Everyday AI Podcast – An AI and ChatGPT Podcast - EP 535: How AI Is Changing Personal Data and Privacy Forever

Episode Date: May 29, 2025

We're so quick to give AI access to see the world around us, but what are the dangers? And what are some powers that you're not aware of? We'll be sharing both as Michael Tiffany, Co-Founder and CEO of Fulcra Dynamics, joins us to discuss.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Have thoughts? Join the convo.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1. Power and danger of letting AI view your data2. Quick emergence of live AI technology3. Kill switch and intelligent data routing4. Local compute and orchestration requirementsTimestamps:00:00 AI Advances: Power and Privacy Concerns05:34 Fulcra: Pioneers in Streaming Data Storage08:09 "Hacker Cyborgs and AI Privacy"12:08 AI Surveillance and Privacy Concerns15:02 "Experimenting with Custom AI Assistant"16:40 Calendar and Location Data Insights21:46 "Smart Local Monitoring Strategies"24:09 Miniature AI Models Revolutionize Technology28:36 Experiment with Personal AI ControlKeywords:Generative AI, AI technology, Google Gemini Live, Gemini's AI, AI agent, Microsoft Copilot Vision, personal data, privacy, data security, artificial general intelligence, superintelligence, live technology, AI observability, AI assistance, AI models, multimodal models, world models, local inference, edge AI, small language models, Frontier models, cloud-based models, Enterprise software, on-premise software, Cloud Software, AI Orchestration, Local Compute, Hardware, Biohacking, Personal Data ControlSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live and Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. Consumer models and AI technology is getting so good and cool.

It's scary, right? Without any real tech know-how, you can go use as an example, Google Gemini Live and Gemini's AI can instantly see your screen. You can use chat GPT's advanced voice mode to interact with a, neural low latency AI agent that can see the world around you, right? Microsoft co-pilot vision can see parts of the web that you're browsing that even you can't see. So there's obviously great power in letting AI see you. And then when you throw in all your data, I mean, the possibilities are endless, but there's dangers as well, right? Should we be getting all our data to these big companies?

What are the downsides, right, of using these things and giving them your personal data? But I think regardless of where you stand on the topic, I think today's conversation is an important one because this is the future of generative AI, whether you want it or not cameras embodied AI. It's going to be everywhere. So generative AI isn't just a large language model you sit and quietly use in the silence of your own home or office. Generative AI is a live technology, and so we have to understand the power and the danger. All right, I'm excited for today's conversation.

I hope you all are too. If you're new here, hello. My name's Jordan. This is Everyday AI. This is your daily live stream podcast and free daily newsletter, helping us all not just keep up with AI, but how we can use this all to get ahead to grow our companies and careers. I want you, dear listener, to be the smartest person in AI at your company. And here's your cheat code, our website, Your EverydayAI.com.

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All right. This is technically we're debuting the show live. It is pre-recorded. So if you are tuning in for the AI news, it's going to be in the newsletter. Don't worry. But I think you're going to want to listen to today's conversation. I can already tell it's going to be a banger. So enough chit-chat from me.

I'm excited for our guests for today. So please help me welcome Michael Tiffany, the co-founder. and CEO of Fulcra Dynamics, Michael, thank you so much for joining the Everyday AI show. It's a pleasure to be here. All right, I'm excited for this one. Michael, tell us a little bit about what you all do at Fulcra Dynamics. All right.

We built a personal data store for all of the data that your life produces from wearables. So we collect a lot of biometrics. You can stream your calendars in, your location. The idea is to take all of your information producing systems and bring it together under your control into one place. So you can see it, make it truly yours, explore it, but also connect it with a helpful AI agent. So, I mean, who's your average customer, is it just like dorks like myself who, you know, maybe have like an Apple Watch or, you know, a couple wearables and they just want to biohack their life?

Or, you know, what's, like, who's your average customer and what's everyone using your platform for? It's, I'd say there are two different, you know, big customer types. One really is the biohackers, right? You have multiple wearables. And if you're living that kind of life, it's sort of annoying that every single thing that you buy comes with its own dashboard. That dashboard is probably only on your phone. And so if you want to see everything, you've got to like check five different screens. And sometimes what you want to do is see everything and you want to see it on your laptop on like a big screen or on your desktop where you have a really huge screen. So biohackers are loving Fulcra just to bring everything together. and have one visual place. So similarly how businesses have business intelligence dashboards. This is a personal intelligence dashboard for all your smart devices, right? Oh, totally. Yeah.

