Brian Bell (00:01:17): Hey, everyone. (00:01:17): Welcome back to the Ignite podcast. (00:01:19): Today, we’re thrilled to have Dina Blikstein on the mic. (00:01:21): She is a partner in the Intellectual Property Practice Group at Haines Boone in New (00:01:25): York, (00:01:26): co-chair of the firm’s artificial intelligence practice, (00:01:29): an expert at the intersection of patent law and emerging tech, (00:01:32): and a frequent speaker and writer on AI, (00:01:34): patent eligibility, (00:01:35): and tech regulation. (00:01:36): Dina brings both technical expertise from her early career developing (00:01:40): high-frequency trading systems and seasoned legal perspectives shaping how (00:01:44): innovators protect and deploy their technologies.
Dina Blikshteyn (00:01:47): Thanks, Brian. (00:01:49): It’s always great to be here and do a podcast.
Brian Bell (00:01:51): Yeah. (00:01:51): So I’d love to get your origin story. (00:01:53): What’s your background?
Dina Blikshteyn (00:01:54): So my background is in computer and electrical engineering. (00:01:57): So I was a computer geek. (00:01:59): I did a lot of computer engineering, computer science work. (00:02:03): Ended up on Wall Street and then decided after spending about five years there that (00:02:09): I needed more human interaction and (00:02:12): And went to law school at night. (00:02:13): So then lo and behold, (00:02:15): you know, (00:02:15): we can fast forward 15 years later and I am still doing technology, (00:02:20): but on the legal angle. (00:02:22): And now I’m also, you know, throw AI into the mix. (00:02:24): So extremely math heavy. (00:02:26): So now it’s computer engineering, (00:02:28): computer science, (00:02:30): AI, (00:02:31): and...
Brian Bell (00:02:31): Yeah, (00:02:31): so you very much live at the intersection of law and IP, (00:02:35): but also like AI and technology. (00:02:37): Yeah, very interesting. (00:02:37): I had the same realization 20 years ago when I worked on Wall Street, (00:02:40): you know, (00:02:40): staring at a spreadsheet and analysis all day, (00:02:44): every day was not for me. (00:02:46): I enjoyed the work, but not 80 hours a week or whatever. (00:02:48): How do you think your technical background shapes your approach to IP and later AI?
Dina Blikshteyn (00:02:53): Quite a bit, (00:02:54): actually, (00:02:54): because with the technical background and then also working in the industry, (00:02:59): I know how technical systems work on a perspective of a developer. (00:03:04): So when I talk with inventors about either helping them obtain patents or (00:03:09): invalidating patents, (00:03:10): all that background comes into play. (00:03:12): So it’s a lot less theoretical and more hands-on. (00:03:16): And then applying that to the legal concepts.
Brian Bell (00:03:19): What are some challenges you faced when working on patents and cutting edge tech fields?
Dina Blikshteyn (00:03:25): Challenges when I help inventors get patents?
Brian Bell (00:03:28): Yeah. (00:03:28): What are some of these challenges around, let’s just take AI. (00:03:32): We’re an early stage venture capital firm. (00:03:33): We’re backing, you know, around a hundred early stage firms a year. (00:03:38): As founders approach us, you know, how should we counsel them? (00:03:41): Obviously get them in touch with you, (00:03:42): but what are some of the challenges that they face, (00:03:45): you know, (00:03:46): when they’re trying to protect their IP?
Dina Blikshteyn (00:03:48): So, you know, the biggest challenge that I’m seeing is IP is not a priority, right? (00:03:54): And neither it should be when you’re just a startup, but that comes with consequences, right? (00:03:59): So in US, (00:04:00): you have a one-year grace period to file a patent application from an earliest (00:04:04): disclosure. (00:04:05): And other countries, it is an absolute bar. (00:04:07): Essentially, you have to file for a patent application before any disclosure. (00:04:11): So at the time the startups actually figure out they need a patent and want to file (00:04:16): a poor patent, (00:04:17): it’s just too late. (00:04:18): And the patent protection is not available to protect their crown jewels. (00:04:22): Now they can file on improvements, (00:04:24): but more often than not, (00:04:25): you want to protect the core technology. (00:04:27): And sometimes it’s just from a legal perspective, it’s too late.
Brian Bell (00:04:30): Maybe you could define for the audience what the notices versus the actual patent. (00:04:34): There’s a lot of entrepreneurs that listen and probably don’t know the difference.
Dina Blikshteyn (00:04:38): All right, (00:04:38): so when you file a patent application, (00:04:41): right, (00:04:41): you essentially get a date from the patent office of when the patent application is (00:04:46): filed, (00:04:47): right? (00:04:47): It takes about three years to have the patent issued, (00:04:50): sometimes a bit more, (00:04:51): sometimes a bit less. (00:04:53): But your protection starts from the filing date. (00:04:57): Now, (00:04:57): if you’re a startup going 100 miles an hour and trying to develop technology, (00:05:02): you will be talking to different VCs or you will be talking about it publicly or (00:05:07): submitting submissions to different conferences that outline your technology. (00:05:11): All of that is public disclosure. (00:05:13): And that starts the clock for filing the patent application. (00:05:17): So in the US, you have one year to file from the earliest disclosure that you make. (00:05:22): And that’s without the NDA. (00:05:23): And then the rest of the world, you can’t do any disclosure.
Brian Bell (00:05:26): Yeah, (00:05:26): maybe tease that out a little bit, (00:05:28): you know, (00:05:29): because a lot of the startups we back, (00:05:31): obviously, (00:05:31): are U.S. (00:05:31): based. (00:05:32): How should they be thinking about the international filings, if at all?
Dina Blikshteyn (00:05:35): So you want to file where A, you can protect the technology and B, where you’re doing business. (00:05:41): You don’t need to file in every single country in the world. (00:05:44): And we recommend against those types of filings. (00:05:47): So if you’re in the tech space, typically it’s U.S., Europe. (00:05:51): And then you pick several countries in Asia. (00:05:54): It can be China, India, South Korea, for example, sometimes Japan. (00:05:58): But it always goes back to the business case, right? (00:06:01): Where will people use your technology and where it will be sold? (00:06:06): And also how much of an appetite you think you’ll be protecting it worldwide. (00:06:10): Now, I know if you’re a VC, you’re looking at it the opposite way, right? (00:06:14): Where is the technology being protected? (00:06:16): when you’re looking at different companies. (00:06:18): So it’s always a calculus, (00:06:20): but it’s a business calculus more than a legal calculus of where a company should (00:06:25): be filing versus what would be the long-term benefit from those, (00:06:29): right? (00:06:29): And you don’t need to file on everything, right? (00:06:31): You don’t need to file on every single technological improvement, (00:06:34): especially for startups, (00:06:36): like our recommendation, (00:06:37): one to two patents. (00:06:39): You can start in US and then you can decide whether you want to file globally down (00:06:43): the road and make sure that’s directed at the crown jewels of the technology.
