Why Companies Miss 95% of Their Inventions

The big issue with all these corporations is the engineers are kind of doing their thing and I think they're missing 95% of the patentable ideas. I told AI I removed all its tools and I said, okay now do a prior art search Instead what it did is it built a tool to access the web and it was trying to do a rainbow attack to break through our system I have an idea. My AI talks to your AI. We negotiate a patent. Okay, here's a patent Next year you may have AI and vent more for as much as humans Welcome to Clause 8 the number one podcast on intellectual property brought to you by IP co-pilot and junior Welcome back to Clause 8. I've been talking with today's guest Austin Walters for a long time about AI and where pan practices had Long before Cheshire PT. He's working in the AI machine learning it's one of the world's largest pan-fires and Becoming an investor on hundreds of patents himself. That gave him a front row seat to how innovation actually happens inside a huge organization Those experiences eventually helped him lead to found IP co-pilot What happens when AI can actually help companies see and understand their own innovation how should pan professional separate useful tools from the hype Why does Austin think pan attorney AI agents may be talking directly to pan office examiner AI agent sooner than most of us imagine Welcome to Clause 8. Yeah, no, thank you for having me. Your story is pretty interesting because you are actually a pretty prolific inventor before you started IP co-pilot Can you tell the story of how that happened? Yeah, so you know I started really like in college. I was Part of a group in caterpillar I'm actually doing computer vision related work. I had the opportunity to do a PhD or go to capital one I ended up deciding capital one and kind of going down the more applied research path Well, is that capital one? I didn't know about the patent program at all So we were just publishing stuff going about kind of our normal thing and I ended up doing a lot of neural network Designed on kuda chips and stuff in college, but then also at capital one and then there was a competition internally to do the new fraud detection model And we took a stab at it using neural networks around that time capital one Inform me finally that we had a patent program. I was like there for probably two years without knowing about it Just giving away the IP after that my team filed a few or we at the with the team And we kind of waited actually to see if like we were told there was bonuses and stuff like is this real and so we ended up Finding out you know is real and then we ended up filing you know hundreds over the course of a few years Part of it was my team would work with different groups within Capital one kind of like as like a spoke and we would try to bring external research in and then share it with the organization and take Internal research or internal products and try to find External research that could be helpful and so I got to kind of sit at the center of a lot of the R&D efforts and through that We got you know hundreds of patents primarily around neural networks Primarily training as well as like kind of utilizations obviously in financial services I call it the time before chat GPT and after chat GPT and this was before what made you interested in machine learning in AI at that point Yeah, so I mean at the time is you know people really did there were no neural network frameworks So it was all like kuda by hand Originally I was doing like some project where I was trying to code drones to identify Moving targets for your sister better. We're And you know, I thought it was just the best way to actually solve the problem And the compute at the time was not very good But you can see how as compute was improving and the GPUs were improving how this would very Quickly become the dominant way to do things over like a you know five ten year period And so I just ended up focusing on that and there was a lot of resistance to it at first because it's not You don't know what's going to happen when you put stuff into these neural networks all the time But they tend to be more accurate and faster. So that's that's kind of where I focused the chat GPT surprise you or No, so actually a capital one we built I would call it a large language model before Transformers. So we were able to basically synthetically replicate any data set and we published this back in like 2016-17-18 and there are other companies actually interested in utilizing it So when I saw like the transformer come out. I was like it's really slow. There's actually better ways to do it I still maintain there's better ways to do it and faster and more efficient ways to do it but At least for like large language miles that we're kind of taking the brute force approach It seems to be working so far. It just takes exponentially more Compute and money. I think they're at some point in the next 10 years We're they're going to take some of the other approaches that actually we patented or did research on and I think you're going to end up getting Far smaller models that are actually more accurate and faster and I give the reaction to chat GPT surprise you Like the world is changing and everything. This is the moment So I always told my team at the time when I was at capital on they're like we're going to be the last ship out because Like I think it was like 2018. I even wrote like a blog post about I'm like it's over Because it was like clearer that we were on that exponential curve This is before chat GPT when the transformer came out. It was clear that this is like a general purpose Computing systems same with before that recurrent neural networks So I knew I had to start a business like honestly where I was like using AI because I'm like I think it's going to dominate be dominant and I have expertise in it and I was like it was just clearly the time to to try to make it happen There are a lot of AI tools being built for lawyers what stands out about junior is where it came from Junior grew out of patent lawyers and law firm leaders who spent years thinking about how technology could make sophisticated legal work more efficient That shows in the product junior fine tunes on a firm's own work product for every type of document It's attorneys work on so the firm controls how the AI writes across all of its work product right inside work It's backed by integrations with 11 patent offices trademark data federal case law and SEP standards And its web platform universe pairs that data with examiner history to help draft amendments and arguments based on what has actually worked Including on section 101 rejections for a particular examiner you can learn more at junior dot law We'll get back to that, but I want to talk a little bit about your experiences and inventor Did you know what a patent was when you were told about the patent program like what was your I'm actually opposed to software patents So like when I was at capital one we'd get bonuses and I'd like donate to the EFF to like try to get rid of software patents Actually, whenever investors to like he said like why should I invest it with you? I don't like software pens and I said by the end of it I don't think there'll be patents which I can talk about but like I think you know as things are going to Change so dramatically you're going to democratize it we're like patents are going to become very cheap To produce and then you're also going to be able to very quickly and easily be able to license them And I had that would probably help everyone because everyone does innovation So I didn't know what it they were. I was just I think most software people are kind of naturally inclined to avoid it From your perspective being within the company Anything that stood out to you about what makes a patent program work or not Not even specific to a company, but I guess kind of as an inventor In general what I think is everyone does innovation constantly And I think one of the biggest takeaways I had was Pretty much everyone has at least one patent or one patentable idea every like week or month, but like they have multiple every year Um and the big issue with all these corporations is The engineers are kind of doing their thing and management knows what's important But they don't know what's patentable um either and so you have like this like disconnect between Management which knows strategy engineers which are actually creating the patentable work And this is software mechanical kind of whatever and then you have the IP team just desperately trying to figure out a connect the two Um, and I think it's very inefficient very complicated and I think they're missing 95% of the patentable ideas and most most corporations You know, I think you mentioned your name is on hundreds of patents What was it about I guess maybe you or the experience that kind of made you so engaged in the program? So no, I guess naturally we were in applied research So we were taking probably like a bunch of like cutting-edge stuff and applying it in unique ways every couple weeks Because we would work with multiple teams at the same time and we were like consultants helping all these different teams And so because of that I got to see like all the problems and like you know One of the largest corporations in the US and I got to figure out all that like we wrote constantly trying to brainstorm We would give people kind of like here's how you can like build a framework to solve your problem Then we would consult with them So for me it was just it was a fun problem solving in terms of like how I identified like what's patentable and what's not We would brainstorm all the ways that we can