What happens when a 40-year-old legal data company decides its employees should start building their own software? This week on we talk with Best Lawyers CEO Phillip Greer and Senior Vice President of Research and Product Strategy Elizabeth Petit about an internal AI transformation that reaches far beyond adding ChatGPT to the corporate toolkit. Best Lawyers is experimenting with generative engine optimization, internal agentic systems, vibe coding, and an AI development environment where employees across research, finance, marketing, and other departments build applications around the company’s data.

Greer begins with a challenge facing every law firm marketing team: traditional search is changing. Google AI Overviews and answer engines such as ChatGPT, Claude, and Gemini increasingly give users answers without sending them to the familiar list of blue links. Greer argues that SEO still matters, but law firms now need to think about Generative Engine Optimization, or GEO, and the signals AI systems use when deciding which sources deserve trust. Structured data, schema markup, substantive content, and third-party validation all become part of the equation. For Best Lawyers, its long history of peer-reviewed rankings offers an interesting advantage. The company’s data serves as an independent signal that AI systems might weigh differently from content produced by a firm’s own marketing department.

Petit explains how Best Lawyers is applying the same thinking to legal marketing through Smithy AI, a system designed to help attorneys and law firm marketers develop profile content without endlessly copying the same biography across websites. Smithy draws from Best Lawyers’ structured information and existing lawyer content to produce a starting point that attorneys and marketers then edit. The larger goal is authenticity. As generative systems make producing generic legal content almost effortless, Greer argues that distinctive expertise, voice, and credible third-party signals become more valuable rather than less.

The conversation then moves inside Best Lawyers, where Greer has taken a far more unusual approach to AI adoption. After building a secure data layer connecting systems including SQL databases, HubSpot, Gong, Google Analytics, and accounting data, he created an internal Best Lawyers App Store where employees use natural language to build applications against company data. What began with roughly 30 percent of the workforce vibe coding has grown to around 40 percent, according to Greer. Petit describes building research and KPI dashboards despite coming from a research rather than software engineering background. Projects that once required Excel formulas, Power BI reports, development queues, and weeks of waiting now sometimes move from a question at 9:30 to a working internal application by 10:30.

That shift also changes the role of professional software engineers. Rather than spending their time building another reporting screen or internal form, Best Lawyers’ engineers increasingly concentrate on architecture, data infrastructure, performance, governance, and the guardrails surrounding employee-built applications. Greer describes moving parts of the company’s data architecture toward Elasticsearch and developing “Bestie,” an internal agentic AI team member. Yet speed introduces another problem. Petit and Greer describe an “AI vampire” effect, where instant feedback encourages people to keep working because the machine never gets tired, goes home, or stops responding. Human judgment includes knowing when the human needs to stop.

The discussion closes with what these changes mean for legal practice itself. Best Lawyers introduced Artificial Intelligence Law as a formal practice area in the 2026 edition of The Best Lawyers in America, reflecting how AI work has spread across technology, intellectual property, privacy, employment, compliance, litigation, and counseling. Looking further ahead, Greer and Petit do not expect AI to erase the billable hour overnight. They do expect clients to ask harder questions about what they are paying for, how legal work was produced, where AI contributed, and where the lawyer’s judgment created value. If AI makes routine production dramatically faster, the economic question for law firms becomes less about how many hours technology saves and more about how firms explain, price, and defend the value of human expertise.

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[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

Transcript

Greg Lambert (00:01)
Hey everyone, I’m Greg Lambert from The Geek in Review, and this week we are talking with Phillip Greer and Elizabeth Petit from Best Lawyers. But first up, here’s a word from our great sponsor and partners at Legal Technology Hub.

Marlene Gebauer (00:15)
I have Sarah Glassmeyer here from Legal Technology Hub, and since it’s almost time for ILTACON, Sarah’s going to share with us how you can use the Legal Technology Directory to prep for ILTACON.

Sarah Glassmeyer (00:30)
Yeah, so this is probably one of the more exciting times of the year for legal tech, kind of leading into ILTA. Everyone’s excited to see all the new changes, see people we haven’t seen for a year. So Legal Technology Hub can help you pregame and get ready to get the most out of it because, while you’re there, it does seem like you live now in Nashville and you’ve been there your entire life, but it goes fast. You only have two and a half, three days to talk to vendors.

So a couple of things I would suggest you do. One, look at the list of who is exhibiting at ILTACON. If you go to the ILTA website, they have a legal tech directory, but guess what? That is us.

We partner with ILTA. It is a mirror of our directory. You can see the little badges, and that will tell you who is exhibiting at ILTACON. Don’t wander in there. Think ahead and think, what am I looking for this year? Am I looking for a new kind of document automation tool? Am I looking for a new AI legal assistant?

So you do some filtering, make a list, see who’s going to be there, and start thinking ahead. Through the directory, you can see, these people don’t integrate with X tool, so they’re kind of off the list for us. You can do a little pre-filtering, pregaming, and figure out who you want to talk to, who is going to make the most of your time.

And then from there, if you are back on our platform in the Legal Technology Hub directory, if you’re logged in, you can make notes. So as you’re wandering through the exhibit hall visiting the people you want to visit, you can make notes saying, here’s this person’s email address that I spoke to and I want to follow up with them, or maybe show Bob this when I get back to my office, or these people are off the list, forget about them.

And so you keep track of who you’ve talked to, what you’re thinking about things live, and it’s all in one place on the Legal Technology Hub directory, not a swag bag full of business cards and flyers. So that’s how we help you get the most out of what you’re doing.

But also, I do suggest wandering around the exhibit hall, because everyone who’s there does have a listing on Legal Technology Hub, so you can double-check. But I think it’s good to wander, see who catches your eye, have a spontaneous conversation, because you might find a vendor you never considered or something you weren’t thinking about ahead of time. So it is a fun time to get yourself absorbed in what’s happening new in legal tech.

