This week we welcome Paula Reichenberg, founder of Neuron, for a sharp and thoughtful conversation about legal translation, artificial intelligence, and what happens when professional expertise collides with tools that look polished but still miss the mark. Paula shares her path from M&A and capital markets law into business school, legal services, machine learning, and finally legal tech entrepreneurship. What started as frustration with inefficiencies inside law firms grew into a translation business, then evolved again as machine translation improved and forced a harder question about survival, adaptation, and quality.
Paula explains how her early company, Hieronymus, found success by handling sensitive, high-stakes legal translations in Switzerland, especially where precision and confidentiality mattered most. But as machine translation improved, the market for average work started to disappear. Clients began doing more on their own, leaving only the hardest, highest-value assignments for specialists. Rather than ignore the shift, Paula leaned into it. That decision led her back to university, into data science and machine learning, and toward building Neuron, a company focused less on replacing expertise and more on improving the process around imperfect AI output.
A central theme of the discussion is the uncomfortable truth that many users do not care as much about excellence as professionals do. Paula makes the point with refreshing honesty. AI often produces work that is mediocre, but for a large share of users, mediocre is enough. That creates both a market shift and a professional dilemma. In legal translation, as in legal drafting more broadly, the issue is rarely whether AI produces something flawless. The issue is whether the user notices what is wrong, has the time to fix it, and has the systems in place to improve the result efficiently. Paula argues that the real value is not in claiming perfection. It is in helping experts find the mistakes faster, correct them with less pain, and avoid wasting hours doing work that feels like cleanup on aisle five.
The conversation also digs into trust, user behavior, and the strange authority people give to AI-generated answers. Paula recounts how, in one negotiation, a party trusted ChatGPT’s answer more than a human tax lawyer’s detailed explanation, even when the AI response was wrong. That anecdote opens up a broader discussion about confidence, presentation, and why polished outputs often feel more persuasive than expert judgment. Greg and Marlene connect that idea to legal systems, translation quality, and access to justice, especially where technology might offer better service than overworked and underfunded human systems. The result is not a simple pro-AI or anti-AI position. It is a grounded look at where human excellence still matters, where automation fills gaps, and where the future may split between mass-market convenience and premium, highly tailored expertise.
Looking ahead, Paula sees consolidation coming to legal tech, along with a growing push toward seamless interfaces that bring best-in-class features into one place. For Neuron, that means becoming an embedded layer inside other legal tools rather than forcing lawyers to juggle yet another standalone platform. Her crystal ball view is both stylish and sobering. She compares the future of legal services to retail and fashion, with more ready-to-wear solutions for everyday needs and a smaller, more exclusive market for bespoke legal work. It is a vivid way to frame what may be coming. The legal industry is not simply moving toward automation. It is sorting itself into tiers of service, quality, and expectation. And if Paula is right, the future belongs to those who understand where “good enough” ends and where true expertise still earns its premium.
Listen on mobile platforms: Apple Podcasts | Spotify | YouTube | Substack
[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
Transcript
Greg Lambert (00:00)
Hi everybody, Greg Lambert from The Geek in Review and I am here with our friend Nikki Shaver from Legal Technology Hub. ⁓ Nikki, you have a new event series ⁓ coming out called the Demo Dozen. Tell us more about that.
Nikki (00:15)
That’s right. Hi, Craig. Hi, everyone. So actually, we held our first one of these on February 19th, hosted by Stephanie Wilkins. You’re right. It’s called our Demo Dozen event. At these new LTH events, which are free to attend, by the way, you can log in virtually to see 12 curated providers offer demos of their products.
It’s an easy way to get up to date on what’s new in the market, whether that is brand new products or existing ones with new features. We’ll be offering these throughout the year. And our next one is coming up on May 19th. So if you’re a vendor listening and would like to participate in upcoming demo dozen events, let us know at legaltechnologyhub.com or reach out to me or Chris Ford on LinkedIn. If you’re a buyer or other participant in the market and you’d like to learn more or attend one of
these events, make sure you follow our page on LinkedIn. It’s at LegalTechHub. Or if you follow along at LegalTechnologyHub.com, we have an events drop down on our page. And if you follow on that, will see registration links for all of our upcoming events, including Demo Dozen And so keep an eye out for that. It’s a great way to know what’s what in the market.