No, we can get buzzwordy there. It's like the single plane of glass or your life analytics. Yeah. Okay. And then kind of similarly, right? Like, just riffing on like bringing enterprise norms to consumers, people don't have a data lake. Like there's no place to plug an AI into. So the other category of users of Volcro are people who are using us as that data link.

So you collect all the data from all these systems. And then you teach an AI to do function calling against your repo. And bam, you've got a personal AI. Amazing. And give me quick rundown. So, you know, kind of assume, you know, watches, like, I mean, what other, you know, connectors or hardware or software do you all post? pull from. We really shine when it comes to the data that's like not already in files. Like, it's super easy to upload files to an AI. Like, no one needs help for that. And there's plenty of cloud

storage that'll store files. But how do you store, how do you make your own copy of like your calendar or your heart rate? Right. Like that's a continuously updating stream. And there's no streaming data store for consumers. So we had to build literally the first one. So that data, that data tends to be like biometrics. I think we store your location history better than any other alternative. Like all those continuously updating things, virtually any IoT device, if you have smart stuff in your house and you want to make your own copy of that, that's a place where Fulco really shines.

You can also upload arbitrary files. There's a library function. The idea truly is to take your desilued data from whatever. source and and and give you a single home for all of it. So we'll absorb whatever, though I'd say that the unique strengths tend to be the streaming data. So I definitely want to dive in a little deeper on your personal side and personal experience of all this. But before we get there, I want to zoom out and just answer the question, right? Answer the question of this episode title. What are both the power and the danger of letting a,

I see you at all times. I will. I'll start. Yeah, I'll do it in that order. And I'll very much make this personal. I want to be a cyborg and I think I can be a cyborg before implants are possible. Like if you look at consumer tech, this is a magical, this is a magic ring that knows when I'm stressed, which is like beyond comic book technology. Like this is an amazing thing.

Is that just the aura? Yeah. just an or a ring, right? Orr rings are magical. If you look at the total device footprint I have from a smart bed, connected scales, I got, you know, a car that's practically a computer with four wheels, the capabilities are really high if all of that stuff was brought together and was like really unified under my control. So I've been leaning into how all of these devices, can actually like augment my cognition in real time and make me effortlessly quantitative. But I'm a hacker. Like I was a teenage hacker. I joined Ninja Networks. I've done hacker

hijinks for my entire life. And so in my pursuit of being a cyborg, I also just cannot give up my security lens. And the danger here, the opportunities that we can all be like cognitively enhanced. The danger. is that it's really hard to delete stuff out of the latent space of a, you know, transformer model. Like, you know, you give it data and it adds it into a latent space and it's to some extent like not really yours anymore. So if we're going to use these models practically, there needs to be like an undo button where, where I can opt in to share with, you know, Claude or chat GPT, my location and my heart rate. Seriously, my custom GPT knows I'm doing

this podcast interview right now and knows what my heart rate is. But you need to be able to revoke that decision and go, actually, no, stop. Like you can't access this anymore. So I think the future I'm trying to bring about is one where we can safely interface AI with your personal data, but that has to be a two-way door. You have to be able to actually change your mind later and say, never mind, you're cutoff. Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't

really get traction to find ROI on Gen. A.I. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft, and NVIDIA have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for chat GPT training for thousands or just need help building

your front-end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to your everyday AI.com slash partner to get in contact with our team. Or you can just click on the partner section of our website. We'll help you stop running in those AI circles. and help get your team ahead and build a straight path to ROI on Gen AI. So you very clearly laid off a little bit of the power and a little bit of the danger as well, right? Like you have to still have, you know, some hold of your data and security.

But, you know, what happens, right? Because I think, you know, as the conversation in early 2025 has already shifted from large language models to artificial general intelligence to super intelligence, right? So what are maybe the dangers as we look down the road? Because yeah, more and more people now are starting to use your advanced voice mode, your Gemini Live, right? Like all of these live AI assistants that are so easy to use and actually really, really good. So, you know, like what kind of dangers are we looking at in the, you know, maybe medium term as we have this quick emergence of new live technology that can see us? But then it's like, yo, like we're already talking about super intelligence now?

