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Hey, I've got a question for you. When was the last time you drove over to another law firm near you, sat down over lunch and traded every business tip you've got? Talked about what's working, what's not and what to do next? No, see, that's what maxlukan is for. It's real conversations with law firm owners who are actually doing this building, leading, scaling and willing to share what's working right now. You could keep doing it alone, but let's be honest, it's slower, harder and way more expensive than getting in the room and shortcutting the learning curve. As of this recording, we've got 20 seats left to this year's event. Skip the guesswork, go to maxl.com and grab your ticket before they're gone. Get ready because here's your host, Tyson Mutrix.

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Welcome back to the Guild Live show. I'm Tyson Mutrix and today it's going to be a bit of a shorter show so I'm going to try not to get too preachy at the end. So no promises with that one, but I do got an interesting one where we're going to be talking about a little bit about negotiations, chat GPT usage. That was kind of an interesting thing that came out this week. And then an article about anti Chat GPT which I hate the name of this article. I also kind of hate talking about what I'm be talking about, but I'm be talking about it. It's because of the company that's involved. Top 50 mistakes. We'll go through some of those. I thought that was kind of an interesting thing. And then we're going to talk about Charlie Kirk at the end. You don't want to tune to the end. I completely understand. But I'm not going to get political. I promise I'm not going to be. It's not gonna be a political rant, but I do, I do like it's something I want to address. So I considered, just so you know, I considered actually spending the entire episode, but I figured that's probably not why people tune in and so I, I wanted to make sure I was respectful of that. But I do think I've got some things I want to say about it and so I'm going to talk about it a little bit at the end. Let's get into the show. The first topic we're talking about is it's an interesting article that I found that was about. Not an article, it was a post and it was about negotiation tactics and I thought it Was really kind of an interesting thing that I would, would share with you. So I'll put this on screen so you can see what it is. All right, so here's the post and I'm not going to go through all of it but they, they, what they did is they looked at negotiators that were rated as effective by both sides, have a track record of success over a substantial period of time, have a low incidence of implementation failure. So those are who they chose. Okay. So. So in the preparation stage, results show that in comparison to the average negotiator, skilled negotiators, they considered about twice as many possible outcomes and options. So that was interesting. So about five compared to about two and a half different options. I thought that was an interesting thing. They spent about three times as much time on anticipated areas of agreement. That's to me makes a lot of sense. You want to focus on those things that. Where, where are we aligned? That's. I think that's a pretty smart thing. By the way, the reason why I think it's important everything's a freaking negotiation. Whether you're doing family law or criminal defense, personal injury, estate planning. I mean you might be negotiating with vendors about different fees on things. Like there's a lot of negotiations that go into running a business and that's why, that's why I want to bring this up. So that's. If you're wondering why are we talking about negotiations, this is why they considered about twice as many long term issues. 8.5 versus 4. I'm not going to all the numbers because that's just not as important. Plan specific sequences. So negotiating issue A then B, then C about half as often. So they planned specific sequences about half as half as often. That's interesting. So they didn't plan. They plan specific sequences less. That's interesting. Set ranges. So instead of a fixed point more often. That is interesting. I will say that is something we do when it comes to our. We go with a range. We're looking for a range is what we're trying to do. They spend a similar amount of time on preparation suggesting that they planned not more often but more efficiently. So spend a similar amount of time on preparation, huh? So they were just more efficient with their time in preparation. I think it might say that might suggest that. Also could. You could say this that preparation not as important. That's another thing you, you might want to. Might weigh another way of looking at it. Jeremy Danielson, thanks for the kind message. Just got a kind of message from Jeremy Dallison so really appreciate it. But. So that's. I think that could be another suggestion. You could say that that's. That's what that is. But all right. Then we go down to when analyzing behaviors during face to face negotiations, Rackham as the person put together found a continuing theme from the planning stage that skilled negotiators avoided creating disagreement and conflict. They did so by doing the following Use only about one fifth as many irritator words. Those are words such as generous offer fair, reasonable. Fair is definitely something that's what Chris Voss talks about. You know, don't use the word fair. So those kinds of words can irritate the other side. Make about half as many immediate counter proposals. So immediate counter proposals tend to be seen by the counterpart less as proposals than as attempts to block or oppose their proposals. Interesting thing using verbal attacks or defenses only about 1/3 as often I think that makes a whole lot of sense using behavior labeling areas of disagreement agreement only about 1/3 as opposite so these are some of the things I'm not gonna go through this whole thing as you can see it's a. It's a pretty long post but I thought that was an interesting thing. So some. Some things to think about when you are negotiating but let's get into the next topic today and that is chat GPT usage This is an interesting one for sure because they it's broken down this is something that OpenAI released let me show you this where and this is from a Greg Eisenberg post but this is this diagram that you're