Build a workspace of AI agents

Foreign.

The podcast that makes artificial intelligence practical, productive and accessible to all. If you like this show, you will love the Changelog. It's news on Mondays, deep technical interviews on Wednesdays and on Fridays an awesome talk show for your weekend enjoyment. Find us by searching for the Changelog wherever you get your podcasts. Thanks to our partners at Fly IO. Launch your AI apps in 5 minutes or less. Learn how at Fly IO.

Welcome to another episode of the Practical AI Podcast. This is Daniel Whitenack. I'm CEO at PredictionGuard and and I'm joined as always by my co host Chris Benson, who is a principal AI research engineer at Lockheed Martin. How you doing, Chris?

Oh, I'm feeling pretty chipper today. It's a good day to talk about AI.

Yeah, yeah, I feel quite chipper as well, especially as we've got our guest today, Scott Meyer with us, who's founder and CEO at chip, which is you can find at Chip AI, I believe is the link. But yeah, Chip is awesome. Also Scott is awesome. And also Scott is, is along a good friend because he's a fellow member of the the Silicon Prairie. Not living on the, on the coast, but out here in the middle somewhere where AI is really blossoming. If you didn't know it is.

And it gives an unfair advantage for those of us in non metro areas. You know, like the ability to leverage AI to have the power of 10 people in a place that doesn't have enough people to do the jobs. It' perfect. It's a perfect solution. So it's great to be here live from Fargo. Just like the movie. It's fantastic to see you all and be heard by all of you listening.

Yeah, yeah, Scott, we'll get into all the cool stuff you're doing with Chip and some of the things you've learned through that. But I'm wondering if you work in the space of, I guess we might put it like low code, no code, AI assistant builders. So for maybe audience members that aren't as familiar with that space or maybe they're just kind of wondering what's out there, you know, as of today, could you paint a little bit of a picture for us for kind of what sorts of tools are out there and then maybe that would kind of motivate some of the unique things that you thought should be out there but weren't, which would maybe kind of highlight some of the things you're doing with Chip.

Yeah, no, it's great to be here. I think the stat that blows my mind is that almost 50% of Americans use AI every week. But 7% of businesses use AI, which is obviously a lie because 50% of Americans are using AI every week, and they work at those companies. So what's happening is the businesses aren't. They have no idea what's going on. It's like the early days of cell phones, when everyone would come to work with their own cell phone, their own laptop, do whatever they wanted to. And eventually we got to this point where you get a company email, you get company apps, you get like the standard way to do it. And I think the risk right now is that, and the opportunity is those who are willing to have agency and try stuff have unfair advantage, right? So I can go do my work with AI and if my colleagues don't know and I don't have a culture of sharing, like, all of a sudden I'm a super superhuman. The number one thing I tell businesses when I meet with them is, you should have a lunch and learn once a month and just have people say what they're doing. Because just that horizontal sharing of AI practices and ideas is all you need to build a culture of acceptance. And what makes AI so unique is it's not top down, it's the CIO or CTO saying, I bought this thing. You guys all go use it. It's each individual figuring out how they can use it for their specific tasks. And what I've seen is admin assistants, you know, marketers, interns, right, they're all going to use it differently and often even know better how to use it because they're the ones doing the tasks. And that kind of motivated what we built with Chip, which is how do we just make AI as easy as possible to use? Our, you know, kind of our motto is AI for all. And I think I've spent most of my professional career working on bridging a digital divide, because maybe like you, you know, people that work and live alongside me in Fargo aren't always taking advantage of the latest technology. Right? And so I kind of feel like it's both a passion and mission to bring what's happening and make it accessible to those around me. In 2009, I started my first company, and I was trying to tell businesses there is this thing called social media they should use right before there are Facebook pages and Facebook ads. And it feels like that to me again, almost 20 years later, where it's like this amazing power is right here. And the best time to start learning is now. And with tools like Chip and others that we can talk about, it's Actually better now than ever for people who aren't technical because it's not about technical ability, it's about knowledge and agency. And I think we all have that. So happy to give a landscape. I think that already went off track from your question, but hopefully that gives you a starting point.

No, that's awesome. What would you say are kind of some of those things that might make AI hard to use? And here, you know, mostly we're talking, of course we've talked about a lot of things in the show, but mostly we're talking about kind of what typical people would consider AI now, which would be kind of generative AI language models, maybe vision models, et cetera. So like what, what can make those difficult to use? Or how might people get disillusioned as their exploring the technology?

