Where AI Actually Helps and Fails in M&A Legal Work

18:30Aaron Binstock:But when you are prompting the specifics and the more information you can feed it, the better. If you can feed in precedent documentation or forms that you've used, then that's helpful. One thing that we have found a lot of success with is called reverse prompting. You'll give it a very specific prompt, take this provision and make it more buyer favorable. And we want this cap and we give it all the details that you have. But then at the end of the prompt, you say, is there anything else I can tell you to help you do a better job on this project? And it will actually, the AI tool will ask you questions to help it do better.

19:05Aaron Binstock:So for example, I was putting together a checklist for a group of founders that's starting a new LLC. It has some complicated nuance to it. And it was putting together, again, just a list for them to use as an agenda for a meeting to have some preliminary discussion around considerations to think about as they're structuring this new business. And it came out with these questions. And then in the reverse prompt, it said to me, is this a Delaware LLC? Yes. Is it a multi-member LLC? Even though I was saying owners or founders in my prompt, it understanding in its language that it was a multi-member LLC, that makes a big difference in terms of how you think about a document like a LLC agreement or other preliminary considerations that are going to be important to figuring out the relationship between multiple founders of a business.

19:49Aaron Binstock:That is a good lesson I would always ask it. Do you have any questions for me after you've given the initial prompt? Because usually when you iterate like that with the tool, the AI is able to help you create a better result. I like that reverse prompting. I've seen the models improve where they're generally improving your prompt before they execute it. And then you can always prompt to create the prompt, which is, I think, really helpful if it's more of a complex output that you're striving for. So then you sort of understand you can make more of a complex prompt and then you can tweak it and then make it something repeatable or turn into a skill, depending on which LL I'm using.

20:24I think you got a great point on this reverse prompt where you get the output and it's like, okay, what could I have told you to make this result better? And then it's turning around asking you those questions.

20:33Aaron Binstock:Yeah. And this is all evolving really quickly. The AI is getting better. There are people who are really good at the prompting. I've got a guy in our technology transactions group who is at the forefront of beta testing all these products for our firm. And I was giving him that example I gave you earlier about marking up the asset purchase agreement and how it did some things well, but some things it didn't do well. And he said, well, the more specific you can be and target it to specific provisions, the better. You could actually build a prompt out that says, make this particular purchase price adjustment provision more buyer favorable, make this indemnity, and go sort of prompt by prompt.

21:04Aaron Binstock:But you can have it build a very complicated prompt that sort of takes a long document, breaks it into pieces so that it then is more focused and does a better job doing a markup of a more complicated document. Some of that sort of prompting it to give you the prompt or using agents to sort of like create prompts is where this is all headed. And it's only going to get better. I very much agree. That's exciting to see how that's evolving. Do you have any other examples? I use it for lots of different things. There's the standard use case of just getting smart on a subject, whether it's tell me about this, it will pull information and provide you sort of treatise level information on things.

21:36Aaron Binstock:In terms of helping with email and inbox management, we can take a very complicated thread where clients and teams have been going back and forth on a particular subject that may be very nuanced and it will summarize the email thread. Super helpful when you're trying to get up to speed quickly. If you're new to a client relationship and you're trying to get up to speed on their existing documents, it can provide decent summaries of existing documentation. It's not going to replace your review of them, but it's enough to understand the situation and understand what the high-level terms are. So I use it in those cases.

22:04Aaron Binstock:On the drafting stuff, a lot of the examples I already gave, we also use it for just working with particular clients. If you're working with a repeat buy-side client or a sponsor who's doing lots of deals and they've got one of those playbooks, you can take all their prior deal docs from that client. If you've done 20 deals with a client, put them into a sandbox in the AI tool and have it create benchmarking. So almost the way an ABA deal term study or whatever, create a tabular review chart that says, all right, this is everything that they've agreed to on survival periods for reps and warranties or caps or baskets and break them all down by whatever categories you want it to create.