Brian Bell (00:06:48): So you probably talked to lots of founders who think they have really unique IP, (00:06:52): but upon review, (00:06:53): they don’t. (00:06:54): Tell us some of the common traps or pitfalls or ways that founders actually don’t (00:06:59): have a patentable IP that they think they do.
Dina Blikshteyn (00:07:02): Right. (00:07:03): Okay. (00:07:03): So there’s usually two groups of people that one group thinks that everything’s (00:07:09): novel and the other group that thinks that nothing is novel and we’re going to file (00:07:13): a patent. (00:07:14): For if you’re money conscious, especially in the beginning, you can always do your own. (00:07:20): search to see what’s out there and the easiest and free way to do it is to do (00:07:26): google patents and then you can kind of see what’s already out there something to (00:07:30): remember is that most patent applications don’t get published right away there’s (00:07:36): about an 18 month lag unless a patent issues before that so you’re a bit behind the (00:07:43): technology when you do when you do those searches (00:07:45): But at the same time, (00:07:46): doing a search can give you a fairly quick sense of what is out there (00:07:51): technology-wise.
Brian Bell (00:07:52): So how should a startup think about patent strategy versus other forms of (00:07:55): protection like trade secrets or trademarks?
Dina Blikshteyn (00:07:59): Okay. (00:07:59): So there are four types of protections, right? (00:08:03): Then you mentioned three of them and the last one is copyright. (00:08:06): And they all work together, right? (00:08:09): So a lot of startups think, well, we’ll just do everything a trade secret. (00:08:13): Well, (00:08:13): It doesn’t work for a number of reasons. (00:08:17): One, when you shop around, you are doing the disclosure under the NDA. (00:08:21): And then you want a risk of someone saying, (00:08:23): well, (00:08:23): I’m going to file a patent application on your technology.
Brian Bell (00:08:27): So tell us more about that. (00:08:28): There’s a disclosure under NDA. (00:08:30): What does that mean?
Dina Blikshteyn (00:08:31): Disclosure under an NDA is when you sign a document. (00:08:34): It’s essentially a non-disclosure agreement where if I’m a startup, (00:08:38): I’ll say, (00:08:39): I will tell you about my invention and you agree not to disclose it outside of our (00:08:44): conversation, (00:08:45): right? (00:08:45): Does it always work practically? (00:08:47): Maybe yes, maybe no. (00:08:49): Right. (00:08:49): You can also have instances where you have developers leave. (00:08:52): Right. (00:08:53): And while they can’t take the source code, (00:08:55): because that would be covered under an NDA as well, (00:08:58): or non-compete agreement, (00:08:59): they can still take the know-how and try to build their own product. (00:09:03): Right. (00:09:05): And then the other one, (00:09:06): and this is always true in the AI space, (00:09:09): since it’s such a hot and emerging field, (00:09:11): a lot of talent likes to publish at conferences. (00:09:14): And what do they publish on? (00:09:16): Whatever they are inventing, whatever they’re coding, whatever their research is in. (00:09:21): So while you as a company or startup think that something can be a trade secret, if you have a (00:09:26): paper that publish something the same or similar, (00:09:30): that is the genie out of the bottle that you can.
Brian Bell (00:09:32): Yeah. (00:09:32): So like if I’m a AI researcher at Google, (00:09:35): you know, (00:09:36): name your favorite company and, (00:09:37): you know, (00:09:37): I really, (00:09:38): I’m on the research team. (00:09:39): I’m not in the applied engineering team actually shipping products. (00:09:42): I’m doing like primary research. (00:09:44): There’s this kind of push that I, you know, if I’m a PhD, I want to publish. (00:09:47): What are some considerations for organizations as they scale? (00:09:51): to kind of maybe say, (00:09:52): hey, (00:09:52): researcher, (00:09:53): maybe don’t publish that in archive or at the next, (00:09:57): you know, (00:09:57): AI conference. (00:09:58): We’re going to, how do you, how should organizations think about that?
Dina Blikshteyn (00:10:01): You know, it’s always a balance between, you know, small, medium and large organizations. (00:10:06): And if you have, if it’s a large organization, there’s typically a program in place (00:10:12): Where they say, (00:10:12): yes, (00:10:13): go ahead, (00:10:13): publish, (00:10:14): but we will file for a patent application the day before you do the submission. (00:10:18): That way, the technology is protected. (00:10:21): And if you’re a researcher wanting to publish, (00:10:23): you can go ahead and submit your paper for the publication. (00:10:27): If you’re looking at the startups or you’re looking at medium-sized companies, (00:10:30): it becomes more problematic because at that point, (00:10:33): they have to make a business decision. (00:10:35): Do we submit or not submit or do we file for the patent application or not?
Brian Bell (00:10:40): Are there any, to the degree that you could speak about, any interesting stories? (00:10:44): I mean, (00:10:45): you could change the names to protect the innocent, (00:10:47): but are there any interesting stories where you were surprised or where you’re (00:10:52): like, (00:10:52): okay, (00:10:52): this is like a shoo-in, (00:10:54): this is definitely a protectable IP and it wasn’t or vice versa? (00:10:57): You didn’t think it was a protectable IP and it ended up being?
Dina Blikshteyn (00:11:00): I’ve seen a lot, (00:11:01): a lot of cases where I didn’t think something would get patented, (00:11:05): and lo and behold, (00:11:06): it was. (00:11:07): And not only was it patented, the protection was also extremely broad. (00:11:12): So that’s the surprises you have, especially in the tech space. (00:11:15): Right now, (00:11:16): in the healthcare and AI, (00:11:17): I expect to see a lot of those types of claims there as well. (00:11:21): Because you have to remember, (00:11:22): every time there is a new technology, (00:11:25): the patent office examiners have to come up to speed on the technology. (00:11:29): And while they’re coming up to speed, (00:11:31): that’s really your chance to file for patent applications and get broad claims, (00:11:36): right? (00:11:37): Because then once the field gets saturated, the claims will become lengthier and more narrow.
Brian Bell (00:11:42): Yeah, (00:11:43): I mean, (00:11:43): the most famous example in the current platform shifts that we have is the (00:11:47): Transformer architecture out of Google, (00:11:49): which was published in 2017, (00:11:51): right? (00:11:52): But since they publish it, it’s not protected. (00:11:54): Yeah. (00:11:55): I bet Google, (00:11:56): do you think Google’s like, (00:11:56): oh man, (00:11:57): maybe we should have, (00:11:58): you know, (00:11:59): instead of publishing that research, (00:12:00): we should have just, (00:12:01): you know, (00:12:02): protected it with a patent?
