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The Double-Edged Sword of Letting AI "See" you
In an increasingly digital world, artificial intelligence technology continues to permeate everyday life; connecting us, simplifying tasks, and effectively "seeing" us. However, this advanced technology is not without its challenges. Harnessing AI's potential effectively and responsibly can revolutionize businesses and industries, but it's a balance between leveraging its capabilities and maintaining ethical practices and security.
The Power of AI Monitoring
AI technology holds unique potential for self-monitoring and understanding individual and business behaviors and patterns. Innovative creations like smart rings, smart beds, and other IoT devices can gather biometric data, calendar appointments, and location history in real-time. Incorporating these technologies into everyday life can provide profound insights, streamlining decision-making and enhancing productivity.
With the aid of AI, businesses can take unprecedented strides. For example, AI models can parse an employee's calendar, looking ahead and generating recommendations based on the anticipated schedule and location. This kind of proactive, insight-driven approach can lead to improved operational efficiency, better use of resources, and potentially transformative business outcomes.
The Dangers of AI Visibility
While advances in AI bring pleasure and convenience, they also bring unique challenges and potential risks. A central consideration is privacy, arguably a basic human right. Stripping individuals of privacy can induce paranoia and fear, diminishing trust and creating an unhealthy society.
The introduction of AI models into surveillance systems can lead to 'intelligent' monitoring. This means each camera installed for security purposes becomes an active observer capable of interpreting what it sees. This rapid transition from privacy to constant surveillance poses a significant societal risk and can potentially lead to misuse if not properly regulated.
Safe Engagement with AI
For AI to offer its full benefits, it must 'see,' or gain access to, relevant data. Businesses can ensure safety and security while interacting with AI by setting boundaries and retaining control over the data shared with the AI models. Implementing this control could involve the use of kill switches or off buttons that allow the retraction of data access to AI models.
On-Premise vs. Cloud-Based AI
The future of AI is split between local compute and cloud-based frontier models. Local or edge AI refers to algorithms that are performed on the data-generating device itself, providing increased privacy and quicker response times. Cloud-based models, on the other hand, offer enhanced computing power and increased data storage. An optimal AI deployment could involve a combination of both approaches for maximum efficiency and effectiveness, taking into consideration the type of data, its sensitivity, and the required AI capabilities.
Conclusion
As we balance on the brink of extraordinary AI advancements, businesses and individuals must approach this sophisticated technology with caution. Focusing on measuring what matters, retaining control of data and its usage in AI engagement, and drawing lines around privacy and security can enable the efficient use of AI while mitigating considerable risks. Therefore, embracing AI and its transformative power must go hand in hand with ethical practices to ensure a transparent and productive future.
Topics Covered in This Episode
1. Power and danger of letting AI view your data
2. Quick emergence of live AI technology
3. Kill switch and intelligent data routing
4. Local compute and orchestrations requirements
Podcast Transcript
Jordan Wilson [00:00:17]:
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 ChatGPT's advanced voice mode to interact with a neural low latency AI agent that can see the world around you. Right? Microsoft Copilot 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 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.
Jordan Wilson [00:01:33]:
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. Alright. I'm excited for today's conversation. I hope you all are too. If you're new here, hello. My name is Jordan. This is Everyday AI.
Jordan Wilson [00:01:58]:
This is your daily livestream, 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. There, you can sign up for our free daily newsletter where we recap this show and every other show as well as, keep you up to date with everything happening in the world of AI. So, you can also go to our website, sort 450 shows by category. So whether you wanna know about the legal sides of AI, guardrails, ethics, marketing, HR, whatever you want, it's all categorized on our sites from the world's leading expert free for you on demand. So make sure you go check that out. Alright. This is technically we're debuting the show live.
Jordan Wilson [00:02:49]:
It is prerecorded. So if you are tuning in for the AI news, it's gonna be in the newsletter. Don't worry. But I think you're gonna wanna listen to today's conversation. I can already tell it's gonna be a banger. So, enough chit chat for me. I'm excited for our guests, for today. So please help me welcome Michael Tiffany, the cofounder and CEO of Fulcradynamics.