22:41Aaron Binstock:Super useful detail to have at your fingertips when you're working with those clients because they may say, what did we do in this deal? Or what do we typically do? And to have that information at your fingertips can be really helpful. I like that. The summarize and then benchmarking. What about if you're doing a deal? It's like a cross-order deal. There's a batch of employees in Denmark. You're trying to figure out what are the HR laws that we have to abide by doing this transaction there and those things. You start leaning on AI for that or do you still... Yeah. I wouldn't rely on the AI for anything.

23:09Aaron Binstock:And fortunately, I haven't had a bunch of Denmark employees who recently in any of my deals, but recently had a deal where it was an odd one for business reasons. The party just decided, even though it was a US acquisition to use a European lockbox style purchase price adjustment in the deal. And that created some weird timing things with respect to the way certain accounting items were adjusted. I was able to ask some questions overnight when my UK tax partner was still asleep to be able to just, again, get up to speed quickly before I could talk to an expert on it. What would the impact of using this lockbox in this style transaction be to the way this particular thing gets adjusted?

23:47Aaron Binstock:And it was able to give me a really good response and even go beyond that to say, how is this negotiated and help me with that. And when I talked to my UK tax partner in the morning, a lot of it was pretty spot on. Again, I don't trust it. Do you remind me this? I forgot the locks box. How does that price adjustment work? Just generally? Yeah. You basically block the balance sheet as of a certain date. It's usually before you sign the agreement. Say, okay, we're going to take the end of a, and that's going to be the adjustment. And then there are certain items that are leakage or permitted leakage that are allowed from that date to closing and you adjust for those.

24:19Aaron Binstock:But otherwise, you're essentially fixing the balance sheet date prior to the deal signing. What benefit does that have? Because most people just do it right at close. U.S. deals, we use more of a networking capital adjustment, which is typical where you do it as the closing date. Obviously, it's simpler than trying to having to calculate a closing date balance sheet and you're not adjusting for impact of the deal or things that have happened since people have been thinking about the deal. But it's not a very common tool. So it's less aggressive negotiating at the closing table, I would assume.

24:46Yeah.

24:46Aaron Binstock:It can be. Plus I've still seen people fight over the leakage and debt to equity bridges. And there's a lot of things that come up in those contexts that still get a fair amount of negotiation, but just different way of doing things. Between like a prompt that really works well and one that you have to like redo from scratch, you sort of differentiate that when practice? The more simple from my perspective, the better it tends to work. I wouldn't say things you have to redo from scratch, but there are prompts that need to be refined. And again, like what we were talking about earlier, where you iterate with the AI to be able to create something that is actually a useful markup.

25:21Aaron Binstock:Sometimes for more complicated things, it requires that iteration because it's trying to understand the context and you need to give it more detail than you may have initially given it when you prompt. By feeding more information into it, it's able to provide a better result. Have you seen an NDA that prohibits the use of AI? I haven't. Are you seeing those? I just talked to somebody about it the other day. They were permitting putting the data in any kind of AI product. Yeah, now we've got clients that say, we prefer not to have you use AI on our work. That's a professional ethical thing that firms need to be cognizant of.

25:50Aaron Binstock:If you're telling a client, we're going to use AI on your work and they say, please don't, you need to be able to have a process. That's even one I haven't heard of because I'd be like, by all means, use all the AI you want. Yeah, so we've got a centralized lookup tool. So when you're in the AI tool, you can see before you start using a particular client matter, has this client opted out of using AI? What drives it? I guess there's two conversations to have here. There's sort of with the counterparty permitting them to use AI and then your own client that doesn't want you to use AI. What is the driver?

26:20Is it just, hey, we're a conservative company? We're kind of like closer to the Amish and want to stay back in time and not top to AI just yet? Like, what is the thinking behind that?

26:29Aaron Binstock:It probably varies depending on the client. And some people are more comfortable with the technology than others. There's concern over how the data gets used. Again, when we negotiate our licenses with these enterprise level tools, we're very careful to make sure it's not training on client data, that the client information is staying on premises so that you're not worried about blowing attorney-client privilege, for example, or having that go into some aggregate information source that it's then using to provide information to other people because we're not going to let it train on our firm's data to then go out and make that available, even if it's on an anonymous basis to others.