Dina Blikshteyn (00:12:04): I have a counter story to that. (00:12:06): So this is one with, it’s publicly known with OpenAI. (00:12:11): So when OpenAI became public and the HIGPT came out, (00:12:15): It was all about, (00:12:16): well, (00:12:16): we don’t need patents because all this technology has existed long before. (00:12:21): And then long behold, after ChatGPT became popular, (00:12:25): they started filing for patent applications on improvement and they were getting patents. (00:12:30): And they were also filing under track one, (00:12:32): which essentially for track one, (00:12:35): you pay an exorbitant fee in thousands of dollars and you can jump to the front of (00:12:39): the line at the patent office. (00:12:41): So you can-
Brian Bell (00:12:42): So they have like the Disney fast pass for patents where you pay some (00:12:46): extra money-
Dina Blikshteyn (00:12:47): Exactly. (00:12:47): And the patent office will review you next. (00:12:49): Exactly. (00:12:50): So you can pay for-
Brian Bell (00:12:50): Yeah. (00:12:51): Then it’s where you can get a number of patents. (00:12:54): pursue that route. (00:12:56): So what I started seeing is all of a sudden you have open AI who went from the (00:13:00): mantra, (00:13:00): we don’t need patents to filing a lot of patent applications in the LLM space. (00:13:06): How has this platform shift? (00:13:08): I mean, obviously you started 15 years ago in law, you said. (00:13:12): So you kind of started during the kind of the mobile and cloud kind of boom. (00:13:16): How is this AI platform shift the same or different from kind of the previous (00:13:22): platform shifts that we’ve lived through?
Dina Blikshteyn (00:13:24): You know, it’s, it’s, I can’t say it’s different, but we are on this cups. (00:13:29): There’s a lot of innovation going on. (00:13:31): So let’s, (00:13:32): a lot of companies are just filing for a lot, (00:13:34): a lot of patents just to have their piece of the pie. (00:13:38): And then, so I’ve seen that a lot in the beginning around 2018. (00:13:42): picking around 2022, (00:13:44): where you can actually file for a patent application and get claims that were (00:13:48): fairly broad from the patent office. (00:13:50): Right now, (00:13:50): you’re sort of seeing the field being saturated a bit, (00:13:54): but now you’re seeing innovation based on technology types. (00:13:58): So, (00:13:58): for example, (00:13:58): AI and healthcare is becoming extremely popular, (00:14:02): and I see a lot of filings in that space.
Brian Bell (00:14:04): Makes sense. (00:14:05): So I get my patent. (00:14:07): I’m scaling, revenue is going up and to the right, everybody’s happy. (00:14:11): But then I notice three, four, five competitors crop up. (00:14:15): How do I inspect and prove that they’ve somehow stolen my IP? (00:14:21): I’m, you know, I’m looking at it and like, this is, this feels the same as what I’m doing. (00:14:26): Like it’s, it’s, you know, same input gets the same output. (00:14:30): There must be something in that black box that I’m not seeing that I’m curious or suspicious of. (00:14:34): Right. (00:14:35): So how do you prove that that’s from the outside looking in?
Dina Blikshteyn (00:14:37): It’s, it’s pretty hard, right? (00:14:38): It’s not always easy. (00:14:40): You have to look at the claims of your patent. (00:14:42): And again, (00:14:43): the claims, (00:14:43): not the entire specification, (00:14:45): because claims is what is where the protection is. (00:14:49): And you can, (00:14:50): search online and you can see if the claims map to their technology. (00:14:54): Now, (00:14:54): if you’re sure, (00:14:56): you can always file a complaint with the courts and that would open up discovery (00:15:00): where you can actually go into their systems and see how they work.
Brian Bell (00:15:04): So is it an impartial third party that does that investigation? (00:15:07): It would have to be, right? (00:15:08): Because if company A is accusing company B, (00:15:10): it’s not like company A’s engineers, (00:15:12): the plaintiffs are going to get access to the defendant’s systems, (00:15:16): right?
Dina Blikshteyn (00:15:17): So the plaintiffs may not get access, (00:15:19): but their attorneys may get access and they may not be able to disclose to their (00:15:23): client what they’ve been looking at or they hire an expert to do that. (00:15:27): And the expert can tell the attorneys, yes, there is infringement. (00:15:30): No, there is no infringement. (00:15:32): And then the attorney can take that to the client.
Brian Bell (00:15:34): Really interesting. (00:15:35): Yeah. (00:15:35): So the attorneys get involved, (00:15:36): but then you guys have to, (00:15:38): you know, (00:15:39): have somebody like you on staff that understands code, (00:15:41): understands the space, (00:15:43): but also you can hire experts in discovery to, (00:15:46): to sort of make that determination.
Dina Blikshteyn (00:15:48): Exactly. (00:15:49): And, and, you know, you don’t always pursue the litigation route, right? (00:15:53): Sometimes until there is infringement, (00:15:55): sometimes it’s your former employee who opens up their own company. (00:15:59): So at that point, (00:16:01): you can kind of see that they’re all doing almost the same thing and that may be (00:16:05): covered by your patent. (00:16:06): So sometimes it’s just a communication saying, (00:16:09): I believe you’re infringing my patent and you can send the system the system (00:16:13): letter. (00:16:13): Would that always work? (00:16:14): Probably not. (00:16:15): But you also have to do a calculus. (00:16:17): How much money is at stake?
Brian Bell (00:16:19): Yeah. (00:16:19): So what percentage of patent cases end up in like a settlement? (00:16:24): Like what are some of the outcomes and rough statistics around this space? (00:16:28): This is completely fascinating. (00:16:29): I have no idea.
Dina Blikshteyn (00:16:30): Right. (00:16:31): So it’s not an easy question to answer because there’s a lot more complaints filed than trials. (00:16:37): So someone gave a statistic that it’s about 5% of cases actually make it to trial (00:16:43): and most of them settled before that. (00:16:45): Right now, the question is, when will they settle? (00:16:48): Will they settle in the beginning? (00:16:50): Will they settle closer to the trial? (00:16:52): That depends on the discovery that can take, (00:16:55): whether there is actual infringement or what else is going on in the case. (00:16:58): There’s also a parallel proceedings called post-grant proceedings in front of the (00:17:03): patent office, (00:17:05): right? (00:17:05): So say if I have a patent and I sue you for patent infringement, (00:17:10): you can file a petition at the patent office saying that that (00:17:15): patent is invalid, right? (00:17:17): And now the proceedings from the patent office, they’re very quick. (00:17:21): They’re about a year from institution to the final decision. (00:17:25): So roughly 18 months from start to finish if you count in the preparation time. (00:17:32): So now you can use the post grant proceedings to force settlement and litigation. (00:17:39): So there’s a lot of different strategies that you can take.