Jordan Wilson [00:03:09]:
Michael, thank you so much for joining the Everyday AI Show.
Michael Tiffany [00:03:12]:
It's a pleasure to be here. Alright.
Jordan Wilson [00:03:14]:
I'm excited for this one. Michael, tell us a little bit about what y'all do at Fulcradynamics.
Michael Tiffany [00:03:20]:
Alright. We're a we built a 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.
Jordan Wilson [00:03:43]:
So, I mean, who's who who's your average company, like, customer? Is it just like dorks like myself who, you know, maybe have, like, you know, an Apple Watch or, you know, a couple wearables and they just wanna biohack their life or, you know, what's like, who's your average customer and what's everyone using your platform for?
Michael Tiffany [00:04:00]:
It it's I I I'd say there there are 2 different, big customer types. 1 really is the biohackers. Right? You have multiple wearables. And 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 and so if you wanna see everything, you you've gotta, like, check 5 different screens. And sometimes what you wanna do is see everything and you wanna see it on your laptop on, like, a big screen or on your desktop where you have a really huge screen. So so biohackers are loving FulcrA just to just to bring everything together and have one
Jordan Wilson [00:04:36]:
visual place. So similarly, how, you know, businesses have business intelligence dashboards. This is a personal intelligence dashboard for all your smart devices. Right?
Michael Tiffany [00:04:44]:
Oh, totally. Yeah. No. We can get buzzwordy there. It's like the single plane of glass Yeah. Or, you know, your your life analytics. Yeah. Okay.
Michael Tiffany [00:04:52]:
And then, kind of similarly, right, like, just just riffing on, like, bringing data nor bringing enterprise norms to consumers. People don't have a data lake. Like like, there there's no place to plug an AI into. So so the other category of users of Volkra are people who were using us as that data lake. 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.
Jordan Wilson [00:05:21]:
Amazing. And 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 do you all pull from?
Michael Tiffany [00:05:34]:
We really shine when it comes to the data that that's, like, not already in files. Like, it's super easy to upload files to 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 or or your heart rate? Right? Like 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 like biometrics. I I I think we store your location history better than any other alternative, like, all those continuously updating things. Virtually any IoT device, if if you have if you have smart stuff in your house and and you want to make your own copy of that, that that that's a place where Fulkar really shines.
Michael Tiffany [00:06:22]:
You can also uplay upload arbitrary files. There's a library function. The idea truly is to take your desilo 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 the, like, unique strengths tend to be the the streaming data.
Jordan Wilson [00:06:41]:
So, I definitely want to dove 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 of this episode title. Yep. What what are both the power and the danger of letting AI see you at all times?
Michael Tiffany [00:07:02]:
Right. I well, I'll start. Yeah. I'll do it in that order. And and I'll very much make this personal. I wanna be a cyborg, and I think I can be a cyborg before implants are possible. Like, the if you look at consumer tech, this is a magical this is a magic ring that knows when I'm stressed, which which is, like, beyond comic book technology. Like like, this is an amazing thing.
Jordan Wilson [00:07:28]:
Is that just the aura?
Michael Tiffany [00:07:29]:
Yeah. Just an aura ring. Right? Aura rings are magical. The if you look at the the total device footprint I have from from a smart bed, connected scales, I got, you know, a car that's practically a computer with 4 wheels. The the capabilities are really high if all of that stuff was brought together and was, like, really unified under my control. So 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.
Michael Tiffany [00:08:09]:
I joined Ninja Networks. I I've I've done hacker hijinx for my entire life. And so in my pursuit of being a cyborg, I I also just cannot, you know, give up my security lens. And the the danger here, the the the opportunity is that we can all be be, like, cognitively enhanced. The the danger is that it's it's really hard to delete stuff out of the latent space of of a, you know, transformer model. Like, you know, you you give it data and and it 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 ChatGPT 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.
Michael Tiffany [00:09:11]:
But you need to be able to revoke that decision and go, actually, no. Stop. Like, you can't access this anymore. So so I think the the 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 cut off.