Marlene Gebauer (02:46)
Yeah, serendipity is definitely part of the ILTACON experience, but this is great advice in terms of using the Legal Technology Hub directory because it can be overwhelming, and this will allow you to focus on the vendors and people that you definitely need to talk to before you leave.

Marlene Gebauer (03:14)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (03:21)
And I’m Greg Lambert, and today we are exploring how a legacy data institution is completely rearchitecting itself from the inside out for the generative AI era.

Marlene Gebauer (03:34)
We are thrilled to welcome Phillip Greer, CEO of Best Lawyers, and Elizabeth Petit, Senior Vice President of Research and Product Strategy. Phillip and Elizabeth have been leading a radical internal and external transformation at Best Lawyers, turning the organization into a pioneer for agentic workflows, vibe coding, and generative engine optimization. Elizabeth and Phillip, welcome to The Geek in Review.

Phillip Greer (03:58)
Thank you so much for having us. We’re quite excited to be here today.

Greg Lambert (04:03)
Right. Well, Phillip, I’m excited about this because I think a lot of us have been hearing about SEO for years, and now recently, with all the AI, we’re hearing more about generative engine optimization, or GEO.

Phillip Greer (04:18)
Mm-hmm.

Greg Lambert (04:20)
Last year you noticed that there was a massive drop in traditional web traffic due to all of these AI overviews that you see with Google and others, to where people are getting answers rather than getting links out to the websites and then going and finding the answer themselves.

So you talk about your pivot away, or, well, I’m sure you still use SEO, but you’re also having to implement this new generative engine optimization, and that differs a lot from the SEO that a lot of us here at law firms have grown up on and realized, in order for us to get high up on the Google rankings, we need to be good at that.

So how does GEO differ from traditional SEO? And why is it that these third-party reviews or peer reviews are becoming the primary, what you call a trust signal, that keeps firms from being compressed out of the AI search answers that we need to get involved in?

Phillip Greer (05:26)
Yeah, so I think it’s helpful to give a little bit more context. You started that process.

We’ve all been chasing SEO for over two decades, right? Google came out, and then, how do we get to the top of that Google ranking so that our customers and new clients can see the work that we’re doing? And we did that through so many things, backlinking efforts, keywords, thought leadership pieces, marketing associations, marketing partnerships with different types of organizations, et cetera, et cetera.

It’s been popular to say that SEO is dead. So first off, I don’t think SEO is dead. Traditional SEO is still important, and it’s the foundation of AIO.

So a few years ago, Google changed their search engine, and when you started searching, all of a sudden the middle of the funnel, or that blue-links world, started to go away, and you were using that Google Gemini. So you would say, “Hey, I need to find a lawyer for an issue I’m having,” and then it would give you a summary.

And then what was happening was so many law firms, and any type of business, were losing 15, 20, 30 percent of their web traffic because the blue links aren’t being clicked anymore. The users are going in, they’re getting that overview, they’re getting what they need, and they’re kind of moving on.

Well, those original AIO types of overviews were being fed from those SEO efforts.

Marlene Gebauer (06:51)
Yeah.

Phillip Greer (06:51)
The shift that we did over the last couple of years is, first, make sure that our content is structured in a way that, when these LLMs such as ChatGPT or Google Gemini or Claude are coming to our website and looking at our data in traditional SEO forms, we’re tagging it and presenting it in ways that are helpful.

So you think about SEO stuff you used to do. It was all about meta descriptions, keywords, page titles, and then, again, we talked about some of the backlinking techniques. Those things were structuring it so that, when the crawlers are finding your website, they know how to get that data.

Well, that’s kind of amped up with AIO and then eventually what we’ll talk about with GEO. You not only needed the meta descriptions, you needed to have good schema markup. You need to describe sections on the page, to be able to ask questions, answer questions, so that when the AI engines were gathering data, it wasn’t only finding it, they could start to interpret it, because that interpretation is what becomes a little more important when you think of things like ChatGPT or Google Gemini.

It has to take the structured data, and then it has to interpret it and create insights and say, okay, now you can start showing up in these overviews.

The latest shift, and I think it’s the biggest shift, is this GEO, and that was your opening question.

Greg Lambert (08:14)
Right.

Phillip Greer (08:14)
The generative engine optimization approach. And that is making these AI systems not only get your data, but understand that they can trust your data. And so, how do you trust data?

Data has to come from quality places, things that are not produced by a marketing staff. So we’ve all gamed the SEO world. Every year, Google changed the algorithm again. What do we do? How do we get to the top? We spend more dollars. We make sure that we have more content. We flood the internet with it. We do backlink strategies, right?

That would work for SEO, and then it would work until they changed the next algorithm again.

With GEO, you have these AI systems that have some insight and intellect in the sense that it knows whether or not the content is being produced by you, your law firm, putting more information out there, or are you reading a service like Best Lawyers that’s known for producing peer review surveys and, ultimately, lists of lawyers? It’s an annual process. It’s been around for 40 years. It’s got a strong standing, a big domain authority. It is a trusted source. It’s third party to the industry that is putting out their data, right?

So that type of association is important. That’s where GEO plays a role.

So we think about our customers and clients who are part of Best Lawyers, who make those rankings. They get a leg up immediately by making it onto our list because they’re getting a signal, an AI signal in the industry for these LLMs to say, okay, if they’re on Best Lawyers and they’re ranked in corporate law, then their value has a higher weight.

And there are other types of services that you should be paying more attention to, and you want to make sure they’re credible. And so that’s why, when you think about what is your plan going forward, I think for law firms you should continue to think SEO is important, but it’s not the only thing. And that’s the takeaway.

SEO is important. It’s not the only thing. Structuring our data is important, and let’s make sure that we’re also thinking deeper about the schema so that we can have questions asked and answered. And then how are we putting our data in? How are we associating? What are we saying? Go see us on, go connect with, what type of services, and what’s their credibility level?

