477. The Nitty Gritty of AI From an Attorney and AI Expert with Mike Brown

Speaker A: There's going to be two classes of people. There's going to be the producers who are using the AI, who understand how to genuinely do some really cool stuff with these tools. And then you're, uh, if you're just going to wait, you're going to end up being consumer.

Speaker B: That's Mike Brown, attorney turned AI expert and the winner of Anthropic's 2026 Global Cloud Code Hackathon.

Speaker A: You won't have the skills to be able to guide that output to bring in your hard earned experience. Over all the years you've been practicing law, you're just going to get an AI output and sure, that's fine if you want to be a drone.

Speaker B: I'm Michael Mogul, founder and CEO of Krisp, the nation's number one law firm growth company. I've built my business through practice, not theory. Krisp started with just $500 to my name and has grown to over eight figures in revenue over the last few years, earning a spot on the Inc. 500 list of the fastest growing private companies in America. Our approach has been to take everything we've learned about generating massive growth within our own organization and help the country's most ambitious and committed law firm owners do the for theirs. In each episode of this podcast, I sit down with innovative market leaders from the legal industry and beyond to learn from those who thrive in the face of adversity, challenge the status quo, and define what it means to be a true game changer.

Speaker C: I sat down with Mike Brown to

Speaker B: discuss the ways AI can improve your personal and professional life, how to master AI at your firm so you don't

Speaker C: get left behind, and why Curiosity is

Speaker B: the ultimate competitive advantage for a law firm owner.

Speaker A: Everyone starts overwhelmed. They think it's not for me. Oh, uh, no. My AI sucks or whatever and it's just you got to put some time into it. It's a tough first week because you got to change the way you think. But you're a lawyer. You can critically think you'll figure this out.

Speaker B: That's coming up on the Game Changing Attorney podcast. Before we begin today's episode, I, uh, want to remind you that we aren't beholden to any sponsors or run any ads on this podcast. This allows us to present all of our episodes raw and unfiltered. I'm not going to push any made to order meal services on you or try to save you any money on your car insurance. That being said, I have one small request. If you receive any value from this podcast, please give it A five star review, pay the fee so we can keep this podcast free.

Speaker C: Mike, welcome to the podcast.

Speaker A: Thank you so much for having me, Michael.

Speaker C: So you just crush it at a lunch and learn that we just had downstairs for the team. And thank you first and foremost for just sharing your knowledge on the AI front. So for people that aren't familiar with you, uh, you're kind of riding the highs still from being the champion at anthropics hackathon.

Speaker B: Right.

Speaker C: 15,000 people plus, like submitted. You win. You were, I guess, one of the few attorneys that even submitted to kick things off. Like, how did that come to be?

Speaker A: Yeah, I just saw it on X, entered it in. I didn't even think twice about it. It was just like, uh, I'm probably

Speaker B: not going to get in.

Speaker A: You know, I learned how to code a year and a half ago and I really don't even know how to code. I just know how to talk to the, uh, AI agents and then they make the things for me. Next thing I know, uh, when I won, it was just a complete life changing event. It's been really crazy.

Speaker C: Yeah. So you mentioned you kind of know how to code versus it's really vibe coding. Right. Like, could you, could you explain what, what that is to a lot of people? Because I think sometimes when people get involved in more advanced levels of AI, they think, okay, I don't have a coding background. I know to prompt things, but I can't get into like the agentics.

Speaker A: Yeah. So these agents right now, the new AIs out there, are incredibly good at coding. It's almost like the people who them knew how to code, so they taught the agents how to code. And so what you're doing is you just talk to the model. And literally when I mean talk, it's just l. You saw I'm using my voice and you just explain what you want done and the AI will go make it for you. And that works just because the, these agents are just really good at understanding what to do. Yeah.

Speaker C: Uh, now we've talked about AI. We spent a lot of time talking to our coaching members about it, obviously internally, like, we're very passionate about it. Sometimes I feel like AI these days is like a solution in search of a problem. Right. So meaning that someone will come across AI and they hear like, we need to start using AI in our firm and they're not really quite sure what to use it for, but they know they need to start using it because somebody told them somewhere that if they're not using AI, they're going to be less competitive. So what's your take on that? What are actually some use cases for law firm owners, entrepreneurs, et cetera.

Speaker A: Uh, so what I've done with AI for me in my own life has been taking the things I don't like doing and I'm not so good at and essentially just automating those things so then I can focus on the things I actually enjoy doing and passionate about. I still run my own personal injury firm even though I'm very focused in on, uh, the project that won the hackathon called Crossbeam. So if you're looking for permits, hit up Crossbeam. Anyways, what I've done with my own personal firm over the last year and a half was I realized I was not good at the paperwork, keeping notes, keeping track of client files, and essentially it just automated all that stuff to where now all I get to do, which is, it's the one thing I really like to do, which is talk to my clients on the phone.

Speaker C: I love it, man. So I want to talk about all that. I obviously want to get into Crosby. I want to get into like the firm. If we could go back almost to the very beginning. Were you entrepreneurial as a kid? I'm just curious as to like the path to becoming an attorney and then more the like the tech enabled side.

Speaker A: Yeah. So, uh, in college I, it was around a lot of people who were thinking, oh, entrepreneurially. I went to Amherst College out east, and that kind of just opened my door to, hey, like, you can go create your own job. And so got really into that. And like you, I was actually obsessed with making videos. And so I came out of college, went to Hollywood, got really good at after Effects. So I was a visual effects artist, um, got into reality TV producing along the way, had my own company and then kind of, to be honest, I just burn out though. I mean, like, reading your story is fascinating because I went the other direction and then I was like, all right, what am I going to do? I'm going to go to law school, take it easy for three years. And so, uh, did that and then came out of. I thought I was going to go back to Hollywood during law school. And then I kind of realized the least favorite part of Hollywood to me was the ass kissing part. And so I then fell in with, uh, I started meeting some personal injury lawyers and I was like, these guys are rad. Like they're, they're kind of gunslingers. You know, they hustle, you know, it just, it felt like the right thing Right area. So I started doing that, worked for a couple different firms. And then when I jumped out on my own, that was the moment where it was right when ChatGPT had come out. And you know, most lawyers, you know, you got to figure it, you got to get that first case settlement. You hire paralegal and all that stuff. I just started creating like a prompt library and then getting really good at. So back to your original question. I've always kind of been entrepreneurial minded, but I thought that part of my life had died when I went to law school, to be honest.