Jordan Wilson [00:09:36]:
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 GenAI. 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 100 of their employees on how to use GenAI. So whether you're looking for ChatGPT training for 1,000 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 everydayai.com/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 GenAI.
Jordan Wilson [00:10:40]:
So you you very clearly laid off, a a little bit of the, power and a little bit of 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 what happens? Right? Because I think, you know, as the the conversation in early 2025 has already shifted from large language models to artificial general intelligence to superintelligence. 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 of new live technology that can see us, but then it's like, yo. Like, we're already talking about superintelligence now?
Michael Tiffany [00:11:36]:
Right. Right. Yeah. Well, let's talk about a way in which society can go sideways, which is we can all become paranoid. Like, the, I I I think there's something deeply important about privacy. We we should probably consider privacy a human right, a basic human right. And and why? Because when you take privacy away, it it messes with your head. Right? We do not want all of our fellow citizenry to to be, like, paranoid about about who's watching, right, and and what data is being collected.
Michael Tiffany [00:12:08]:
So, so a principal way that this can go wrong is that, now that your superintelligence is seems to be within grasp and and it can lift everyone up with by being a helpful thought partner, that that has to be driven by, let's call it observability to use the the nerdy term. And, that observability needs to needs to have some, you know, privacy protections or or it'll create this feeling, like, like, the the feeling that we're always being watched, which I just think is is not a good feeling. That that that that that's not a way in which we want society to head. Right? And and and that is that is a a near term risk because think about the number of, for instance, surveillance cameras that that that, you know, just have a security purpose, that have ever been installed across the entire world. Well, we we don't think of that as too creepy because because there isn't an infinite number of people who are, like, literally watching every camera. Mhmm. 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.
Michael Tiffany [00:13:25]:
That's crazy, and and and it's like it it's not gonna 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. Bam. We we we now have intelligent eyes behind every single screen. So so so that transition from, you the safety of privacy to, wait a minute. You can't be paranoid enough, might happen much faster than than society is ready for.
Jordan Wilson [00:13:53]:
And I think it's important to call certain things out because AI and large language models move extremely fast, especially if you've been sleeping the last, like, you know, 4 or 5 weeks. Right. You know, because the reality is all these models are multimodal by default now. Right? Like, as an example, the, you know, older, quote, unquote, older GPT 4 models, it was technically using 3 different models under under the surface. Right? But now with the o or Omni model, it's all one. So these models, Gemini 2 point o as well. These these models are multimodal by default, at least Gemini. Right? It understands video.
Jordan Wilson [00:14:28]:
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 in the more that we give them. So, I actually wanna rewind a little bit, Michael, and you you kind of gave us a bullet point list of, you know, hey. You know, I have a smart ring and, you know, smart bed 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.
Michael Tiffany [00:15:02]:
Oh, okay. Alright. So, the I I've experimented with all kinds of things. And and so so I'll walk you through some of the things that I've I've found extremely valuable and and then the the duds. The, I'll I'll go back to 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 full core data store so so I could share all kinds of of real time, you know, systems with with this GPT. I named it operator. And in in one of my early test queries, I was about to get on a plane to go to a hacker conference, ShmooCon. And and I asked operator, where should I have breakfast after my flight tomorrow? The operator did what I expected, which is it made the function call, got my calendar information in a JSON blob, parsed it.
Michael Tiffany [00:15:55]:
It did something better than I anticipated, and I did not program this either as prompt or 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 5 restaurant recommendations that serve breakfast near my hotel, which is brilliant. Like like, I was expecting it to give me recommendations maybe near the airport, maybe in the in the city within Washington DC. It 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 think almost like chasing that magic that that, like, woah.
Michael Tiffany [00:16:40]:
You did better than I than I kinda asked for, since then. And, and as it happens, I I would say giving models access to my calendar, has been, has been extraordinarily fruitful. It, there's a whole bunch of inference that's available when you do this about just, like, who you are as a person. Like, I I literally did not explain, like, who I'm married to, who my children are, but but you you can get that from, from the calendar looking at recurring, you know, calendar reminders, which was wild. And and so then that was that's been a source of of proactivity, right, to to to, like, be a good person, which which is a lot of fun. Location turns out so, my location history is constantly being generated from from my phone, and and and now I have access via my full core data store, and therefore, any AI hookup to the location history, is able to access it. It turns out that lots of memories the the way you encode your memories in your meet space neural network, it often uses the hippocampus to to to, like, encode things relative to location. So when you faintly remember something, there there's sometimes, like, a location angle to that.
