty Bent: Yeah, I mean, I try. I attempted to make a meme yesterday because I thought it was just the whole Neo launch was very funny. There was a bunch of funny still pictures that came out of. Oh yeah, that demo video that they shared online. But it's half jokingly, but you could easily see this. It's just a play on the fight club meme. Remember this? The robots you're trying to step on. We're everyone you depend on. We're the robots who do your laundry and cook your food and serve your dinner. We make your bed. We guard you while you're asleep. We drive the ambulances we direct your call.

[48:59] Mark Suman: Yeah. Well, in this image right here, this is a great representation of the current iteration of the popular AI apps, is that Neo has this like really soft veneer on the front, right? And these two eyeballs that are supposed to kind of look like Baymax and make you feel like you just watch Big Hero 6 or something. But one of the memes I saw was an artificial intelligence video of it ripping off its own skin. And underneath it's like the Terminator T1000 or whatever with like red glowing eyes. And really that's what it is. If you were to pull off that veneer, it's. It's this scary looking robot of metal and gears that could just like totally wreck you.

[49:58] Marty Bent: Yeah, I saw, I mean, the, the attempt to waifu the humanoid robots already is, is very strong. It's like I saw somebody like a woman robot companion robot, if you will, for people who can't find the human companions. And they ripped off the face and it looked like the T100. It was like, oh God, people are going to be welcoming these things into their homes. And that's the thing. I mean, not to take too much of a black pill and to sort of push us back into the direction of this stuff is useful. It's here. If we do it the right way, it can be incredibly beneficial to all of our lives. There is a correct way to do this, is there not?

[50:45] Mark Suman: Yeah. Yeah. So let's unplug the dark pill chip and insert the white pill chip now, or floppy disk, whatever metaphor you want to use from your generation. But the. We can have our cake and eat it too here with AI. And that is we just need to make these systems verifiable. I was going to say vulnerable verifiable. Right. We need to have open code, we need to have verifiable standards, we need to have cryptographic proofs, we need encryption. We can build these systems. I kind of look at them with like, we're like three things, right? The first one is that they need to be open, so we need to be able to see the code, we need to know what's going on.

[54:42] Marty Bent: What would you say to people who push back and say it's great and all? It's an ideal way to do this. However, the feature parity with the top line models just isn't there yet. Is that a true statement? Will it be true forever? If it is and I guess what is the roadmap for Maple moving forward to reach feature parity and make it so you can't basically not tell the difference when you're using Maple verse chatgpt?

[55:10] Mark Suman: Yeah, the top line models are better than the open source models, but the gap is closing and very quickly. 5 or whatever we saw within just a matter of A few weeks that open source models came out that were like 80% as good as. Now that gap is closing. It's like 90, 95%, you know, and a lot of benchmarks are getting really close. Some of them, they match. And so we're seeing the gap close. And then also you look at like, okay, what's the, what's the delta there? 3% maybe? Let's say we get that. Good. Well then you start to look at, do you even need that final 3% with what you're doing as an everyday average user?

[57:40] Marty Bent: And what are the different power user archetypes that you're observing right now?

[57:46] Mark Suman: So we definitely have just everyday consumers who want to use it for their own personal life. And then we have a lot of people from the legal industry. We have lawyers signing up because they've been told by their bar associations not to use ChatGPT because it breaks client attorney privilege. We have financial advisors, we have attorney or sorry, accountants coming on, therapists, a lot of people in the like medical adjacent field that are starting to use it. And then we have some app developers in that space as well. We have app developers who are making health care apps that are not HIPAA compliant. Like they don't have to be held to HIPAA standards because they're medical adjacent.

[59:37] Marty Bent: Yeah, it feels like this will have to become a standard, particularly for these sensitive use cases. Lawyers with confidential client information, doctors with confidential patient information accounting, that's like at tftc I've always wanted and now I can do it with Maple that you guys have file upload. But just taking our QuickBooks and uploading a PDF or an Excel file of our books and analyzing it and trying to think like, okay, how can we make our business more fiscally responsible? It's like, I would never do that with OpenAI, but I feel comfortable doing it with Maple because I know you guys can't see what our books look like.

[1:00:24] Mark Suman: Yeah, no, definitely. So that's. And like I said kind of earlier in this conversation, it's like you. You're starting to see the value of having a private AI where you don't realize that you were holding back certain things. Maybe you do, maybe you, like consciously said, I want to upload this, but you are self censoring. You know a lot when you use ChatGPT because you simply just don't feel comfortable. Or you think about the apps on your phone. You know, if you're on an iPhone, you've got that health app, that little white icon with the heart, and all sorts of personal health information's in there, like how many steps you took, what your heart rate was, if you have an Apple Watch.

