Fresh from AALL in Cleveland, Greg reflects on a conference filled with legal information professionals who understand how technology performs under real working conditions. These librarians purchase products, train users, support law schools and courts, and often serve as internal advocates for legal technology. Their expertise makes vendor engagement especially valuable, yet major product announcements were scarce. Marlene balances Greg’s conference report with stories from her hiking trip through Zion and Bryce Canyon, plus a brief comparison of Ohio and Utah karaoke culture.

The conversation turns to the rapid growth of innovation attorney positions across law firms and legal organizations. Greg and Marlene describe these professionals as translators who connect legal practice, technology, workflow design, and organizational change. Firms are searching beyond traditional legal career paths for people who combine technical fluency with strong interpersonal skills. For law students and junior lawyers facing uncertainty around AI, these emerging roles offer broader career options beyond the familiar associate track.

Marlene explores the growing use of AI personas and simulations for professional development. Deposition witnesses, opposing counsel, negotiation partners, and drafting reviewers now appear as interactive characters with distinct goals and behaviors. Lawyers receive a place to practice, make decisions, and receive feedback before working with clients or appearing in court. Greg connects simulation-based learning with legal fiction, including his Beyond the Model series, which uses a fictional law firm to explain AI systems, business pressures, and changes in legal work.

The discussion takes a serious turn with a reported AI benchmarking incident involving an agentic model, a breached sandbox, and unauthorized access to Hugging Face resources in search of an answer key. Greg and Marlene examine the episode as a warning about containment, accountability, and excessive faith in technical guardrails. From there, they consider the renewed importance of knowledge management and security as AI systems gain access to documents, financial information, client data, and institutional expertise. Greg predicts growing attention around AI harnesses, structured software layers designed to guide model behavior and produce predictable outputs.

Marlene closes with examples of AI moving into client intake, business qualification, and workflow decisions, including an AI legal receptionist designed for smaller firms. The larger shift involves moving beyond simple tool adoption toward redesigned workflows, staffing models, pricing structures, and client service. Token costs are creating immediate budget pressure, while clients are questioning which AI expenses belong on their bills. Greg and Marlene argue firms must connect AI spending with legal judgment, measurable value, and responsible delivery, rather than treating consumption as a proxy for progress.

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

Email: geekinreviewpodcast@gmail.com

Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

LINKS:

Here is a chronological list of the substantive topics and sources referenced in the episode:

Transcript:

Marlene Gebauer (00:00)
Hey, this is Marlene Gebauer and Greg Lambert of The Geek in Review

Greg Lambert (00:04)
And this week we thought we would just have a little chat with each other, catch up on the latest in legal technology, AI, and talk about what could possibly go wrong if an AI tool breaks out of containment and hacks into another company’s database. I’ve never seen that movie.

Marlene Gebauer (00:20)
But first up, let’s get a bit of wisdom from our sponsor, Legal Technology Hub.

Stephanie Wilkins (00:25)
The Gen AI conversation has been advancing faster than a lot of people can keep up lately, but some new developments have brought older concepts like prompting back into the spotlight thanks to a trending new topic, token cost. Token cost moved into the spotlight recently as tools like Claude gained traction in legal, because most plans come with token limits, as well as the ability to request limit increases, which has resulted in tales of astronomical bills for some users. Recently, Legora also announced that it’s moving its Agent Pro offering to consumption-based pricing, which means it’s billing by what the agent does rather than by a flat seat license. And what agents do is consume tokens. Eventually, other providers are sure to follow suit. What many don’t fully understand is just how quickly token usage can add up. A few extra follow-up questions, a document pasted in twice, a chat continuing long after it should have been reset. If that sounds familiar, token consumption compounds faster than you might expect, and you may be looking at a higher token usage than you think. And you might not even realize it until you’ve hit your usage limit or worse, seen the bill. This is a blind spot we’ve been unpacking in one of our latest article series on Legal Tech Hub: how to get more out of tools like Claude without burning time, decreasing accuracy, or racking up unnecessary bills. We’ve covered topics like what tokens actually are and why they function as a hidden meter running behind every chat. When to reset a conversation versus continue in the same chat. And what everyday prompting habits, from repasting whole documents to burying five questions in one prompt, might be driving up both cost and inaccuracy without you knowing it. Head over to legaltechnologyhub.com to read the full series and learn more about how to get the most out of your token limits and your usage of tools like Claude.

Marlene Gebauer (02:04)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer

Greg Lambert (02:11)
And I’m Greg Lambert and we thought this week we would catch up. I’m just getting back from beautiful Cleveland, Ohio, Marlene’s favorite town in all of America.

Marlene Gebauer (02:21)
Ha ha

Greg Lambert (02:22)
And I was there for AALL. Our friend Jenny Foster got the conference kicked off and completed without a hitch. Congratulations. And now that she’s rotated off and Jessica Whytock is president, Jenny is a nobody anymore. So welcome to the club, Jenny.

Marlene Gebauer (02:39)
Yeah, not true, not true. She’s going to be working behind the scenes. I seem to recall how that goes.

Greg Lambert (02:45)
Yeah.

Marlene Gebauer (02:46)
But yes, I was sorry to miss AALL, but I was actually I wasn’t sorry to miss Cleveland.

Greg Lambert (02:52)
No, you weren’t. You weren’t sorry to miss Cleveland, I can tell you that.

Marlene Gebauer (02:57)
But I was doing some great stuff. I went on a trip to some of our beautiful national parks. I went to Zion and went to Bryce Canyon and did quite a bit of hiking for a week and I went with a very good friend of mine and it was amazing. It is breathtaking.

Greg Lambert (03:19)
Was it breathtaking from the view or from the hikes?