looking at has been all over the Internet so you've got it's broken down into multimedia we got this other unknown area practical guidance seeking information self expression technical help writing the by far the two categories that are the biggest as you can see practical guidance so creative ideation health fitness beauty or self care how to advice tutoring or teaching and of the practical guidance tutoring or teaching was the highest not a surprise writing so argument or summary generation edit or critique provided text personal writing or communication translation or writing fiction Writing fiction really at 1.1.4% in that case cute and then you got the technical help 7.5% data analysis mathematical calculation if you were using for mathematical calculation early on you were getting a lot of bad numbers your data was probably terrible it's pretty good now but early on not so good Computer programming for I surprised computer program is so low that's really surprising to me. Maybe it's just because the general population that's just a low Low percentage but it is really helpful with us when it comes to things we're doing with, with our, with coding and all that. Then you got the seeking information, looking for specific, specific information. I don't know how they're categorizing this over other things because I could lump that easily into other things. Purchasable products and then cooking and recipes. That's a little less than you think. I think cooking recipes a little bit more. And then you get this other known, other or unknown and then you got multimedia 6%. So really if you, you, if you break it down, practical guidance, seeking information and writing, that's where the bulk of everything. That was kind of an interesting thing that I would share with you all. So let's, let's get into the next one. This is where it's. I just hate even bringing up this company. But this is a. There are some bold claims in this article that I wanted to bring up. So that's why this is why I wanted to talk about it. All right, so this is an article from Venture Beat and in this one the Anti Chat GPT Thompson Reuters Multi agent system slashes 20 hour tasks to 10 minutes. That is the claim that I was like what? Not a huge fan of Thompson Reuters. Don't tell anybody. The Anti Chat GPT Thompson Reuters Multi agent. That's why it's like why is this the Anti Chat GPT? That doesn't make any sense. It's not true. It's just something that they wanted to put in there to make the article sounds fancy. You can ignore that part of it. The slash is 20 hour tasks to 10 minutes. That is what's interesting to me. Pretty dang bold claim. So what if the future of enterprise AI isn't about speed but depth? This is Thomas Reuters, Westlaw's BET company's deep research platform that was specifically designed to take its time working an average of 10 minutes. This allows the multi step research agent to plan, execute and pull by the way working average 10 minutes. If that's their bold claim as to how it's the Anti chatgpt you can do the deep research and it takes a long time. I've had some that taken said well over 10 minutes but it's taken over 10 minutes to do the full analysis. So that's kind of a garbage claim. But this allows the multi step research agent to plan, execute and pull perplexities the same way by the way from a deep curated data set of more than 20 billion docs up to date, case law, blah blah blah. Blah blah. Unlike retrieval augmented generation systems. Rag. I've talked about RAG before on the show. Deep research is designed to eliminate errors and hallucinations providing. I mean here's the thing, here's I just like you can create a RAG system that builds this stuff in. So this is kind of a. What they're going to try to do is going to sell you on this really expensive product that you could probably build yourself using their stuff. I can say right now you can probably build an AI agent that will go in and research in their systems. Researching the billions of documents. No one's researching billions of documents. We all know that you're researching a specific subset of the billions of documents is what you're doing. That's just fancy, you know article talk. There is sign that fancy. It's just article speak is what it is. But what you've got is you can build a system with RAG or without rag. You can build it though that analyzes, goes and searches their systems and then takes the output and then you have all these checks and balances. You can actually do that. We're doing that with a lot of other things inside the firm. So I think this is just a lot of bold claims that are. They probably built out an AI, A, a team of AI agents basically. And then that thing is they're just, they're, they're mass, they're scaling that to be able to sell to everybody. That's what they're doing. So if you want to, if you want like a pre made one, fine, great, go for it. It's probably really, really expensive. Multiple agents in the mix. A rich data data set. Multiple Westlaw isn't wedded to one underlying model. The team has a multi model strategy with different models chosen based on their effectiveness with different tasks. That's what you do and that's how you build out these, these agents. Anyways, where. Okay, what's the task? Okay, go this way. What if it know it's more complicated? Go that way. That's, that's, that's all they did. That's exactly what they did. They just built out something that I guarantee a lot of you could build out with some vibe coding. That's probably what you could do. But I think that's enough with this article I wanted to touch on in case you've seen it. It's kind of an interesting thing and then the next thing I want to get to is the top 50 mistakes because that's a longer article and I want to make sure we have time to talk about that one as well. So here we go. And you can't see that. I want to make sure you can actually see it. There you go. There we go. I will build and sell companies worth many millions. Here are the top 50 mistakes I've seen kill startups. All right. That's. I thought it was kind of interesting. That's why I want to go through this. So try to clear some of the screen space so you can see. Thanks. Thinking you have all the answers. Let's go. Ignoring the impact of compounding which. That's an interesting thing. How making little bitty steps can have significant impacts over time. That's a. I think that's a really good