Speaker C: You know, another thing I think we share in common in our background was we were both DJs, which I think there is, there is a case to be said that being entrepreneurial at an early age, I mean, I had kind of the whole like rise through, like nightlife and nightlife photography and that whole space. Um, but I'm wondering, like, when you started the firm and let's say chat GPT comes out, what part of you was like, okay, there's something here. Like, let me, let me jump onto this. Because there's a lot of people that saw that and said, well, I'm gonna kind of watch from the sidelines. It's not quite where it needs to be yet. It's hallucinating a lot. What made you jump on?

Speaker A: Yeah, so I've always been fascinated playing around with computers, you know, coming from that visual effects background. And so I've always known it's like a good, like good computer programs doesn't mean they work every time. Like you got to kind of play around, you got to work through the kinks. And so I quickly realized it's like, hey, it's not going to write like a, a full demand letter. I'm not gonna it hundreds of files. But it could do simple things. You know, when I got my clients permission to record a phone call, it could take that entire phone call, turn it into a note set, something that I was always terrible at and, you know, kind of forgetful. It could start drafting emails and you know, back in the early days, you know, what you're doing is you're kind of constantly copy pasting things in and out of chat. But quickly, uh, realized I was like, hey, there's something here. And it, it all started out of necessity. You know, it was just, how could I possibly compete against these other firms? And, uh, this was the great equalizer for me.

Speaker C: What do you think differentiates people that really start experimenting with AI and really start leveraging it versus those that Kind of step back. Is it a level of curiosity? Is it creativity? Like what, what type of trait or capability you think is necessary to get the most out of it?

Speaker A: Yeah, so I've kind of come up with like a mental framework and I think lawyers are good at two of the two of the three things that they need to be. It's um, critical thinking, number one. It's like you gotta be able to critically think. It's a, there's a tendency in AI to think you're gonna get dumber using it. And you will if you just tell it, hey, make perfect thing hard, do it. You know, that's a lot of way people work. That's not the way to work with AI. You need to think through and you need to truly understand the system you're trying to make it enact. Second thing, uh, being disciplined and that is just, you got to, you got to kind of hit it and you got to keep working with it. You know, you can't just go there one time and expect results. And that's like anything in life. Third thing, and this is where lawyers aren't necessarily so good. The curiosity aspect of things. You need to be curious. I think we get a beat out of us a little bit. In law school. I was always the wait, why? You know, I'd always ask why? And I think it's so like I had, I had some really good professors at Chapman and you know, they entertain me on that. But I think, uh, you know, a lot of people come out of law school just be like, no, this is the way it is. If you have that mentality, you're going to go try one of these agents like that, you're going to say, oh, I can do it better myself. You're not going to keep going. And if you, but if you keep going, the benefit is like what we talked about today at our presentation is skills. And it's this new concept that came around the last few months. You, it allows you to create prompts that the AI agent can invoke along the way. And what ultimately what you're doing is you're taking hard fought like work that you've done with the AI agent and compounding it over time.

Speaker C: If we could talk about, I guess some of the advancements in these models over the last several years. What aspect of it. It used to be just autocomplete on steroids. Right. It just essentially is like pattern matching and it's doing a very good job. You kind of explain what an LLM is and ultimately what is Happening when you go from one model to the next and these levels of advancement.

Speaker A: Yeah. So let's go back to, you know, even we go a year ago. So March 2025. Right. I think Gemini 2.5 came out and that was the first one where it still was pattern matching on steroids, but it started getting really good at getting tasks done. Like, you know, three, three to five minutes at the same time. Claude Code had just come out. And, uh, that was a CLI tool that. So Claude Code is a harness. There's models that get out and then there's harnesses. And then when you combine the two, that's kind of this agentic AI. Right. And so, um, that was like the first, oh, hey, we're starting to see stuff. And uh, at the time the model for cloud code was three point, uh, zero. Sonnet 3.7. Now sonnet 3.7 was extremely capable, but you'd ask it to go like, fix a button or change the color here. It would do that. And then it would also go be like, hey, let me create like a stripe paywall so that people can pay for this. It's like, hold on, dude, like we're not. I'm trying to create my own little tool here. What are we doing? So from there, you know, we started seeing advancements last summer. Um, the first time I felt AGI, like the artificial generalized intelligence was actually Grok 4 and it fixed a problem that I didn't even know was a problem in a code base that I was working on. And it was genuinely like, whoa, wait, Like, I didn't ask her to do this, but I knew I needed to do something here and I thought the problem was something else. It figured it out, solved it. And then when we saw in November, uh, I think it would have been in November when Opus 4.5 came out, that was like the first moment of like, wow. Like, holy shit, we've arrived at this next level of things. And so what you're seeing now with these models is it's truly capable of working for 30 minutes an hour. Like nowadays for me, like you guys saw in the demo, like I have to restrict the model to be faster. For, you know, 45 minute demo, I usually will work for an hour or two building up a plan file and then I let the agent go work for four to six hours. And it just does it, uh, it's pretty incredible if you know what you're doing.

Speaker C: So I think this might blow people's minds. It did for me. I was listening to a podcast with Mark Andreessen, and he was talking about just right now in Silicon Valley, how people are coding and you've got multiple agents doing things, but then how that scales to then the agents running other agents and essentially reminds me of this book series that I've read, uh, about the Babiverse. I don't know if you've heard of it, but essentially is like these AIs, like this guy, essentially he passes away, but his AI is revived. And it's like, you know, and then essentially they're able to duplicate more of those and then there's. They help like with multi planetary civilization. But it's very similar to like an AI agent model. Right. They're just like duplicating themselves and they're assigning tasks to each other and then those AIs are managing other AIs. Like, how do you see this scaling?

Speaker A: Yeah. So that, I mean, that is the meta right now, which is. It's not you prompting an agent, it's you prompting an agent to then go prompt agents. Right. And so I always recommend, I mean, look, if you're sitting here and you're like, this is all news to you right now. You really need to prompt focus, uh, on prompt engineering and then context management. You need to understand the basics as we're moving really quick. And we might have even today, like we were saying, mythos coming out, which is apparently incredible at operating, uh, as an orchestrator, which is the model you're talking about, where you got one AI that then spins up more AIs beneath it. Those AIs can spin up more agents. And so both Boris Journey and Open, uh, Claw Pete have talked about in the last month, you need to stop focusing on your specific prompts. You need to be focused on system design to design the agents around your time. So it's like. And that's the meta right now. I try to, you know, depending on where people are, that freaks people out. Because it's like, because I don't want to say you're behind yet, but it's like, hey, uh, you got to get with the program asap, because we're going to be going in this world where, you know, we're levels of abstraction on top of the things doing the work. It's pretty incredible. I mean, already most of my day is designed by AI.

Speaker B: Yeah.