27:03Aaron Binstock:So I think there's some concerns about that, the privacy elements of it. There's some concern still about how accurate it is and doesn't miss stuff. It's as great as associates and human partners are, like attorneys and humans miss stuff too. It's a little bit of cost-benefit analysis when you're thinking about whether it makes sense to use it to figure out, listen, is it going to do a 99.9 % good job and maybe it misses something, but like a human could as well. We're going to save hundreds of thousands of dollars in the meantime. Yeah, sometimes in certain use cases, if that makes sense. But yeah, with different clients and their willingness to use it or putting a provision into their NDA that says you won't use AI, it's just a level of comfort and maybe they're thinking about some of these different considerations.

27:39Aaron Binstock:And listen, again, like a lot of our clients are technology companies. And so they were like almost asking us to use AI even before we were using it to the level that we are today. It was expected that we'd be doing it for efficiency purposes. I'd say we probably don't see it as much, but definitely I'm sure there are companies that are more conservative about it.

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29:26It's the counterparty that's, hey, I'm going to provide this diligence information, but I don't want you to put in AI. Or I just was talking to somebody about it where they're starting to see that. And to me, I'm like, I would be a little concerned because there's some reason that...

29:42Aaron Binstock:Yeah, you mean to check it or something like that? Maybe there's a view I'm coming from operating deal room, for example, where we're providing a workflow product for the buy side. And a big use case now is putting all these AI features in there that allow you to, I don't want to use it to automate, but definitely automate, I guess is the right word. Parts of your diligence, that's something that's come up. They've worked on deals and the seller is, I don't want you to put my company information into an AI product. They're probably worried about the privacy element of it, right? And that information going into an AI tool where they don't have any control or understanding of where their information is going.

30:20Aaron Binstock:Listen, I'm sensitive to that. Again, it's part of the reason why with the tools that we use at the law firm, we're very sensitive about how the license works with those tools so that we understand where client's data is going. If you're my lawyer, how are you going to put them in place so we can use AI on this deal? Well, I mean, again, I've had conversations with clients about exactly that. Is your information staying on Cooley servers or is it going somewhere around the world? And is it training on your data? No. But getting them to understand that initially can be helpful. The defense deals and the security deals that I do, there's a lot of unclassified and sensitive information that can only be, for example, seen by US citizens.

30:53Aaron Binstock:And you don't want foreign nationals to be looking at it under certain government rules. There are considerations like that where you have information that you're worried about, like who has access to it, where is it going. Law firms get very sensitive information like that in all kinds of contexts, not just that. You can understand why they would have it, but understanding how the AI tool uses the information and where it's putting it can be very helpful in explaining to a client that may have concerns about it, whether it's being used in a way that they're comfortable or not. And listen, again, mindset prerogative to say exactly that.

31:25Aaron Binstock:I don't want to use it and I understand it's efficient, but I just prefer it not be used. That'll change over time. Without anything, people will become more and more comfortable with technology. But understanding how the tech works and how the information is used is key. That's a good point. Now I just started recollecting some of the stuff like the ITAR classified information is probably one of the highest levels of security classification for documentation in the US. And I remember there was a period of time where you couldn't even have this stuff on device with Wi-Fi. So they would actually have laptops where there was completely removed Wi-Fi and you would actually have to physically deliver this laptop.

32:01And that was still in the cloud era. You still had to do that. So I can imagine now, I can imagine you putting that stuff through AI. Back then, that's how reserved it was around security.

32:13Aaron Binstock:And even when you think about information like that, like law firms are sophisticated with this, but we'll have our intake team clear certain attorneys on a particular matter. So if I've got a matter that's dealing with information like that, and I know that I can't have non-U.S. citizens having access to it, we can make it so that our document systems provide access to attorneys that are cleared on a particular matter. And we do that routinely. But yeah, AI takes it up a notch when you're thinking about, okay, not only that, but now you're putting it into this tool that's manipulating it and using it to provide an outcome.

32:45Aaron Binstock:The diligence use case you're talking about, there's a lot of efficiency there that can come from feeding the data room information into that tool. Think about how many hours we spend as junior lawyers digging through contracts to look at change and control provisions or anti-assignment language. And now you still have to look at it, but it can pull all the information out and put it into a chart for you and saves hundreds of hours. Does that mean the legal bill is going down? In those types of deals, you would usually figure out ways to make it a manageable project. It may have been the case that you'd create thresholds where you're going to look at the 100 top contracts or whatever.