Michael Tiffany [00:18:03]:
You're like, oh, yeah. You know, Bob said something to me. Like, you're trying to remember that thing, but you remember where you had the conversation. Yeah. Then you can locate that. So, so here's, like, a wild stringing things together. You you want to remember the details. You can get to a location.
Michael Tiffany [00:18:24]:
Then from the location, I can get to a time stamp so then I can find it in my, you know, AI transcript, you know, driven by whatever. Otter, for example. So the the lots of, like, following the threads to to essentially have AI assisted memory. Your brain is like this rich data store, but your lookup system is nondeterministic. Right? Like, you don't have a good search function on your brain. So if you can 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 I've also, I I've tried random stuff.
Michael Tiffany [00:19:03]:
I especially wanna understand my own patterns, like, my own patterns of of eating, because tracking your your eating is is a chore. So I was like, can I outsource this chore to AI? Right? Can can I use a, especially an image model to to to make this easier? And so so I've tried some weird stuff. Here are 2 things where, like, I'm still tweaking. One is just using a a cheap webcam and pointing at the refrigerator to just catch me when I'm when I'm, like, snacking as a way of, it it it's actually a way of, like, not doing my work. Right? Yeah. You you know, I like I wanna procrastinate and I get up and go look at the fridge, see what's in the fridge. So it it it's been interesting to, to monitor that. I I I also tried like, this is almost good.
Michael Tiffany [00:19:56]:
I installed smart breakers, so I'm 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 to do effortless tracking about my, like, about when I'm doing cooking. Right? Now that turns out to be noisy. Like like, this is almost good. There there there a future experiment of mine will probably use, like, a camera pointed at at the stove to, like, try to capture what's literally being cooked along with power monitoring. And we and 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? You know, are there seasonal variations? So, like like, these are works in progress though. I would say that broadly what I've been most happy with is almost like that.
Michael Tiffany [00:20:47]:
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 the big memory.
Jordan Wilson [00:20:56]:
Yeah. It's so so interesting, Michael. It's it's like you've almost, you know, big brother yourself with Yes. Dude. Some people are fine with it. Right? Even me, I'm like like, I'm I'm hearing you talk. I'm like, okay. I want I want Michael to be like my personal biohacking, like, mentor.
Jordan Wilson [00:21:12]:
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 for your own personal privacy. I mean, do you have a do do you have a kill switch? Do you have an off button? Like, you know, because people are probably thinking like, hey. Yeah. This this could go bad in the future if if, you know, AI goes off the guardrails.
Michael Tiffany [00:21:31]:
I know. Right? Okay. So, yeah, 2 responses there. Like like, you're not the first to make that observation. We we've been kicking around at Fulkar the idea of, like, some high end consulting services like the the AI SWAT team. Right? We're just gonna show up. We're gonna set everything up for you. Like like like, just what do you care about? Okay.
Michael Tiffany [00:21:46]:
Like, we'll figure out how to monitor it, which I think would be kind of a fun business. But the kill switch is everything. Plus you need to be smart about about, like, intelligent routing. So so for instance, I'm I'm, like, a huge fan of fan huge fan of foundation models, but but I don't wanna use them everywhere, especially when it comes to experimentations with, like, self monitoring with cameras because you're gonna capture stuff that you don't want anyone else seeing. Right? 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 like, this is not something to necessarily send to OpenAI. So so in that particular case, you wanna hook that camera up to a local image model that's, you know, small parameter model you can just run on, you know, some some local to you computer, you know, an old laptop or something, that's that's doing the preprocessing, maybe discarding a whole bunch of stuff, and and pulling out the the intelligence that matters for my, you know, silly food tracking.