I'll say almost, almost every excuse people have not to use AI tools is fear. They are scared of a blank page. And this is the same with technology. For 20 years I taught entrepreneurship, I started entrepreneurship centers and all these students with amazing ideas and you know what, 90% of them didn't do anything because they had to actually go do something, right? And it's like you just have to start. And I'm convinced the biggest challenge in AI is change management. It's just getting people to start. And I think this happened when Google first came out. You know, it's a blank screen, blank prompt window. Like what do I say when I can say anything? It's actually quite intimidating. And so that's the challenge, I think with AI is like anything's possible. So where do you start? I tell everybody the best place to start is to create your digital protege. Like just tell AI what you do and have it help you do those things. AI is great at what you hate and so find those things that you hate doing or that take a lot of time and start there. You've maybe seen that quote. I really love that. You know, I want AI to do my dishes and laundry so I can do more art and music, not AI to do art and music so I can do more dishes and laundry, right? So I think we all have dishes and laundry in our day to day life. And so let's use AI there first because that'll be the. You'll get more motivated to do fewer financial analyses or fewer, I don't know, copy editing because that's kind of annoying than you would like making music because maybe that's fun for you, right? So start with things that you don't like. One thing I find fascinating about research On AI is actually having knowledge makes you better positioned to use AI. I think about AI as like the rebirth of the Renaissance person. It's like if I want to create a picture on AI that looks like Picasso, but I don't know Picasso's name, it's really hard to describe that. Right. If I want to make a blueprint of a Georgian architecture building, like, how do I explain that if I don't know what Georgian architecture is? And so whatever area you live in or work in or care about, you have like expertise, right? You can talk about it all day. And that's a great place to start with AI because you can go say those words like give me, I don't know, a hierarchy of Pokemon characters and you can name all the things and have it rank, order it. Like I have no idea what I would say for that. Right. But I can talk all day about saunas and have the AI help me improve my sauna, find new water buckets, look at different ratios of time in the sauna, because I care about that. So find some things that you know about that you're passionate about and start asking AI about it so you can go deeper. I love it.

I'm curious, quick follow up on that. Because you raised a point that I hadn't really thought about, but I've observed it many, many times and you brought it to the surface here. I see people who are totally comfortable getting on the search engine of their choice and search topics and they've been doing that for years. But as soon as they pull up, you know, a chat with a given model, they're really struggling with that and they're really. That that's what you know, like from. I'm just curious, as you've clearly thought about this quite a lot, what is the difference and why are people so easy to go to search and yet struggling with, with that model, you know, that has the same text box in.

Front of it, part of its exposure. Right. Just history. But I also think there's something quite vulnerable about AI where it's really a two way conversation. Search engine is, you know, very much like, like the old card catalogs. You know, I remember my first year of elementary school, I learned card catalog and then the next year was told never have to touch that again. But it's the same, that worked the same. Right. I'm just going to go find something. But with AI, it's probing back and forth and actually you can get, you can get pushback and it kind of identifies how you're thinking about things. So I think there's some vulnerability around that and plenty of like blank page problem of just not knowing where to start. So start by creating a protege. Start by diving into areas you care about. And I always tell people a great framework to get started is what I call the RIPE framework. So R I P E. And it's just a way of like four sentences to put into AI to get good answers, which is the role. So like you are an expert, I don't know, copy editor. The instruction like read through my paper and improve it parameters. So make sure it's very concise and don't repeat a lot of the same points and examples like here's a paper I wrote before that shows my kind of tone, you know, if you just do those four things, a role, instruction, parameter, example, like you're going to get awesome output that's personalized and much more effective and less robotic than just going there and saying write me a paper.

Yeah, I've had this kind of hypothesis, I guess going around in my mind. I'm curious, Scott, on your, on your take on this because you've seen a lot of people now you're always interacting with people on Discord or wherever, trying to get their assistants to do this or that. What have you found to be kind of the qualities that make up someone who is just really proficient at honing in the instructions, the data integration, the, the configuration of AI systems? My hypothesis is sort of, this is almost like a, I think if we took a bunch of hostage negotiators and had them log into to AI systems to try to, you know, either get them to do things that they wanted them to do or to jailbreak them, I think they would be like amazing at this because a lot of times it seems to me, you know, not that I feel in danger physically or something, but it's like people can get disillusioned with this. It's like not quite what I want. How do I get you to do what I want you to do? How do I like warm you up to this idea? So yeah, I'm curious on the qualities that you've seen in terms of people that have become good at configuring these systems, prompting understanding how to, you know, pull in integrations or when and where to do that. Any thoughts?

Yeah, I mean people who are great at this are kindergarten teachers or parents of 3 year olds.

Maybe also hostage negotiation also.

Yes, basically the same job title.

There's some similarity there maybe.