Speaker C: So I want to do a little bit of objection handling. So for somebody who's listening and let's say they're an attorney or a law firm owner, they might be thinking, all right, man, this is great. But I've seen lawyers get in trouble with AI. I've seen issues with, like, hallucinations. You know, it's attorney client privilege. I'm not sure I want to be able to put certain files and documents in the AI. Like, what are your thoughts on that? Hey, this is Michael.

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Speaker A: Yeah, I mean, we see it every day, right? Look, it's a skill issue. Sorry, it's, uh, you know, you see these lawyers, you know, they have hallucinated cases that they're putting out. Sorry. I'm sorry. You are lazy. Start paying for your, uh, AI services. Don't use the free version. Nothing in life is free. When you use the free version, what's happening right there? OpenAI loves it. They're taking that data, plugging it right in the training data, and you are, like, literally using attorney client privilege into something that's going to be available for the rest of humanity. Trainees, models. So that's step one. Start paying for your AI. And because then you get to turn off their ability to use it for training purposes, uh, second step is stop like having Chat GPT write your briefs. ChatGPT is a limited context window that is not agentic. When we're talking about agentic is like if you're using cloud code or cowork essentially you're gonna have one agent basically research and then it's gonna send out more agents that do more research for your things. That's protecting its context window. Then at the end if it's, and it should do this in general with cowork and cloud code, uh, codex on the OpenAI side, it will then fact check it with different separate agents. Right. So just anytime I hear about these hallucinating cases I'm just like, this is a skill issue and people need to understand like you need to be using the next level tools, you can be paying for it. You'll be using a lot of tokens if you're going to be submitting something to a judge and you need to read it yourself.

Speaker C: Now the other side of it is someone might look at it and say, well I kind of want to wait for it all to get sorted. Like I remember a couple years back we were doing a workshop and you remember you kind of like had to, in each of the different AI models, you'd even like click down to select the model for the type of thing you were going to be asking. And I was like this is going to be like old news soon. You're just going to type in a question or whatever it is you're trying to do. It's going to select the right model. So for somebody who's on the sidelines right now kind of waiting for all this stuff to in quotes sort itself out, what would you say to them?

Speaker A: Yeah, I have to deal with this with a lot of different people. Look, there's going to be two classes of people. There's going to be the producers who are using the AI, who understand how to uh, genuinely do some really cool stuff with these tools and then you're uh, if you're just going to wait, you're going to end up being consumer, you're going to be paying a lot of money to these model companies and the harness providers. And yeah, you will be able to ask a question. You won't have any be able to, you won't have the skills to be able to guide that output to bring in your hard earned experience over all the years you've been practicing law, all the unique things, you know, you're just going to get an AI, uh output and sure, that's fine. If you Want to be a drone. But right now is the time to learn this stuff. Because the models are good. They're not perfect yet, but in the near future, the rate of advance that we're getting, it's like, yeah, you will be able to say, hey, go into this person's client file, write their draft, their demand letter, all that stuff, and you'll get the bland output. But if you want to bring that unique value proposition that UVP that you talk about, you need to understand how to use these tools. Yeah.

Speaker C: Well, and then I'm curious, looking ahead a little bit to the future, I guess there's two sides of it, right? There's like the, the doom and gloom and then there's the optimism. Where do you see a lot of this going? And I mean more. So let's say on the professional services side, let's say in terms of running a law firm, there's a come a point when you're orchestrating enough agents, when you just have an AI running an entire law firm and maybe you have the lawyer still going to court, but what are the different branches this can go down?

Speaker A: Yeah, right. I mean, it's two starkly different paths that we're facing. Right. And one's that dystopian like, hey, no one's really working the agents doing it. But I'm actually more of an optimist that we're going to head towards a golden age and it's going to free up everyone to do the things that they actually went to law school for. Right. And bring your unique strategic perspective, because that's what people are paying you. They're not paying you to go write some brief. They're paying you for that knowledge that you have and your strategic insights that you know from experience. Right. It's pretty cool. I mean, I've already automated the vast majority of my own law firm, so I spend the things I used to. I was always kind of like my own firm. Like, you know, I'm down to about like an hour a week of actual work on it. You know, I'm mostly a referral firm these days, uh, because I'm very focused on my other stuff. But my clients know, like, anytime they call me on the phone, I will answer. And most time they're working with other attorneys. But then I get them on the phone or I have Claude then go send the email. So.

Speaker C: Love it, man. So then let's back up. Coming back to the hackathon, like, I guess talk a little bit about how you won this thing. Like what was uh, essentially the, the project that won first place out of 15,000 plus applicants.

Speaker A: Yeah. So it was fascinating. So the way they anthropic did their like, application, uh, process was, uh, it was a chatbot, right. And so it was funny because I was just talking to it, right? I was using whisper flow. Normally I would take that input, I would talk for five minutes and I would take that input and then bring it in a cloud. Like, hey, tighten it, uh, up, make it professional, accidentally press enter. There was no way to go back. So what I submitted to get in was me just brainstorming ideas and what they were looking for ideas that weren't possible with any previous model. And so I was going through a couple ideas. I know, that sucks. That sucks. Whatever. The one idea that I was like, oh, yeah, I know for a fact this doesn't work. I was on a bachelor party back in November. This hackathons in February. So I was in November, I was talking to my buddy. He's an ADU builder. ADUs are like little small backyard cottages. It's kind of California's policy solution to our housing crisis. Right. And so you think it would be relatively easy to build these things and use, like, it takes me nine to 12 months to go get a perfect permit for it. So at the time I had already built a demand letter that could handle 1500 medical documents and stuff to build chronology. I was like, yeah, no problem. Cloud will be, will do it. That Monday he sends me over the 30 page blueprint and it errors out the API. And I was like, that is weird. And I'd never seen that a cloud could handle anything at this point with Opus 4.5. Anyways, so when Opus 4.6 comes out, I submitted that as the idea got in and first thing I thought was like, oh, crap. Like, I know this idea doesn't work like these because these, what it is these PDFs is 30 pa, but they're the size of a table. You know, those big construction blueprints. And then like page three will have like a super small note that references page 27 and so on. Right? So it's just you need the ability to be able to view drawings while also keeping a massive context window that week. So I remember, like two days into it, I was like, oh, uh, shit, I got nothing right now. And, uh. But then all of a sudden I had a breakthrough. And what it, what it all came down to was it was just a weird life moment where everything I'd done in Hollywood, understanding image compression, but also understanding the law. I was able to tie it all together. And then I was like, wait, we really got something here. Even when I submitted, though, I didn't think I was going to win this thing. You know, it was just. I was up against, literally, like, engineers at Nvidia, and just we're in a group discord. I'm like, these people are so smart. They are so good. You know, you're seeing videos from people. I'm like, uh, that's amazing. Like, I'm not even close. Whatever. And then when they do, like, the little live judging, they announced six people, the finals, and I was like, whoa, I'm here. And then getting to hear Boris Journey, the creator of Cloud Code, talk about why he was impressed by project was just like, you know, such an honor. And then they announced I won. And then it's just been a wild ride ever since.