one. Disregarding the law of funnels. It's for any action of a user or customer needs to take is. To take. Is considered the top of a conversion funnel. The goal is to get them to the bottom. I've talked about funnels somewhat recently and we'll be releasing something announcing over the conference that will talk. It's got funnels in it. So that's all I'll say when it comes to that. Hiring based on experience. So I think that's an interesting thing and I think that this is. Right. That sounds crazy at first, but you want to hire the right people. Okay. Focusing on the right people then experience. I think that's important. Focusing on scaling too early. That's it. Yeah. So this is an interesting diagram about really plateauing. That's kind of an interesting thing. Wearing too many hats. I think a lot of people know about that. Comparing your work in progress to others. Finish work. Big problem in the legal space. Trying to resolve unbounded problems. To be. To be solved effectively and efficiently, problems must be segmented and bounded. First, split your intractable problems into small digestible challenges with a single goal in mind for each. Second, ensure that their solution is bounded to a finite solution space. Not realizing this is almost always a recipe for wasted resources and disappointing outcomes. Being frightened of incumbents. That's interesting. You see that a lot of times with lawyers. Oh, these. The new person's coming into town. Oh my gosh. It's. They're awful. Yada yada yada. I'm fearing the pivot. You know, that's a good one. Where sometimes you have to pivot. Somebody think that things aren't working the way you want them to and you gotta. You gotta pivot. So. Yeah, very good. But. All right. So I want to get to. He said I want to. I've got to get done a little bit early today because I've got the. We got a quarterly meeting today. So I. What I do want to do is I want to get to my. My last topic, and that is the. The Charlie Kirk assassination. So like I said, this isn't going to be political. It's not something I ever do on the show, but I'm just part of this is just something. Over the last week, it's. I mean, I kind of had to pull myself out of a hole because one. It was just a really shocking thing to see a human being murdered in that way. And so that was odd in front of millions of people realizing that his daughters and his wife had seen it, they saw it happen was a shocking thing. The fact that someone would do that over someone's speech was shocking enough. But what really, really just bothered me as a human was seeing how people react. That is what. And that's what I want to talk about. I want to talk about the reaction part of it and the humanity part of it and how it really worries me as a profession, because I saw a lot of lawyers react in a way that I never thought was what was ever possible. And it really made me. It made me think of a lot of things I've observed over the last few years when it comes to litigation and how I've seen people treat each other and how I've seen people become very vicious. I think it's a really scary thing because it's interesting. You saw people celebrating the death of this person and you saw people criticizing his faith, so talking about Christians in a negative light. It was really interesting because the thing. The things that they were. They were celebrating were, could have been just as easily applied to your neighbor, could have applied to anybody, could apply to me. And it really is bothersome, and I highly doubt it's something that would have been said to a person if you were in the same room with them. The message is. Is I would like to convey is just remember that we're all humans and the vast majority of us are in that middle 80%. And there's a 10% on the far right and 10% on the far left that, that have done a really good job of tearing us apart. And don't let it tear at your humanity, because it is something that once you go down a certain path, once you go far enough, I think it's kind of hard to. To come back from that. And so I. I really hope, I mean, there are some of you, that this is going to go right through one ear now. The other, I'm not gonna. I'm not talking to you. I'm talking to the rest of us is really what I'm talking to. That's. I'm not talking to those of you that think that a murder of a person in front of his wife and his kids was justified. I'm not talking to you. It doesn't apply to you. I really don't care if you. I don't care if you hear me or not. This more applies to the rest of you that do have a heart, you have a brain, and that you. You care about humanity in general. So I hope that you will, when interacting with people online, in person, when you're dealing with other. Other attorneys or you're dealing with vendors, you're dealing with anybody that you remember that you're actually dealing with a human being that you're not doing, dealing with someone that's just a bot. It's just not a computer program. These are real people, real lives with real families. And that I'm hoping that you will. You will act accordingly because the things I saw this last week, I lost a lot of respect for a lot of people. I just did. I. People that I thought somewhat highly of or very highly of lost every ounce of respect. I read. And so as. As Facebook learned, as, as the line in Facebook that the Internet's written in ink and not in pencil, just remember that the things you say online, you can't take them back. You've rung the bell. I'm hoping that. And here's the thing, some of you may not have meant it, even meant it, but you know what? The people that see it, they feel it and, and they think you meant it. So hopefully we can get better. That's what I'm hoping. So hopefully you stick around for the end. You know, I. It's one of those things where it's. It's. Obviously, I wouldn't want to have to talk about something like this, but it's something that I feel like I kind of needed it, needed to address. But I hope that we can get better from here. Hopefully we can all start to interact better with each other, but hopefully you have a better week. Hopefully things get better for everyone. Hopefully that we all start to treat each other with respect, but hopefully. Enjoy the rest of your week and leave me a comment if you agree, disagree, whatever, but otherwise, thanks for watching. Appreciate it. I hope you got something from this episode and I will be seeing you later.