That’s what is creating the big buzzword for the last year, the AI signals. And all those together create a strong AI signal for your law firm.

Greg Lambert (10:39)
Yeah.

Well, one of the things that I’ve been hearing from marketing folks is much more long-form articles, client alerts, more showing your expertise in an area. Are you also seeing the generative AI or the AIO, and it sounds like a song,

Phillip Greer (10:58)
Yeah.

Greg Lambert (11:00)
or the GEO relying upon, instead of straight-out structure and how things are presented, deeper content as well? Are you seeing that?

Marlene Gebauer (10:59)
Yeah.

Phillip Greer (11:13)
So first off, I refer to this as the E-I-E-I-O because it is 100 percent that effect. The Old MacDonald version.

Greg Lambert (11:18)
Yeah, there you go. The Old MacDonald version.

Phillip Greer (11:24)
For a new world. Yeah, so I think the goal is not only to have longer content, but it’s authentic content that speaks to your messaging.

Greg Lambert (11:31)
Okay.

Phillip Greer (11:31)
So you can create a lot of long-form content with AI, but the problem, if it’s not authentic to your brand and your expertise, you’re going to lose a lot of credibility.

So, thinking about what is it that makes your lawyer special? What makes them stand out? How do you speak on it? And how do you keep that authentic?

And sometimes it’s not directly in the world of law for the authenticity to stand out. So figuring out what your niche is there and then driving it home. That is what is going to create unique content that gives you that authority and will stand out among these GEO initiatives because they can say, this is not a whitewashed, white-labeled version of the same type of content that continues to be posted online. It’s something unique.

And if you have a series of this type of content, it can create that narrative of, okay, this is the type of lawyer, this is the type of firm, this is the type of credibility, this is the type of messaging that you can expect, and stand out among your peers. So yeah, I definitely agree with the longer-form opportunities there.

Marlene Gebauer (12:39)
So Elizabeth, for law firms trying to adapt to this new AI search environment, creating unique, machine-readable content is a difficult hurdle. Best Lawyers recently launched Smithy AI to help lawyers and marketers scale their profile content. How does this tool solve the duplicate-content penalty while keeping human editorial control firmly in the loop?

Elizabeth Petit (13:05)
Yeah. So first of all, we’ve always viewed ourselves as partners with lawyers and law firms. So the products that we build and deliver, we want to solve your problems. And one of the problems we know that you have is too many things to do and not enough time to do them.

So one of the things that Smithy AI was developed to address is getting lawyers to fill out their profiles.

Have either of you gotten lawyers to fill out their profiles? That’s a tall order.

Greg Lambert (13:35)
Yeah, all the time. All the time.

Phillip Greer (13:37)
Yeah.

Elizabeth Petit (13:41)
So the easiest thing, the old way to do this, the easiest thing to do is that most teams would copy and paste whatever you finally got written on your own website. You’re going to go copy and paste it everywhere else on the internet. And you got penalized for that.

So we wanted to, again, partner with law firms and develop an easier way to assist you with your workload.

So what Smithy AI does is it reads our structured data and the content you’ve already developed on your lawyer profiles on your website, and it makes a recommendation for you. Now, everybody knows that AI is not a replacement, it is an assistant, and you should still use your human judgment.

So it does say, recommended generative content. It pulls in information about the awards, things that it thinks are appropriate. You review it, and then you publish it with the click of a button.

The name for Smithy AI does come from one of our original co-founders, Gregory White Smith, who was a Pulitzer Prize-winning biographer.

And so we also wanted to name it in his honor for both founding Best Lawyers and as a writer who was a craftsman as well. So Smithy is developed to help you with your mountain of work, both with profiles, but also there are features now to help with press releases, email announcements, social assets. So we’re trying to be your extra hands when you’re looking at the clock, looking at your watch, looking at your calendar, and you don’t have enough hours in the day.

Greg Lambert (15:18)
I’m curious when it comes to that because I know, for example, one of the things that got pushed out on platforms like Substack is there’s kind of that AI content identifier.

Phillip Greer (15:34)
Mm-hmm.

Greg Lambert (15:35)
And so is that one of the things that you try to look for with using a tool like Smithy, giving them something to work with, but then, is it a full copy and paste, or is it something that they take and then they kind of do their own smithing on it as well? How do you encourage your clients to use the tool?

Elizabeth Petit (16:00)
Yep, so we envision it to get you like 90 percent of the way. So it’s not a copy and paste. It doesn’t pull exactly what you’ve already written. You can also hit that button a few times and say, slightly rewrite it, slightly rewrite it, polish it for me. So it can iterate a little bit for you.

But as with all wordsmithing through AI that we all do, we all know we do this, you should never take whatever it spits out for you and then publish that. This is where human judgment always needs to be that final set of eyes, that final check before you are comfortable publishing to your website, our website, or anywhere that you publish content.

Phillip Greer (16:44)
Yeah, and I would say also, going back to your point about the long-form content and where I was going with brand authenticity. So one of the great things about Smithy is, as Elizabeth is saying, we’re going to get the way you’re talking about yourself on your profiles, and we’re going to take the data we have on Best Lawyers and put it through our own system to say, how do we think it should build content?

So it’s going to have your unique voice with our data, but you still want to ask yourself, is this me?

If not, you do have the ability to be replacing it with another version of yourself. And I know with AI, it’s a whole different topic where there’s a lot of, what does it mean to clone a lawyer? Well, we don’t want you to clone your brand. We want you to play a role in helping that editing of the brand.

I think back to college, the hardest thing was always to start writing the paper. The first paragraph is always the hardest. The thing about Smithy is it’ll write the first four paragraphs for you, but you should come in with that closer. You should come in with that closer and do a little bit of editing on the first four that it produces for you.