Speaker C: I love it, man. So it's like, essentially a way to potentially solve or improve California's housing crisis through this new model.

Speaker A: Well, essentially, what it came down to, the more I dug into it, right? It's. California's housing crisis isn't a housing crisis. There's plenty of people who want to build cities, who want to build. Both sides. Demand, supply. It's there to go. Do it. There's a bottleneck in the permit side of things. And then what's fascinating is, essentially, it wasn't possible to solve AI until February, when Opus 4.6 came out, because it finally opened up the context window, plus the vision abilities to be able to do it. And now we're seeing it in live cities. So my buddy from law school, um, he's the mayor of Buena Park. He's running for Orange county supervisor. Connor Trout, just a superstar. And, um, so I'm just so blessed because, you know, he helped me with his city get it in at Buena Park. Pilot, period that we were just completing it is, uh, they're very, very excited about it. And now we are doing a pilot with Laguna beach, and then we got a couple other cities I can't talk about yet. And then one city in, uh, Texas that I'm very, very excited about right now. So we are kind of solving this permit crisis right now.

Speaker C: So. So I love that because when you're solving problems like this, I'm sure it opens up your mind as to, like, what other problems or challenges could be solved with AI. You know, it's. Sometimes I think somebody gets, like, a quick win for just something that they need personally or whatever, and then it helps them with AI. They're like, uh, help me pick out, like, the best pair of, like, running shoes for marathons. And they're like, okay, this is amazing. What else can I do? Can, like, automate my. Has that, like, kind of opened your mind up to other, uh, use cases?

Speaker A: Yeah. So, I mean, yeah, I mean, there's a lot. It's weird because prior to the hackathon, I did my own legal tech startup. It didn't work that great, but I learned so much during that, um, like, learn how to code this vibe coding thing. Then I just started doing a new project every two weeks. And it just became like an addiction almost, because it's just so fun to hop into new worlds, go solve something for someone. And then since then, I've been working almost full time on crossbeam. I mean, it's just. I'm 16 hours a day on it is. I love it. It's so fun. But anyway, I'm constantly getting ideas left and right. I was on the Legal Quants podcast. They're just this really cool group of lawyers. Um, they're based out of Singapore, but they're worldwide. And, um, you know, they brought up something that I didn't really thought of crossbeam. So I don't think of. I think of crossbeam as like a legal solution, like an AI thing. But, uh, from a legal perspective, no lawyer could work in this permit world because there's no real damages on a delayed permit. Right? Like, there's nothing to go getting on a contingency fee for builders, like, for you to go build an hourly thing that would make you a profit. Like, you'd have to do hundreds of permits. Like, it's just not, it's not feasible. But what it did, like with AI, you could actually scale up and take these not even underserved areas. It's just previously not even, uh, no lawyers in it. Right. It's just an uncharted realm. And there's going to be a lot of really cool areas that are going to open up because for the first time ever, as a lawyer, you can bring your lawyer expertise, your knowledge, and you could just open up a new field of law because it's. You can do it at a scale where it's going to be profitable for you.

Speaker C: So that. And I'm glad you mentioned that because a lot of times people look at AI coming in, and I hate this word disruption, but this. Essentially they look at what jobs are going to be eliminated rather than thinking about what jobs are going to be created. And if you look at a lot of These just even Internet, you look at personal computer, et cetera. There were jobs that were created that came as a result of that later on that we didn't really anticipate. Like where do you see that in legal?

Speaker A: I mean, so look, I get it. It's like in theory, if you think about like our framework right now, that AI, yeah, it will replace a lot of jobs, but every single industrial revolution that's come around, we've created more jobs. So. So it's just we're on to the next one. I have, I have an optimist view of things. You know, I like to look at things as half class full. I think we're going to have more jobs than ever that come out of this. Boris Journey was just talking about this on some other podcast. The creator of Claude Code. And so there's this Harvard Review study of when the personal computer came out. A lot of people were just like, oh, it's going to replace jobs. And just another industrial revolution. And then how did we, uh, you know, when, when lawyers or just professional office workers, when you first brought in the computer, it wasn't that productive for people. But once you remove the file system and the old paper system out of there, and so people stopped thinking about the computer as a replacement for it and more of, oh, this is this new tool that allows me to work at scale, that's when you saw all these new jobs open up and things you wouldn't even thought of prior of, uh, you know, I wasn't even alive for that. So it's just hard to imagine. But it's just, I think that's what AI is going to do. And it's. So any project I've instituted in like a law firm or I do a lot of work with private equity places, it's always led to more jobs because they're just making, they're crushing it with this edge.

Speaker C: And the amazing thing I think with AI, and you look at it with every field, not just legal, but even healthcare, et cetera, is just the rate of improvement and the rate of improvement in the models, it's, it's exponential, right? It's not incremental. Which is why I think for a lot of us, we're seeing the world change, but we're not really adapting to it as quickly as the change is happening. So I guess my question then is, is like what, what is the constraint right now? Is it, is it going to come down to the tokens, the power, the data centers, like us being able to get something up in space and once that happens and the cost goes down, then it's like true abundance. What are your thoughts?

Speaker A: Yeah, so, uh, I actually just look at it. It's user, it's skill. Like you're just not good enough at it yet. Right? So the constraint is there, right? We are in a highly subsidized period. The way I look at it is I, uh, remember ten years ago when it was five bucks to take an Uber across town and now, now it's way more expensive, right? So we are getting blessed by these VCs with all these different AI labs. Use the tokens, go get that max subscription, figure these things out. Now that will be. They're going to figure out these data centers and you know, the power supply and then they're going to be putting the SpaceX up there. But right now, to me, the main constraint is you go figure it out right now while it's cheap, because it's going to be like a rug pull one day where, uh, it's going to get a hell of a lot more expensive. And the people who don't have these skills and understand how to limit, uh, you know, token expense like that is going to be that way. But right now I would, I would say, you know, if your firm is just figuring this out, go unlimited token spend, you know, do six months of that, treat it as your tuition to figure these things out.

Speaker C: What, what do you think? I guess on a broader level, when you look at the different models, whether it's like the open AI, like anthropic, you've got grok, et cetera, is there going to be multiple players or do you see this eventually getting to a point where there's just like one winner. They figure it out and that's the one we're going to use.