33:19Aaron Binstock:Now you can look at all 10 ,000 contracts and it takes the same amount of time, but it's pulling out the provisions for you. Kooli has been in the press recently about this, but I think the value that clients are paying for is changing. This isn't going to be an overnight thing. But if you look at time it takes to do something and the fact that law firms go by the hour, put two and two together and see that that has some problems in terms of an economic model. That's what I was curious. I'm surprised it's not changing faster. We both know there's a very short list of firms that do charge by essentially like deal points like a banker.

33:46I was under the impression that's probably going to catch on more or at least some kind of hybrid model where there's something attached to like actual deal value.

33:53Aaron Binstock:It'll be interesting to see how it evolves. If you look at other like outside of law firms, like other professional service models. Consulting has probably been the closest rate. They've gone to more of a project-based billing where they're either charging for completion of a project or success or whatever. It'll be interesting to see if the legal market evolves in that way. But there's also some firms that do this today to some extent on larger matters, like on M &A, where they will charge a fixed fee for successful closing of a matter. And so that's the investment banker model, where it's like the deal closes, it's this and whatever this is, maybe higher than what the aggregate hours may have been on the bill, but also they're not charging if the deal doesn't close.

34:31Aaron Binstock:It'll be interesting to see where law firms go with this. Right now it's hourly and that's pretty common, but potentially it is like a contingency based on a percentage and then there's a flat fixed fee. Yeah. And you see, I mean, again, hourly is probably the predominant billing method today, but like to your point, there are other models out there. And I think it goes back to like that judgment. AI is reducing the time that it takes and making certain things more efficient, but it's not replacing the judgment in that sort of trusted relationship. They're advising a team or a partner on things that are nuanced and complicated, and you have to really be in the trenches on the deal dynamic to give them a view on it that aren't just comparing it against what's the market.

35:12Aaron Binstock:That judgment that leads ultimately to successful transactions is the part that's valuable. I want to be clear on this. I want to just kind of understand, just for the sake of understanding, and I'm definitely cheap. I'm publicly cheap upon what I'm trying to do or be efficient as possible. But I don't want to be cheap with the legal counsel because if anybody you don't want to be cheap within the deal process is your legal advisor. You can cut corners in a lot of places in the deal, but it shouldn't be with your legal counsel. That's your best bet. So I definitely want to work with the best attorney I can find.

35:42And they should be happy. I love it. I'm curious about just like what this industry sort of future looks like, especially now with that many signs, we got different certifications and some of the stuff was like right towards early career practitioners breaking into the industry. And I'm curious about the legal track because I talked to incoming lawyers or people considering it. Even my own kids, I've been kind of steering them away. I'm like, I don't know. Between before it was always the cliche when you come up with immigrant parents, it was go be the doctor, lawyer, engineer. That was your three career options.

36:13And I've only given my kids one. I'm like, just go be an engineer. Give me like your take. What do you, your kids are young though.

36:20Aaron Binstock:11 and 14. They're the same age. I've been thinking about it too. We got, I mean, my wife and I and parents, friends in our community have had a lot of discussions about this because we're seeing how it's disrupting our professions. And it's interesting to think about how you advise your kids, what's going to be a good career path for them, given industries that may be replaced or changed in a drastic way. Yeah, it's hard to say. There's things like being a chef, things that are like have an artistic element to them, which in some ways, laws like that too, like the creativity aspects of it, that piece of what we do will be hard to replace.

36:50Aaron Binstock:in certain professions like that, it's hard to see AI changing. Others like pure coding, we're seeing a lot of jobs already being eliminated in the tech world for years now. It'll be interesting. Again, we're sort of early stages here, but it definitely is having me think twice about what career path and what type of higher education I would suggest for my kids. Are you hiring less like incoming associates in the firm now? We haven't been. I don't think this is an overnight thing. And so I think that when you think about that skill set and what junior associates spends their time doing, that's evolving.