Right, right. Yeah. Well, let's talk about a way in which society can go sideways, which is we can all become paranoid. I think there's something deeply important about privacy. We should probably consider privacy a human right, a basic human right. And why? Because when you take privacy away, it messes with your head, right? We do not want all of our fellow citizenry to be paranoid about who's watching and what data is being collected.

So a principal way that this can go wrong is that now that superintelligence seems to be within grasp and it can lift everyone up by being a helpful thought partner, that has to be driven by, let's call it observability to use the nerdy term. And that observability needs to have some privacy protections or it'll create this feeling like the feeling that we're always being watched, which I just think is not a good feeling. That's not a way in which we want society to head. And that is a near-term risk because think about the number of, for instance, surveillance cameras that just have a security purpose that have ever been installed across the entire world. Well, we don't think of that as too creepy because there isn't an infinite number of people who are like literally watching every camera. however, you add an AI model that can understand what's being seen. And every single surveillance camera that's ever been installed becomes an actual watcher that's interpreting what it's seeing.

That's crazy. And it's like it's not going to take years of effort. It's almost a light switch. Like we just take the feed that already exists. We add the AI to it. We now have intelligent eyes behind every single screen. So that transition from the safety of privacy to wait a minute, you can't be paranoid enough might happen much faster than society is ready for.

And I think it's important to call certain things out because AI in large language models move extremely fast, especially if you've been sleeping the last like four or five weeks. Because the reality is all these models are multimodal by default now, right? Like as an example, the older, quote unquote, older GPT4 models, it was technically using three different models under the surface, right? But now with the O or Omni model, it's all one. So these models, Gemini 2.0 as well, these models are multimodal by default, at least Gemini, right?

It understands video. It understands audio, right? People think they're just some text machines, but they're not. And as world models become more and more popular, more and more available, these AI systems are going to know a lot. and the more that we give them. So I actually want to rewind a little bit, Michael. And you kind of gave us a bullet point list of, you know, hey, you know, I have a smart ring and,

you know, smart bad and all this. Can you just give us the full rundown, but also say, here's what I've learned from allowing AI to kind of see everything about me and how that's impacted your decision making. Oh, okay. All right. So the, I've experimented with all kinds of things. And so I'll lock you through some of the things that I found a extremely valuable and then the duds.

The, I'll go back to an eye-opening experiment. I did coincidentally, literally a year ago today, which is when I first got my own custom GPT, like up and running with access to my Fulcord data store, so I could share all kinds of real-time systems with this GPD. I named it operator. And one of my early test queries, I was about to get on a plane to go to a hacker conference, Schmukon.

And I asked operator, where should I have breakfast after my flight tomorrow? Operator did what I expected, which is it made the function call, got my calendar information, and a JSON blob, parsed it. It did something better than I anticipated. And I did not program this either as prompt or anywhere in the tech stack. It found the flight. Great. It looked ahead in my calendar, saw where I was staying, saw the hotel that I was booked at. And it specifically made recommendations about where I should eat, give me five restaurant recommendations that serve breakfast near my hotel, which is brilliant.

Like I was expecting it to give me recommendations maybe near the airport, maybe in the city within Washington, D.C. It was extra insightful by looking ahead to realizing, you know, eating near the hotel would be much more convenient than eating near the airport. So I've been almost like chasing that magic that that like, woe you did better than I then I kind of asked for since then. And as it happens, I would say giving models access to my calendar has been extraordinarily fruitful. There's a whole bunch of inference that's available when you do this about just like who you are as a person. I literally did not explain like who I'm married to, who my children are. But you can get that from

from the calendar looking at recurring, you know, calendar reminders, which was wild. And so then that's been a source of, of proactivity, right, to like be a good person, which is a lot of fun. Location turns out, so my location history is constantly being generated from my phone, and now I have access via my Fulker Data Store, and therefore any AI hook up to the location history is able to access it. It turns out that lots of memories, the way you encode your memories in your meat space neural network, it often uses the hippocampus to like encode things relative to location.

So when you faintly remember something, there's sometimes like a location angle to that. You're like, oh, yeah, Bob said something to me. you're trying to remember that thing, but you remember where you had the conversation. Then you can locate that. So here's like a wild stringing things together. You want to remember the details. You can get to a location. Then from the location, I can get to a timestamp so then I can find it in my AI transcript

driven by whatever, Otter, for example. So lots of like following the threads to essentially have AI-assisted memory. your brain is like this rich data store, but your lookup system is non-deterministic, right? Like you don't have a good search function on your brain. So if you can use the AI to help you with search, then it'll get you to the thing that triggers like the full memory out from your brain. So that's been tremendously helpful. I've also, I've tried random stuff.