Brian Bell (00:17:41): That’s fascinating. (00:17:42): Are there any famous examples where it made a company successful or broke a company (00:17:48): where a company was like, (00:17:49): wow, (00:17:49): we got to shut down or eventually just kind of petered out because there was a case (00:17:55): decided against them that was existential. (00:17:57): I don’t want to talk about them.
Dina Blikshteyn (00:17:59): Publicly well-known ones, you know, like the Amazon buy now button comes to mind, right? (00:18:03): They had the buy now button. (00:18:04): They, they put a patent on it. (00:18:06): Other people tried to copy it. (00:18:07): And I think Amazon sued them and won. (00:18:09): Right. (00:18:10): There’s also an eye for eye case, (00:18:12): which is actually a few, (00:18:14): that one I can probably talk about because it’s a, (00:18:17): It’s more than a decade old, right? (00:18:19): Where it was also post-grant proceeding that I think ended up in a $400 million (00:18:26): verdict in district courts, (00:18:27): right? (00:18:27): So that is an example of a startup taking on Microsoft, right? (00:18:32): And Microsoft did not want to settle.
Brian Bell (00:18:33): Wow, they took it all the way to trial. (00:18:35): Took it all the way to trial.
Dina Blikshteyn (00:18:37): And that was a $400 million verdict in 2010, 2011.
Brian Bell (00:18:42): What was the technology that was in dispute?
Dina Blikshteyn (00:18:45): It was the Word document technology, like XML type technology.
Brian Bell (00:18:50): Right, right. (00:18:51): And what was the startup in this case?
Dina Blikshteyn (00:18:53): i4i.
Brian Bell (00:18:53): i4i, I don’t recall them. (00:18:55): And maybe that’s indicative of this $400 million ruling kind of put them out of (00:18:59): business, (00:18:59): basically.
Dina Blikshteyn (00:19:00): Well, no, no, they won. (00:19:02): They won against Microsoft.
Brian Bell (00:19:03): They won. (00:19:04): Wow. (00:19:04): So what were the particulars of that case where they were able to argue and win a (00:19:09): settlement or win a ruling?
Dina Blikshteyn (00:19:11): So, I mean, Microsoft alleged that the patent was invalid and not infringed, right? (00:19:17): And they did not want to settle. (00:19:18): They took it all the way to trial. (00:19:20): And I guess they were wrong.
Brian Bell (00:19:22): That’s crazy. (00:19:22): So from that situation, (00:19:25): how can, (00:19:25): you know, (00:19:26): what are some of the lessons from that case that startups can take away and apply (00:19:32): as they’re developing new IP?
Dina Blikshteyn (00:19:33): So you should really talk to an attorney. (00:19:37): or a patent attorney when you, (00:19:39): it doesn’t have to be one, (00:19:40): it can be several, (00:19:41): but some people understand the technology. (00:19:43): Because a lot of what I’m seeing on my end, (00:19:46): especially with startups, (00:19:47): they can say, (00:19:48): well, (00:19:48): we can write our own patent application, (00:19:50): but they don’t understand.
Brian Bell (00:19:52): I’ll just plug it in.
Dina Blikshteyn (00:19:53): Exactly. (00:19:54): And ChatGPT will give you claims. (00:19:56): Would it give you defensible claims? (00:19:58): Probably not. (00:19:59): So that’s when the skill comes in, (00:20:00): to make sure you have quality claims that you can get through the Patent Office. (00:20:05): Because there’s a huge difference if you look at a claim, a claim that’s 150 words, (00:20:10): or that’s 500 words. (00:20:11): It’s a lot more narrow. (00:20:12): Narrow claims are harder to infringe.
Brian Bell (00:20:14): Yeah, and so AI is changing how this is done. (00:20:16): You recently joined the advisory board for Solve Intelligence, which is a portfolio company. (00:20:21): It’s how we got in touch. (00:20:22): What is it about their approach or about AI that’s kind of changing how this work (00:20:26): is done and maybe accelerating patents and IP?
Dina Blikshteyn (00:20:30): Solve is really a cutting edge of what they do. (00:20:34): And the way Solve works, it minimizes the busy work that an attorney needs to do. (00:20:40): And it actually lets us focus on the legal work, like how to draft the best possible claims. (00:20:46): Do the claims have support in the specification? (00:20:49): Like, (00:20:50): What are different claim types that we can do and what can we direct it on? (00:20:55): Because remember, say you have a system with a client and a server, right? (00:20:58): You can write claims directed at the server and do it directed at the client, (00:21:04): and you can do it directed at both, (00:21:05): right? (00:21:06): Now, (00:21:06): keep in mind, (00:21:07): there’s probably two different entities who own the client and the server, (00:21:11): right? (00:21:11): So those claims may not necessarily be the best ones for infringement, (00:21:17): especially if you’re looking to enforce it against one party. (00:21:20): So with Solve, (00:21:22): Solve can help me draft spec while I’m focusing my legal expertise on the claims to (00:21:28): make sure good quality claims are written and those get through the patent office.
Brian Bell (00:21:33): Interesting. (00:21:34): So it’s accelerating the legal work that can actually get done, (00:21:38): which is what any good technology does. (00:21:39): It’s a leverage.
Dina Blikshteyn (00:21:41): It’s a leverage to minimize the busy work and focus on the high value legal work. (00:21:47): It’s like the same thing as asking a question out of any chat bot. (00:21:52): Depending on the type of the question and the type of the prompt, (00:21:55): you may get a good output or bad output.
Brian Bell (00:21:57): Right, right. (00:21:58): So there’s this meme or idea floating around in tech circles that there are no moats. (00:22:03): Execution and speed is your only moat. (00:22:05): Would you agree with that or are there still moats given Java AI?
Dina Blikshteyn (00:22:09): I think there are still modes. (00:22:10): It’s just you need to figure out what they are and where the boundary is.
Brian Bell (00:22:14): Yeah, (00:22:15): so there’s lots of startups out there, (00:22:17): you know, (00:22:17): at the application layer, (00:22:19): you know, (00:22:19): with a platform shift, (00:22:20): like large language models have been, (00:22:22): you know, (00:22:23): there’s this disparagement where, (00:22:25): oh, (00:22:25): you’re just a chat GPT rapper, (00:22:27): you know, (00:22:28): you’re just sitting on top of their tech and you don’t actually add any value. (00:22:32): How do you respond to all the application startups out there that are kind of (00:22:36): building on top of foundational models? (00:22:39): What should they look for? (00:22:40): What are some areas that are commonly patentable?