Greg Lambert (01:30)
And having these very compacted ⁓ demonstrations really helps people like me to evaluate things very quickly. So thanks for doing this.
Nikki (01:40)
Thanks, Greg
Marlene Gebauer (01:49)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.
Greg Lambert (01:56)
And I’m Greg Lambert, and today we are delighted to have Paula Reichenberg, founder of Neur.on, on the show. Paula, we’ve had a few technical difficulties, but I think we’re going to fight through them now. So welcome to The Geek in Review.
Paula (02:11)
Thank you so much for having me.
Marlene Gebauer (02:13)
So, Paula, you began your career as an M&A and capital markets lawyer, and then you moved into translation services with Hieronymus. You obtained an MBA, then undertook data science training, and now you’re the founder of Neur.on. So can you walk us through that journey? Because that’s quite a jump. Explain how you came to decide legal translation combined with AI was definitely the game you wanted to play, and tell us what motivates you there.
Paula (02:33)
I think the right order was that I first decided to leave the legal world. As an M&A lawyer, I always wanted to sit in my client’s seat because we kept giving them advice that they would not follow, and then they would come back a couple of months later and complain and say, you know, “I would have wanted to do that instead.” So that’s why I did the MBA, to try and switch into business.
In the MBA, we had an exercise where they said, “Okay, think about your last employer.” I thought very hard about my last law firm. “Did you have any inefficiencies?” I was like, my God, where do I start? So you had to list them, and then you had to come up with a business idea out of those inefficiencies.
At the time in Switzerland, we didn’t have anything like paralegal services and so on. Menial tasks were organized in-house, and translations in Europe are a big thing. You would work on your M&A transaction the whole week, and then on Friday nights a partner would knock at your door and say, “You speak French, don’t you?” It was like, no, I don’t. And then you would spend your whole weekend doing translations for the rest of the firm.
So that was one example. The company I started then, right out of my MBA, because we won the business plan competition, which is extremely weird, because service company ideas never win business plan competitions, and for very good reason. Running a service company is a very bad idea, as all lawyers know. You need to keep working if you want to keep making money, which is a totally inefficient business model.
And so that’s what I did. I started the company offering paralegal services, trying to organize large due diligence projects.
Greg Lambert (04:15)
Yeah.
Paula (04:40)
And that was a nightmare, because lawyers at the time in Switzerland were not the best managers. One thing they could outsource really easily was translations. So we became, in a very short time, the legal translation agency in Switzerland, specialized particularly in confidential cases and really hard topics that other translation agencies just would not touch.
So this legal minion task that needs to be super precise was a niche that carried us for a while. Then machine translation started to become a thing, not at the time when it was just statistical and always there to give you a good laugh, you know, the inside joke of translators. But when it started producing good results, I really thought about this MBA case where we had studied Kodak. I always clicked with that case. How can you kill your own company within a couple of months because of a new technology?
And I thought, I’m not going to Kodak my translation business. So we started developing our own Swiss legal translation models with external partners. And we discovered very early on what lawyers are discovering today, that data is king. If you don’t have good data, you cannot build good models. It is always this managing of the data.
So we decided to internalize these processes because no one was, of course, good enough to handle legal data properly at the time in Europe, at least multilingual data on top of it. And that brought me back to university. I had to take some accelerated classes in machine learning and data science. We started with innovation projects, hiring our first engineers, which again, for a lawyer, is very challenging. You know how to hire lawyers, but try to hire an engineer. First of all, you have the impression they hate you. You talk to them, you smile, they don’t smile back, and you always think, “This person was good, but they’re going to say no, they hated us.”
So no, we hired really good people, thanks to excellent professors. You need to have some insiders. And that’s how the journey started for us to transform the company into a half-tech company. That’s where the idea came from. A lot of people, even today, I get the impression that I’m living Back to the Future because a lot of law firm customers tell you large language models are so good, just wait a couple of years and they’re going to replace lawyers completely. They’re lay people and they don’t see the mistakes that large language models make.
So they think the next version is just going to be better and the next one is going to be perfect. At the time, of course, the same thing happened with translation models. People who don’t really speak languages see the results, they’re amazed. The results are amazing, but they’re far from being perfect.