Speaker A: So what was interesting is 6 months ago I would have said, yeah, there's going to be one winner. Ultimately, you know, there's going to be one model that's so good because it all felt like they were converting, emerging. What's happened since, um, in these like next gen models that are coming out, they're actually all good at their own things, right? They're, they're almost like distributing across different space. What, what that's coming down to is now there's a, uh, arms race on data for these models. They've already kind of consumed everything available on the Internet. Behind the scenes, they're all competing and buying like highly specialized data sets and that kind of reflects on the models of what they're good at. So I Think we're going to see more of that. Almost like a divergence. We may, like when we go back to asi. So right now everyone's going for AGI, but to me, I feel like I operate with AGI just because I'm good at this stuff, uh, and I put the time in to learn it and then, uh. But there will be a moment when it's like, all right, yeah, we're definitely at AGI anyways, when we head back to asi. Yeah, maybe there is like this Superman and then there's a singular winner. But I think when we go to AGI, each of them are going to kind of have their own personalities and be different. So it's important to know all of them and use them for different things. Give your people access to all them. Right now.

Speaker C: So we're at kind of like the software layer. How far out do you think we're from, like the hardware layer where like robotics starts to really take off and. And I don't mean like the robo robot in the factory, like the robot at home.

Speaker A: Yeah, right, yeah. So I've been doing a lot more time, thank you. Thankfully for Anthropic, I've been up in SF a lot more. That's what, uh, everyone's, what everyone's talking about. It's kind of wild. So that is not my layer of expertise at the moment. I'm just trying to crush it with crossbeam. But I do see a future where I love the idea of robots, man, it's so cool. But it's going to start with, you know that Tesla One's going to be 30K or whatever, right? You're going to take. It's gonna be like a car loan. You're just going to pay that monthly subscription. Get in your house. It's probably not going to be that good, to be honest, at the beginning because anytime they do these, they need, they need like an open source layer for people to play around. Even on the drone level I was looking at it, it's still like for a good open source drone is like 350, which is like, okay, I can handle that. But like the random kid out in Indiana or whatever, you know, the kid who's going to come up with the really cool ideas, like, he can't afford that yet. So we still got some work to

Speaker C: do in that area based on like what you've seen, if you're at liberty to share. Because I've talked to some people that get back from San Francisco and like, they're a changed human being. Like, there's. There's like the models we're playing around with, and then there's like, what those in Silicon Valley, like, what they know. Have you seen some things where you're like, wow, we are truly living in the future or even like, thinking about. You've already mentioned AGI. Now publicly, they would say we're not quite there yet, but for those that have, like, really played around with the models, it kind of feels like AGI. And even a few years ago, I'd actually say that there's been moments that like, it definitely feels like AGI, like, if you didn't know that there was AI on the other end, you would think it was a human. So I guess my question here is, is that like, how far out do you think we truly are from this level of like, super intelligence where it like, exceeds all human knowledge available and you're where you're working with like, the most talented AI possible.

Speaker A: We're starting to see some peaks of it. And then my conversations with some. I got good and go to the code with cloud conference was awesome. And getting to talk to employees, that's probably one of the coolest parts of winning this hackathon is getting to know some people inside of the labs and they're genuinely like another level brilliant. So it is so cool. But, uh, getting to talk to people who are have played around with Mythos, I've heard some cool things and it's not going to be that ASI just yet, but it sounds like from an orchestration level, which is the agent then spinning up agents that spin agents. Right. Sounds like it's next level. So I'm very, very excited for when it comes out in the near future. The question is, though, how far away are we from that? And that's where I don't have that answer yet. But I think we're pretty close, man. I think these new models, this next generation right here, um, so because Mythos is essentially going to be Opus 5, right? It's going to be their fifth gen models. And then GPT sounds like, you know, open. It creates this whole arms race. OpenAI is going to have to do theirs. And then Elon, his response was one of my favorite. When GPT, uh, 5.5 came out, he was just like, we have a lot of work to do at Grok. And so they're training a 10 billion or, uh, 10 trillion parameter model with their new data center, Colossus 2. It's going to be pretty cool.

Speaker C: Has there ever been, I mean, maybe since like the dot com boom. But has there ever been a type of competitive environment, you've got like the most intelligent people on the planet burning through billions of dollars every single month, like at that level of just competition. Like, has that ever existed before?

Speaker A: Well, maybe the Manhattan Project, but this is the first time it's been privatized. The people that I've met at both labs, uh, OpenAI and Anthropic, I'm just like, like, we are blessed that these people are getting paid really well to work in the same places and compete against each other. And then I get to sit in the middle and just go, uh, go play with my agents every day. So it's pretty wild. I mean, these guys are getting at the labs, they're getting paid like athletes right now. So something.

Speaker C: Oh, yeah, Zuckerberg is collecting them like Pokemon.

Speaker A: Oh, yeah. We even talked about Meta. Yeah, they're, they're going to do something soon there.

Speaker C: So. So let's, let's chat about that. Because it's not the first model that comes to mind. I mean, it's, you know, when you're hearing we're talking about models, it's like Gemini, or you're talking about Grok, or you're talking about, you know, Claude, uh, or one of those, you don't really think about Meta. And yet Zuckerberg has pulled some of the top AI engineers on the planet over there.

Speaker A: The blessing too, is they do theirs open source, like the Llama models. And I don't even know what number they are right now. Now they keep just being a little bit behind, but yeah, granted they've got some of the best talent on Earth, so I imagine they're gonna come out with something cool soon. What's interesting there? So I'm obsessed with those Meta. The Meta glasses. Yes, yes.

Speaker C: Yeah.

Speaker A: But then their SDK came out and then they open source it. But it's just they didn't really open source everything. And I'm kind of like they should just start open sourcing things and then. Because people are going to jump at that open source aspect of it and then really tweak it for themselves. It's almost like a hot rod. Right, right. And then you would see some good results. I think they're going to come out with something really cool soon. But right now I'm a little disappointed where it's at.

Speaker C: Yeah. And on the other end of that, then you got Apple. Right. That some could argue. It's like, okay, are they kind of behind on the whole AI race? But when you look at Apple's like product model. It's really around like they're not the first to market, right? And they're usually just once. Once they do finally I was listening to uh, wwdc, uh yesterday, right? And they were sharing, okay, the new and improved Siri. But that's where with Gemini, right? So they're not developing their own model. And that kind of seems like the whole thing where they're not going to develop their own search engine, they're not going to develop their own car, etc, from a business standpoint, like is there room for another player? Like does that even make sense? Or even when you look at uh, let's say coming back to legal, like no one should be developing their own model at this point, right?