Dina Blikshteyn (00:22:43): Okay, (00:22:43): so your question, (00:22:45): if I convert it to patent terms, (00:22:47): it’s patenting the LLM model itself or is patenting the system as a whole? (00:22:52):
Brian Bell (00:22:52): Yeah, it’s probably the system that sits on top of the LLM, right? (00:22:54): Because the LLM is, you can swap it in and out like a database, right?
Dina Blikshteyn (00:22:58): Right, right. (00:22:59): I mean, unless you’re the LLM provider and you’re actually looking how to make the LLM faster. (00:23:04): So if you’re an LLM provider, that’s where you’ll be filing your patent applications. (00:23:08): If you are looking at the entire system, what we would recommend is (00:23:12): filing a specific components, right? (00:23:15): How do you have an application? (00:23:17): How is it different from whatever has been filed? (00:23:20): Are there any improvements that you’re doing on the input or the output? (00:23:24): Like what is your final output product, right? (00:23:27): Can I file a patent application on that? (00:23:30): So you’re looking at the entire system, (00:23:33): you’re figuring out where the value is, (00:23:35): and you’re trying to direct the patent applications on that value.
Brian Bell (00:23:40): What are some famous, because patents, they last for a couple decades typically, right? (00:23:45): About 20 years?
Dina Blikshteyn (00:23:46): 20 years from filing.
Brian Bell (00:23:48): From filing, not from awarding, but from filing. (00:23:51): 20 years from filing for utility patents.
Dina Blikshteyn (00:23:55): Yeah.
Brian Bell (00:23:56): Which is interesting. (00:23:56): I think in pharmaceuticals, you develop a compound, some new drug. (00:24:09): The 20 years is really lucrative. (00:24:12): you know, the semaglutide GLP-1 patent, like that can be very lucrative. (00:24:17): But with technology, technology moves so fast, right? (00:24:19): There’s every year or two, there’s some new model, some new platform. (00:24:25): How do technology companies think about this?
Dina Blikshteyn (00:24:28): I see where you’re going with this, Brian. (00:24:31): But I think if you look at what the age of the patents, (00:24:35): when they’re being asserted, (00:24:37): they’re all over 10 years old. (00:24:39): So even though the technology has changing, (00:24:42): If the patents are drafted well, they would apply to that future technology. (00:24:47): That’s why if you look at big companies, (00:24:48): they also file multiple patent applications in different fields and on different (00:24:53): technologies because they won’t assert all of them, (00:24:55): but they’ll assert maybe a handful.
Brian Bell (00:24:57): I see a lot of startups wrestle with closed source versus open source. (00:25:01): How do you advise clients, (00:25:03): if you have advised clients in this area, (00:25:05): to think through going open source versus closed source?
Dina Blikshteyn (00:25:08): Yeah, and it’s a catch-22 for startups because they do like to go open source. (00:25:13): You can still use open source where the end product is something that’s inventive. (00:25:18): You’re not getting a patent on open source. (00:25:20): You’re getting a patent on your entire system or a portion of that system. (00:25:25): So you can still use open source and have something be inventive and patent. (00:25:31): Right. (00:25:31): It’s tricky though. (00:25:31): Like if somebody submits a pull request into a repo and the license says, (00:25:36): now I own that, (00:25:37): you know, (00:25:37): I’m the company and now I own that. (00:25:39): It’s tricky legal waters, right?
Brian Bell (00:25:41): It is tricky, but you won’t be getting a patent on that request. (00:25:46): You’re getting a patent on something that’s specific to you and to your technology (00:25:50): and to your area. (00:25:51): Because the way I look at it, open source, it’s a tool of how to make your invention work. (00:25:57): And you have to combine multiple functions of that open source to achieve a result. (00:26:02): So that combination may be.
Dina Blikshteyn (00:26:05): How has, (00:26:06): you know, (00:26:08): AI regulation and law in this area kind of evolved over the last five or 10 years?
Brian Bell (00:26:13): It’s evolving and it doesn’t stop to evolve. (00:26:16): Right. (00:26:16): So especially with this administration, they’re very pro AI innovation. (00:26:22): So what they’re doing is minimizing legislation on the federal level with a Trump’s (00:26:28): executive order. (00:26:30): And essentially, (00:26:31): they’re saying develop AI as quickly as possible for national security reasons. (00:26:36): Right, because some of the states were trying to develop their own laws. (00:26:41): So you got to do this in Colorado, (00:26:43): you got to do this in New York, (00:26:44): you got to do this here and there.
Dina Blikshteyn (00:26:45): You’re correct. (00:26:46): So the states are trying to step in. (00:26:48): And if you look at the previous administration, (00:26:50): there was a lot of proposed state legislation that has never been enacted because (00:26:57): there was an administration change. (00:26:59): So if you’re talking about the Colorado AI Act, (00:27:02): so that was, (00:27:03): I think it was like 2024, (00:27:04): I think it was May 2024, (00:27:06): it was enacted. (00:27:07): It was supposed to go into effect of January 1st. (00:27:10): Now it’s delayed until June. (00:27:13): And this administration has been very forward in its thinking that states should (00:27:19): not legislate AI on a state level. (00:27:22): It should be done on the federal level. (00:27:24): And at the same time, (00:27:26): this administration wants AI to develop with as little oversight as possible.
Brian Bell (00:27:32): Now compare that to Europe. (00:27:34): You had an EU AI Act that they’ve been forced with different levels for different (00:27:40): types of systems with high risk, (00:27:42): low risk, (00:27:42): medium risk. (00:27:44): And then all of a sudden you have startups who have trouble innovating in Europe (00:27:50): because they don’t know which of those risk categories apply or if they apply. (00:27:56): So there’s a school of thought that the EU AI Act actually is stifling innovation, (00:28:03): especially for the smaller and medium companies.
Dina Blikshteyn (00:28:05): Yeah, it’s kind of interesting, right? (00:28:07): Because some governments will try to, (00:28:09): it’s very unusual that a government kind of steps in and tries to regulate (00:28:12): something before it’s harmful. (00:28:14): Usually regulations are more of a reaction to harm already inflicted, (00:28:19): like the smog in California, (00:28:22): right? (00:28:22): You had all the regulations around carb and clean emissions and stuff like that, (00:28:28): which was a reaction to all the smog sitting in LA and stuff and other places in (00:28:33): California. (00:28:33): It’s really interesting that states would try to go, (00:28:35): oh, (00:28:36): AI could be really powerful and harmful, (00:28:38): so we’ll try to get ahead of it. (00:28:40): That’s kind of an unusual stance I don’t usually see from governments.