Yeah. I mean think about talking. I mean people. I say an intern, but that's even too too experienced. Think about talking to my three year old Sebastian. If I tell him three things to go do in order, there's no way he's going to get all three of them done, right? Like go to the bathroom, pick out some shoes, grab your snack, go to the car, like that. That's not happening. I have to be like, go to the bathroom. Good. Now this, right? And now this. It's very step by step and I think what's interesting is there's two models or two types of models emerging in AI and you guys maybe have your own language for this. But you know, I think about linear models like 4.0, Claude Sonnet 3.5 and we have reasoning models now like O3, Deepseek and now Sonnet 3.7. And it's like the reasoning models. Actually that's like talking to an intern who you can give a ton of stuff and you just let it go. But if you're doing a linear model, that's very much need to do that step by step. First do this, then do this, then do this. Because the biggest I think frustration people have is that AI too quickly tries to get to an answer before it has all the details and things get lost. And so with Chip, you know, you can prompt your AI tool and then anyone can use it. And so we've found is like flipping the relationship is really powerful where the AI prompts you to get what it needs and then gives an answer. So you can even, you know, on Chip you can build this in so you don't have to type it every time. But on any AI tool you might say like before you write the paper, before you create the, you know, strategy, before you create the, I don't know, the press release, make sure to ask me these three things, right? And force it to get all of that information step by step, just like you do with a three year old and then you go to school and then you write the paper and then you do the thing, right? So I think that's really fascinating though seeing that divergence with reasoning which is like don't go step by step, just give all the context and it's going to work through it on its own versus the three year old linear that's like needs that guidance. So yeah, I think at the end of the day, hostage negotiator and parents, you got this.

Well friends, today's ever changing AI landscape means your data demands more than the narrow applications and single model solutions that most companies offer. Domo's AI and data Products platform is a more robust all in one solution for your data. It's not just ambitious, it's practical and adaptable so your business can meet those new challenges with ease. With Domo, you and your team can channel AI and data into innovative uses that deliver measurable impact. And their all in one platform brings you trustworthy AI results without having to overhaul your entire data infrastructure. Secure AI agents that connect, prepare and automate your workflows, helping you and your team to gain insights, receive alerts and act with ease through guided apps tailored to your role and the flexibility to choose which AI models you want to use. Domo goes beyond productivity. It's designed to transform your processes, helping you make smarter and faster decisions that drive radio real growth. All powered by Domo's trust, flexibility and their years of expertise in data and AI innovation. Data is hard. Domo is easy. Make smarter decisions and unlock your data's full potential with Domo. Learn more today@AI.domo.com Again, that's AI.domo.com.

So Scott, maybe we'll come back to kind of the tooling itself. Could you maybe kind of circle back and describe some of the. Maybe people aren't familiar with some of the kinds of tools that are out there. Especially maybe there's programmers that have interacted with APIs that are listening to the show. Maybe there are people that have explored one tool or another. Maybe there's people that haven't explored anything yet. So could you maybe just help us kind of form a mental model for the kinds of AI tools that are out there? And then maybe that would lead into a discussion about kind of some of the things that, that were really on your mind in terms of needs that weren't being addressed in that ecosystem.

Yeah, I mean, if you want to think of like a simple two by two matrix, I think there's a really clear like vertical versus horizontal and like closed versus open dichotomy. So you can think about horizontal tools doing a lot of things across modes. Right. So ChatGPT can write, it can create images, it can code, it's really good at all of those. But if you want to just make images, like midjourney is probably better. Right. It's a vertical image generation tool or Pika is really good at video generation, which some of the general horizontal tools aren't as good at. And my sense is like horizontal is going to win, but there's always going to be a need for people who want the Maserati of AI. Right. If you're only doing Code like you're going to probably be in cursor going deep into using these tools. Whereas someone like me, I'm going to do the vibe coding where I can use a tool like Lovable or Bolt and just try stuff or repl it. Right. So I think horizontal, vertical and then I think, you know, kind of open, close. So there are tools that let you use it on their platform and you don't necessarily know what's happening. So that would be obviously like ChatGPT or Claude. You can't change the kind of rules underpinning it. Also you have to go to their website, you can't brand it, you don't really have much control over the privacy. And then more open tools are ones that you could put on your own site. You could add privacy into it, you could brand it. So that's what Chip is. We want to bring the power of AI tools like ChatGPT and Claude to your website. Add privacy so the files stay locally, add your own branding. You can see the chat log. So just a lot more control, obviously, like Prediction Guard, same thing, right. Where you can bring AI into your own cloud. So a little bit more work obviously with an open tool where you more power also you get more options. So I think that's kind of like the lay of the land. And I think it's just like when you look at the Internet broadly, like it started with text because that was easy to send across wires, and then music because MP3s were smaller than video, and then video. See the same thing with AI, right where it started with text and code because that's text heavy. Starting to get pretty good images now. Video is still coming. Not quite there yet, but getting better every day. So I kind of see that evolution happening. Yeah. And I think what maybe is a surprise is that people thought the value was in the large language models. And I think what's become really clear the last month or two is it's actually going to be in the customer relationship and making this stuff easier to use. Deep Seq is the model that came out of China a couple weeks ago. And you know, if I look at what chips AP like cost per API call is, it's gone down 90% in 18 months. Right. So just think about the value of these large language models becoming more commoditized and then what, what is what people are signing up for is like the experience of signing up and creating. So I can go to replit and say I want an app that you know, is tracking my to DOS and get it in A few minutes. It's all on top of the same power, right? It's all on top of ChatGPT or Claude. Just like Chip, you can use any model underneath. But it's that end user experience which maybe isn't so different than the web. Right? There's protocols underneath, but you still use the browser that you like or the web app you like because of how it works, not necessarily that it uses FTP versus something else. Right.