Greg Lambert (17:52)
Phillip, I want to turn to something that you’ve done that I think we’ve seen a few CEOs do, and that was there at Best Lawyers, you kind of famously mandated that your entire team continue doing their job, but also look at becoming essentially software builders.

Phillip Greer (18:13)
Mm.

Greg Lambert (18:14)
Now you’ve got, and I think the last estimate was about 30 percent of the company, vibe coding their own apps. And Elizabeth, I think you’re a prime example of this. You’re a researcher, not an engineer, yet you were involved in building a real-time KPI dashboard there at Best Lawyers.

So it seems like people are taking you up on your challenge, Phillip. So I want you to walk us through something that you’ve built that takes the worst part of your job off the plate. And then, once the repetitive data wrangling is automated, how do managers shift their focus from doing that kind of grunt work to focusing on more of the higher creative work?

Because that’s something that we’re insisting our lawyers do. So I’m hoping you’ve got the secret sauce and can help us on that.

Phillip Greer (19:20)
Yeah, I’ll start here, and then I’ll want to pass it over to Elizabeth. But I’ll start.

So the end of last year, I’ve been playing with a lot of AI and product development for years now. My background is engineering. I used to build a lot of software. I built a lot of the core systems we still use here at Best Lawyers for producing our rankings and doing our survey processes.

And then I’ve been the CEO and running it for quite some time. And when AI came on the scene, I started experimenting. What can we do? What can we do?

And about December of last year, I had the breakthrough. Claude Opus came out, and its coding abilities were far superior to every other AI coding that had existed before.

So I had this wild thought where, what if I build an environment that was safe, that our team could fail fast in and not worry about hurting our data, and it wouldn’t go and be spread out to the outside world? It wouldn’t be trained on outside systems. We wouldn’t have to worry about them doing some shadow IT type environment where they’re taking sensitive data and going straight to ChatGPT and dumping it out there.

So I felt an obligation to create that environment. And it’s an exciting time because it felt possible. So I spent all of the holiday break in December and then all of January building out, first, an environment.

And what I mean by that is a place where all our data connects. So I needed to create this API layer so that all of our data, when I say our data, I mean our SQL data, our accounting data, our HubSpot, Gong, Google Analytics, any type of systems that we are accessing and using on a daily basis, I wanted it all to come to one place and then inform itself and know about its own data.

So I created a data lake, if you will, custom. And ultimately, I don’t know, for those who are a little more technical on these calls, it’s a deep coverage model where the data in Gong understands how it relates to our data inside of HubSpot, and that understands how it relates to our data inside of Google Analytics, and so on.

So when you’re asking questions, it references each of the data sets, and it knows what a firm is or a lawyer is inside of those areas.

That’s a complicated way of saying I made a place where all our data came together safely and made sure it knew how it was associated.

And then I needed to create that safe environment. So I built a system called the Best Lawyers App Store, where you could go in and start vibe coding through our Claude environment.

And when you vibe code, you’re safely in that App Store. It’s using only our data sets, and you can build, eventually, what we now are using as an operating layer for most of our business.

So I did this. I put all this together. I got it in a prototyped form, and I met with the team at the end of January and I said, “All right, all right, I got an idea. Everyone’s going to become a vibe coder. Every department’s going to be a builder. Elizabeth is going to be a builder. Nancy is going to be a builder.”

I went full Oprah Winfrey on, you’re going to, you’re going to, you’re going to.

Greg Lambert (22:25)
Yeah.

Phillip Greer (22:26)
And they were all on board. I was like, okay, no convincing needed, you’re totally in for this.

Elizabeth was one of the first people I sat down with. I think I spent all of 30 minutes with you, Elizabeth. It was a short session. I got you set up in the environment. I gave you the tools, told you how the data works and how to connect it, and I said, go and dream and build.

And Elizabeth, I’d love to hear your take on it. That’s my more technical explanation of what happened. I’d love to hear what your side was.

Elizabeth Petit (22:56)
Yeah.

Phillip Greer (22:56)
I’d love to hear what your side was.

Elizabeth Petit (22:58)
Yeah. So I knew Phil had been working on this passion project. Phil’s always got multiple passion projects going. So I knew he’d been working on this, and then he made a statement and he’s like, “I’m going to ask you guys to come join me in this.”

And then he did that ping and he’s like, “No, I mean it.”

And I’m like, “Yeah, I know. I’m here. I’m ready.”

So my personal mantra has always been, my team knows this, work smarter, not harder. And that doesn’t mean we don’t work hard. I love to work hard. But it means work more efficiently. If there’s a better way to solve your problem, do it the more productive way. Don’t do something the same way over and over again because you’ve always done it that way.

So what vibe coding unlocked for me was this new ability to work smarter and not harder, and take some of the repetitive, mundane parts of everybody’s job and make them more fun, make them more creative, automate them.

Whether it’s through KPI reporting that eats up time every day, or taking some of these important projects or problems we want to solve that sit on that shelf, that you know have value, but you never have the time to take off the shelf.

And say, okay, now that I have optimized some of this basic operational work, I’m not spending all day, two days of the week, pulling together my KPI reports, let’s take a bigger, meatier project off the shelf and let’s go start working on that.

Phillip Greer (24:38)
Yeah.

It’s been exciting watching how everybody came to the table to build.

Elizabeth built research dashboards to monitor the process for Best Lawyers as the nominations were coming in and voting was happening. Nancy, our head of content, developed expense-paid tracking systems for all of our budgets that were going out for paid media, as well as her marketing team developed this marketing dashboard that pulled in our Google Analytics and gave full-funnel conversion visibility.

I mean, we went from, like a lot of companies, working heavily in Excel and Power BI or Tableau. We’ve stopped using Power BI. We don’t use any Tableau. We do everything now inside of our dashboards that are developed independently by departments.

And the only time we’re in Excel these days is when we’re exporting data sets to give to our business partners or if we’ve got to send them out somewhere. But we work primarily now inside of our BL App Store environments.