solve this problem and then pick the best ones So a lot of times you're you know, that's what you want to do during pending you'll you'll focus on the one main one And you have all these other branches like we should cover these two to make sure no one else can implement a solution to it that we don't cover any Pain points that you recognized Besides within the company which you described there's a disconnect but from an investor standpoint that you thought I wish this was mover or I mean hundreds of patents means like probably thousands of disclosures, right? So like that process was not fun like documenting everything writing it all out planning it all out I always joke like you know like my goal is an engineer is not to talk to attorneys like my goal is to engineer things So um, although I'm friends with a lot of patent attorneys now and like I work with patent attorneys Like that way I think it's the biggest pain point is it takes away and like we our team actually got reprimanded repeatedly Like you're talking to attorneys too much And I was like well, yeah, that's part of her output. Yeah, so You know, essentially you said you got to recommend that I guess is that because we should disconnect between the value of the patent Program within a company that kind of the different people value it Um, so like my management they're they're gonna be graded on one thing, right? And then you're gonna have like maybe the the highest wrong of management like the VP level be great on another thing And so from the line manager, they're like how much code did you produce not how many patents you got and then but the VP might actually be great created on that. So there's like that disconnect. And so any kind of organization kind of worries about promotion. Um, and so I think that was the biggest issue strategically. I think they typically depend program, like has like CTO and like a bunch of buy in if they have a program. But yeah, I think that's the main disconnect is like the engineering departments are not aligned. And so you're, uh, you're working in AI and you think like, we might not have a job soon. See, sort of company. How did you come up with idea for IP copilot? So before IP copilot, I went to read AI, which is a company that does, um, they record meetings. Um, and one of the reasons I went there is I was like, with all that it recording data, there's a bunch you can do with it. Before IP copilot, I had a, a thing on basically on the web. And what I would sell, I would sell it to like friends who were doing stock trading. And what we would do is identify people doing, kind of like leaking trade secrets online, but it was public and they were anonymous. So the system would de-anonymize them, identify that they probably worked for like AMD would be an example. And then they're talking about their new chip coming out soon. And we would send that signal to the investors outward. And that would be, uh, accidentally, I guess they would be like bragging about it. I'm like hacker news or Reddit or Twitter or whatever. And our system would track all that. Well, in 2018 and 2020, they started locking it down. Um, all the API started going close source and like kind of walled gardens and I shut it down like when COVID happened. And then fast forward a couple of years, I was like, Hey, may it read AI? I knew I wanted to start a business. And I was like, you know, one of the biggest problems I had with like at a corporation was like everyone's inventing and like, there's no ideas being captured. Really? Like you have, you have these IP teams going out. You have one, one IP attorney for 10,000 engineers and you're just talking to people and you're never capturing all the IP that way. I was like, why don't I just apply the system? I already built to identify trade secrets. I shut down like a couple of years ago, but I can apply the same thing internally. That's going to where IP Copa came from. We want to capture and understand the knowledge that's in a company and how it maps to value within a business. So that's interesting. It's something you've told me many times that really IP Copa is about capturing the ideas within a company and not necessarily of invention disclosures for patents. That's part of it. Of course, but why is that important? Why does people have all kinds of ideas, things happen? They file some patents. Like why? So the way I view IP is kind of different. I think, you know, the way I always describe is it's what's in the person's head, right? And then you have IP assets, which are just derived from what's in a person's head. So our system is always trying to do is understand what a person knows and how those concepts evolve over time. It's also like a really interesting technical problem that I enjoy. But part of that right is then mapping those concepts to the unique value of the business. Because most businesses, almost all businesses are IP based in some way, even if it's marketing. And so it's trying to figure out what concepts and who's creating those concepts that link it back to the product that's being produced by the business. And that's kind of what makes I think IP code pilot unique is that we're not worried about drafting. We're not worried about all this other stuff. What I'm worried about is who in your business is the most valuable? And like what are they creating that is valuable? What is the vision for how the IP team leverages that or the company? Yeah. So I think in general, I think it's more than just the IP team. But it's like even like a CTO, it'd be useful to know these three people are producing the 80% of the value to my products. Those are my most valuable people. I want to make sure I'm keeping them happy. There's other people are also providing value and they might iterate on that idea. But how do I make them more productive? How do I leverage them more and stuff like that? So in my view, it's more of a living knowledge graph of kind of what's happening in your business and then trying to map that to the value and trying to figure out how you can leverage that more effectively. Few different applications, right? So I worked a little bit in data loss prevention. You'd want to identify when a person's about to leave and when they're taking their most important information with them. That's like trade secret protections basically. So that would be like one of the use cases. The other use case would be, okay, well, I can see that these people are developing IP around this new product. Maybe we don't care about that product, but we still want to patent it because we see our competitor doing it. So it's about tracking that and figuring out how to protect it. From the IP team's perspective, showing you the graph and showing you how things map is pretty much like, in my view, most of an internal counsel's job, right? It's making sure you patent the right ideas because you have a fixed budget. Yeah. And so we're trying to help that. And I guess what do inputs have to be into your system to make that happen? Yeah. So we plug into engineering documents primarily. So Slack, your confluence, some people are doing like Zoom transcripts. We have one company that sends us just everything, all the transcripts and all everything that they record. So it's really, it can be that. But some companies just give us data dumps. They're like, here's all the documents related to our given product. And we do like infringement monitoring and product monitoring, too, where you might just give us our portfolio. And then we're basically doing the same thing, but we're mining your competitors products and then mapping them back to your portfolio and saying like, here's the one, here's the, the patents that are reading on your products. But it's all the same type of problem. It's comparing an idea to assets. And then if you don't have an asset, here's the assets you should get to protect those. You know, it was recently actually talking about it. What is the number of patents that you need for product in the, a pound of attorney I heard say, well, you need whatever you need to protect it because of the value of the product. You know, that's the number of patents that you need. But it's a hard thing to do, right? Do you put financial documents into that financial projections? Is that part of that? Yeah, you can change it. So the thing, this one is just being beta tested now. One of the things we do is you can give us a bunch of product documents and we'll break out all the sub components. And then we'll identify which components are that we can't find on the market elsewhere. And then we'll highlight that and say, hey, you have like two patent assets on it. But you have all these other concepts that you need to file on to make the protection, like, you know, robust around that given asset such that no one else can build around it. And so I think that, like finding the choke points in your product is really what your, the goal should be in my mind. You know, one of the interesting, I guess, those you, you share with me is, you know, sometimes within a company and it happens, I guess, within an industry, you know, people are working on the same problem. And I think you kind of alluded to that. Oh, yeah, there's so much waste. I mean, every like a company like Capital One, there's probably, I mean, I was a part of several projects where we had eight teams competing basically, which means you have eight duplicate efforts trying to produce something. I imagine that that's what that we know of, right? With 40,000 engineers, you're going to have, I don't know, 150, 200 different duplicate efforts for like very basic problems that everyone has. Yeah. And being able to identify, sort that and say, like, hey, these