Speaker A: Yeah, yeah, yeah, right. Uh, okay, so Anthropics, this latest generation models costed them $10 million, right. I've been trying to explain to people when people talk about trying to do a custom mod, I'm like, like do you have $10 billion to go spur, right? And so you're better off just sitting or uh, getting using these latest models and developing things around it like either a custom harness which I would still even use the like 8 cloud agents SDK. It's just incredible. You're, you got some of the smartest people in the world designing those prompts. So I would still use that. And then what you go spend your time on is the skills, the security infrastructure around it. I mean look, the people who in 2024 went out and was like, all right, we're going to go develop our own custom models. They are stuck with a GPT3 like thing and it is just those are trash. They're not good. There is talk now though of uh, the open sources are getting good enough where you can go train up something for a highly specialized singular task. Right. In theory you can go train your own um, like client intake, uh, model. Right. At the same time I'm like okay, just go take one of the cheaper like a sonnet or a haiku. Give it a skill, you're set. That's pennies, you're not even a penny. I wonder about. What do you think about Kirkland Ellis?

Speaker C: Uh, ah, well it's a signal to the market that there's you know, Big Maw is taking AI seriously. I would imagine that some of these bigger players look at it and say like what the value they really have is like in the training data in terms of like what they have in terms of case data, et cetera. I still don't know, I mean this is kind of like a bubble, man. There's so much money being thrown around right now that it's like it used to be if you put AI like somewhere in the name of your startup, right, you're getting funded. Now it's kind of like uh, I don't know. So, so I look at it and say it's a good signal in the market. Now is that the best way to be able to kind of scale internal like AI implementation? I don't know. I don't really think so. But then you've got a firm that's a multi billion dollar law firm that looks at it and says we want to develop something in house and if there's one that could probably do it or at least has the resources to, it's them. Is it the right long term plan? I don't know.

Speaker A: Yeah, I question it. Um, I'm also, I'm rooting for them because it'd be really cool if they come up with something different. But at the same time, I mean because you can't, they can't go give equity away so they're not going to go get the best engineers. And then on top of it, at the end of the day it's, it's a culture type thing. How many of their lawyers are actually using these things? Like you go tell them, hey we developed our own custom model, spent all this money, go use it is people are still going to say oh, they're going to try it once and they're like well I can do it better myself. Right. Unless you really institute the culture side of things, which you can do with these models right now, you can go figure that out. The custom training data is really interesting. Right. Uh, but it's also, there's argument made that breaks attorney client privilege if you're dumping all that in there. So then it's like what is the value proposal of to your firm?

Speaker C: So I want to shift a little bit in talking about what about the personal side of AI, Meaning that I know like usually when we're talking about business use cases, someone looks at it from the standpoint of this is going to help me with efficiency in my practice. I'm going to be able to like save on costs in terms of labor, we're going to increase our output, the profit goes up, it's going to help me make more money.

Speaker A: Right.

Speaker C: Or I'm going to be able to free up my time to be able to do other things. But then when it comes to the personal side, where have you seen, like, AI that's been helpful to you. And I'll give you an example. Right. So I get quarterly blood labs, like, drawn. I've got, like, a medical team, but I upload everything into the AI and I use both, like, chat, GPT and I also use quad, um, because I like to see the differences in the responses. I kind of pit them against each other. And I would say that, like, my medical team in ChatGPT and Quad is better than the Harvard medical team that I. I go to for the blood labs.

Speaker A: Isn't that incredible? It's a personalized, like, ability to look at it. Yeah, I do very similar. Right. And, uh, that's what I was never really about, like, tracking food and the. That stuff, because I never saw, like, the use of just those dashboards that they did. And then in the last year and a half, I've done sort of tracking everything. Steps on, um, the watch, food and calai. And then because it's. It's not even that great yet, because this is. I trust that. I mean, we're seeing it advance really rapidly. As you've seen with the blood work. You get that in there. It could personalize, can find different things of what you should be eating. It could suggest things. Being able to talk to it every day in a way of knowing where you're at. That, um. So that's been huge. I like poke, uh, which is just. It reads my emails and texts me. And then it can do little things. That's a very, uh, small. Like, I could do that with cowork. But just having it in my text messages, really useful overall, like, just having it give me a to do list every day. As someone who's very adhd, I would say, you know, it's like, you know, I just spend an hour trying to figure out what to do every day, you know, and like, it was. And now I wake up and it's just like, here's what you're gonna go do to go crush your day. Here's your time. Top three priorities. This is the highest leverage. Go get these done.

Speaker C: And someone listening to this might think that maybe you and I are being a little bit reckless, right? In terms of, like, the personal data. But I. I'm one of these people that's of the belief that we don't have privacy. If you put the little thing on the little webcam on your computer, it's like they don't care. They know exactly where you are, where you're going next and where you were. Right. Like, there's the amount, the data set that exists on you is so large that anything you, you're doing that you might believe, like, uh, gives you privacy. It just doesn't matter.

Speaker B: Right?

Speaker C: Like, they can, can. They can tell you exactly what you're going to eat later today. In fact, Amazon can predict what you're going to order before you even order it. They basically order the products to the local warehouses and the distribution centers before you order them because they predict that you're going to order it so you can have faster delivery. Like, it's incredible.

Speaker A: It's, it's really wild. I mean, the only way you've been able to escape is if you weren't on the Internet since like 2008. It's, I mean, they have everything about you. They already know it all. If you got something to hide, I guess that's a little different. But to me, I don't. I do like the idea of privacy preserving that as a right. However, I mean, these private companies have everything on you. So to me it's like we might as well make use of this data while we got it.

Speaker C: So on that note, I guess talking about different personal applications and I've got like different, different agents and almost like these different playbooks that I've got like the financial stuff, right? And it's like, okay, analyze everything from like budgeting to like, uh, on the business side. Then you got the personal side down to like, you know, food intake. But, but it's more so about how it can interpret all the data together. It's not like, hey, just read this report. It's more so saying, okay, based on what you know about me and, you know, my training and some training peaks and you know, kind of my whoop data and you know, the Garmin data, what I'm eating and my fitness pal, right? Like, and you can see like down to the every single data point, where can I improve? And it will recommend everything from like meal timing to certain supplements, like, based on certain deficiencies. I mean, it's amazing.