Brian Bell (00:28:44): I think it depends on the state, (00:28:47): because we have the same issue with the privacy law, (00:28:49): because the EU has a really firm legislation on the privacy law. (00:28:56): The GDPR 10 years ago. (00:28:58): And in the United States, there’s no federal privacy law. (00:29:01): So you have states like California who are trying to regulate it on the state (00:29:05): level, (00:29:05): and some states do so more so than others. (00:29:08): And it seems like AI was going in that direction until this administration has been (00:29:14): actively trying to discourage that.
Dina Blikshteyn (00:29:17): You remember my big, beautiful bill, because who could forget that? (00:29:22): As part of that bill, (00:29:24): they wanted to impose a 10-year moratorium on the states for AI legislation. (00:29:29): Now that failed. (00:29:31): But what if you start reading Trump’s executive orders and subsequent orders? (00:29:36): What he’s saying is if the states would try to legislate AI in a way that we don’t (00:29:43): agree, (00:29:43): that would impact the state funding.
Brian Bell (00:29:45): Interesting. (00:29:45): So yeah, (00:29:46): this administration, (00:29:47): it’s all for, (00:29:49): you know, (00:29:49): developing AI with as, (00:29:51): I don’t want to say as little oversight as possible, (00:29:54): but they seems like they want to do, (00:29:56): they want to give companies a chance to develop AI and then try to legislate it. (00:30:00): Right. (00:30:00): But then again, (00:30:01): if you are a startup that just keep in mind, (00:30:04): there are frameworks that are in place, (00:30:07): right? (00:30:07): Such as NIST and ISO that have that, (00:30:10): uh, (00:30:11): that have the risk analysis, (00:30:13): the risk assessment frameworks for AI. (00:30:15): that companies still use. (00:30:17): It’s just at this point, instead of maybe seem as more optional than mandatory.
Dina Blikshteyn (00:30:23): Yeah, so you threw out a couple acronyms there that the audience may not be aware of. (00:30:28): Maybe you could define NIST and ISO. (00:30:30): I always forget when this stands for. (00:30:32): What is the spirit of that? (00:30:34): How does it matter to founders?
Brian Bell (00:30:35): Okay, so NIST, it’s a risk assessment framework for AI, right? (00:30:39): And the other one is ISO 42001. (00:30:43): And it’s all about AI governance. (00:30:45): What is a high-risk system? (00:30:46): What is a low-risk system? (00:30:49): How do you implement a risk framework so your AI is deemed safe and ethical? (00:30:56): And how do you apply proper AI governance, (00:30:58): depending on whether you develop your own AI tools or whether you use third-party (00:31:03): AI tools?
Dina Blikshteyn (00:31:04): You know, we talked about the last five or 10 years of changes. (00:31:06): How do you see it kind of unfolding over the next, (00:31:08): you know, (00:31:09): remainder of the decade, (00:31:10): over the next four or five years?
Brian Bell (00:31:11): In U.S., I think the next two years, there’s going to be a lot of innovation happening. (00:31:16): After that, it will depend on whether there is or isn’t a change in administration. (00:31:20): So we may get, (00:31:21): if there is a change in administration, (00:31:24): we may move more towards pro-AI governance. (00:31:28): Then go back to, (00:31:29): I don’t want to say EU AI Act, (00:31:31): but something that’s similar and moving more in that direction. (00:31:34): If there isn’t, (00:31:35): there’ll be continuing a lot of AI innovation with not as much federal oversight. (00:31:42): I think what’s going to happen is something that’s happening with the copyright (00:31:45): law, (00:31:46): where it just has to go through courts. (00:31:48): And at some point, (00:31:49): when we get a Supreme Court case to see what can be done about AI and copyright, (00:31:54): and if that is infringement or not. (00:31:56): But at this point, it’s just weaving its way through courts.
Brian Bell (00:32:01): Yeah, (00:32:01): kind of the most famous right now with this current paradigm shift is probably (00:32:05): OpenAI versus New York Times or New York Times versus OpenAI. (00:32:08): I think there’s about like 39 different cases that are found. (00:32:12): Mostly in California and New York, some of them are consolidated. (00:32:16): But there is a question of whether training AI model on copyrighted data or not copyrighted.
Dina Blikshteyn (00:32:23): data is fair use and not and different judges apply though the four factors (00:32:28): differently how do you think if and when it makes it all the way to the supreme (00:32:31): court if you had a crystal ball how do you think the fair use kind of comes down (00:32:35): with with ai models
Brian Bell (00:32:36): it will be fact-specific. (00:32:38): Because, you know, the problem is not fair use or using your books on AI, right? (00:32:45): It’s the fact that all these LLM models, (00:32:47): they can create something that’s extremely similar to artists’ work. (00:32:53): And then the value of the entire work just crashes.
Dina Blikshteyn (00:32:56): Tell us more.
Brian Bell (00:32:57): Okay. (00:32:57): So if I’m an author or if I write songs as a company right now, you can buy my song or my book, (00:33:06): And because that’s valid, that’s not infringement. (00:33:09): And then you can use it to train AI model. (00:33:13): And now what a lot of AI providers are doing, (00:33:17): they’re putting, (00:33:18): I don’t want to say it’s firewalls, (00:33:19): but essentially they’re putting, (00:33:22): I guess, (00:33:22): I see firewalls on the inputs and the outputs. (00:33:25): So you can get content that’s the same as my work. (00:33:28): So they’ll change a word here and there. (00:33:31): So that’s not infringement. (00:33:34): But at the same time, (00:33:35): once trained, (00:33:36): that model can still produce work that’s very similar to my own. (00:33:39): That’s in my style, but that uses different works, right? (00:33:42): So there is no... A university would train a human being to read a bunch of books. (00:33:47): And, you know, you could argue that a PhD is just regurgitating other people’s ideas. (00:33:52):
Dina Blikshteyn (00:33:52): You’re right. (00:33:53): You absolutely can. (00:33:54): But now you look at the speed, right? (00:33:57): How fast can a PhD replicate my work as opposed to how fast an LLM can do it? (00:34:02): Right. (00:34:02): And at which point, a value of my work would just plummet exponentially.
Brian Bell (00:34:06): Yeah, that’s really fascinating. (00:34:08): So I think it’s going to be the same thing as what happened with Napster. (00:34:12): There’s going to be a change in the industry, (00:34:15): right, (00:34:16): on how work are getting evaluated and where the value is. (00:34:20): Because I remember we went from CDs with the music to now individual songs that can (00:34:25): be downloaded to your playlist. (00:34:26): I think at some point with AI and copyright, we’ll see the same type of change.
Dina Blikshteyn (00:34:31): Right. (00:34:32): I think it’s a little different because, you know, I ingest a book and train on it. (00:34:36): There are ideas in that book that I can then summarize, right? (00:34:40): Or regurgitate. (00:34:42): And so you think ultimately, (00:34:43): we’re going to have to develop a system to compensate the authors of every book (00:34:48): ever written that’s still like under copyright?