[1:01:43] Marty Bent: I think bringing this back to McConaughey, his vision of what he wants, is there an argument to be made that actually would be better on an individual level, like doing what you just described within Maple, instead of trying to do that with something like ChatGPT, does the response, the inference, get corrupted by all the other data that they're collecting? Could you make an argument that by leveraging something like Maple, which gives you basically a sandbox and the secure enclave that's yours, and just feeding it data specific to you over time will actually result in better outputs than if you were to do this with ChatGPT because you're sort of thrown into an ocean of data being provided by other users that those models can access to?

[1:02:31] Mark Suman: Yeah, that's a good question. Possibly. I don't know the scientific answer to that right now, but you potentially could. I mean, you think about how it just gets to know you so much better. I think ChatGPT could probably build a similar product to that. It just wouldn't have the privacy angle. But then again, you would be self censoring on ChatGPT without realizing it because you know inherently that it's not private. So yeah, maybe you do get a better experience because you are opening up. It starts to learn you better and more intimately and can give you better responses that Chat couldn't. You asked because maybe it's like mixing in your results with a lot of other people.

[1:03:29] Marty Bent: Yeah, and that's actually I'm happy brought up business model because that's something that we glossed over earlier that I think is really important to dive into, which it's becoming clear that a lot of these models are injecting advertising into their business model and the outputs can be heavily influenced by the advertisers providing revenue to the model providers.

[1:03:52] Mark Suman: Yeah. And they market it as a feature. I can't remember what, which Igpiti is calling it, but Sam Altman goes on there and says, hey, great news everybody. While you're sleeping, ChatGPT is thinking for you and when you wake up, it's going to give you this snapshot. A daily, you know, news brief of all of the wonderful shopping that you can do today. I've gone through and found all the products that you want to buy and here they are right for you. Given all this information. I know about you and it sounds great and convenient, sounds awesome for some people. But it's literally just here's all these advertisements that we want to throw in front of your face. But we're going to spin it as we're doing you a favor, doing you a service. Yeah.

[1:04:35] Marty Bent: Ads we ever get through, get, get away from them. This episode brought to you by Becky on Chain Obscure Silent and maple. And maple. Unofficially we should put a maple. We should, before we post this, get a maple sign up. Code for tftc. Just throw it out there.

[1:04:51] Mark Suman: We need to do that. We don't have one right now, but I'll just make an executive decision. On air. Business on air. 10% off TFTC code TFTC. When you sign up. There you go.

[1:05:03] Marty Bent: Business on air. It's a timeless tradition here in the TFTC family of podcast.

[1:05:10] Mark Suman: Yeah.

[1:05:12] Marty Bent: Last thing we need to touch on, because I think this is particularly important to you in me, which is our children are going to be growing up with this stuff and the importance of making sure that this is done correctly so that the children don't get corrupted. Because our children particularly are in an age where their minds are very malleable and their emotions are very malleable. And you don't want Sam Altman and Zuckerberg and the founders of Anthropic controlling the malleability of, or how our children's brains are changed over time as they're learning and growing up with these tools.

[1:05:55] Mark Suman: Yeah. So the question there then is, let's talk about family and let's talk about kids using AI. Man, that's a whole nother rabbit hole to go down. But it's. It is scary, right, that you're just going to hand over your kid to talk to this engine that has been trained on the brain, the output of the world, and everything that comes with it. We try so hard as parents, and I know every parent has their own threshold. Some parents lock everything down, some parents don't lock anything down. And then there's this in between. We try very hard to be selective and say, all right, we're going to introduce this technology at this point, you know, in our child's life, and we learn, okay, that was the wrong one with that child.

[1:10:41] Marty Bent: You don't want the kids getting one shotted by the LLMs. Yeah, there's plenty of adults getting one shot at by the LLMs. It would be, I mean, going back to what we were discussing earlier, this gentle nudging, this subconscious nudging towards a political worldview that is dictated by the people that write the system prompts. You don't want that. If you thought schools were indoctrination camps, this steps it up many orders of magnitude in terms of its effectiveness.

[1:11:10] Mark Suman: Yeah, definitely. We go back to that anchoring bias thing we talked about, right? If you are a seven year old child, there are so many things in the world you've never been exposed to and so you have all these anchors that could just be dropped right into your brain by an AI that's going to introduce a topic that maybe you as a parent would not want to introduce to them yet. And suddenly it's going to put this anchor in their mind as a seven year old and now as they grow up, you are going to have to be fighting against that and try to pull them away from that and say that is not the view of the world that I would love for you to have.

[1:12:30] Marty Bent: Yeah, I mean, I think I discussed this with you. My older boys school, they go to a Catholic school and the administration's very on top of things, very tech forward. They have a robotics class, they're really good at STEM stuff and they're already back to school. Meeting they basically threw out, like, hey, we want to be out of the curve. We're going to put together an AI task force. I sent an email like, hey, I would like to be on this to make sure that we don't mess this up. Like, what we're discussing right now is how do we control our child's or children's interaction with this technology in the house.