When you’re asking for data and looking for new trends and analysis, it used to be, okay, we’ll export everything out to Excel. We’ll write a bunch of complicated formulas. I’m Googling how to write that complicated formula.

Greg Lambert (25:55)
Yeah.

Phillip Greer (25:55)
My God, why do we have to write these complicated formulas?

Greg Lambert (25:58)
Yeah. How do I do that VLOOKUP table again?

Phillip Greer (26:00)
Yeah, that VLOOKUP table.

Elizabeth Petit (26:01)
Yeah.

Phillip Greer (26:03)
My God. And then you always have the one person in the office who’s your Excel person, and that’s what their life is all about. “Ask me about my sheets,” you know?

But now everyone goes straight into their environment. The data’s all there in our data layer. It’s trained, it’s learned, it knows itself in the deep coverage model.

And you work through our App Store and ask it questions. And instead of developing an Excel sheet, you develop an entire new page that is the best visual representation of your data and answers a question. And you use that to start making decisions.

So our speed to decision is pretty great. A week-long, two-week-long Excel project is now done. We have an idea in a 9:30 meeting. By 10:30, we have a new page that’s been pumped out through our finance team that’s explaining the answer.

And then we need to make that next decision and, okay, how do we execute on this data?

Greg Lambert (27:02)
The follow-up I have on that, and this is something I’ve been writing on, especially since you have so much data that you’re working with, and it sounds like you’ve solved part of the problem through creating these data lakes or this ability to feed information into the right places so that you set up your AI to access clean information in a relatively simplistic way rather than having to go out to 15 different places, some external, some internal. You’ve got it all built.

So the topic that I’ve been writing on a lot, and I think this is going to be, I thought it was going to be 2027 fodder, but it might hit before then, is a lot of us are building these harnesses around both our data and our AI tools with the expectation of a lot of this information.

You don’t need to dump everything into the LLM and get an answer. You need it to be precise, and you need it to be consistently right so that the inputs and the outputs are accurate.

So I know, Phillip, you designed the initial, you vibe coded the system around it. Do you have a technical team now that keeps that up to date, updates or adds new things that come in, or are you still able to use the talent that you have on staff that they build on top of what’s already built?

How do you take that 9:30 question and turn it into a 10:30 report?

Phillip Greer (28:47)
Yeah, so I built all the environment for the initial App Store, and my intention was always to hand off the day-to-day maintenance because adding more data to our data lake or data API library is going to be a constant workflow.

And any company who’s working with any type of data environment, governance, or what have you, whether it’s SharePoint or an actual Azure data lake, et cetera, you’re going to have to have somebody managing that.

So once I got everything built, the initial systems, we found one person internally who had the most interest in the AI infrastructure and the future of AI and how it’s used in programming. He was one of our programmers, and his name’s Seth.

And so I trained him for a week on the system, and he took it over. And now he is running that API library.

And people, all the vibe coders, as they need more data or they need more feature abilities in the App Store, they go through Seth. And it’s been a good system.

Because my original vision was, I knew what Elizabeth and I knew what our research and our finance and our content team would need to get started, but I didn’t know what they were going to build, right? And that’s the wild thing, what they end up building and how they use the data.

I’m watching some of the requests come through and some of the systems they’re building, and I know because I designed the actual data that came together, and I would go, that’s not going to work. They don’t have that data. That’s not going to work. They don’t have that data.

So I had that moment where I was like, I need to hand this off to someone. I’m the CEO of this company. I can’t keep adding to that API layer.

Greg Lambert (30:28)
You don’t want to be the head vibe coder and CEO?

Phillip Greer (30:31)
Head vibe coder and CEO.

So I got Seth involved, and now whenever they start to build with more data, he continues to extend the actual API layer.

But what is great is the actual building is still happening from the non-builders. Well, they used to be non-builders. I’d call them all builders now.

That 30 percent number, as of, I onboarded a few more people this last week, we’re up to 40 percent of our company who are vibe coders now. It’s growing because the mentality is, if you want to do this, we want to help you do it. There’s no reason you have to sit on the sidelines.

So I handed that over, and then what I realized quickly by handing this over is there’s an opportunity to build more things.

So that’s been kind of exciting. And we can get into it. But I built out what we lovingly call here Bestie, which is our first type of AI support team member that uses our different models and data sets.

So yeah, you have to have someone who’s going to own it. You’ve got to have someone who’s going to make sure that they’re adding to it. But that’s the easy part.

The harder part is thinking about what you want to build. Because when you can build endlessly, narrowing that scope down to building things that are functionally helpful is that next muscle you have to start learning how to train.

Greg Lambert (31:55)
Yeah, it’s not asking what do I build, it’s what should I build, right?

Phillip Greer (32:01)
Yeah. One of the opening things you mentioned was about, how do you open up room and bandwidth for creativity?

Well, you have to allow yourself to be creative. And there’s a lot of people who work in an office environment that have not worked that muscle in a long time because you make that Excel report, you put it through Power BI, you drop it out as a PDF, and then you take it to the board. And you do it again, and you give the lawyer the thing.

The creativity is lacking.

And now all of a sudden, all of that administrative burden is done within 9:30, the idea, 10:30, the actual result. Now what’s the creative solution that you move forward and make something functional to execute on?

Marlene Gebauer (32:43)
So transforming a workforce as quickly as you have doesn’t happen without some friction. Phillip, you’ve spoken about the AI vampire effect causing extreme decision fatigue.

Phillip Greer (32:52)
Mm-hmm.

Marlene Gebauer (32:53)
And Elizabeth, you’ve had to manage an existential crisis among your software engineers. How did you help these technical engineers transition to feeling like they’re authoritative system architects rather than feeling displaced?

And how are you managing that cognitive burnout when AI offers answers in seconds instead of in weeks?

Elizabeth Petit (33:20)
Yeah. So first of all, the existential crisis is understandable.