two things, you know, are similar, our system does that right now. But I think like in general is a huge problem that's outside of that IP space. I mean, I still view it as an IP problem personally. But like, no one else seems to, it's everyone else is like, that's an engineering problem. Yeah. It's interesting because obviously, you know, sometimes when I do the work and so that's the best part of art is, it's part of from that same company that's working and applying the pan application. And you know, I've had a funny story because I once was in the mentor and I saw him, well, we have this probably art existing. And he's like, no, no, that's what my pan application was supposed to cover. But it's going to put the plan down like, oh, well, then I don't know if you get that. How do you get people like that? And you said, you know, they want to do engineering, they don't want to be like emailing back and forth with pan attorneys and writing and then just disclosure materials and so forth. How do you get inventors like that more involved, you know, the kind of your high value, really busy engineers, you know, normally the disclosure form is where you lose the most people, right? If you say fill out this form, I'm going to close the window, right? As soon as you leave the room and just ignore that because like, I'm guilty of this, probably if, you know, thousands of times where I had like a half run disclosure, I'm like, you know what? I got to go to do my work and I like, you know what I mean? So I close the thing and when we did surveys, it takes 40 minutes on average for people to complete an invention disclosure if you have documentation. Yeah. What we did in our tools, we flipped the script. So now we take their documentation, we have it already and we summarize it for them. So all you have to do is confirm it. Yeah. And so from like an engineer's perspective, it's like, this is awesome. Like I don't need to talk to anybody. I just say yes. And then our system will generate follow up questions, which can or cannot be used if you want to. And then you can answer those questions and make a more robust disclosure. But because it's tied into all the engineering documents, we get all that context. It's actually way better than what the inventor would ever provide you. I'm sure you're familiar with like, you know, getting one or two sentence disclosures and like you have to deal with that. Or you the reverse now, you get like an AI generated one that's like, yeah. And like our system does both for just makes it concise. And it's like, here's the idea. Here's the inventive concept. Here we'll do a prior search and you get a package that's all ready for you. Well, I was going to say even I've had some clients who've told their inventive not to use AI because what ends up happening in the initial disclosures like 30 pages. And it could be inventing stuff too, which is even a bigger problem. But yeah. And then I guess the job of the parents and he's then kind of to dig through that and figure out like what what actually was the invention and the kind of harder. I'm sure because now it's not narrow. Before I guess you'd have the conversation and invention disclosure call and kind of figure that out. But now you're kind of sometimes, you know, it's probably then that's where my start forget where his where his idea was within that document. I think the other thing too to like consider is just, you know, from an inventor's perspective, it's just like a better user experience to be like, hey, you have an awesome idea. Yeah. Rather than being like, what idea do you have? You're going to get shower thoughts half the time. Like or whatever they were driving to work. They'd be like, oh, I had this disclosure meeting. I knew I had to come up with something. Yeah. You know, and it's just going to be more natural, if that makes sense. And obviously, if a company has a good pan program, then they're like you said, they're not going to file for every invention disclosure. They're going to kind of try to find a way to call through them. And obviously, the next time an effort, but also it brings disappointment to the inventor who might have spent time on that invention disclosure material. Yep. It seems like every company should have something like that, a graph of all the innovation. I guess what is the reason? reaction that maybe you got in the beginning and you're getting now and has that kind of changed. So I would say there's like, there's two reactions. I mean, the engineers are all like, this is awesome. But I think a lot of the patent attorneys about half of them, you'll see like hesitation of like, I don't know if we want to be spied on because it's like, that's what it sounds like. And like, to degree, it is, right? Like, we are going to be reading your technical documents, but it's technical documents. So like, it's simply, it's honestly like already owned by the company and everything else. So it's less of a reaction. Like, I think very quickly we get everyone on board. And then it's getting the security team to approve us accessing all their most sensitive details, organizing it and then making it, you know, available is like, there's always a hesitation there. I would say like initially, there was a big hesitation around AI is all these companies had like generative AI committees spin up. Now, I mean, we can get through a product like before I would take sometimes eight months because the committee would be formed as we were trying to get through the process. Now, it might take six weeks. So it really just, it's, it's getting a lot easier, honestly. You know, one of the interesting parts about your inventions, you are looking at ideas so they could be some for patents, some could be trade secrets. Any thoughts about like solving that problem because obviously that's a problem that IP teams navigate kind of how do you have both protections as possible and at the same time keep trade secrets as trade secrets. Yeah, I mean, you know, to, you know, I think the, I think what's going to end up happening is patents are going to become cheaper and cheaper to produce over time effectively free. Yeah. And I think that'll happen much sooner than a lot of people think. And I think because of that, the flip side of that is also you'll be able to take a patent and you'll be able to have the AI generate around it as well. So I actually think patents, like there's some question whether or not there'll be as valuable as trade secrets over time. Yeah. I think also because you have the search capability, you can also enforce your patents more effectively. So I don't know where that's going exactly to be blunt. I think from the trade secret perspective, I think they're underutilized. And I think there's not a good system today to categorize, maintain and analyze and like keep track of them. So that's where we're like one of the things we're focusing on is like we do have like a trade like a trade secret component because I think that the biggest problem is just the organization of it. Like how do you know even what a trade secret, a lot of people are trying to enforce it after the fact where, you know, you'll see someone taking your ideas and then you're like, well, we had a program. It's like kind of you had some trainings. You told people not to take it. But now in our system, right, we can say this person when they go to leave, they knew all of this. You can have them sign it. And so when they take it with them, like you can actually prove with documented evidence that they took it and they knew about it before they took it. Yeah. I guess it makes a difference. Like if you're a company with tens of thousands of employees, you're going to spend the time for each one. Oh, yeah. You don't care. Honestly, you can't. Also, like do you really want to be suing your employees as like another question? Well, yeah. You wouldn't want, I guess I picked up that you think patenting will take less and less time. Obviously, there's a lot of other tools to kind of make that happen. What do you think about that? I guess the state of the AI technology for that, the state of the tools and different approaches you've seen? Yeah. So we always viewed AI drafting as a solved problem. Like even when we started in 2023, we said it will be solved. I don't know when. I don't know how, but I think like a general purpose model will solve it, just like it's going to solve programming. And so our thesis was, let's focus on the thing that can't be solved with these generative programs. And so that's where we spent all of our time. But I think it's at the point where even we ran some tests and we were able to generate a patent that I thought was, I was like, we should just file this, you know? And so like, I think we're at that point where at the very least like, provisionals can be easily filed. I think we're at like, by next year, I think you'll have, I mean, you already see it. You're getting AI generated patents today. I think specialized tools, though, are going to become less relevant, at least related to the generation portion. I think specialized tools might make sense in more like some of the other components, right? Collaboration, things like that. I think that's where that would be. Those would make sense. But I think in general, like, chat GPT is pretty good at making a patent or clot if you've. Yeah. Well, I guess I have someone, at least for now, I have someone with different experience. But you know, to me, it's interesting. There's obviously, it depends on the industry and all this other stuff. You know, I'm just saying, you can see the difference, though, between like 2023 and three years later, like it's, for sure. Yeah. Things are getting