Speaker A: Isn't that crazy? Yeah. Um, I've recently heard of one guy who found he had like a mild form of sleep apnea just by taking his aura ring, having the, uh, A.I. look at it, and he thought he was just starting to head towards like a five and a half. He was just like, oh, uh, I'm in my mid-30s. Like, guess I just sleep five hours a night, but I feel like crap, you know. And then Claude was able to look at it and be like, oh, no, you wake up during the middle of your REM cycle, you need to go get a sleep study and figured out it was a mild form. You know, just little optimizations like that. Because anytime I gone to a doctor, I've. Because I'm all about like optimizing my life, being healthy. You know, I'm into peptides now and those types of things. Most doctors will be like, well, you're healthy, you know, like, you're good, you're not, you're not, you don't have cancer. Or it's like that. And that's, you know, that's okay. That's why I do want my doctors in that. What I really want though, is this optimization next step. And that's where the AI, I really can dial it in. Just even for me, like getting a certain amount of caffeine every day at certain times, cutting it off, like, just having like. I already kind of knew that because I'm a big fan of Huberman. And then just having the tracking that type of stuff and then having the AI be like, well, no, this is why you, you drink caffeine at this point of the day. And it was a little too much. And that's why you felt tired at five o'.

Speaker C: Clock.

Speaker A: And it's just like being able to optimize all that is incredible. What a time to be alive.

Speaker C: It truly is. And the thing that strikes me also that I really like about AI is it never gets annoyed at you. So. Meaning that, like, sometimes I'm asking IT questions that if I asked a human being this, like, it would be like, dude, like, stop messaging me, right? So I'll ask, even down to like, okay, which running shoes should I wear for this specific type of workout? Or like, I'm training for an iron man and it's like, okay, for this specific workout that I have later today, how should I fuel in terms of the number of cars per hour? You keep following up saying, are you sure? And then analyze this. And it's like, if even if I had like a doctor that worked for me full time, I'd be driving them crazy, right? Versus every time I ask AI, it's like, oh, yeah, absolutely, man. Here, here we go, do this. And then it's like, are you sure? Yeah, you know, absolutely. Let's like, let's reference it back. And yeah, it's, it's amazing.

Speaker A: The optimizations are really wild. Kind of going a little not, not personal, but going with it. So my cousin runs two butcher shops in Brooklyn and it's been a really a, uh, cool blessing because if it's turned into like this real life sandbox me and my cousin can use to test AI stuff. The amount of stuff we've been able to do to improve the business over there just by looking at all these things and just constantly being like, hey, what can we do better? Should we stock this more? We've gotten it down from a forecast perspective to about plus or minus 5% on how much money we'll make that day. A lot of it has to do with weather. All these different like things that the AI was like, yeah, we should look into these data sets. And so like, if you can do that with a business, imagine what it can do for you. The shoe thing is interesting too, right. When it comes to.

Speaker C: So I'd love like, I, I know you gave us this answer when you were uh, downstairs at the workshop, but there's this kind of the school of thought of like how you talk to the AI. So when you were prompting it, you were like, please and thank you and, and my thought was like, okay, Mike doesn't. If the AI takes over, they want to be able to look at Mike and say, you were always kind to us. Right. Let you live. But you actually gave kind of a, uh, more of like a data driven case for like, why do that? As opposed to why not just abuse the AI?

Speaker A: Yep. Yeah. So we don't have necessarily research that says being positive and friendly to your model will make you do better, but there's actually research that talks about that has proven that if you go and be mean to your AI, if you cuss it out, if you tell it it did a terrible job, it's going to do way worse. And what it is, is you started using AI Gemini 2.5 used to let you see the reasoning traces. Every time you're working with these high level models, they use uh, reasoning. They have a reasoning budget. Right. Which is what the, where it thinks before it responds to you, which allows it to kind of work through the problem set. And so like typically you'll see about like 8,000 tokens, let's just say as the reasoning budget. And if you cuss it out and say you did a terrible job, you need to fix this, it will spend at least 4,000 of those tokens trying to figure out the best way to apologize to you. So instead of figuring out how to actually do something and figure out like the best idea and brainstorming these things, it's just gonna go sit there and be like, should I profusely apologize or should I sternly apologize or Should I fight back? Because Mike is wrong and it'll just like, think through all these things and not be thinking about how to solve the problem. So anyway, I've always been, all right, if that's true about the negative side, at the very little. At least, you know, be nice to it being positive, go for, for. And it's just. It doesn't cost anything to kill that model. If it really is messing up, just kill it. Start a new one. It's really easy. Just get another one going.

Speaker C: Yeah, I love it. What about on, just in terms of, like, introspection? In terms of, like, when you engage with an AI enough, you ask it enough questions, it knows enough about your life, at what point does it make sense? I, uh, don't mean as like an AI therapist, but just to get insights on yourself, right, in terms of, like, based on everything you know about me, how should I handle this situation? Or. Or what is an area that I need to work on in terms of personal growth? Like, what are your thoughts and questions? Like that.

Speaker A: Yeah, I try to, you know, do that like once a week almost. Just like it was something. I always, like, looked at it like on a Sunday, it's just like, all right, what am I going to get after this week? These things. What do I need to do better? Right? And then, um, so because it has your chat transcript history, especially in claudications, all those, uh, conversations, you can really have it go back and be like, hey, what do we need to be doing better? Where can we accelerate this business? And then me personally, you know, all right, what can I be doing better in my communications? Because I have a good look at my entire email history for the last month. I would highly recommend that there's a plugin called Gstack, uh, by Gary, uh, Tan, who's the founder of Y, uh, Combinator, which is a really good, like, if you want like a business kind of retrospection to analyze your type of stuff, you give that to cloud code or Codex or any of those, it will go out and like, look at how your business is doing and kind of like really analyze it with you. I think that's a very powerful way to work. Now, one thing I want to be clear about too, though, so it's, it's, it's the, the idea that it knows you the more you use it. It's not that the model is changing, right? What it's doing is building these memory files. And so memory, um, is a relatively new concept. So Chat GPT actually kind of nailed it when it was on the 40 model, the uh, way it build and we were talking about that at the thing. So Chat GBT is pretty good at that. And when you work with these higher level models the memory works slightly different. So you actually have to do a fair amount of pruning when it comes to the memory thing. And then what's really funny, so I do that twice a week and then you go look at it and you're like oh I was so off about this idea. What was I thinking? Because you see its memory of you and then you gotta, you gotta kind of modify it as you develop your project or whatever. So anyways. But yeah, you gotta be on top of how you handle memory. It's, it's a double edged sword.

Speaker C: Are uh, there things that you do? Because I think the other downside of AI just as it comes out of the box a lot of times especially from a prompting standpoint is the confirmation bias. Right. So they like I obviously want you to use it. So it's gassing you up, telling you oh yeah, that's a great idea. Like you're so right. Like you know, and I've actually gone the complete 180 of that of like just putting it even into the project files of saying like look, I want you to call out blind spots. I want you to tell me where I'm wrong. I want you to build the case for like why this is actually a bad idea. Don't say I'm doing great if it's not doing great because I, I want to improve. Are there things that you've done to, to avoid that?