These team members of ours, many of whom Phil and I have worked with for over 10 years, went to school to be experts in these fields. They are craftsmen. They are builders. This is their superpower.

And then seemingly overnight, IKEA came to town, and no longer is the craftsman superpower the same. And that’s hard. And I understand that, and we did understand that.

And it helped that Phil was a developer and came from that background, so he understood that as well.

And it was always our goal, continues to be our goal, to use AI to upskill our team members as an opportunity for professional development, whether you are an engineer or another member of the business team. It is not to replace anybody’s job.

And so while the business team is learning a superpower they never thought they would have, they never thought that they were going to be this type of craftsman, and they’re not going to be this technical craftsman, they do some things.

What it has done for our engineers is it has allowed them to also build faster, solve more complex problems. All the things that AI does. It allows for better debugging, it allows for better regression testing, it removes some of that mundane work that they were doing.

So we went into this as, we are all going to learn this together. This is new emerging technology. We don’t have all the answers, and we want to figure out how this makes the most sense for you and your roles, and us within our business.

Phillip Greer (35:16)
Yeah. At the end of January, at the same time I was meeting with the executive team, Elizabeth pulled all of our engineers together in an in-person meeting, and we had a Q1 huddle.

And I sat there and explained to them that I’ve built this App Store. I’ve got this huge data layer. All our data comes together nice and clean. It’s learning off of each other. And now everyone’s going to become builders.

And it was a lot of disbelief because, again, craftsmen, like, okay, I make quality code. There’s no way a machine’s going to do it.

And so there was a lot of disbelief.

After that, I met with Elizabeth and team and taught them how to vibe code, and they started building apps.

We brought everyone back together again and showed them what these non-builders were putting together and how it was pulling data from all of our systems.

And then even our engineers were in the mindset of, my God, this is a real thing. This is not some fancy fad happening, Phil says everyone’s going to be doing this. They’re building financial systems. They’re building research systems, marketing systems, sales systems.

And it was clear that they wanted to be part of it too.

And so Elizabeth put on, I thought it was an intelligent thing, as we had the adoption, every two weeks we’d have sessions where we’d encourage the engineers to show, in a safe space, the other engineers what they’d been vibe coding.

Because there’s also this stigma, like, I’m an engineer. I don’t vibe code. I know how to code.

But that’s not what vibe coding is. Vibe coding is not a replacement for, I don’t know, a good analogy. Here’s a good analogy.

I play guitar. But when someone says, “I play Guitar Hero,” and they’re pushing those buttons on the little plastic machines,

Marlene Gebauer (37:02)
Ha ha ha.

Phillip Greer (37:03)
I would go, “Ha, you don’t play guitar. I play.”

It’s not that. It’s a completely different thing. It’s not analogous to Guitar Hero versus playing guitar.

Both people are coding. Both people are building. It’s only one person’s doing it craftsman style, line by line. The other person is effectively doing it through a learned generative system that knows how to put all the pieces together, and you get to be that creative fountainhead.

So watching them come along the journey, it took a little while. We had to walk with them. We had to have these sessions where they would show what’s possible.

And the way it’s aha moments. After you start to realize, I made my day a little less boring, or I removed this ridiculous, redundant task that I have to do over and over, you start to go, now what do I do?

So that’s been a fun thing to watch. And I’m using the word fun because everything’s hindsight. In the moment, it was like, come on, guys, you don’t understand. I promise we’re not playing Guitar Hero. This is real. We’re producing real things.

Greg Lambert (38:06)
Yeah. I can’t imagine the eye strain from the eye rolling from your engineers when you first announced this.

Phillip Greer (38:16)
Yeah. Yeah, it was significant.

Marlene Gebauer (38:16)
Ha ha ha.

Greg Lambert (38:20)
But I’m wondering, Phillip, how do you leverage the intelligence and the experience of your engineers? Is there anything that they help people like Elizabeth better understand about structure and what the end results are and how you want to get there?

How do you leverage that expertise to help the non-engineers?

Phillip Greer (38:50)
So Elizabeth made the comment that our goal was not to reduce our engineering staff in this process. We wanted to support and invest more.

And they ultimately have, to the point you’re making, this understanding of our data.

No matter how much we bring it together and add to our API and have it learning on itself, they understand the repository, where it comes from, how it was put there, how it’s designed, the schema markup, how you scale it, and when you’re having more and more people access it, how much you pull on those data sets.

I’ll give you an example.

One person was writing an analytics system as a builder, and they were saying, give me all of our analytics in our BL App Store. We’re talking about hundreds of millions of data points, and they couldn’t get their app to do it. It was crashing constantly.

And so we had to have one of our architects come in and go, let’s walk through this.

And what they built, you’re using the word harness or harnesses earlier, or guardrails, they decided to change our API layer to understand that the user doesn’t understand the data when they say, give me all of our analytical data, give me all of our financial data.

So it’s my job to create ways that I feed it to them in an appropriate way.

Because when you have access to all of this and you don’t know how it’s formed, functioned, and put together, you create a lot of overly inefficient, heavy systems.

So what we’ve been doing is we’ve opened up the doorway and the communication to say, go talk initially to Seth. He’s in charge of the App Store. And then from there, you spend some time meeting with one of the engineers and asking questions about, how do I make my app do something a little faster? It’s getting stuck here.

And they walk you through their why and the how.

Because what they’re doing is they’re not only in charge of where the data is coming from, they understand it, and they’re building better ways to feed it.

So we had a big session with our engineers where we’re saying, how do we feed things even faster to our API layer?

So we had an architectural meeting where we’re trying to understand, if things are coming from SQL and those are our bottlenecks, it’s single-server environments, how do we extend that further? Let’s have a stopgap where it feeds into a system like Elasticsearch.

I’m so sorry for all the technicality here. Let’s have it feeding to a faster system.