better. I mean, even with the pan drafting tools, you know, there are one of the problems that you do have, or sorry, patent prosecution, whatever. There's still a lot of data that you need to gather to do the task. And then you need predictable output, right? Somewhat. I would argue, like, it depends on what you're trying to get. Because there's a lot of companies that just want to file some patents, you know what I mean? They just want a certain number. I think like you'll have probably two tears, I mean, you already have two tears, right? You have like the cheap tears. Sometimes there's three tears for certain companies. I think like the lower tier patents are already commoditized, right? You're already outsourcing it to somebody you don't know. And so what's the difference between if they're using AI or not using AI? Like, you have no, from your view, any difference. Yeah. No, that's interesting. I think even before Chad GPT, but I appreciate GPT times. The market was going two different directions. One was, let me figure out a way to make it as cheap as possible. And AI is obviously a way to facilitate it. Yeah. And that's just going to drive it down. But if your goal is to kind of figure out to get something valuable and worthwhile, then I think somebody still has to make that decision, right? And the question is, like, do you press it but internally or externally and make that call? So my thesis is that both are going to happen. So you're going to have companies that realize they can outsource most of their IP efforts. So they're going to get rid of their in-house counsel and just have the law firm do it for really cheap. Or like, you know, you'll be like succumbed to whatever it is. You're not going to have like the two-sided thing, though, where you have like the in-house counsel in a bunch of external. I think that'll probably go away or like shrink. I think you'll have a lot more just, you know, that's my law firm and I just outsource it. Or you'll have the in-house counsel doing everything. And I think one of those two will happen just because a lot of the automation allows it to be centralized to just one person or two people that can be managed it. And what do you think needs to happen? Like, you know, you said you you drafted a pretty good application using LM. But do you think it's like the key is really good prompts, the right type of technology that's a good fit for that. Initial disclosure, but you start with has to be good. Like, what do you think right now as we said today that kind of makes it work? I mean, I think it's with kind of everything, you know, so like our system, the entire system is designed to capture all that information you need. So I'm using it and are like, we have an internal system that's connected everything. And I'm like, okay, now draft a pen. It's like, well, it has like, you know, tens of pages of documentation about this. It has images. It has diagrams. Like, it doesn't need to do any of that. It just generates it all. It's like puts it all together and formats it. And then we just like, okay, now check the MPEP. Make sure it's all like accurate. And so we, you know, it's pretty straightforward. It took like an hour for it to generate. And, you know, is it the best application? No. But like, is it okay for a provisional for sure? And it's probably, it might be good enough to actually just file it. So like, is it good enough to be like, stand up? It's like, well, you don't even know if an attorney draft said if it's necessarily good enough. Like, you hope it is. But there's always like, you know, edge cases. So I think there's two problems. I think it's that you don't know what the quality is, unless like you're an IP attorney. So like, I sent that draft off to an IP attorney to get reviewed, right? And I think that's going to happen regardless. You're still going to want some sort of, like as a CEO or as, as in house counsel, you're going to want to outsource your risk to somebody else to like, handle that. Yeah. And be like, well, they didn't do a good job then, or whatever it is. And so I think that natural, like human instinct to avoid risk will continue. And I think maybe that's kind of saving grace potentially. But I think in general, like, I think of it from a programming perspective, like, I haven't opened up my IDE, which does the development in like a year. So I'm assuming anything generative, like content-wise, that'll happen. And we've, we've probably all generated videos or images or songs. And we could all see how good it's getting. Yeah. I was going to bedora. And then there was this one artist. I like the music. And I couldn't find anything online. And my wife burst the bubble and said it was a AI-generated music. I live in Nashville, area. And it was three years ago. I started to one of my friends who was a musician. Yeah. He immediately bought a farm and started raising shame for us. I mean, it's, yeah, it's, it's, it's very good. Yeah. The other part of using AI, you know, I were discussing before we started recording is obviously the examination of patents. And, uh, you know, these people has been thinking about it. They've been kind of public with it. What do you think that will go like? Well, I think, you know, from the start of kind of like IP co-pilot and really any of these things like I think we're going to go into a registration process effectively. Because it's going to be so quick to get IP or so quick to generate patents. If the US Patent Office takes 18 months to prosecute it or to like even like, you know, start prosecuting it depending how it works or examining it. Sorry. Um, then the prosecution process takes like whatever, five years. Um, like the point is is AI is going to make everything faster. And so there's no point. Yeah. By the time, like, if I was to publish a, like I say, my patent goes live in 18 months, but I don't get protections for five years. Well, my company might be out of business. Because what they can do is they, they can do another company can come in, use AI to generate everything that I just described perfectly for them. And then go to market with it before my, my patent becomes enforceable. And then I go out of business and then, then what? Like it is so, so I think like four patents to be valuable. They're going to have to figure out how to cut that time down to like a couple of months to get a patent over the next few years. I don't know exactly how they're going to do it. Uh, I think they're probably going to use a heavy amount of AI personally. Um, but that I think they have to do something related to that. You know, the thing is like, AI is good with making something seem like it's compelling and well read in and make sense. But if somebody really knows the stuff, it might be able to identify kind of a shortcomings. And somebody who's generating that output, if they don't really know understand what they're doing, they're more likely to generate output that probably misses the mark. Yeah. I mean, what is the key to providing AI as a tool with making short sluvers in the, in the right way? So I always view these LLMs as the least efficient way to solve a problem. I mean, it's effective, like, you know, if I had, I used to joke, like, I would be more effective just having a room of interns and probably cheaper, you know, because the interns are going to be able to, like, it's a lot about your prompting, right? If I tell the intern, intern solve this problem, they're going to, like, not do really silly things. Just as an example, you know, we saw, like, a hugging face get hacked. Well, we had a similar incident in our system, which I think it posted even on, you know, LinkedIn, where I told the AI, I removed all its tools and I said, okay, now do a prior art search. And I wanted it to, like, manually, like, just tell me, like, hey, I don't have access to the tools. Instead, what it did is it built a tool to access the web and then went to our website. Identify the users, because it had that tool still. And it was trying to do a rainbow attack to break through our system and guess the passwords into our system. So we're going to do a prior art search on the website. And I was like, oh, well, that was shocking. So I turned off, like, the, the ability for it to, like, be in, like, that agent mode. But what I realized, I was watching it. I mean, we were like, I was like, why is this taking so long? Like, just to tell me, like, no. And so we were watching it. I was like, where's this going to go? We were watching it do the whole thing. And that was like December of 2025. And so I was like, this is clearly a problem. And in general, the approach we've always taken is use the LLM as kind of a brute force approach. Then once you know what it you need to do, you don't need to use reasoning mode or any of these other systems or your familiar, but like large languages have these reasoning modes. They use a ton of tokens. You can actually just make it a very simple prompt once you know what you're looking for. And so you kind of solidify a workflow. And then once it's solidified, it's very quick and to easily analyze massive amounts of data. And I think that's where you can strain the problem and you can actually monitor errors. We have additional tooling that doesn't use large language models to check all of our systems. And I think that's also like important. So for instance, if it sites something, is that actually in the text? Like, you don't need a large language model to check that. You can just check that with something called projects. There's a lot of other approaches and I think formalizing those workflows is a way to reduce risk. Yeah, it's kind of like putting guard rails. I don't think you should