Could you talk a little bit more about that end user experience, both the good and the bad? Because I think kind of going back to, to what we were talking about before, it's one of those barriers and you know, there's a set of people that are totally bought in across a whole bunch of different industries, but there's also a very large segment of the population that still really hasn't engaged. You know, they're hearing about it every day in the news and everything, but they're just intimidated and haven't done so. Could you talk a little bit about the landscape of being on both sides of that barrier for different people?

I mean the biggest increase in use that we see with AI is putting it where people already are so they don't have to learn a new interface. Right? So if they can engage with AI via a Slack channel or via WhatsApp or via text message, like way easier, right? And so I think it's fascinating to see there's a lot of amazing UIs out there, but it's still like getting people there. It seems like time to value is really important with the tools. So like the faster you can show somebody an outcome and that's I think where a lot of the new kind of text to app tools like Lovable and Bolt are, are really exciting for people because they can get something quick, which makes sense. I think that's kind of like how all UI is, is like how do you get someone to the value quickest? I actually think like the default UI we are accustomed to with ChatGPT is, is not great, you know, like for someone to come in there and use. You know it's interesting that ChatGPT was a research project, it was not supposed to be a consumer app and it just became that on accident. And so I think there's a lot of improvements to the UI to come to make it easier for people to use and you see those already coming into play where there's pre built ideas, Autofill, you know, connect to data sources. You know, the most common way people use Chip is by duplicating an existing app. Right. So it's like solving that blank page problem is really important, I think, for any AI tool. So the easier you get people to motion is key. Yeah, yeah.

I'm intrigued. You made me think of something. So, like, for those that haven't seen Chip and what Scott and team are building, you can go in and create individual assistance that as Scott mentioned, you can kind of control and configure, make the way you want, connect the data sources you want. And often I think in my conversations in the past with Scott, I've heard him talk about how people are creating sort of proliferating these. Right. You create one to do this and like one to do that and one to do this and you clone this one to do that. Because it's not quite, quite that, which is a different, It's a different paradigm than the sort of like, here's a chat interface. This chat interface is going to do everything that we want it to do. Could you talk about that element of it a little bit and what you've seen there? Because I also see this on the business side, like when we engage customers, the kind of tendency it seems, from my perspective is to say, hey, how are we going to build our internal AI and get it to do all the things that we want it to do? But it's like a single, in their mind, it's a single thing, right? It's like this is our tool and it's going to be the tool to sort of rule them all. They're thinking very singularly in that way, which definitely does not seem to be kind of how people are engaging in the way they're building assistance in your tooling. Any thoughts?