Brian Bell (00:34:51): Potentially, right? (00:34:52): Assuming, but you have to remember for copyright infringement, (00:34:56): at least non-derivative copyright infringement. (00:34:59): It has to be the same thing. (00:35:01): It’s the same output.
Dina Blikshteyn (00:35:03): So there’s derivative works and non-derivative works. (00:35:06): Maybe you could like, what’s the difference?
Brian Bell (00:35:08): So the derivative works are the works that can be different from the actual work, (00:35:14): hence it’s a derivative. (00:35:15): So I’ll give you an example with a Mickey Mouse. (00:35:18): Right. (00:35:18): Recently, Disney changed how Mickey Mouse and Minnie Mouse, how they all look. (00:35:22): Because the copyright on the Mickey Mouse has about to expire if it hasn’t already. (00:35:27): So that second slightly different Mickey Mouse, Minnie Mouse, that’s the derivative.
Dina Blikshteyn (00:35:32): And that’s copyrightable?
Brian Bell (00:35:34): It’s copyright. (00:35:35): You can copyright derivative works for like another 70 plus years.
Dina Blikshteyn (00:35:39): How do you think that applies to books and AI and articles in New York Times now?
Brian Bell (00:35:44): Well, that’s right. (00:35:46): So that’s, (00:35:47): that’s sort of the issue because if, (00:35:49): if you have a vote base going through the AI model and you have the AI model (00:35:56): provider, (00:35:56): that’s modifying the content, (00:35:58): right. (00:35:59): It’s not direct infringement anymore because the input is different from the output. (00:36:03): Right. (00:36:03): The question becomes, can it be a derivative?
Dina Blikshteyn (00:36:05): And if the court rules that it is a derivative work, (00:36:08): then the IP copyright flows back to the original?
Brian Bell (00:36:12): Potentially, yes, but it will be fact-specific. (00:36:15): I think if you start looking at it just in terms of policy, (00:36:20): it’s not the fact that AI can infringe one work. (00:36:24): It’s that the value of the author’s work will become extremely low just because AI (00:36:31): can replicate the author’s style. (00:36:36): which may not fall under copyright infringement or under derivative infringement.
Brian Bell (00:36:40): Yeah. (00:36:40): That’s like, (00:36:41): like almost arguing that like a book review or a Wikipedia page is, (00:36:45): you know, (00:36:46): infringing on my copyright of like be as an author. (00:36:49): Right. (00:36:50): But not really, right? (00:36:52): Because it’s just writing a summary of the book. (00:36:54): Your book report doesn’t infringe on my copyright necessarily. (00:36:58): But if you write a whole other book, that’s effectively the same book, just rephrased.
Dina Blikshteyn (00:37:03): It can be a different book. (00:37:04): You can just be in my style, right? (00:37:07): Or think of it as songs, right? (00:37:09): If you have, (00:37:10): you know, (00:37:10): all of a sudden you can have 100 songs that sound like Taylor Swift, (00:37:13): but they’re not Taylor Swift. (00:37:15): Right. (00:37:15): I’m sure Taylor Swift would have to have something to say about it.
Brian Bell (00:37:18): Yeah. (00:37:18): There’s a famous song example recently that was like the Robin Thicke blurred line song. (00:37:24): I think the Marvin Gaye estate sued them and said, no, this is, you’re copying Marvin Gaye. (00:37:31): But really, (00:37:31): I think, (00:37:32): I think the court kind of ruled in favor of Robin Thicke and his production team. (00:37:36): They’re like, no, it’s not, it’s not enough. (00:37:39): It’s not similar enough to infringe on the copyright.
Dina Blikshteyn (00:37:42): Right. (00:37:43): I think the issue with LLM is just they can do it so much faster. (00:37:49): Right. (00:37:50): So whereas you can come up with one song in the month, maybe. (00:37:53): Right. (00:37:54): All of a sudden you can come up with a hundred in a day. (00:37:56): Right. (00:37:57): And you’re seeing this in music right now with AI-generated music. (00:38:00): You can generate hundreds of songs per day, (00:38:03): potentially, (00:38:04): that sound just like a number one song right now.
Brian Bell (00:38:06): Exactly.
Dina Blikshteyn (00:38:07): Yeah. (00:38:07): So that’s where the underlying problem is. (00:38:09): I think you see it more in music than in books now, (00:38:12): but it’ll go into the books, (00:38:15): it’ll go into paintings and into the artwork. (00:38:18): It’ll go into everywhere. (00:38:19): That’s why I’m saying, from my perspective, I think there’ll be a shift. (00:38:23): on how business in the art space is being conducted and how artists ultimately get compensated.
Brian Bell (00:38:30): It’s a really interesting thing that we’re teasing out here, (00:38:33): which is the legal professions and our construct of legality and copyright, (00:38:39): how it kind of shifts with the times. (00:38:42): Right. (00:38:42): Because I think what you’re describing is, (00:38:44): you know, (00:38:44): yeah, (00:38:45): if I made one song in the 80s, (00:38:47): that sounded like Michael Jackson’s like, (00:38:48): whatever. (00:38:49): Great. (00:38:49): But now because you can take Taylor Swift to make 100 songs that sound like her (00:38:53): instantaneously, (00:38:54): the legal apparatus and frameworks need to adapt to that reality.
Dina Blikshteyn (00:38:58): Exactly. (00:38:59): Yes. (00:38:59): And that’s the argument that Taylor Swift’s IP lawyers are making.
Brian Bell (00:39:03): Probably. (00:39:04): I mean, right now it’s almost with AI, it’s like trying to have the legal framework adapt to AI. (00:39:11): And sometimes it works, sometimes it doesn’t like, just like with the copyright and fair use. (00:39:16): Right. (00:39:16): So it ultimately either be a Supreme court decision or, (00:39:19): you know, (00:39:20): the federal government decided they would have to legislate.
Dina Blikshteyn (00:39:24): At the federal level, (00:39:26): because otherwise you’re going to have state sporadic legislation that is born to (00:39:30): anyone any good.
Brian Bell (00:39:31): Right. (00:39:31): Which would be the next step. (00:39:32): If it gets all the way to the Supreme Court and I get a ruling I don’t like, (00:39:36): well, (00:39:36): then I go turn around and go to my lawmakers, (00:39:39): my elected officials. (00:39:40): I say, hey, this is not fair.
Dina Blikshteyn (00:39:41): Exactly. (00:39:42): But that takes time.
Brian Bell (00:39:43): On the other hand, you can also make an argument, you know, playing the devil’s advocate here. (00:39:48): that you need to let technology develop to figure out where the issues are. (00:39:53): And you can’t stifle innovation with too much legislation because then people would (00:39:57): just go to other countries to do the same thing.