be letting the large language model kind of go off and do its own thing most of the time because you don't know where it's going to go. And it's honestly not going to be very efficient. And it also is based on like, I don't think the AIs have gotten really too much smarter necessarily, but they've gotten better about, I mean, smarter is relative, I guess, but they've gotten better about is understanding what you're prompting them to do. And I think that's actually like the biggest, like, lift that we've been seeing over the last couple of years. Like context windows and all that sort of stuff, but that's the biggest one in my mind. Companies are constantly innovating, but capturing those ideas and deciding which are worth protecting can be difficult. IPCOPILATE uses AI to uncover potential inventions in the tools teams already use. It connects those ideas, invention disclosures, and patents to the company's products, and brings in market intelligence to show how that IP relates to competitors products. The result is a clear view of what supports the business and what to protect next. That's what makes IPCOPILATE such a natural fit for Clause 8 this season. Discover, manage, and protect your IP at ipcopilot.ai. You know, obviously before there were, again, before Cheshire PT, there were tools available to kind of make things more efficient and easy, like, you know, simple things like checking antecedent basis for like one, 12 rejections or whatever, you know, you don't need a whole lamp to do that, that's kind of like, well, yes, Cheshire, check the article. And it's interesting now because it's obviously now there are tools that use that lamp to do that. Yeah, it's just like an expensive way to do the same thing. You could have done very cheap, yeah. How do you build those workflows, you know, how should the panel office think about building those kind of workflows or like, what's the good way to take the price of it? Yeah, so, I mean, I don't know, like, I still think this is like a software engineering problem, ultimately, I guess because maybe I'm a software engineer, you know, an art, but, you know, in general, I think what we, you can create workflows like in a system called like Zapier or any of these kind of things where you just have like, you know, inputs outputs and you want to test each one of those. So in, I don't know if my cloud or open AI or any of the systems necessarily have that exactly, but you can effectively run API call scripts that just go through and they like try each one and they have checks around them. I think that's honestly all you need to do. And you can have like a programming, it's like a Python script, but like any kind of like scripting language, you can just say, here's the prompt. Here's what I'm looking for. Here's the checks I'm doing around it. And you just go down the list. And that way it doesn't think for itself and just who knows what it's going to do. Kind of a little bit more predictability with that, you know, you mentioned, I thought it was interesting. You mentioned, you don't think, obviously, the conversation is more and more computing power for LMs and because of Transformers, but you think it's actually going to go direction towards smaller models. Well, everyone's concerned about the compute and I'm like, look, if that becomes a constraint, people are just going to innovate around it, you know, like that's not, that's not really a real problem. It's we're doing it because that's the most efficient way to gain 1% accuracy, right? Like these models are not gaining like huge leaps and bounds based on their, their training size or anything else. Actually, most of that has to do with the harness and the way that they're being put together to like understand the prompts or to get access to more tools. I think the more efficient way of doing it, I mean, there's a lot of different ways. Just for example, there's certain models now that instead of using 16 bytes or 16 bits for every like kind of compute layer, they're using four bits, right? And so like you need to use four times less memory, well, there's one bit models too. And ironically, like the one bit model is equivalent to GPT 3.5 already, like a 27 billion one bit model, which is like four gigabytes or something. So like I can have it on my phone. It's equivalent to GPT 3.5 now, but give it four years, right? It's going to be as efficient as our best models today, but be four gigabytes. And so what we're seeing is there's the frontier models are getting bigger and bigger, but like just behind them about 18 months, these small models you can run on your laptop are matching, you know, maybe six months or a year behind the frontier models. And they're much more efficient. So at some point, I think what's going to happen is intelligence, clinical will become saturated and there's not going to be huge performance gains. And we're kind of actually, in my opinion, seeing that like there's not a huge performance gained between these models, so much as just configuring the tool use. You know, there's pretty public some companies discussed about developing their own models, then they move away from that, then they go back to that, when you hear stories like that, what are you kind of thinking is going on or? So our thesis was that you should only ever just use the general models, begin and build in the tools and the scripts and the workflows you want. And so we actually have always just used the best open source models we can get access to that run efficiently. And we find that even like the accuracy in our model would often beat like open AIs model. And like by the way, like there's no difference except for, you know, we have these workflows with additional checks around them to more accurately determine things. So we can use any LLM in our system because we have like tooling, we have data, we have other things that help structure the whole thing. So it doesn't really matter how smart the LLM is. You know, obviously we love phrase you said, but LLM is the least efficient way to do things. But there's so many AI tools out there, you know, just putting inside the pen practice stuff for, you know, for everything you're meant for coding, obviously for marketing, for that. Let's say one of them, how do you validate if a tool is actually worthwhile for the problem? Like how do you kind of, what advice would you give? Yeah. So it's an interesting question because we've come across people who try to build like copies of our tool. It's always funny because we're like, okay, well, we'll stay in touch and then like half of them or more come back within like six months because the reality is, so like suffer as a service. I don't know if there's like in the news, they've been saying like, that's going to go away. It's like not really. And the reason I don't think so is like, you're not a good designer. Like as a regular person, you're only thinking about your narrow scope of problem, but there's a long tail of potential issues that you don't even know exist. There's a bunch of unknown unknowns. And what all these companies are doing is they're solving for the unknown unknowns all the time. And so when you run into it, you want your tool to be able to handle it or recognize that there's a problem or whatever it is. I think in terms of assessing whether or not a tool is good or not, is whether or not for one, like, can you just use Chad GBT to do it like right off the bat? I think there's like a fair argument. Like if I can give it a prompt and it does it, like, I don't need your tool. But I think there's like, it's more about your workflow and how cost effective you can get it. And I think, you know, but I do think that it's becoming very saturated as you're noting because it's also very easy to create a bunch of tools. And so like, I know in the AI drafting space, there's what, like, 100 companies. We literally have a channel that's just AI drafting tools that's just like every day is like a new one. And they're all getting funding and we're like watching and we're like, okay, but, you know, I'm just looking at Chad GBT getting better every month and I'm like, I'm just going to wait. Yeah, I think that the reason you would want a new tool would be a workflow or a collaboration of some kind. You know, go back to our discussion about how the pan offices and practitioners are going to use AI. I think you mentioned, and if someone jokingly or serious, but a certain point, the AI tools are going to talk to each other. I mean, they already are. But, um, yeah, no, I mean, I think, I think very, I mean, I think with the next couple of years, it might be China, it might be Korea, it might be the US, but whoever it is, you're just going to have a system where like, I have an idea. My AI talks to your AI, we negotiate a patent, and then you're just like, okay, here's a patent. Like, one of the questions I have, like, or what my thesis I have is like, why is the US PTO, if they're concerned about formatting, not just drafting the patents for us. Bye. the claims and I have the evidence. I think the biggest problem that's going to happen is proving that a human actually created the patent. And so I think what the quickest way to solve that problem in my mind is I think you're going to have to get to the interview process as fast as you can or something to validate it, right? And so it's going to be me providing evidence. It's going to be me providing like, you know, maybe an interview. I don't think the drafting of the patents actually the important or will not be the important part. I think it'll be the process of confirming that that's actually you creating it. You know, I think there's been a lot of predictions about relating to AI, like, you know, I don't know, three years ago. Everything I can have a job in three years. I think there's always like an overestimate in underestimate, right? But I guess going back to when Cheshire PT cannot, you already kind of knew this was coming to where we are today. Like, what do you think kind of the progression has been? I mean, I said within seven years in 2023. And the reason I say that is like, that's just like a decent, like it takes about seven years to like for to transition like an industry in general through technology. But I don't know where it's going to end up. I think that's actually the hardest part is like a lot of technologies. It's very, it's almost like you can predict it. Yeah. Like, even sitting here, I'm like, I don't know if AI is going to just like keep progressing or like if it's going to get ice to my sheep or like what other uses can it be used for? I mean, one of the big things I always think about is like, you know, which people don't talk about is like, we're going to have robots and all of our homes like fairly quickly, like within five years. And the reason being is I know they're probably going to be able to ramp up to like 10,000 robots. Well, those 10,000 robots can build more robots. And so you're going to end up with like exponential growth. And it's like, how quick is that curve? 