There's, I mean, I think the high level thought is the concept of software is getting turned on its head where software is now an individual sport, not a team sport. You know, you think about if you're the CTO even a few years ago, it's like, I have to do a lot of research, buy the right thing, because everyone's going to use this. It has to fit the most use cases. We have to squeeze everything we can into one thing. And now it's flipped where every single person can build custom software within, you know, we say 60 seconds, right? So you would never build software to, I don't know, write a better introduction paragraph to a grant. But now like someone on Chip will go build an app that just does introductory paragraphs for grant applications because it takes 60 seconds and it saves them three minutes every single time and they do 10 a day. And so it's 30 minutes. And you know, we're seeing the average admin person saving 60 minutes a day on chip, going from 90 minutes to 30 minutes on admin work because they're building specific apps for their specific tools. So you know, today I was looking at one that was getting IRS status from the IRS website, right. And putting it onto a spreadsheet. And it's like nobody is going to go build a SaaS tool that just does that because the market is, you know, maybe 100 people or something. But with AI you can. And so there's definitely no need to have this like laborious top down purchase cycle when you can say just try it. Does this solve one problem, two problems, five problems, ten problems? Great. Imagine the power of every single person in your org being a web developer or a coder. That's what it is now. Now we don't have to bother our IT people or our developers. They can go do the hard stuff. Integrating with antiquated systems, getting our billing to talk to our web, to talk to this. But for my job, I just have a file and I need to get something done. And I'm not going to bother our developer, but I'm going to be my own developer. And I don't know, that's a total flip, right? Where now we're not making decisions for the org, we're making decisions for Scott and I can just build it myself. So the only limiter again is agency like just go, you have to go do it. Most people still won't, even though the tool is right there. But if they can at least try once, it's not as hard as they might think.

So it's a fascinating point you're making there with it, but it does change. Even though you're talking about flipping the, the model over, you know, from kind of catering to the business as a whole to being able to cater to each individual contributor in the business by doing that. I'm curious, you know, that that opens up a lot of possibilities for how you might run the business going forward. Do you have any thoughts on like what that does to the business? If assuming let's in a, in a hypothetical world that you could get your entire workforce to engage in that way, what do you think that does for a business and how might, if you were the CEO of a business, how might you operate in such a way to change that? If you were just everyone's empowered with AI agents that they can make in 60 seconds, what does that do for them?

Yeah, and this is what Chip's trying to build. This is really my. Where we think work is going is we need an umbrella of safety so that our employees can do whatever they want without feeling like they're going to break something. Like right now the fear of messing up is greater than the fear of missing out. And so we need to like get rid of that fear of messing up. So I always say, you know, like the FOMO is greater than the fomo, like we got to get rid of the FOMO because people aren't taking action because they're scared. And so I think if I'm a company, what I'm doing is I have my five to 10 core apps. This is how we work. When you start at Scott Inc. You're going to go through the onboarding chatbot, you're going to get the content creator that writes everything in our voice. You're going to, you know, get the data analysis that's going to analyze the spreadsheets in the same way. So these are the apps everybody uses. This is company standard. This is getting the laptop with pre built software and then underneath that now you can duplicate or build your own to how you work, right? So you have this layer of company wide apps and then I have my Scott apps and maybe they're only visible to me. And a lot of times I might even cross personal and professional potentially, right? Where it's like, here's my workout schedule and my agenda builder for work and my, I don't know, grant writer tool. But since it's underneath this umbrella, we know that it's gonna adhere to privacy. Any personal information will be removed so it doesn't violate any problems. And then the final piece is, yeah, we have the tools, but then we need that monthly or bi weekly lunch and learn where like, hey Scott, what did you build this week? Oh, cool. Let's just duplicate that one click and now send it to Dan. And Dan has similar work. Or, you know, new employee starts, they can look over my shoulder it already the bot's already trained on all the history. It knows what to do so they can jump in. And you know, I always say that AI really raises the floor, you know, like every new employee could start at average or slightly above average. You still need to raise the ceiling yourself, add that special spice, right? Your own ideas. But it's going to make everyone on a whole quicker to get to work and higher, I guess, like higher average across the board. And I always tell, you know, the framework I always recommend is like the AI sandwich. Like just think about The AI interaction starts with you, the human, the bread on top. Then the AI is going to do something that's the meat in the middle, but then you still have to be the human on the bottom to take that output and to improve it, to share it, to repurpose it. And so I think a lot of new people get the bread and the meat, but they forget the bottom piece of bread. And so that'd be like, the work I would do as a leader is here's our tools. You can all use it and you're all going to be good. Like, you're not going to have spelling mistakes. It won't be wordy, it'll make sense. But now how do you get better? And it's going to be like adding your own spice on that last piece of bread. So that's what I would do for Scott Inc. So I think home run.

And part of that too is like developing the muscle memory. So, like, for me, for example, we've been going through fundraising recently. There's always like the same set of questions that come up in diligence and in, in, in questions about the product and all this. And most of those have been answered like 3 million times now in some form. And, you know, now looking back, like, and, you know, we've started to do this actually, but really what would be best is if we just had a little chat that had all of that preloaded into it and could chat over that. But at the time it's like, oh, well, I'll just answer this email that's asking these 10 questions. Right. I can bang that out really quick. But that, I guess there's a muscle memory thing there. And then there is some barrier to overcome to configure the system for future benefit. Right. That you might not see there. So I don't know. Yeah. Any suggestions? Even in your own personal life where you've kind of come over?