37:22Aaron Binstock:The time spent on diligence can be used for time spent doing more complicated things. None of us went to law school thinking we want to sit there and pour through documents throughout the course of our career. So you've gone through that because it was a necessary evil at the beginning of your career and because it's necessary to get transactions done. But it allows somebody who has a world-class education to use their brain in a way that's a little bit more productive. There's still going to need to be associates. Do we need as many over time? TBD. But right now we're not seeing like a massive need to replace.

37:52Aaron Binstock:We're not replacing jobs with it like at that scale. Be more productive or just not use your brain? I would just be good. Carry on. Get this done. Blah, blah, blah. Yeah. I'm out to office early today. I mean, you raise a really good question around and we've had a lot of like good conversations about like what the expectation is for attorneys and junior attorneys. and again, how they're using this tool in a way that's productive, but also that is the proper way to do it. You can't just use it as a substitute for legal judgment. You can't just feed information into a doc generator and say, create this document.

38:26Aaron Binstock:If you've ever created that document and you don't know when you're doing it, what's good and what's bad and what's wrong for this situation, that doesn't work. And it's really not ethical to be practicing law in way. You've got to be able to, in one way or another, understand the way provisions and agreements work to be able to check the AI. You can't just throw up your hands and rely on it. I'm sure there's lots of practitioners out there that have either fed something in and they have to make a lot of changes to it, or they receive something from a junior person on their team, and you're like, no, this doesn't work.

38:58Aaron Binstock:But those are learning lessons, right? And it doesn't replace the practice of law. It's a tool. That's what we got to keep in mind. When you think about those junior attorneys, the big value is to help them learn those skills to make judgments. How do you actually approach that? This is all evolving, right? The way firms are training is evolving too. You need to think about how do you get somebody to short circuit exactly that. Okay. You used to read thousands of agreements and you learn through osmosis and you see how things got negotiated. And now you're having AI do a markup for you. But you need to understand that.

39:32Aaron Binstock:some of the formal training may evolve. I like to use this kind of law school, Barbary, bar prep course metaphor where it's like, okay, you could read thousands of case law and get the one point out of each case that you read. And then at the end of the time, your three years in law school, you have a basis in contracts law and constitutional law and learn the points through that method. And then you go take the bar class and they give you all the substance that you need to know to have that particular topic down in a month's time. And maybe there are ways that we jump to that. What's the conclusion without having to like pour over all those documents.

40:04Aaron Binstock:But I also don't think that, again, like where we are today, we're there quite yet. If you produce that chart that says, here are all the change of control provisions and assignment provisions, somebody still has to check the work of the AI. You can't just go through and read it. There's still a lot of learning and reading through osmosis and some looking at those documents, it's more focused and streamlined than having to go find something in the document, but you're still reading the provision and understanding that particular provision that you're focused on says. A lot of that is still there.

40:31Aaron Binstock:The way that you act with AI can be helpful when you're thinking about engaging with it and prompting. That's using critical thinking alone to think about how you're actually asking the AI to create what you want it to create. You do have to have that foundation. But once you have that, there is a creative element and critical thinking element to prompting with the AI. Things like the playbooks, right? When you're using AI to create a playbook for a buy-side client, you're looking at that. That's a learning tool of itself, creating a summary or creating a playbook. We still do this. Like when deals close, we track data on deals.

41:02Aaron Binstock:The same way the ABA or SRS have these deal term studies that come out. Sometimes pulling those stats out of documents can be a useful learning exercise for an attorney because they're seeing, one, where do I find it? And how is it worded in the particular provision? But also being able to extract those key points and understanding what those look like in the aggregate. And then some of the tools, like you've probably seen this with AI, will actually explain the reasoning that it's going through. Have you seen this? Where it's like, it will give you the result, but as it's telling you why it came up with that, that's useful too in the sense that if it's telling an associate or a partner, we made this change because of this, or we pulled in this particular source, that's what we're using.