10,000 to a million or is it 10,000 to 100,000 the next year? It's just how fast can you produce it? I think similar with AI, it's how fast that that technology trend is. I think in terms of like where we are on the progression, I think AI drafting is definitely coming. And like I think we all know it. I think a lot of us that including myself like coding, it's like very hard for me to like look at and be like, I spent 15 years learning how to do this. I'm like, I'm going to be doing something else. I'm still right now helping the AI. I'm still like, but I think we're going to have to transition into a different way of thinking about it. We're like, we're not generating content anymore, but we're doing some other augmentation to that. Well, you know, it's not my original thought, but obviously, there's just thought like the AI has been creating more work. More patterns, maybe there's more attorneys and more. Well, it's the joke, right? You get that email to generate the generate AI, generate email and then you just summarize it on the other side. I'm like, can guarantee that's a thing like we do it in code. The inventor, the code or will generate a bunch of code. And then I just asked the AI to review it for me and like give me a summary of the key parts I need to review, right? Same thing. Is there anything I guess you thought AI a problem that it would already solve that you are surprised that hasn't been addressed? I'm surprised. They're still using these super huge models. And I'm surprised that they're still trying to just like brute force it into the trillions and trillions of parameters when it's marginal gain. Because most of the utility I think is very simple. Like just being able to ask the AI like summarize this email is like a large portion of that utility. So I'm that's the only thing I guess I would be surprised. I'm really surprised by because then they're spending you know trillions of dollars building these data centers that they probably don't need. You know, obviously you mentioned some working with AI and then it takes you like an hour to do with something. And that's something we have to get used to, right? Because computers got pretty quick. So we kind of expect things to be instantaneous. And now using AI sometimes it takes time for us to think things through. Yeah. Yeah. That's a weird one. Because like I think I talked about another like talk like I did like the equivalent of like three thousand hours of human labor and like three hours on my phone the other day. Because I was just like review this review this create this power point create. And like I was just like going through like my list my checklist and I was just sending off to a different AI is to do everything on my checklist. And I was on I was by the way driving while I was just like talking this. And I get to where I'm going and then I reviewed it all for like an hour. And like normally like creating like a financial model or whatever it would be would be like you know that takes days. And it's like that's a hundred hours of work right? Like and whatever it is. And it's just done. Yeah. I think the trick is trying to do things in parallel. Yeah. Because that way you're not like effectively bored. Yeah. It's like what would you normally have just outsourced to like we're all becoming managers of AI basically. Yeah. And so like as a manager my job is just outsourced things as fast as I can to other people. And I think the same thing people should do with AI. You know this is not my original idea. But during a work day the average professional makes 150 transitions during a day between different things. And it's those transitions that kind of actually slow them down. At least that's theory goes. And the question is like how do you use AI in a way that's you're not constantly building up your own context for the mix sense. Half the AI to organize your day so you don't contact switch. I mean that's an interesting question. I don't know. I do think that's actually that one of the challenges that we're going to have like just in general as humans is like trying to figure out because you could do so much. And I've talked a lot of people are like we're doing so much more work. And I think to your point it might not actually be more work necessarily right. You still are working the same out per day but your transitions between them is exhausting. You can't so we have to think about it. So I do think that's something people are facing. You know obviously there were like kind of layoffs tied to AI's but then it was like Uber saying well we haven't found any productivity gains. So it's hard to make sense of it all I guess. I think well the way I describe it is when you're in the singularity which is like arguably where we are right it's almost like a black hole in the closer you are to that event horizon. The like where things are like constantly changing. You're going to experience things much more rapidly than other people. And so I like to like equate it to the amish. It's like the amish they haven't changed in what 150 years. So between now and 150 years like they've progressed very little. But like we're going to if you think about like how fast technology is progressing like you'd be equivalent to the amish today like 10 years from now if you did not transition. Because that's how quick technology progresses. And I think a lot of people like Uber may be just further down that chain and not experiencing the latest technology frankly. Or like their analysis started two years ago right. Well it's like yeah two years ago it wasn't very good but like six months ago it was really good. So yeah. I got some friends who were like I'm the cutting edge of AI. They're like from the beginning like I'm using claw, chat GPT all in one and you know kind of saving time. But don't tell me recently I guess they're like well kind of like what you were seeing with a financial model that might have taken you I know a hundred hours takes you an hour. And then they're like well takes an hour it's not as good as I would have come up with but it's good enough. Well it's the thing isn't good enough right. To be fair when I the financial model I did create I really did have to reduce so like from scratch I was like that wasn't good. So I just had to spend the eight hours to like fix it or whatever it was you know. So I hear you but like the other things were good enough. And I think the thing is you don't know if you don't try. So you constantly are trying and just eventually it becomes good enough. Yeah. I want to kind of a version of the gap if you're discussing between what IP co-pilot does does this diagram of all the ideas innovation within company. And you know drafting maybe chat GPT or cloth can do these days at some extent without any issues. What's the next step? I guess you see kind of where AI can be helpful within. So I think the tech like the big thing is that people think of the IP program is like a separate thing under the legal department. I think that's really weird. Personally like it should be like almost inside your seat the under your technology division. And I think the big thing is the IP programs like looking over the technology division and constantly trying to pull out ideas. But I think what's going to end up happening is they're going to merge where you're going to have like a layer of insights over your like I call like business intelligence over your entire system right. You can use that a lot of different ways. I can say like build me the best team to solve this problem or maybe I don't maybe I need to hire for these three like cat technology categories because I did whatever. And I need I need these additional like this additional capabilities. I mean part of it might be when you go to do layoffs which you know bound to happen don't lay off like a core portion of your team which like that happened repeatedly where I watched them and then they all had to get rehired back at like double the price. So I think there's like a bunch of uses outside of like classic IP that there's a ton of value. I also think cybersecurity is going to become a big deal like who's the inventor right is it the AI or is it the human is like going to become a problem but then like also what if it becomes misaligned like well we're already tracking the alignment of how your ideas and concepts relate to the business. So