Greg Lambert (41:11)
I think a lot of people that listen to this have heard Elasticsearch before.

Phillip Greer (41:16)
Yeah, yeah, yeah.

So we decided to move our API model from SQL to Elasticsearch, so the first read comes from a faster environment.

And these are the new types of things that our programmers get to think about. No longer are they creating forms on an intranet to feed data back to an end user in an Excel environment.

Now our engineers are freed up thinking, okay, if I restructure my data and take it from a single-server environment to a multi-server replica environment where I feed it faster, what do we accomplish?

How do I create a harness that feeds this data in a way that has nice guardrails, and they don’t have to think about ruining the data, over-encumbering the server? We’re not going to have server crashes.

And those are the projects.

Engineers always talk about, Elizabeth made the comment, taking that hard project off the shelf. That’s what the engineers want to do. They don’t want to have to write one more page on the intranet. They don’t want to write one more page that tells you what your voting reports are.

They will do it because that’s their job.

What they want to work on is making our server faster, building a better backend system, creating a new way to isolate our data and feed it in a multi-tenant scenario.

So that’s when their aha moments and their expertise start to be able to work on those types of systems.

For the first time, those are the types of things where we go, okay, you get the last two weeks of December. This is what you get. Build whatever you want in two weeks.

And now you have more opportunity to start thinking about that longer-term pipeline because each department, they’re servicing their own needs with the data.

Greg Lambert (42:59)
That sounds good, and I think it’s something that we all kind of face for how we transition into whatever the new version of, whether you’re a manager or an engineer, it seems like we’re all taking on more.

And I’ve noticed I’ve not seen people go home earlier on Fridays for some reason.

Phillip Greer (43:23)
Yeah.

Greg Lambert (43:24)
So I’m still waiting for my, it’s time for me to be rolling in income and sitting on the beach the whole time. So perhaps that’s a 2028 goal.

Elizabeth Petit (43:33)
Well,

Greg Lambert (43:34)
2028 goal.

Elizabeth Petit (43:35)
I think one of the reasons for that is because you move so much faster with AI, and specifically with vibe coding, it has unlocked all of this creativity and opportunity that, for those of you who’ve done it, is quite addictive.

Greg Lambert (43:55)
Yeah.

Elizabeth Petit (43:55)
Once you get in there and you start building, whatever you are, you’re prototyping something, you’re building something you’ve been wanting for years.

That used to be this cumbersome process of meetings, requirements, design, getting in the development queue. Three years later, you get something you asked for that you don’t need anymore.

You now all of a sudden get it quickly.

Okay, that’s great. But also, the feedback loop comes immediately. So instead of Friday morning you send off your information and think, I’ll get something on Monday to address, it comes right back to you.

Phillip Greer (44:32)
Mm.

Elizabeth Petit (44:33)
So now we have this cognitive fatigue concern because you are engaging so rapidly that the machine will never get tired. It will never go to dinner with its family. It will never log off for the weekend.

So at a certain point, you have to have the critical judgment to say, I am exhausted. You will never be exhausted, but I am.

And Phil shared this story that he found himself at one point being so in the go, and Phil and I are the same in that way. Once we get on that path, we are running.

And then he realized that he was not making the same little decisions that he would make. And not big, impactful things, but things that usually he would make a decision about.

He had that aha moment that he was having cognitive fatigue. And you don’t want AI to make those decisions for you.

So that’s when,

Phillip Greer (45:33)
Yeah.

Elizabeth Petit (45:34)
you have to remind yourself, AI will never say, go spend time with your kids, you know, go home early on a Friday.

Marlene Gebauer (45:41)
Not unless you

Phillip Greer (45:42)
Yeah.

Marlene Gebauer (45:42)
create an agent to remind you.

Greg Lambert (45:44)
Yeah, exactly.

Elizabeth Petit (45:44)
Yeah, exactly.

Phillip Greer (45:45)
Yeah.

Greg Lambert (45:45)
Exactly.

Phillip Greer (45:46)
And that runs into, you mentioned earlier, the AI vampire. That’s what it is. It’s feeding off of you, you’re feeding off of it, and you have to recognize, okay, I get to walk out in the sunlight. So I need to go do that some.

Marlene Gebauer (46:01)
Tear my eyes away from the screen, yes.

Greg Lambert (46:04)
Yeah, well, I always say when somebody asks me what’s the addictive game that I’m playing on my phone, I say it’s my $200-a-month Claude account. I’m on it all the time.

Phillip Greer (46:15)
Yeah, yeah.

Elizabeth Petit (46:16)
Yeah. Yeah.

Greg Lambert (46:18)
So yeah, you’re right.

Elizabeth, before we start wrapping up, I know that AI, of course, isn’t only affecting how we manage engineers, how we manage managers, and how we all become vibe coders. It’s becoming a part of Best Lawyers content as well.

And one of the things that you oversaw was the introduction of Artificial Intelligence Law as a formal recognition category in the 2026 edition.

Do you mind talking about what that encompasses, how you came up with it, and what you found?

Elizabeth Petit (47:00)
Yep. So, you know, it is always our goal to help connect clients in need of legal services, whether that’s professional or personal, with the right attorney for their case.

And so we always keep our eyes and ears open for developments in the legal industry to add new practice areas as they emerge over time.

So cannabis law, that was one that we added a few years ago because that was bubbling up.

And so about three years ago, we started to see that law firms were adding AI practice groups. And so we added this and started researching AI as its own practice area.

Now, when this first started, I envisioned this would be sort of a subset of emerging tech.

And we have technology law, and that this would kind of,

Greg Lambert (47:51)
This is what the blockchain lawyers morphed into, right?

Elizabeth Petit (47:54)
Yeah, exactly. This is sort of what I thought this would evolve into.

Phillip Greer (47:54)
Yeah. Yeah.

Marlene Gebauer (47:54)
Yeah.