So this last mile was really the thing. How do you solve this last mile that lay people don’t see, but as a professional you enjoy the tool, you see the mistakes, and you think, how can we become more efficient than this? And the sad answer, because it’s full of words that lawyers hate, is processes and data. That’s what we decided to do. This is why we created a spin-off, to really accelerate that. Work with large language models, fine, but work with them through processes and data.
And so that’s how Neur.on was born. The name Neur.on sounds a lot better than “processes and data,” but that is what we could have been called.
Greg Lambert (08:58)
Yeah.
Marlene Gebauer (08:58)
Great.
Greg Lambert (09:05)
So that’s why you hire marketers as well as engineers, to make sure that…
Paula (09:09)
Yeah, yeah.
Marlene Gebauer (09:09)
That’s right.
Paula (09:11)
Thank you for assuming that was done by a marketer, but no, we were desperate, we needed to fill out a form, and we just came up with a name.
Greg Lambert (09:18)
So there you go. Necessity is the mother of invention and the mother of naming your company, if need be.
Marlene Gebauer (09:25)
Mm-hmm.
Greg Lambert (09:28)
Well, Paula, I want you to walk us through the different iterations of what you’ve done. There was Hieronymus, and there was Lex Machina, and now Neur.on, and maybe even something more recent if you want to talk about it. So what were you doing to solve each of those problems in the stages along the way, and what’s on the near horizon for you?
Marlene Gebauer (09:43)
I think you have some news.
Paula (09:57)
Yeah, so Hieronymus is definitely the service company, still a service company. The mix of services it provides changed a lot with the improvement of machine translations, which can give you a glimpse of what may happen in the law firm industry. A lot of easy assignments, where we could at the time also bill huge urgency fees, those were the times, all disappeared.
Everything that could be average got totally taken over by the clients themselves. So there was a lot of do-it-yourself by clients for easy stuff, and the service company became extremely specialized. It became a little jewel of highly specialized people who could then charge a premium, while average work was replaced, unfortunately, by this do-it-yourself behavior of the end clients.
That company, I sold to a larger group that is looking exactly at niching down into very specific industries where really high-quality services are needed. So I think the consolidation of extremely skilled specialists is one trend. So that’s Hieronymus.
Neur.on is also focusing completely on the process part of, if you want, this kind of agentic flow of what you need to do to serve lawyers in all their different use cases. But we also built, at the same time inside Neur.on, a big natural language processing team specialized in multilingual legal texts, which was extremely important at the beginning to build the right models, et cetera.
Now this team has been spun out into a European scale-up called Noxtua, which is offering a European sovereign alternative to non-European players, let’s put it like that, specialized as well in handling publishers’ data from every single European country. That’s also something that we have to face in Europe, how scattered the legal landscape is. Each jurisdiction really has a different legal tradition, different language, different structure of its legal system. So it’s unfortunately not comparable to the states, where you have 50 states that are more or less similar. In Europe, it’s very scattered.
Marlene Gebauer (12:51)
Don’t tell them that.
Greg Lambert (12:52)
Yeah, don’t tell Texas that.
Paula (12:53)
No, no, it’s true that South Carolina would probably disagree very much with what I’m saying.
Greg Lambert (13:04)
Yeah. Well, you kind of mentioned some of the learning experiences you had with how technology was changing the way that you do translations may run parallel to what the technology may be doing now for the legal industry as a whole, or at least parts of it.
And I read something about Mindy Kaling’s keynote at Legalweek recently where she talked about that takeover for writers for television shows and movies, in that she sees the technology taking over the mediocrity of it for us. We may think of that as more of the basic work, the commoditized work. Do you see that running parallel with translations to legal?
Paula (14:06)
I see that very much. A lot of little contracts I’ve had to negotiate recently were directly sent to me by lay people who just told me, “Yeah, I prepared a contract,” and you could see that it was AI-generated. Unfortunately, they didn’t even use a proper legal AI tool. They should have. But this is just happening.
This do-it-yourself attitude, before, we would have involved a lawyer to do that. It would have been done a lot better, but now it’s just a crappy draft. I change what is to my disadvantage, I leave what is to my advantage, and we sign it.
So the reaction translators had at the time was, “No, but have you seen this? This translation is full of mistakes. They’re going to regret it. At some point they’re going to come back and see that everything they did was full of mistakes, and then they will come back.” Maybe.