41:38Aaron Binstock:Having that like explanatory note, the AI result is a good training tool. You have a good point though. You need the experience in the war room. You need to be on the other side of the table yelling at the other, you know? Yeah. Yeah. There's a lot of soft stuff too, right? When you think about as attorneys get more senior, their ability to conduct a call with a board of directors or with opposing counsel on points and to be able to sort of engage with them on that or to run a deal process or to have that one-off call with the CEO to have that sensitive discussion around a particular point. That voice and those things, that's the art that AI isn't replacing.

42:12Aaron Binstock:And that's an area where you're still going to see the attorneys that are like successful practice in building trust with clients be able to do that in a really good way. So if we want to break down basically what AI can own, but then specifically break down like the areas of human judgment where you'd want somebody that's done hundreds of deals to really lean in on? Well, I don't think AI can own anything yet. Again, I want to go back to that. The AI technology isn't owning anything. Even the most basic tasks that it does pretty well, it's not owning them. It's just producing a report. The people that own it are the people that own it.

42:43Aaron Binstock:That's the legal judgment and the need to quality control, check it. And that's why lawyers are trusted advisors. And there's a relationship there because we're the ones owning the result. But it does do things. It does create efficiencies by producing reports and drafting more efficiently. It's, again, a tool for the owners, maybe the way I would think about it. And then, again, a lot of that human interaction stuff that I was just talking about a minute ago is the part that I don't think it's ever going to be able to replicate. It's good at absorbing context, but it doesn't really absorb the context.

43:13Aaron Binstock:I was having lunch with some folks today and we were saying there's all kinds of reasons that particular parties do a deal, deal one way or another about. And it could be informed by market or their practice, or maybe they just don't want precedent out there because it's a public deal or with their name on it because it creates a bad precedent for future deals. There's all kinds of things that the principal is thinking about in deals that inform their negotiating perspective on lots of different things during the course of the deal. again maybe we get there eventually but having ai be able to sort of like understand that global context and have that situational awareness of the overall transaction to be able to advise at that sort of way we're far away off from that but we'll see i'm writing my prompt down take on the persona of aaron benstock at cooley and guide me through this deal and then when i fall short i'll call you tell me to fix this problem you know where to find me exactly what is the craziest thing you've seen in M &A?

44:09Aaron Binstock:That's a good question. You didn't prompt me on this one ahead of time. This is good. I get a real answer from you then. I've seen deals that are at the signing table where a party decides to walk away when everything feels like it's fully baked. And just at the very last second, because they have a change of heart, I've seen big events like economic market changes that have led to odd results. You wake up and the world shut down because of COVID. And all of a sudden, that at least briefly impacts things in ways that are unexpected. Lots of smaller stuff, but I'll let you know if I think of any other ones.

44:40Yeah, next time I want a big dramatic story, but those are all good case in points. You can't predict unpredictable when it comes to M &A. Aaron, thank you so much for taking time from doing your deals to help me become a better M &A scientist.

44:52Aaron Binstock:Yeah, it's my pleasure. Thank you for having me. Fellow M &A scientists, let me know what you think of this. If you're interested in chatting more with Aaron, go bug him on LinkedIn. Mention M &A Science. He'll give you a discount on the first engagement. Maybe he will. We'll see. But reach out to him. I had a great time in this conversation and just, we've had a great relationship with Pooley. You guys hosted events over here at the office and it's just been a great partner for providing different educational resources. Also connect with me on LinkedIn. You've gotten this far on the podcast. You definitely deserve to connect with me.

45:20I love hearing feedback in terms of what you like and didn't like about this. If there's things I could have done better, I'm open to the criticism and if there's topics I haven't covered, reach out, let me know, but mention, mention something in the podcast. It gives so much spam. Until next time, here's to the deal.

46:03have. We're here to help. And if we can't help you, we probably know someone that can. You can reach out to me by email, Kisan, K-I-S-O-N, at mascience.com. Or you can text me directly at 312-857-3711. If you just want to keep learning at your own pace, visit mascience.com for a lot more content and resources. That's where you can also subscribe to our newsletter. Again, And that's mascience.com. Here's to the deal.

46:45Views and opinions expressed on M &A Science reflect only those individuals and do not reflect the views of any company or entity mentioned or affiliated with any individual. This podcast is purely educational and is not intended to serve as a basis for any investment or financial decisions.