I especially want to understand my own patterns, like my own patterns of eating. Because tracking your eating is a chore. So I was like, can I outsource this chore to AI? Right. Can I use especially an image model to make this easier? And so I've tried some weird stuff. Here are two things where, like, I'm still tweaking. One is just using a cheap webcam and pointing at the refrigerator

to just catch me when I'm like snacking as a way of, it's actually a way of like not doing my work, right? You know, I like, I want to procrastinate and I get up and go look at the fridge, see what's in the fridge. So it's been interesting to monitor that. I also tried, like this is almost good. I installed smart breakers. So I'm getting a signal from all of the power usage in my house. And an AI model can apply inference to, for instance,

look at the power to the stove to do effortless tracking about when I'm doing cooking. Now, that turns out to be noisy. Like, this is almost good. A future experiment in mine will probably use like a camera pointed at the stove to like try to capture what's literally being cooked along with power monitoring. And then the power monitoring will also reveal over time rhythms of my house. Like how often are we eating dinner at the same time? are there seasonal variations?

So like these are works in progress, though. I would say that broadly what I've been most happy with is almost like that. It's like the understanding my own patterns and helping me recall things when I can just pull on one thread I get to the big memory. Yeah. It's so interesting, Michael. It's like you've almost, you know, big brothered yourself. Yes.

Some people are fine with it, right? Even me. I'm like, like I'm hearing you talk. I'm like, okay, I want, I want Michael to be like my personal biohacking, like, mentor. Like, I don't know how to do half of this stuff, but like, I'm always good giving all my data away, take everything, right? But you're for your own personal privacy. I mean, do you have a kill switch?

Do you have an off button? Like, you know, because people are probably thinking like, hey, yeah, this could go bad in the future if, you know, AI goes off the guardrails. Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI assistant, now live in the Adobe Firefly app, the all-in-one creative AI studio. Powered by Adobe's creative agent, Firefly AI assistant lets you start with your vision, just describe what you want, and shape the outcome as it takes form with the assistant. The assistant orchestrates multi-step workflows,

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See it today at firefly.adobie.com. I know, right. Okay, so, yeah, two responses there. Like, you're not the first to make that observation. We've been kicking around at Fulker, the idea of, like, some high-end consulting services, like the AI SWAT team, right? We're just going to show up. We're going to set everything up for you.

Like, like, just what do you care about? Okay, like, we'll figure out how to monitor it. Jethink would be kind of a fun business. But the kill switch is everything. Plus, you need to be smart about, about like, intelligence routing. So for instance, I'm like a huge fan of foundation models, but I don't want to use them everywhere, especially when it comes to experimentations with like self monitoring with cameras. Because you're going to capture stuff that you don't want anyone else seeing. I'm not the only person who goes to the refrigerator and opens it.

And like sometimes people are going to be doing that in various stages of undress, right? Like, this is not something to necessarily send to Open AI. So in that particular case, you want to hook that camera up to a local image model that's, you know, a small parameter model you can just run on, you know, some local to you computer, you know, an old laptop or something that's doing the pre-processing, maybe discarding a whole bunch of stuff and pulling out the intelligence that matters for my, you know, silly food tracking, right? Yeah.

So sometimes I think the answer is you want to use a combat. of local models and foundation models and do a whole bunch of scrubbing where you just delete stuff. Second, and I think more globally important is that everyone who works in software engineering understands that you have to measure what matters. This is why we're hung up about observability. So if you don't have observability over the most important metrics, then of course you are not managing those metrics correctly. Well, that applies to life as well. So in order for an AI to be able to help you. It kind of needs to see you. And what's important to me as someone who's worked in computer security for a long time is that that can't be a one-way commitment. I do think that

a lot of tech behemoths are going to say, listen, we run the best model and we already host your email. We already have this data and that data. Why don't you give it all to us? And that freaks me out. I think that you don't want all of your personal information literally right next to the model. You want to grant a model temporary access to your data and you want to be able to say, today I change my mind. And I'm not going to explain myself. I've just cut you off. Yeah.