So the problem is not only that it is taking away the mundane work. The problem is that it is creating a lot of mediocre outputs, but no one really cares about mediocrity in most cases.
Greg Lambert (15:08)
Yeah.
Paula (15:29)
And this is where you see, yeah, for 80 percent of things…
Greg Lambert (15:34)
I don’t know, Marlene just gave me a side-eye for that statement.
Marlene Gebauer (15:37)
Well, I’m thinking about, you know, I think for many people, good enough is good enough, but when you talk about your team, it’s lawyers and linguists and engineers, which is definitely what you need for what you do, but it’s a very interesting combination.
The lawyers are going to be very focused on confidentiality. Both they and the linguists are going to be focused on legal linguistic domain expertise. The engineers are focused more on how things work and innovation. So how do you operationalize that combination in product development and service delivery? I imagine there’s got to be some friction. How do you combat that?
Paula (16:25)
Yeah, I think that’s definitely a more optimistic crew to talk about because they’re not about to be fired. So they’re really good people, and they cannot be replaced by AI.
No, this is true. It’s really this balance of being an expert and being excellent, but also being able to work in a team. And I think that is something that…
Greg Lambert (16:25)
Engineers love lawyers.
Marlene Gebauer (16:27)
Oh yeah.
Paula (16:54)
A lot of companies will tell you that entrepreneurship, or starting from scratch, works better than trying to build with an external team. We started fresh with exactly one engineer, one linguist, and one lawyer. When you build from that and you manage to have trust among the three founders who really represent those domains, then you grow the team with the same communication and the same bridges.
And you’re right, Marlene, because I’m very active in the Swiss legal tech community and in Europe in general. I hear from a lot of legal tech founders, and it is rare to have this triangle balanced. So yes, you’re right. I think it’s the secret.
Marlene Gebauer (17:51)
Supported on all sides.
Paula (17:53)
And with this communication, it works. Yeah, that’s true.
Marlene Gebauer (17:59)
I will say it sounds a little bit like a joke. A linguist, a lawyer, and an engineer start a company, dot, dot, dot…
Greg Lambert (18:08)
We’ll ask AI to fill in that joke.
Paula (18:10)
Let’s ask AI for the end of this story.
Marlene Gebauer (18:12)
Exactly. What’s the joke? What’s the punchline there?
Greg Lambert (18:13)
Ha ha ha!
Paula (18:16)
AI is very bad at jokes.
Marlene Gebauer (18:19)
They’re really bad jokes, I gotta say.
Greg Lambert (18:19)
It does.
Paula (18:21)
Yeah.
Greg Lambert (18:23)
So Paula, we talked earlier about “good enough” being good enough, but when it comes to your potential customers, when they’re evaluating a platform like yours, what tends to matter most to them at the start of the evaluation? And then what proves to be the ultimate value after adoption? Where do they see the strongest returns, and where do you still run into people who are hesitant or resistant to apply these technologies?
Paula (19:04)
Yeah, that was a steep learning curve, understanding whom to speak to and who would understand the added value. For us, coming from the language industry, it’s so obvious that machine translation is crap and needs to be improved. But then you speak to…
Greg Lambert (19:16)
You…
Marlene Gebauer (19:17)
We’re going to quote that.
Greg Lambert (19:20)
Yeah, it’s going to be the title of the show.
Marlene Gebauer (19:24)
Everybody will listen.
Paula (19:26)
But then you tell them, and this is why we exist, to get you from crap to great, either thanks to the proper processes, the proper data, and everything smoothing out so the do-it-yourself becomes efficient. Because this is the problem as well. When you have an output that comes from the machine, and then your lawyer is doing it last minute, and you look at this and say, actually, it’s really bad. Oh gosh.
And then you don’t have the tools to improve it and to automate or auto-propagate your corrections and everything. So you get the impression that you’re doing an intern’s work, but you know it’s super important because you cannot submit something wrong to the court.
So this frustration, having the impression that you were given a tool that is mediocre, and now you have to fix it, I know at every customer conversation when they tell me, “I did this translation with mm-mm-mm and it was so bad, I spent 16 hours correcting it,” you know you’ve got a sale. Because they say, “And with you, it would have been perfect.” And you say, gosh, no. Our models, the only thing we guarantee is that they make mistakes. Guaranteed mistakes.