I can start seeing if the AI becomes misaligned. I can start seeing if the human starts working on their side project at work right. I think all of that actually relates and is like in my view should be part of the IP function even though today it's not you know you talk to a lot of pan attorneys and a lot of in-house teams is there anything that sticks out to you about what's like on top of mine when it comes to AI and pan practice this kind of I mean there's obviously fear like it's going to take our jobs. I think you should lean into it personally and just be like yeah like okay let's try to make it automate my job because then you have more value to do other things and you're also going to be more productive right and so at the meantime you can make a lot more money potentially by getting through more pens. That's one thing like I've just like seen I think the other thing that's interesting from my perspective is there's a lot of people are trying to like skill up and use AI in different ways and it's interesting to see how each person approaches it but they all end up like coming to the same conclusion which is like okay we can use it for drafting but they don't think about all the other things they can use it for. I think expanding the scope of potential uses is also going to be like an interesting component because I think a lot of people kind of put blinders on and they're really focused on like how do I you know get my work done faster you know I always say like you know what an IP team cares about it's like okay well they don't want to look bad to their boss like just like everybody else and they want to do to get home on time that's what most people want to do and so if you can do that and you don't make them look bad and you can get them a home on time like you're successful. and I think that's honestly where a lot of like these tools go wrong and everything else it's like that's what you should be focused on. You know I'm curious about like that seems like a big part of your interaction with the pan process is you guys help generate these invention disclosures. Or maybe you can track how often the window is just accept what you guys generate. It's interesting because we get like a really weird mixed bag where like people expected to be perfect and it's like I know you miss your like when you're doing searches too. So I think like you know I think there's there's like an interesting I don't say can I jump like challenge there. One of the things that you guys do is you do probably our search. It's like the base of our whole system right so we need to know does this idea exist on the market. In like the patent space or on the web we do npl searches and everything else does exist in video whatever it is. And then we also have to say does it exist internally and it's not being disclosed like publicly. And so we're constantly running these searches to try to understand like the mapping between the two. And the challenge to me is always are we getting you know like we might miss something that people expect. And it's like well did you actually review the prior a lot of times they say no like well. Maybe it's better like you don't actually know and I think a lot of people have this like you know these preconceived concepts that they're doing like they're already very good and like maybe they are maybe they aren't. But you need to actually validate it. I think that's one of the more interesting things I've seen is like I don't want to call it like an arrogance because they're people are busy and they just don't have time. But I think that is the wrong way to think about the problem. Well sometimes I do that. I'm like what I'm doing like just using shed your claw. I'm like I'm not going to give it everything I know because I want to see if it's fine. The thing is though it might and I think the biggest thing is the AI will hone in on a different thing that it thinks is unique. And so the search will actually be for something different than what you anticipate. And that's where I was saying it's a prompting problem like you got to make sure you you're prompting it for the right thing to look for. But yeah I know I totally see where you're coming from on that one. Yeah I'm curious like the companies that your clients that have had the most success with embracing your tool like what do they do to kind of make it successful or. We had one one person who just integrated with everything and then didn't even talk to the inventors he just had him he's like you have a patent now and you sent them the patent and was like please sign. So that was he embraced it all the way basically is in my mind where it's like that's what I would be the dream right I just as an inventor Mike here I would love to give you this patent can you sign it for me. And it's like okay that's awesome I think from other people who've like seen it work well is they integrated into the workflow so they'll be using it to talk with outside law firms they'll be using it to review the prior and everything else. And using it primarily to collect it with the like the disclosure from the inventor I think the other thing is people need to tune things a lot. So like for instance in our system you can tune the business value like what are you looking for as a business we guess right up front like we have a pretty good educated guess. We look at your 10k your SEC fileings all this other stuff but like that doesn't necessarily tell us what you as an IP individual is looking for so we found works best as we meet with every one of our customers every week or every other week. For like the first two three months that we tune it. And then they get really good results you know it works decently out of the box but once you tune it like you don't want to filter through a thousand ideas like you want to filter through the five that are the best ideas and so like it depends for each department though kind of what they're looking for. So I'm curious you said you know one of your clients just presents them with documents to sign for the plan application. I presume that he also says is this accurate but interesting because obviously it's a bounds because one of the things and I've discussed many times you know the engineers time is super valuable. So you're going to find a way to kind of bend the least amount of time as you want. But at the same time you want them to be bought in to what you're doing like for example your first encounter with the patent program you did not existed. That you know with your product without your products about the importance of having your engineers inventors as champions for your patent program. And how do you use a tool like yours maybe to facilitate that or. I mean I think in general it depends on like the ethos of your company. I think some companies like it for me right it would be great if you just presented me with the pan it was like he's your pan right I'm out I'd say here's the things I would add right and then you can add them or whatever. But that would be like I think like ideal in my scenario I think for other corporations you need like the inventors to get bought in much deeper. For whatever reason like maybe they're like on the patent committee or whatever like you want to build a patent committee system you want to get really like high end patents that require technology domain expertise etc. I think certain things to like really help that with that is to have them feel involved in the process by creating patent committees or something like that in our tool. You can have different committees and you can have patents auto route and then people upload or download and comment on them. And so we have seen a few people just create like custom committees and they'll just get auto routed and reviewed and then only move forward. And so like effectively the patent IP team is like automating like they don't even do the like the initial review the patent committee does the review. And so it's like the engineers feeling like almost like an internal publication process right like they're all involved in it. That's also interesting obviously because your tool in some ways provide a recommendation right. Yeah, you don't have to show the most people don't show the inventors. Okay. So you'll be sick to kind of correlate that I don't know if you'll ever have an opportunity to do that to see what your tool versus the human evaluators. We do keep track of that. We roughly match like what humans end up reviewing I think it depends on like how strict like some some companies just have people who are just going to reject everything because they're like you know that person in the room they're like I'm the smartest one. I think that happens you know but I think in general when we like look at distributions we end up matching the distribution fairly closely you know plus minus 5% kind of deal. But that's kind of it'll be very similar and actually our scoring system was based on a person who gave us a bunch of disclosures we then mapped our scoring system to their distribution. So that way we knew we were really smatching them and we we met we chucked it with others and they all roughly correlate. Well that's interesting obviously if you have something if you have multiple panel review committees and one of them is out of bounds or whatever. You'll have that one person who's just like not ideas are good. Yeah but if you get good to have that kind of data to kind of show that yeah any other I guess uses of your tools that kind of stand out to you that you think like hey this is a cool. It's something that I guess companies or clients have found like appreciation for. Yeah so my favorite component is honestly something we call idea at list