Michael Tiffany [00:22:51]:
Right? Yeah. So so sometimes I think the answer is you wanna use a combination of of local models and foundation models and and and do a whole bunch of scrubbing where you just delete stuff. Yeah. Second, and and I think more globally important, is that, everyone who works in software engineering, understands that, you you have to, like, 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 were 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.
Michael Tiffany [00:23:29]:
And it what's important to me as as someone who's worked in computer security for a long time is that that can't be a one way commitment. Right? I I I do think that a lot of tech payments are gonna 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 I think that you don't want all of your personal information literally right next to the model. You you you want to grant a model temporary access to your data, and you wanna be able to say, today I changed my mind, and I'm not gonna explain myself. I've just cut you off.
Jordan Wilson [00:24:09]:
Yeah. And it's 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, you know, 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, a small language model. You know, you can't run these huge, like, the original GPT 4 was like 1.7 trillion parameters, but, you know, this recent Microsoft paper said that GPT 4 o Mini, which is a very capable multimodal model, was only 8,000,000,000 parameters. So, you know, Michael, I'm guessing if we have this exact same conversation, January 10, 2026, we are gonna have frontier models that in theory could live on that Oura ring. Right? How does this change the future of of what's possible? As these models get smaller, you can move them on device.
Jordan Wilson [00:25:13]:
How does that change it? But then also, how does this change it for business? Right? Like, 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?
Michael Tiffany [00:25:28]:
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 with, like, a local supercomputer. Right? Mhmm. So if you've been working in enterprise software, especially as 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 you you see the pendulum going back and forth. I I think there's gonna be a resurgence in, like, local compute. Lots of personal computing is essentially cloud based at this point. And and I think that local the the desire for private local inference is going to drive a mix of, like, on prem and in the cloud, for for everyone, which is gonna be a really fun, you know, transition to live through.
Michael Tiffany [00:26:19]:
The, it's not just OpenAI innovating in low parameter models. Of course, Microsoft also has recently released, you know, 54, which which hit awesome levels of performance with only 14,000,000,000 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, you know, amazing demo of 03 suggests that we're not gonna be eliminating, you know, cloud based frontier models for a very, 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 hard core way, if you want really advanced reasoning, that's probably not going to be local.
Michael Tiffany [00:27:06]:
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, you know, enterprise like SaaS engineers, facing every consumer. If you think about it, every consumer is is living a life that's much like the enterprise from techie to go. You're we're a mix of on prem and in 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 so so so so the orchestration burden is real, and it's it's basically totally unsolved. So, you know, if if you're an entrepreneur thinking, how do I build a business that has a moat as as intelligence gets cheaper and cheaper? I I think orchestration is like this giant unsolved problem.
Jordan Wilson [00:27:59]:
So so much that, I want to dive into, Michael, but this this would go on for many hours. Right? But like as as we wrap up today's show, because we've talked about a lot, my my brain's going in a million directions. I'm sure everyone else's is as well. I'm gonna 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 gonna be in your shoes, businesses as well, right, as this becomes shifts from more of a, you know, 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?
Michael Tiffany [00:28:36]:
Oh, it's, wow. Put yourself in charge by experimenting now. Let let like, get started, make your own GPT even without coding skills so so that you're almost, like, training your brain about thinking about ways to bring an AI to bear on on the problem that you face. This is gonna 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 in delegating thinking to the model. So, like, that is the number one thing. And, and then my my second takeaway is, you think about the the data producing devices that are in your life right now and where they live. So who what third parties are you already, like, arming with your personal data, and and, do you want that data to live there? Right? So, start getting control over your own, let's call it data footprint.
Jordan Wilson [00:29:49]:
It's it's so important. I think great advice as, you know, we're we're all dealing with this swirling of innovation and 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.
Michael Tiffany [00:30:08]:
It was a pleasure to be here, and this was an awesome conversation. Thank you.
Jordan Wilson [00:30:11]:
Alright. As a reminder to y'all, that was a ton. I'm not gonna lie. My head is spinning with with possibilities, ideas, all of that. We're gonna 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.
Jordan Wilson [00:30:34]:
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.