But our system tells you where they are and helps you to correct them, helps you to spot them. Instead of 16 hours, you would have spent four hours. So no, it’s not perfect, but it is something where you can say this is the added value. But not everyone, if you have not gone through the pain, you will not get it.
Marlene Gebauer (21:13)
Yeah. I imagine if someone has had the pain, they’re like, let me get my checkbook. Whatever it costs, I need it. But if they haven’t experienced that, then they don’t really understand the benefit.
Paula (21:19)
Exactly.
But I think it’s an issue that a lot of legal workspaces are facing because you create the best features ever, but most people, 80 percent of the people, just use the chat window and do everything there. They never use any of the special features that would save them so much time.
That also, in our naivety at the beginning, totally surprised us. Eighty percent of usage is still on the absolutely simplest thing. So we had to put a lot more effort than we would have imagined into improving the basics. This is really basic stuff.
And then the added value is not necessarily noticed by everyone, but the people who do notice it are the ones who really work with your tool. For them, it’s an immense added value. So you have to cover this whole scope, which is frustrating for the nerds.
Greg Lambert (22:28)
Yeah, to me…
Yeah, well, let me bump my question that I was going to ask later because I think it fits perfectly right here in this part of the conversation. A lot of times, like you were saying, translations to the newbies look deceptively easy, right? It’s like taking one word and translating it to whatever the other language’s word is. But when you’re talking about things like legal documents, it’s not just a word-for-word translation. You get legal systems that are different, drafting cultures that are different, risk assumptions that are different.
And that’s just for the layperson, right? When it comes to lawyers in translations as well, do you think there’s still something they’re fooling themselves about regarding how easy some of the translation tools can take this perfect contract that they wrote in one language and make it a perfect contract for another language?
Paula (23:38)
I think that is still a myth that a lot of people believe in. Then you’ve got the others who notice the difference. But it’s the same with large language models. There are so many people telling you, “No, I tried Claude, it’s better than my lawyer.” And fair enough, those people will still exist.
Greg Lambert (23:56)
That might be true, but they would have a really bad lawyer.
Paula (24:04)
Yeah, they really cannot appreciate their lawyer’s added value. But I had this in a negotiation where it was a complex tax issue involving Swiss tax law, and the other party was not Swiss, so they didn’t understand. We had a tax lawyer jump into the conversation and give us one hour of explanations and everything. They were still extremely reluctant to believe him.
Then I got fed up and said, “Let’s ask ChatGPT.” And they said, “Oh yeah, let’s do that.” ChatGPT came out with an answer, and I said, “Here it’s wrong, let’s correct it.” So that took five minutes, and they believed it. They believed ChatGPT more than a professional. It’s extremely weird.
It’s the same in finance apps. Apparently if you go to your banker and they tell you, “You should diversify your portfolio, let’s put a little bit in Asia,” you’re like, yeah, yeah, I’ll think about it. But apparently if the information comes from the app and it says, “An AI is telling you, you should have at least 20 percent in Asian stocks,” then you say, okay, okay, okay, and click the button. It’s extremely interesting, this pattern.
But I’m going off track here. So yes, not everyone…
Greg Lambert (25:22)
Yeah.
Marlene Gebauer (25:26)
I mean, that could be a whole other discussion about trust in systems and why people do what they do when they work.
Greg Lambert (25:29)
Yeah.
Yeah, well, there was a study done years ago that talked about when they were doing machine learning for the legal system, right? It was setting sentences, or asking if you could get a computer to be your judge instead of a judge, would you do it? People who were not involved in the legal system were very opposed to that. They wanted the human in it.
But the people who had been involved in the justice system, especially on the defense side, were much more willing to allow the machines to do it because they had seen the bias that humans bring in and they thought, well, at least there won’t be that bias. And of course, we’ve all learned that it got built in.
Marlene Gebauer (26:23)
And then we found that no, it just inherited bias. Yep.
Paula (26:29)
Yeah, no, but you’ve got this impression of something official, and it looks so good. There is no spelling mistake. It is just so polished.
So yes, for translation it is an issue. But is it a huge issue? I mean, when you go to translators’ conferences, they’re always telling you about all these mistakes and how bad it is, but you never find court decisions that are the consequence of a bad translation, where the consequence was so bad that it went to court.