Elizabeth Petit (47:57)
But as AI is so dynamic and evolving so quickly, the definition of an AI lawyer is quite nebulous.

Are you counseling AI corporations? Are you focused on data privacy? Are you looking at government issues? Even labor and employment. I mean, AI is truly touching everything.

And so because of that, it started off more as a subset of technology. But we will have to evolve with AI in our awards as this industry evolves so rapidly to make sure that it is reflective of how legal is addressing AI and the needs of clients.

Greg Lambert (48:49)
Are you thinking about

Phillip Greer (48:49)
We didn’t even get it.

Greg Lambert (48:52)
an AI-native law firm category? That seems to be the big buzz phrase these days.

Elizabeth Petit (49:01)
Yeah. We are getting ready to evaluate adding new practice areas for next year. So look for that webinar series coming up soon.

Phillip Greer (49:10)
Yeah, and whether or not we have AI law firms that we recognize, that’s currently not on the horizon.

But it is the question, when do I have my work done by AI and AI Inc.?

Greg Lambert (49:27)
Yes.

Marlene Gebauer (49:27)
Ha ha.

Greg Lambert (49:29)
All right. Well, before we get to our crystal ball question and we start looking at the future, I want to ask you guys to look back in the past. And I think I know where this is going based on our conversation.

Phillip and Elizabeth, what’s something that’s true today that wasn’t necessarily true for you and Best Lawyers a year ago? What’s changed in that year?

Phillip Greer (49:52)
Yeah.

Greg Lambert (49:52)
What’s changed in that year?

Phillip Greer (49:53)
I mean, the biggest thing, it’s everything we talked about, that we have non-builders, and they’re taking their data and they’re solving their own problems.

We have a decent engineering team as far as size because our company is research and tech. We use a lot of tech to build rankings and recognitions in over 76 countries.

And so we always built a lot of custom software, and we’ve always been able to do dynamic things for each department. But there was always a process. You got in line, got queued up, and then it handed off to the engineering team.

Greg Lambert (50:28)
Q3. Q3.

Phillip Greer (50:30)
And now that’s changed greatly because everybody is handling the majority of their own needs internally.

We’re still externally using our engineers. We’re not developing any software. All software that’s hitting our customers is still handled by our engineering team because that’s the right way to handle it.

But for all our internal needs, everyone’s a builder. So that was a big change I’ve seen. That is different now.

What about you, Elizabeth?

Elizabeth Petit (50:59)
I think the surprise that has come out of this is, I’ve been with Best Lawyers for over 15 years now, and I did not expect this career development opportunity.

Phillip Greer (51:14)
Mm.

Elizabeth Petit (51:15)
So I thought that I would continue to grow in leadership or other traditional career development paths, but all of a sudden to have an entire skill set that I did not have last year, that’s pretty amazing.

And I’m grateful to have been given that opportunity.

Marlene Gebauer (51:37)
All right, we’ve looked a little bit at the past, and now it’s time to look in the future.

If you would gaze into your crystal ball and look ahead a few years, as autonomous agents and vibe coding become standard inside both data organizations and law firms, what’s the single biggest shift you see coming for the legal profession’s economic model, specifically regarding the billable hour?

Phillip Greer (52:03)
Hmm. So I don’t think the billable hour is going to disappear overnight, right? But it will shift.

People are going to have more scrutiny of how you’re backing up the value of what’s coming from the work product.

And there’ll be more questions about how did you derive that actual result set.

It was already the question, you hire the partner and you ask yourself, am I getting the partner’s hours or am I getting the junior associate? That’s already been on the table for a long time. And some clients have been wanting to know that. And they want to know, I am paying for the partner’s time.

I think there’s going to be a lot of questions that more clients are going to be asking. I want you to break down the justification. Am I paying for the senior partner’s time, or how much am I paying for the AI’s time?

I don’t know what that’s going to look like as far as how it wants to be shown in transparency, but I think that’s going to be a big question.

Marlene Gebauer (53:01)
How about Elizabeth?

Elizabeth Petit (53:04)
I agree. I don’t think that this is, legal doesn’t revolutionize overnight. We’ve all been in this industry for a minute.

But a lawyer is not going to become twice as productive with AI. They’re not going to bill twice as many hours a day. That’s not possible.

But they’re expected to still bring in that same amount of revenue for the firm.

And so figuring out that balance between what is the appropriate use for AI, but still what requires that human judgment that AI will never replace.

And that’s ultimately what a client comes to a law firm for, not the administrative work that AI assists with. It is the human judgment.

That’s where I feel like, if firms tell their story to clients successfully, they sort of balance that out, get that efficiency that they need, but still have the confidence of their clients.

Greg Lambert (54:04)
Mm-hmm. All right. Well, I think that’s a good spot to end on.

So Phillip Greer and Elizabeth Petit, thank you so much for joining us and breaking down what it looks like behind the walls there at Best Lawyers. Appreciate it.

Marlene Gebauer (54:18)
Yeah, thank you.

Phillip Greer (54:18)
Yeah, thank you so much for having us.

Elizabeth Petit (54:19)
Thank you for having us.

Marlene Gebauer (54:21)
And thanks to all of you for listening to The Geek in Review. If you’ve enjoyed the show, please share it with a colleague. We’d love to hear from you on LinkedIn, YouTube, and Substack.

Greg Lambert (54:30)
And Phillip and Elizabeth, where’s the best place for listeners to learn more about you, more about Smithy AI, Best Lawyers, Bestie, all those things?

Phillip Greer (54:40)
So first off, for more information, go to bestlawyers.com. Also look us up on LinkedIn.

I’m posting a lot of videos about some of the AI work that I’m doing, some of the apps we’ve been building. And yeah, feel free to follow us there and check out what we’re doing.

Marlene Gebauer (54:56)
And as always, the music you hear is from Jerry David DeCicca. Thank you, Jerry, and goodbye, everybody.