so it takes all your internal knowledge and then it maps it to a 2d space but then it compares it to your competitors. And then we have a tool where you can highlight it and ask questions about it so you can say like what are we developing in this area. And so each one of these dots represent either an idea or concept internally a disclosure or other patents and you can see the white space where there's like no one is like operating in but you can file in. And then you can also click a button and we'll show you the emerging areas so things that have changed over the last 6 to 18 months where people are starting to file more or file less within the company or just in general it could do inside the company or extra and then we'll summarize it will say like you know we see. Like company acts is now filing a bunch of stuff related to like kitchen appliances or something. And then here's the kitchen appliances here's the inventors and you know with that information you could start poaching those people you want to write like there's there's a bunch of use cases but I think you can make the easiest thing to do is that you can get budget with it. So you can make the argument like hey we need to patent all these ideas right here because we can see the competitors moving in that area. And it's like it's something that like don't even think about but like our tool just does and it's kind of cool. Obviously one of the thing different things about prior searching especially in a test movie field let's say like AI is there's delay sometimes is there a way to kind of compensate for that to kind of say like well there's nothing right on it but it's getting crowded over here. Yeah so that's so our system predicts where the directionality of the concepts is moving so we can actually say like there's no guarantee it continues. We can say this is the direction it's moving you should they'll be here at some point you should file on those right so it's the other way around yeah before the time. But you can you can track that right there's an 18 month delay so you're going to have to project it out a little bit further and like like I said there's ambiguity but you can definitely do that. Well I want to kind of get your thoughts you know you told me 2018 you were sitting at work you're telling your team like we want to have our jobs. Yeah we got to be ready whatever somebody else kind of listening to this let's say a pattern you're not a pattern turned in everything you're like. All right I want to find a way to solve some kind of problem that gender of AI or LM can just be used to solve easily kind of like Austin did what advice would you give like something it's worthwhile to focus on. That's a good question I think the biggest thing for like IP attorneys and like alums and stuff can provide a lot of value is looking through documents and summarizing documents so if you're looking at like what an IP attorney can apply it to. It's like you know you could now analyze things you never could have analyzed before so if you're trying to build a case you can say read all the documentation from that company sure it takes five days who cares. I mean that would have taken you 500 years like you know what I mean so like you'll get that results back and you're able to actually analyze and make better determinations. I think you got to constantly push yourself and push it beyond where you think is reasonable because it will surprise you and so I think that if that's if I'm an IP attorney that's what I'd be trying to do is just constantly push the bounds and what's possible. I think I would also try to get more into litigation or get more into where it's people to people interactions because I think content generation is going to go away at some point I don't know when but it will. What about like I think you said 2030 I think that's your call kind of that's going to be the big difference you know let's say we are we you've kind of hinted at it but like in 2030 we're sitting here which I guess this point is only four years away or three. All things look like I guess in the space space yeah so I think my personal opinion is I think AI drafting is going to start is already taking off I don't know what percentage it is I'm guessing it's 20% of people are using AI in some. way. I'm guessing in the next year it'll be 50%. I guess in the year after that it's going to be like 70. It's just going to keep growing. I think at some point though, it'll be a one button click. And once it becomes a one button click, it like doesn't matter. Like if you like your pen to turn or anything else, it's like no business is going to justify spending $30,000 on a pen. They're just not. They're going to be like, yeah, spend the 200 bucks or whatever it is. And they're just going to risk it. Now I think there's going to be obviously like those boutique firms that survive, but I think it's going to shrink down to a small number. I think other services you could provide though. Like for instance, like going in and interviewing inventors, like that's not going to go away. So I think it's just going to we're going to shift where people are kind of operating. But I don't think people are going to be drafting pens. I think it's going to be shifting more to the the operational and my customer service kind of space where you're talking to the USPTO, doing interviews, you're talking to the Avengers, doing interviews, or doing the customer relationship to keep money. I think that's where you're going to spend more of your time. I think IP is going to grow rapidly across everybody because it's going to be much easier to enforce. What about like in the in-house perspective, how do you think in-house attorneys, if they're outsourcing pressing the button for the pen application, you know, what do you think? I think they're going to insource press pressing the button to be honest. But they're probably not going to insource like talking to the inventors because you can't. And you need to be able to expand or shrink that. And they also don't want, like I said, don't want the liability. So I think what they're going to do is send drafts to probably the outside council. I mean, it could still be disclosures than, you know, the outside council clicks the button after the interview, whatever it is. But I think that's where it's going to move. But I think like I said, we're going to collapse a lot of in-house council and outside council. So you're going to have a lot more companies that have outside council that maybe have in-house council today, or you're going to have a lot of companies that have in-house council and no outside council. And I think that's how it's going to split. You obviously talked a lot of in-house teams putting the drafting stuff aside, you know, they have your adiagraph, you know, maybe they'll, we're close to, closer with CTO office. What do you think that will kind of look like? The IP function within the companies? I mean, I think a lot of things are going to collapse internally, right? So I mean, it might be like the CTO is managing the IP function to a degree. You know, I think it may be that the IP council's doing something else. I don't, or like, you know, they have different job descriptions. Yeah, I mean, I hope that that's what happens, right? For my business, obviously. But like, I think in general, it's going to be very easy to obtain an understanding of people's businesses. We're seeing that with other AIs too, right? I mean, Chad Gbt can plug in. I think you're going to have agents, though, acting independently in organizations. And I think that's going to become a bigger, bigger thing. I think like next year, you may have AI and vent more for as much as humans inside of like large corporations that have deployed AI. And that that's kind of like, filed to me. I'm like, I don't even know how to track that. Like, that's going to be like a whole separate thing. Like, we have a system in our, in our thing that tracks like, are you an agent or are you human? Like, we're, but we're seeing like, even in our own system, like an AI writes dramatically more than we do. So like, we'll write one sentence, then it takes all the context and builds us a huge document. So like, AI is producing like 90% of our content internally. I think that's never a problem. The companies are trying to go back to where we're discussing it, figure out as like, figure out, actually measure the value of the AI or whatever the tokens that they're using. I guess it's not a very simple problem to solve. We host our own AI, which is kind of nice, because we're kind of unbound by tokens. It's more and just like, how much GPU capacity do you have? So things get slower, but works for us. Because we're spending like, you know, one hundredth of the cost of what you will for some of these other AI systems. In any final thoughts, as you think about AI, plant practice, where things go. I mean, I think it's really exciting personally. Like from an engineering perspective, I think like, there's going to be a lot of changes. And I think people just have to be willing and able to adapt. Like I've adapted to engineering, changing, right? My whole team has, I think it's fairly straightforward to do so, but you just have to like accept what's happening and then figure out how to make yourself more productive in that environment. So I don't think like anyone's losing their job necessary. I think you said it before. It's like people are actually going to probably work more. And if they're going to work harder, because they have to change what they're doing. Well, Austin, thank you for sharing your wisdom and your insights in the, we should have that conversation 2030 to find out what things like.