So in that sense, I agree, it is really bad. And yet you see the luxury sector, and by luxury I mean a private bank working with ultra-high-net-worth individuals, they will not use machine translation to do their general terms and conditions or write to their clients, et cetera. For retail, though, for an insurance company, they do not care so much. Hermès will never translate its website with a machine translation tool, but Amazon does it, and a lot of people are buying their products.
So language quality becomes a sign of luxury. And I think it is going to be the same for court judgments as well. You can have the retail part for all your parking tickets, et cetera, and then you have the luxury part, where suddenly you have a human judge, who then maybe is a better judge than the average of today’s judges, who knows.
So I think there is this, I don’t know, do you have a lot of IKEA in the States? In Europe, it’s just IKEA everywhere. This IKEA-ization, the fact that everything became IKEA-ized, even homes, is amazing. It is crappy quality, everyone has the same thing, but it’s 80 percent…
Greg Lambert (28:33)
Yeah.
Marlene Gebauer (28:34)
Plenty of IKEA.
Greg Lambert (28:37)
Yeah, everywhere.
Paula (28:51)
And they give it to you.
Greg Lambert (29:00)
And you only need one tool, one little Allen wrench. Everybody’s got ten of them in a drawer somewhere.
Marlene Gebauer (29:02)
Yes.
Paula (29:03)
Exactly.
Marlene Gebauer (29:05)
And they give it to you.
Paula (29:09)
I just think…
Marlene Gebauer (29:11)
Yeah, that concept. I just think about how that has all kinds of implications for access to justice. How much justice are you getting if you have one level and another level of commitment from the judicial system?
Paula (29:25)
Yes, and on the other hand, you see there are lots of refugees in terrible conditions coming from Africa to Italy. They are rescued from the water, then they end up in these camps, and it’s absolutely terrible. There you have human interpreters. Those human interpreters are being paid $14 per hour to interpret many different languages.
Because they are not being paid enough almost to survive, and because they have to work so fast, when they do that job for those people, they do not have the time to care properly. So access to justice, yes, there is this whole movement, hashtag no translation, no justice. It is true that sometimes humans also, if you don’t have the financial means to let them work properly, they will not serve citizens.
Whereas now, it is amazing what you can do for interpreting with machine translation and language models. Those people are being served better today, even if it is not perfect, than by understaffed teams of underpaid human interpreters. So I do have hope as well for access to justice. I think technology has its good sides too.
Marlene Gebauer (30:52)
We are too. So, Paula, looking ahead, you’re based in Switzerland. You have a strong foundation there and it sounds like in the EU as well. Maybe this has to do with some of the new changes. How are you thinking about scaling internationally, maybe bringing on new languages? What are the next major features or product areas you’re excited about? Is it beyond translation? What’s going to happen?
Paula (31:28)
I think for translation, the obvious next step for us that we’re working on is becoming the brick for every single legal tech tool out there, because many tools offer some sort of translation feature now. Everyone does. It’s easy to integrate something, but everyone says it’s half-crappy, which is normal.
What is the willingness to change the interface? To be very honest, lawyers are already struggling with the fact that they have to pilot so many different tools and jump from one to the other. I think everyone is dreaming of one interface that integrates the best in class for each feature, and we totally recognize that.
We think that depending on what you need, sometimes you need just good enough and extremely fast. Fine. But then when suddenly you’re looking at those one million SMS messages or text messages you are analyzing, there is one that is just a bit weird and doesn’t make sense. You want to be able to click and deep dive into the different meanings and be able to do that seamlessly.
I think this is the dream of users, not only for translation, but that is the brick that we are. That is what we can offer and what we’re working on.
Greg Lambert (32:53)
Well, Paula, before we get to our crystal ball question, I want to ask kind of two things that run in the same vein. What resources do you use to help keep up with all the changes happening in the market? And I’m also curious whether you’ve found any cool tools lately that you’ve been using and would want to share with us.
Paula (33:27)
Now this question you’re asking a bit too early because I was just discussing with someone the fact of hiring someone to do a certain job, and he answered, “No way, my agents do that.” And this person promised me an interview where I can see how he really believes he can replace someone I wanted to hire with an army of agents.