I mean, we did the same thing, right? Like, we did a raise with Chip and we built a chip chat and it was trained on all of our, you know, slides and everything. And people still want to talk to you. Like, it doesn't mean that they don't get a human.

Right, exactly.

But it gives them the option. And like, you know, the data we're seeing for our users using Chip for, like, customer support, like a chat bubble sort of use case, 70% of them are not clicking the talk to a human button. Like, they just want to know, what are your opening hours? How much does it cost? Who are you? Like, just give me the facts. And as like a busy parent, I get that right? Like, I don't want to make phone calls because I know it'll take five to ten minutes versus a minute if I'm doing it myself. So I think there's that aspect of time efficiency and it is changing habits of going somewhere else or like you said, taking core info and putting it into a repository. What we found most helpful is we have something called Dynamic Knowledge Sources. So if it's a spreadsheet or a folder on Google Drive or OneDrive, anything that gets added into those places is automatically added into your agent. And so I think with businesses, it's important to think about that flow of information and minimizing as much like documentation work as you can. So we always put everything into Notion or Confluence or Google Sheets or Google Docs, make that your hub that is fed into the AI. So everything that you put in that place gets automatically added into your FAQ bot or your, you know, marketing assistant bot or whatever. So I think that's, that's key is like you can ask people to do it, but even better is like not to require more work or even changing behavior because we know that's the hardest part. So maybe it's a BCC email that goes into a spreadsheet that's automated. Right. Or, you know, something like that. So you can kind of decide. The way we do it is we actually look at our chat logs of people engaging with Chip and find the answers that are going unanswered or don't have a great answer. And then we add those things in once a week into our chat so that it improves for the things people are asking for rather than trying to solve for hypothetical edge cases. Yeah.

Well, Scott, we've talked a little bit about Chip. I've described it a little bit. I'm wondering, maybe you've been on this journey of trying to build this easy to use AI tool. Along that journey, have you found, I'm sure you tried various things that did work and didn't work. And certain things have been difficult and certain things have been easier as you reflect on that, kind of as a founder of an AI company trying to build an AI tool, any things that you'd want to highlight in terms of things that were kind of key insights or bumps along the road that in retrospect you look at and kind of make sense or anything like that? Because I think there are a lot in our audience that have maybe ideas for things out there.

Yeah, no, that's amazing. There's so Many, I think I'll take like a non obvious one which is we pretty early on focused on building community. So we have over 20 chip chapters around the world, people teaching one another AI fairly active discord that's been invaluable because those are the people who are bringing back problems and ideas. And being able to build towards actual customer questions is so important. And a lot of times customers don't have time or interest in giving you feedback, which you need. And so what we've done is every two weeks or so basically having free workshops to try to educate our users and anybody. And that's really built a relationship, I think, where we know these people by name, we know where they live, what they do and it makes it a lot easier for them to be like, yo, can you be build this thing? I need it for a pitch on Friday. And we're like, yeah, for, for you of course, because you're contributing, you know, so it's building that, building relationships. And it doesn't have to be hundreds, right? This can be dozens of people who love you. And that's how you really start is like a strong foundation. So I think that one's non obvious. I think technically something that we found maybe an accident and we're trying to lean into now is riding the wave of other people's innovation. You know, like you can only build so many unique pieces and you need to be on top of other parts of the tech stack. And so, you know, Chip is built on top of large language models. So as Anthropic and OpenAI build better models, Chip gets better. And for a lot of our users, they think Chip is doing that because you know, we are their front door to AI. And so as the models get better, Chip gets better and their experience gets better. We partner with folks like Prediction Guard who help us provide better privacy and security. Right. And so we could go spend six months trying to build that, but now we've lost the whole point of what we're doing. Right. And so what is your forte is really important? One thing that has really recently that we kind of focused on is Anthropic has a new protocol called, what is it? Model Context Protocol. It's basically an easy way to connect APIs in to AI tools. And so that's another example of like we've been building one off APIs to all these different tools and now it's like, wow, there's this whole world that's built towards this standard and if we just tap into that now, we can again get better. The More the open source community contributes. So I think that's really interesting to look at. Where are the areas that will move quickly that you can ride that wave and then where do you want to be a differentiator? And you can kind of draw your line wherever the right place is, but probably don't try to draw it on all of them. Pick the ones you're best at. Yeah, I think those are a few and I think just the power of small teams now, I mean you read that a lot of places but you know our CTO hunter who is just like a beast with AI coding and it's like I know our output compared to some legacy teams is just vastly greater. And so I wouldn't underestimate if you're a solo founder, you got a team. You know, we have a couple chip users. There's a guy named Chuck in Colorado who he's building a million dollar one person agency and he's almost there, right. And it's all built with AI automations and he's conducting everything. There's a lot of potential out there. So I would encourage anyone listening like finding a co founder or a team is really, really hard. But you don't have to wait like you can do a lot on your own.