[1:14:12] Mark Suman: Yeah, no, that's true. I mean, good on you for being involved. Right. I wanted to join that task force. Your kids are incredibly lucky. And statistically they're growing up in a home with two parents that are involved. They're going to be statistically more successful in life. And a lot of people would say they've won the lottery of sorts. So I commend you for being involved in that way. But yeah, schools are going to be kind of picking these AIs. And you think about the big fight that's gone on over the last two or three years with school boards has been the books. What books are they assigning to our children for required reading? And it's like, okay, that is small potatoes. To which AI are they going to unleash on our children in school and let them play around with. That's like a thousand times more important than which book are they going to be assigned to read, really?

[1:15:03] Marty Bent: Is no like, going back in terms of like introducing AI to children. My boys are younger 5 and 3, and the extent of their interaction with AI is obviously I don't give them a phone. They don't really interact with screens that much, particularly tablets and phones. But the extent of our use is we'll use ChatGPT voice. And we've named our ChatGPT instance Daryl. And if they ever have just a random question, it's like, okay, let's ask Darrel. They love asking Darrel questions. And it's. And I'm comfortable with it because it's fun, benign questions like, what's the fastest fish in the sea? How long does it take to count to 100 trillion? How is glass made to questions like this. Which is like, all right, I'm comfortable having the interaction with AI be to this extent. But as they grow and their questions get more esoteric and existential, it's like, okay, I don't know if I want Daryl answering these questions for them.

[1:16:07] Mark Suman: Yeah, especially the questions they don't want to come to you for. Right. And it's not that they don't trust you, but as a kid, as a teenager, there are certain things you just don't want to chat with your parents about. And do you want them asking Darrel these questions? Yeah, probably not.

[1:16:23] Marty Bent: Are you optimistic that we can get to this privacy preserving, open source, verifiable future?

[1:16:31] Mark Suman: Yeah, I mean, I'm optimistically. We can build it. I definitely think we can do it. The question is, is there going to be enough public response for it? Are people going to want it? We definitely see if you look at an app like Signal for Texting, people recognize the value in encrypted text messaging. So there for sure is optimism and hope in that model. And if we can capture that same kind of paradigm and bring it over, we're trying to build the signal of AI and make that available to the world. And we're trying to show a model that other people can implement and a pattern that they can follow to build.

[1:18:04] Marty Bent: Let's do it. Thank you for doing what you do, sir. It's very important and I can say as a user of Maple since day one, the UX is definitely getting the parity with the larger models. I've been beta testing the live data feature and that's been incredible. Upgrade in terms of response quality, particularly if you want to talk about something that's happening in the news or something that is topical. It's just been incredible. Again, upgrade in the user experience and the quality of the responses and the fact that I think the other mind blowing fact, I mean, you just mentioned the fact that you've done all this with a team of two is highly encouraging because it's like if the two of you, yourself and Anthony can get it to this point, imagine what can happen when you get a critical mass of manpower focused on building the solutions in this way.

[1:19:19] Mark Suman: Yeah, yeah, definitely. I appreciate. You appreciate using, using Maple from the beginning and helping us test out things and supporting what we're working on. I think it's great. And if we can get a critical mass of people who care about this and are building tools and using those tools, then that scenario of the whole robo taxi, I want to go get a burrito and everything that I do is kind of censored and surveilled. We could, we could affect the community around us, affect society to where these tools are not closed like that and they're actually open and verifiable.

[1:19:50] Marty Bent: Let's do it.

[1:19:51] Mark Suman: Yeah, let's make it happen.

[1:19:53] Marty Bent: Thank you. Thank you for your work. Thank you for joining us on such short notice. We caught up yesterday. I was like, we need to catch up on the podcast because I think people need to hear this message and need to act. Go sign up for Maple use the code TFTC business on air, 10% off. Play around with it, give feedback. And if you're interested, do you have any calls to action for people may be wanting to help out on the actual construction of this model?

[1:20:19] Mark Suman: Yeah, go to try Maple AI and we have all our links in there to GitHub. We have a Discord as well. You can hop in there and chat with us. It's becoming very lively. We have some very passionate users. So if our service goes down for like 30 seconds or a minute, they're in discord saying, hey, Maple's down and then it comes right back online. So go in there. There's some passionate people that would love to chat with you. And then we also have our developer API and there are people in there talking about that too. So if you are a builder who just wants to tinker around, come sign up.

[1:21:38] Marty Bent: We'll link to all that in the show. Notes. Go seize the day. Peace of love freaks. Thank you for listening to this episode of tftc. If you've made it this far, I imagine you got some value out of the episode. If so, please share it far and wide with your friends and family. We're looking to get the word out there also, wherever you're listening, whether that's YouTube, Apple, Spotify, make sure you like and subscribe to the show. And if you can, leave a rating on the podcasting platforms, that goes a long way. Last but not least, if you want to get these episodes a day early and ad free, make sure you download the Fountain podcasting app. You can go to Fountain FM to find that $5 a month gets you every episode a day early ad free helps the show gives you incredible value, so please consider subscribing via Fountain as well. Thank you for your time and until next time.