I mean, I believe he knows what he’s talking about, yes. So for me, my magic tool is probably relaxation podcasts that help me sleep at night. It’s not really an agent, I think. I’m not sure it’s a magic tool, but I think if you want to keep up with everything that’s happening, and everything is just so interesting, you can stay up all night reading through so many interesting news items everywhere. So sometimes switching to some sort of meditation or relaxation podcast is my boring tip.
Greg Lambert (34:25)
There you go. I like that.
Marlene Gebauer (34:27)
I’m sure a lot of us are like, yeah.
Greg Lambert (34:28)
Don’t forget to breathe.
Yeah.
Paula (34:42)
Yes.
Greg Lambert (34:51)
That’s great advice. I like that a lot.
Marlene Gebauer (34:55)
Turning it off allows you to focus more.
Greg Lambert (34:57)
Turning it off.
Paula (35:00)
Yes.
Marlene Gebauer (35:01)
Okay, so Paula, we have our crystal ball question next. If we fast forward a few years, what do you think is going to surprise us most about the legal tech ecosystem and how lawyers are going to work within it?
Paula (35:16)
I think there is going to be consolidation, of course, because the company that allows you to do what we were mentioning before, to get the best-in-class feature for everything in one place, that tool will be so popular. It just needs one interface for everything.
But that is not a surprise. You asked for something that would surprise us. I really see the legal industry going through the same phases as, I don’t know if in the US you read Émile Zola, probably not. Émile Zola wrote a lot of very depressing stories, but there is one about the first grand magasin in Paris.
You have all those little shops selling hats, custom-made dresses, and everything. Then you see all these women running to this grand magasin to discover the latest fashion and everything. The small shops think this is just a whim, it’s not going to last. They think, we’ve been serving the family for 250 years, we are the only ones understanding our clients, et cetera.
So I think it is sad somehow that some old craftspeople become less numerous, because you still have wonderful hat makers, just fewer of them. That is something sad. On the other hand, we all love going to grand magasins when we’re in Paris. So I think that is the positive aspect. It is like IKEA. We must admit that we love going to IKEA.
Marlene Gebauer (37:05)
You…
Paula (37:09)
The meatballs.
Marlene Gebauer (37:13)
The meatballs are great, yeah. And I’m thinking again back to what we were saying earlier, couture may still exist for special things, and that may make it even more valuable if everything else is ready-to-wear, if you will.
Paula (37:37)
Yeah, no, and that is really what happened to my first service company. Now it is the luxury end of the industry, and it has a lot of success because of that. So I think, yeah, if you ask Loro Piana, they are also quite happy about the turn of events, and their clients are too.
So yes, I am looking forward to discovering the Loro Piana of both the law firms and the legal tech world.
Marlene Gebauer (38:09)
Well, we’re going to end it on that fashionable note. Paula Reichenberg, thank you for taking the time to speak with us.
Greg Lambert (38:12)
Yeah.
Paula (38:17)
Thanks to you. Have a great day.
Marlene Gebauer (38:19)
And of course, thanks to all of you for taking the time to listen to The Geek in Review podcast. If you enjoy the show, please share it with a colleague. We’d love to hear from you, so reach out to us on LinkedIn and Bluesky and Substack, all the places. Reach out to us.
Greg Lambert (38:34)
Substack and all the places, yeah.
Paula (38:38)
And on European platforms too.
Greg Lambert (38:41)
The Substack is the couture of our trifecta there. Paula, is there any particular place that you would want to point listeners to learn more about you and what you’re doing?
Marlene Gebauer (38:44)
That is.
Paula (38:55)
Well, LinkedIn, I think. Please connect on LinkedIn if you’re interested in multilingual challenges or legal tech trends in general, rather than fashion. I talked a lot about fashion, but that’s not my…
Marlene Gebauer (39:13)
Are you going to any events this year that people should know about?
Paula (39:19)
This year I’ll be a lot in Europe. Being now closely associated with and having taken up the position of VP of Legal Innovation at Noxtua, I am going to have the honor of attending most European events. So that is why I missed Legalweek this year for the first time.
Greg Lambert (39:43)
Hmm.
Paula (39:45)
But I hope as well to meet you again at the TLTF Summit.
Greg Lambert (39:51)
Yeah, that’d be great. Well, thank you, Paula. And as always, the music you hear is from Jerry David DeCicca. So thanks, everyone.
Marlene Gebauer (40:00)
Thank you, bye.