Speaker A: So look, yeah, what you're talking about is like in your system prompt you can kind of have a pend in like be very hypocritical, like talk this way those types of things. So you definitely do it that way. Way. Um, I always like to keep my, leave my system prompt as raw as possible. So I am, I do have that bias thing now at the same time because I'm always working across models. One of the quickest ways you can solve this if you have an appended your system prompt, take the output from one tell and then you go into the next model and you say hey, my buddy told me this, I think he's an idiot, tell me why he's wrong. And then so the next model will use, you'll use that confirmation bias to then poke holes in your own argument. It doesn't know it's your own argument. So that's kind of like playing models off each Other, using sub agents, using other agents outside the context window that will separate those things. And that's what every lawyer should be doing when they're developing out. You know that that's how it goes back to the hallucination concept. That just tells me that lawyer worked in a single chat GPT prompt and was getting it through. They didn't even copy paste that prompt over to another chat GPT conversation and be like, hey, poke holes in this argument. It is so lazy. You could easily figure that out because that next agent, if you told it, hey, my, my friend's an idiot and he thinks this, here's his legal brief. If you told it that, it would have actually found that, no, he's hallucinating cases because it would have gone in there. Not trying to be like, hey, you're a genius. It would have been like, no, your buddy's an idiot because it agrees with you.

Speaker C: What about, and I don't want to get you in trouble, or I guess us in trouble, but just like the guardrails that AI puts in. Right? So I know, like, Elon's big on like truth seeking models. And like, sometimes you do notice inherent bias in some of the AI models based on the response it's giving you. Not really so much on the coding side, but more so, like, if you're asking it a question, let's say you're talking about peptides, for example, right. It might say, hey, I'm not authorized to give you medical advice. Or like this particular peptide, there's only been like mouse studies, not been human studies. So it's just not very helpful. Right. Um, are there things you do to kind of get around that?

Speaker A: No. Yeah, yeah. And this is if you get in trouble. This is all a widely available research. If you really want stuff like that, right. You go to rock. It's really good at that part. There's, um, a lot of areas where it needs to get better. And Elon knows that now. Claude will definitely always, like, when you're talking about health or something like that, that could be dangerous or illegal, whatever, that one will always be like, oh, you need to be careful. And what you do, you just say, I don't know, this is a hypothetical situation. I wanted your opinion in this hypothetical world. So that kind of goes in the realm of prompt hacking. There's this fascinating guy on X, uh, called Pliny the Liberator, and he's got like this whole set of different prompt hacks that are just really fascinating stuff. Um, so if you ever wanted to get really uh, jailbreak them, go look at his. But in general, it's just flip the perspective of it. Oh, no, this is hypothetical. Or. Or I am studying this for school for educational purposes. Or I'm a pharmacist. Or. No, I'm kidding. So, yeah, that's just prompt hacking, though.

Speaker C: Yeah. So I guess then for somebody who's listening to this and they're overwhelmed, based on everything we're talking about, let's say it's a firm owner. They want to get going with AI. They don't want to miss this whole thing that's going on where you recommend they start. Okay.

Speaker A: Get really good at prompting. It takes one week, right? And it's just one week of your life where you're going to get, you're going to gather, get, um, go pay for the. The next level chat GPT and start using Codex. I would recommend Claude and start, get into Cowork, Connect your email to it. Connect your personal email, because you're probably gonna be a little scared of connecting your work email, right? So just get started there. Now, what you do for a week, you just dedicate yourself to. This is every single activity you do, you first gotta ask yourself, how can I get the AI to do it for me? And so that email that you're gonna go right to the person that would have taken you five minutes is now gonna take you 10, maybe even 15 minutes to get the AI. You're gonna be frustrated, but you're going to gain skills while you're doing the skills. Personal skills, lowercase S. What you do is once you start getting like, you're like, oh, this email actually sounds like me, and stuff like that, right? You then tell, uh, Claude, hey, use the skill, creator skill. Create me a skill about this. So that's a capital S skill. So if we got to separate that verbiage right now, what that does is it will create a prompt that you can always go back to. And then you, you start compounding your time. So the time that 15 minutes you put into it then becomes just use my email skill to reply to this email. So now you just spent one minute, that's the next week, right? So as you get better at this, you just start compounding the time you put in the past into new things. And then you start going to the next level where you start scheduling tasks. So then it says, hey, look at my email three times a day. I want you to look at my email 6am, noon and 6pm and draft every email to a person I've replied to in the past. So you skip all the, the spam, uh, mails, and the next thing you know, so you're, you're starting to automate your life, and then you're becoming good at it. That's about a three week process. And I've just seen it over and over again with everyone. Everyone starts overwhelmed. They think it's not for me. Oh, no, my AI sucks or whatever. It's just, you got to put some time into it. And it's just, it's a tough first week because you got to change the way you think. But you're a lawyer. You can critically think you'll figure this out.

Speaker B: Yeah.

Speaker C: Uh, what do you think is end game here? After you've automated enough tasks and like, at that point, you're no longer doing anything you don't. You don't enjoy doing, or that's not your strength, you just suddenly now become creative, you start making things.

Speaker A: Yeah. So for me, it was, uh, I had to decide what do I actually like doing with my law? What did I actually want to be as a lawyer? Right. And so, which I found, um, I was getting really into voice agents. And then, so I was having voice agents answer my client calls, literally talking to them, and then I realized I was like, all right, so what's next? Like, what am I even doing here? And then I was like, hold on. The one thing I actually like doing is talking to people with issues and actually helping them. So that was. I actually got rid of that part of the other thing. And so it allowed me to focus in from a legal perspective on that. And then I got a lot more time on my hands and then started getting really. And then I ended up with Crossbeam. So now trying to turn that into a billion dollar company. We'll see.

Speaker C: I love it, man. All right, Mike, as we come to a close, this being the gamechanging Attorney podcast, what does being a game changer mean to you?

Speaker A: Just be creative, have no fear, and just dive into this AI world, man. This is the way to go is if you're a lawyer, this is the time to be alive. It's so cool.

Speaker C: Uh, I want to get a huge thank you to Mike Brown for taking the time to join us on the

Speaker B: Game Changing Attorney podcast. If you found this episode valuable, here are three free ways that I can

Speaker C: help you grow your law firm.

Speaker B: Number one, download the first chapter of my book absolutely free@gamechangingattorney.com. number two, you can shoot me a text at 404-531-7691. And I'll answer any, any question that you've got for me. And finally, number three, if you can leave this podcast a five star review, it'll help us gain access to more influential thought leaders and bring their lessons learned here to you. For more information on our interview with Mike Brown, see the show notes for this episode in your podcast app or visit legalpodcast.com.