It's interesting because, you know, Michael, you were there talking about, you know, as an example, you know, local inference, edge AI, you know, small language models. We talked about it on this show earlier this week, but some interesting research from Microsoft came out that gave, essentially they figured out model parameter sizes, right? And if you're not that big of a dork, right, certain edge AI, right? So offline essentially, you know, small language model. You can't run these huge, like the original GPT4 was like 1.7 trillion parameters.

But, you know, this recent Microsoft paper said that GPT40, mini, which is a very capable multimodal model, was only $8 billion parameters. So, you know, Michael, I'm guessing if we have this exact same conversation, January 10th, 2026, we are going to have frontier models that, in theory, could live on that aura ring, right? How does this change the future of what's possible? As these models get smaller, you can move them on device. How does that change it? But then also, how does this change it for business, right?

I get it. We're coming at it from this biohacking, which I love, the personal biohacking angle. But as it becomes more powerful, how can this change what we can do for also our companies and careers? I think that people who have been working in enterprise software have a profound advantage in predicting the future of personal computing right now. Because one, we see Nvidia coming out with like a local supercomputer. So if you've been working in enterprise software, especially as we cycle between like believing in on-prem, you know, believing in client server, which is now rebranded as, you know, cloud, right? Like, you see the pendulum going back and forth.

I think there's going to be a resurgence in, like, local compute. Lots of personal computing is essentially cloud-based at this point. And I think that local, the desire for private local inference is going to drive a mix of, like, on-prem and in the cloud for everyone, which is going be a really fun, you know, transition to live through. The, it's not just Open AI innovating in low parameter models. Of course, Microsoft also recently released, you know, 5-4, which, which hit awesome levels of performance with only 14 billion parameters. Amazing. So I think these models are going to be within reach of local hardware. But the addition of inference to get better answers, as illustrated by the amazing demo of 03 suggests that we're not going to be eliminating,

you know, cloud-based frontier models for a very long time. Instead, we're going to have this mix. So you'll have some local compute. And then when you want to really think through a problem in some sort of hardcore way, if you want really advanced reasoning, that's probably not going to be local. It's probably going to be, you know, cloud-based. So incredibly, there's going to be like this incredible burden of orchestration, the kind of stuff that we've all been struggling with as enterprise like

SaaS engineers, facing every consumer. If you think about it, every consumer is living a life that's much like the enterprise from techie to go. We're a mix of on-prem and the cloud, multi-device, right, from multiple manufacturers, they don't all work together, but people don't have middleware to plug all that into. So the orchestration burden is real. And it's basically totally unsolved. So, you know, if you're an entrepreneur thinking, how do I build a business that has a moat as intelligence gets cheaper and cheaper, I think orchestration is like this giant unsolved problem.

So much that I want to dive into, Michael, but this would go on for many hours, right? But like, as we wrap up today's show, because we've talked about a lot, my brain's going in a million directions. I'm sure everyone else's is as well. I'm going to ask you to bring it all back for us. What do you think is the one most important takeaway for people to understand, right?

Because more and more people are going to be in your shoes, businesses as well, right? As this becomes shifts from more of personal biohacking to, oh, our company can start doing these things as well. What's the one most important thing that you want people to know about the power and the danger of letting AI see you? It's, wow, put yourself in charge by experimenting. now, like get started, make your own GPT, even without coding skills, so that you're almost like training your brain about thinking about ways to bring an AI to bear on the problem that you face. This is going to put you almost instantly on the leading edge, because operationalizing these models requires almost like a feel for it, right? So you need to train your tacit expertise

in delegating thinking to the model. So that is the number one thing. And then my second takeaway is you think about the data-producing devices that are in your life right now and where they live. So what third parties are you already like arming with your personal data?

and do you want that data to live there? Right? Does it start getting control over your own, let's call it data footprint? Ah, that's so important. I think great advice as, you know, we're all dealing with this swirling of innovation and data and technology and AI, you know, in the early parts of 2025. I think that's great advice that you just gave us all.

So Michael, thank you so much for joining the Everyday AI show. We super appreciate your insights. It was a pleasure to be here, and this was an awesome conversation. Thank you. All right. As a reminder, you all, that was a ton. I'm not going to lie. My head is spinning with possibilities, ideas, all of that.

We're going to be breaking it all down in today's newsletter. So if you haven't already, first of all, why haven't you? But you need to go to Your EverydayAI.com. Sign up for that free daily newsletter. Also, everything you need to keep up. It's all there in on our website. So if you haven't already, go to Your EverydayaI.com. Thank you for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks y'all.

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