I'm curious you actually started to get in for a second to the next question I was going to answer and that was you mentioned like privacy and security and partnering with Prediction Guard for that. As you're thinking about these, these different concerns that weigh in on various industries and you know, there'll be you know, legal concerns, things like you know, HIPAA in, in the medical world and, and every industry has its own set of concerns that are kind of external but are, are binding the work in those areas. And as you are kind of kind of unleashing people's potential with the work that you're doing, those kind of have to find some sort of balance. How are you thinking about the constraints versus the unleashing that we talked about and finding a balance so that people are unleashed while they're still having to be held to account, you know, by whatever those constraints in their industry is.

Right? Yeah, I mean I think regulation is always going to trail the innovation. And so I would say as a company, as an individual, like look at yourself first before worrying about the regulatory environment. I think about privacy pyramid as what we tell our customers, the bottom of the pyramid. The first thing you should do is just think about what are you okay sharing and not sharing and just tell people again FOMU is Greater than fomo. People will not take action if they think they're going to get in trouble. Even if it's hypothetical, not real. I don't know. That fear from elementary school sticks with us. And so the first thing you have to do is remove the fear. And the best way to do that is just to say what the rules are. As long as people know the rules, they'll work within them. But if they don't know what they are, they're afraid that whatever they do will get them in trouble. Right. So hey, just don't upload customer, you know, data. Like that's our rule. Great, that's a great place to start. Now go do anything else or, you know, no customer data and don't integrate with these files. Great. And the second level of the pyramid after, you know, kind of just best practices internally is then going to be like human protection error, I call it, which, you know, one thing Prediction Guard offers as well, which is like encrypting pieces of information that get added that shouldn't be right. So if I add a phone number or, you know, a Social Security number or something like, it gets removed for me because I made a mistake, that's fine. Like we make mistakes. But best practices and then cover, cover other people's mistakes up as they make them. And then I think the top of the pyramid is where you actually say, you know what, let's put it in our own environment. So that way if we can share whatever we want without having to worry and you know, that's where you can run an open source, large language model in your own cloud infrastructure. Whatever you share is in your cloud infrastructure. So you know, some businesses have to do that. So if you are in finance, healthcare, like you're probably going to want to do that anyway just for regulatory reasons. Some people want to do that because they know they're going to be sharing data that might be sensitive. But I think for most of us, like to get started, just follow that basic best practice of like think about it before you share it. And if you're working with a team that might make mistakes or are contractors who aren't following your rules, like add in that second level of like human ear protection.

Scott, as we, as we kind of get near to the end here, I'm wondering if you can maybe share just a few standout use cases of maybe things that you've seen people do with chip that have either surprised you or stood out in a, in a way like, oh, I didn't expect people would do this. That you know, or things that are like, oh, I didn't even know. You know, I built the platform, but I didn't even know that was possible every day.

That's my favorite part of chip and AI generally is like, we really are building the tools and we don't know how people will use them. And it's so crazy to see what people do with it. And I mean, the most common use case is, I always say, like, there's kind of five areas that people use all the time. It's like operations, marketing, sales. I call it company search. Like finding stuff in your Google Drive, basically. And what's the last one? Like, data analysis, like reviewing financials and things like that. So those are like, the most common, but in terms of, like, fun, weird ones. Like, we had somebody who launched a Canadian tariff checker, and so, like, as the tariffs on Canada were released, you could actually search any product and it would source, like, where they were coming from, from, and tell you what the change in price would be. That was, like, totally interesting. One of my favorite use cases, a guy named Tyler Hansen, he's in Sioux Falls, South Dakota, and he runs an H Vac company. And he put in all of the training manuals for all of the equipment that they service. So then his technicians are on the, on the ground. And instead of having to, like, be in the bathroom watching a YouTube video, which I know has happened, when my H Vac guy comes, right, he's like, actually learning how to do. Do the thing that I asked him to do. Like, they can actually pull up the specific model via their chip chat and get instructions on what to do and how to service it and parts. And that one's really fun. There's a contractor out in Washington. He uses it to create supply lists. So he just puts in square footage and what people are going to build, and then it'll spit out, like, how much wood he needs, how many nails, like, whatever else. Again, like, things I know nothing about a lot of people doing it for, like finding HR policies, finding. Let's see, there's a car dealer that's using it to find cars to purchase, like, to then resell, right? So it, like, searches through autotrader and, you know, Craigslist and wherever else to find vehicles. Just so many things, right? And every day I'm. I'm encountering new ones that are so fascinating. The, the fun part is we integrate with, you know, APIs and webhooks, so really, like, any tool can get pulled in. And a lot of times chip ends up Being a front, front end to an AI tool that's talking to their software. So chip becomes the way they communicate, but then it's pulling their own data. So that's super fun. Personally, I have a Scott bot, you know, that's the one I use every single day. And so, like, I can write things very quickly and remember people that I've talked to. So it can, like, brings in past conversations. And so that helps me quite a bit. So, yeah, those are a few random ideas. I haven't built the West Lafayette tour guide yet, but we do have some travel AI tools out there, so I bet we could do that too.