Brian Bell (00:40:00): Really fascinating. (00:40:00): Well, let’s wrap up with some rapid fire questions. (00:40:02): What’s a misconception about AI and IP you’d like to debunk?
Dina Blikshteyn (00:40:06): That IP does not apply to AI. (00:40:09): It does. (00:40:09): There’s a lot of AI patent applications that are being filed and a lot of AI (00:40:14): patents that are being issued by the patent.
Brian Bell (00:40:16): What regulation or policy development do you think would most help startups building AI today?
Dina Blikshteyn (00:40:21): I would look at NIST or ISO frameworks for AI governance. (00:40:26): Otherwise, the way the environment is right now, you can develop AI, just develop it.
Brian Bell (00:40:33): What’s the most surprising trend you’ve seen around founders and IP strategy?
Dina Blikshteyn (00:40:37): The resistance to IP.
Brian Bell (00:40:39): Yeah, not a lot of...
Dina Blikshteyn (00:40:40): You know, (00:40:41): startups want to file for patents and when they realize that they do, (00:40:44): it may be too late.
Brian Bell (00:40:45): What’s a bit of advice you give every early stage founder about working with legal counsel?
Dina Blikshteyn (00:40:50): Shop around to find a legal counsel that you like, has expertise and that you can trust.
Brian Bell (00:40:56): Where do you think the next wave of AI innovation is coming from?
Dina Blikshteyn (00:40:58): I think it would be in healthcare. (00:41:01): There is a lot of AI movement in startups that applies to healthcare. (00:41:07): to help develop drugs, particularly in drug discovery. (00:41:11): So I think we’ll be seeing a lot of innovation there.
Brian Bell (00:41:13): Yeah, that’s really exciting. (00:41:14): And healthcare and medicine tend to lag, you know, five or 10 years. (00:41:17): Other industries are just, there’s a lot more regulation there.
Dina Blikshteyn (00:41:20): You’re absolutely right, Brian. (00:41:21): But at the same time, (00:41:22): it also takes, (00:41:24): you know, (00:41:24): five to 10 years to go through all the clinical trials and find out the drug that (00:41:28): you like. (00:41:29): Right. (00:41:30): And AI can speed that process up.
Brian Bell (00:41:33): Yeah, very exciting. (00:41:34): Reading and hearing about, (00:41:35): you know, (00:41:35): basically models of humans where they can test every compound all at once, (00:41:39): you know, (00:41:40): against like human simulations and kind of know exactly how that compound will be, (00:41:44): you know, (00:41:44): digested and will impact your systems.
Dina Blikshteyn (00:41:47): Exactly, right. (00:41:48): It’s things up. (00:41:49): I mean, the problem you often run into there is that those compounds may be creatable or not. (00:41:57): So you have to find a compound that you can create in the lab and then apply.
Brian Bell (00:42:02): Right, right. (00:42:03): What’s a piece of tech or legal writing that changed how you think about your work?
Dina Blikshteyn (00:42:08): It’s not. (00:42:08): So I like Brian Garner, right? (00:42:11): Just for legal writing, active legal writing. (00:42:15): I guess if you’re thinking of it from a technical perspective, (00:42:18): non-boring legal writing that doesn’t want you to sleep and that sounds like you’re (00:42:23): reading a statute, (00:42:24): I would say... (00:42:25): For all startups and also, (00:42:28): you know, (00:42:29): attorneys who are just starting out read how Brian Garner writes.
Brian Bell (00:42:33): If you could change one thing about how the legal community engages with AI (00:42:37): developers, (00:42:38): what would it be?
Dina Blikshteyn (00:42:38): I think the legal community needs to come up to speed on math and AI systems. (00:42:44): So they actually know what they’re talking about instead of just using the buzzwords.
Brian Bell (00:42:49): What’s a part of your work that you wish more founders understood deeply?
Dina Blikshteyn (00:42:52): That attorneys, (00:42:54): there’s some attorneys who actually know how AI works and we don’t just focus on (00:43:01): legal. (00:43:02): We’re also looking for the business case. (00:43:04): which I think where miscommunication often lies because there’s this perception (00:43:08): that attorneys are a bunch of naysayers telling you what you can’t do. (00:43:12): We really are there to guide you to make sure you don’t get into legal trouble and (00:43:17): at the same time also expand your business.
Brian Bell (00:43:19): Yeah, (00:43:19): that’s always the funniest part of working with lawyers and attorneys is how (00:43:23): cautious they are. (00:43:25): They’re trying to keep you out of legal hot water. (00:43:26): That is like your primary function.
Dina Blikshteyn (00:43:28): It’s like... (00:43:29): Yeah, no, I agree with you because that is our job, right? (00:43:32): But at the same time, you can legally stifle the company to non-existent.
Brian Bell (00:43:38): Right, right. (00:43:38): So looking forward five years, (00:43:39): what’s a development in AI law, (00:43:43): this area you hope to see realized?
Dina Blikshteyn (00:43:44): I think more law firms will be using AI and you can kind of tell that what’s solved (00:43:51): and how fast they’re growing. (00:43:52): I think the value actually will be, the AI would help rid attorneys of the busy work. (00:43:58): and help them actually focus on the legal work. (00:44:00): Right. (00:44:01): It’s almost as if everybody now with AI has a team of people working under them, (00:44:06): whether you’re, (00:44:07): you know, (00:44:07): a paralegal or associate level person all the way up to a senior partner. (00:44:11): Now everybody kind of has with AI kind of these agentic systems that help them get (00:44:15): their work done. (00:44:16): Right. (00:44:16): And they’re great, right? (00:44:18): Especially if, when you start looking at a number of these systems, they are fantastic. (00:44:24): But at the same time, (00:44:25): and I think all attorneys need to know how AI works and also reviewing the results (00:44:32): because there’s tons of cases all over the country. (00:44:35): with AI creating fake case law and inciting those in motions and then those being (00:44:42): caught by judges and the other parties, (00:44:45): right? (00:44:45): So there’s a lot of traps that you can fall into, (00:44:49): but at the same time, (00:44:50): using the AI the right way and verifying what the AI product is a huge step for (00:44:57): attorneys.
Brian Bell (00:44:58): Thanks for coming on, Dana. (00:44:58): I learned a ton. (00:44:59): So glad I have this podcast because I get to talk to experts like you and learn something new.
Dina Blikshteyn (00:45:04): Thanks. (00:45:04): Thanks again for the time.
Brian Bell (00:45:05): All right.
Dina Blikshteyn (00:45:06): Thanks, Brian. (00:45:07): And thank you for having me.