So very cool. And while you're building that tour guide, I might give you a location or two as well.

Okay, There you go. Yeah, that's awesome.

So really cool. Use cases there as you like. That's gotta get you thinking about, like, the possibilities. So, you know, you come at it with your own mindset and the things that you have, your customers are. Are teaching you every day about what the new possibilities and boundaries might be. So where does that. Where does that take you? Like, when you are. You're kind of done for the workday, your brain's decompressing, but you're still kind of just working on things. What's going through your head about where could things go with this? You take what you're driving and the folks you're working with are driving. You're taking what your customers are showing you that never thought about. And that's got to leave you with some pretty cool ideas about what the future might hold. But can you share some of those ideas with us?

Yeah, I think. I mean, I reflect at the end of the day in a lot of ways because I have four kids that are 11, nine, seven, three. And I just really try to think about, like, what does society look like when this is more present and, you know, what does education look like? I spent a lot of my life in education. We work with a lot of schools who use it for tutors and advisors. And, you know, what's the value of a credential saying, you know something when the pace of change is like, way faster than four years? Right. I think ultimately, you know, I imagine this technology has to fade away from being AI and just being a part of what we use. And it helps us lean into the things that make us weird. You know, I think about AI is the world's best cover band, and it needs, like, the originals to cover. And so I think it really forces us to be more unique as individuals and create something new. Rather, we're going to use AI for a lot of the quick answers, and it's going to be average. It's going to be the middle of that bell curve. And that'll be fine for most work, but again, we have to raise the ceiling ourselves. And so I think it makes me feel like I want my kids and hopefully myself to just get good, really good at whatever weird, interesting thing we care about. Yeah. And I. Man, I don't know, I think agency, again, like, I keep coming back to that. But how do you instill a lack, like a fearlessness in people? Because it feels like, first of all, most people aren't aware of the pace of change. And as they become aware of it, it's either I'm scared, I'm going to back away, or I'm going to lean into it. And I think we just really need to lean into it. And I don't know, I think it's exciting because I'm in Fargo and I couldn't, you know, learn to be a nuclear physicist in Fargo. Right. But now I could, like, I can easily go down that path and learn what I need to connect with the resources, you know, showcase my work. And this has kind of been my dream since my first company in 2009 of like, really given. Give. Giving anyone, wherever they are, a chance to. To build and AI is just like the next step in that process. And I know a lot of people still will find reasons not to, but it's going to be just on that agency piece like you can. So I don't know. I think a society where everybody has a chance to build and create is incredibly exciting. It's going to be more competitive. You know, everyone around the world has equal access to the same models as NASA and, you know, like the Defense Department. Like, it's kind of wild that you can log into these things for free and have the same power as everyone else. So that's an opportunity if you. If you take it. I think I saw there was a recent study the World bank did in Nigeria, and students who were using ChatGPT as a tutor for six weeks had the equivalent of two years of education. And it's just so many of our problems are problems of access, and I think a lot of those access problems go away. And then what happens when another 1 billion people come online with education who don't have it? Now that's just better for us all. We can come up with really exciting solutions to our problems.

Well said. Yeah, that's a great way to end. Thanks for joining, Scott. I encourage everyone to go create your first chip chat on chip C H IPP AI and have some fun. Explore that weirdness, as Scott put it. I love that. Thanks for joining, Scott. It's been great to chat.

Great to be here, guys.

All right, that is our show for this week. If you haven't checked out our Changelog newsletter, head to changelog.com/news. There you'll find 29 reasons, yes, 29 reasons why you should subscribe. I'll tell you reason number 17, you might actually start looking forward to Mondays.

Sounds like somebody's got a case of the Mondays.

28 more reasons are waiting for you@changelog.com news. Thanks again to our partners at fly IO to break master Cylinder for the beats and to you for listening. That is all for now, but we'll talk to you again next time.