Marlene Gebauer (03:22)
It was from both. We were doing about ten miles a day and as you can imagine a lot of it was up. So we saw a lot of hoodoos at Bryce Canyon and saw great vistas at Zion. There was one hike that’s called the Narrows and you walk through a river pretty much the whole time in waterproof boots. So, pretty cool.

Greg Lambert (03:44)
Sounds perfectly safe. Glad you made it back, Marlene. So we’re filming in the morning so I’ve got my cup of coffee. I pulled out my Wolverine mug. So for those that are watching the video. Yeah? No, don’t worry about it. Only one of us

Marlene Gebauer (03:53)
He’s showing off because I have a Starbucks and I said, I got to go get a cool mug. And he’s like, No, don’t worry about it. So I have my boring Starbucks cup.

Greg Lambert (04:02)
Only one of us can bring a cool coffee cup at a time. Well, let me jump in and talk about what my experience was at AALL. If you’ve never been

Marlene Gebauer (04:12)
Yes, please. Besides karaoke.

Greg Lambert (04:15)
Besides karaoke. Well, man. All right. Well, let me talk about karaoke first. So we actually

Marlene Gebauer (04:18)
I just posted it on LinkedIn, so I figure it’s fair game. Get it over with, yes.

Greg Lambert (04:23)
had two karaoke events the same night. They kind of overlapped a little but you could go to both. Lexis did one and BankruptcyData along with our friend Andre Davison from Harris County Law Library here in beautiful Houston did a great job. I can tell you this, the DJ was not expecting a bunch of librarians on a Monday night to bring it and the librarians brought it. So I

Marlene Gebauer (04:49)
As usual.

Greg Lambert (04:50)
I think we saw a range of songs. My favorite was one of the librarians who got up who was still in his shirt and tie from the day and got up and sang Ice Ice Baby from Vanilla Ice and brought the house down. So good stuff. Thanks to Andre Davison for sponsoring that. And I even sang the Bowling for Soup song “Ohio”, a song about Texas and

Marlene Gebauer (05:15)
Mm-hmm.

Greg Lambert (05:17)
posted it on LinkedIn. So if

Marlene Gebauer (05:19)
So check that out, folks.

Greg Lambert (05:21)
If you’re curious to hear my singing voice, luckily the sound isn’t very good, so I’ll just blame it on that.

Marlene Gebauer (05:28)
That’s it.

Greg Lambert (05:29)
As far as the conference itself, I think again, second year in a row and I’m gonna call out the vendors, especially some of the big ones. These librarians are the people who buy your product, who use your product, who have to sell your product inside the law firms, who teach your products to law students, who provide them to the government, and absolutely no announcements out of AALL again this year. Harvey was a bronze sponsor this year. So thank you, Harvey, for doing that, but they didn’t send anybody. And I’m telling you, they would have been the belle of the ball because everyone wanted to talk Harvey and Legora while we were there. But one of the things that you have to know about the librarians and the library conference is again, these are not the people who install and support your products on the network. These are people that use it. So they were telling us about what’s working, what’s not working, what’s on the horizon, what students expect, what lawyers expect, what judges expect. I talked with Steve Embry while he was there. Steve was there, and Bob was not. Probably Bob’s got a little Cleveland bias too, maybe. I don’t know.

Marlene Gebauer (06:48)
See? Do we need to talk?

Greg Lambert (06:50)
But yeah. Yeah, you two could be good friends.

Marlene Gebauer (06:54)
Okay.

Greg Lambert (06:55)
But Steve, he and I were talking and He was like, this is just an amazing conference of people who understand how the technology works, how the products work. So kudos to Jenny for pulling it off. Great conference in Cleveland, Ohio. Hey, Cleveland rocks. I don’t

Marlene Gebauer (07:15)
Rocks.

Greg Lambert (07:15)
care what you say, Marlene.

Marlene Gebauer (07:16)
Cleveland Rocks. Cleveland Rocks. I told you where to go in the Rock and Roll Hall of Fame. So I have many good memories of my time in Cleveland.

Greg Lambert (07:21)
Man. Yeah. And I told you yesterday when we were talking, they had a women in rock exhibit in the 1990s and early 2000s.

Marlene Gebauer (07:31)
Mm-hmm. Yeah.

Greg Lambert (07:32)
It was fantastic. It was amazing. Yeah. So, all right, Marlene, what do

Marlene Gebauer (07:34)
Yeah, that’s cool. Well, I

Greg Lambert (07:38)
do you have up next?

Marlene Gebauer (07:39)
Well, I’ll quickly share that I also had a karaoke experience while I was away in Utah. Karaoke in Utah is very different than karaoke everywhere else. Yes.

Greg Lambert (07:49)
Karaoke in Ohio

Marlene Gebauer (07:51)
let’s just say, you know, you have to order food if you’re doing karaoke in Utah. Unfortunately we got there very late so we were not able to get a song in. However,

Greg Lambert (08:03)
Okay. Wait, wait. Tell people what very late is in Utah.

Marlene Gebauer (08:08)
Yes. Nine o’clock is very late. It’s very late. So they were playing “Goodnight, Sweetheart”. And so I jumped up and I finished it in an empty hall. Yeah, right. And there are no posts.

Greg Lambert (08:21)
It’s Utah’s version of closing time. So closing time’s a little too spicy.

Marlene Gebauer (08:29)
There are no posted videos, nor will there be. So sorry about that, everybody.

Greg Lambert (08:32)
That’s too bad. That’s too bad. Yeah.

Marlene Gebauer (08:35)
So, what was I going to start with? I’m noticing, and I’ve been talking to other people about this too, lots of innovation attorney jobs everywhere. It just seems that they’re in lots of different places. Yes.

Greg Lambert (08:49)
Yeah. I’m hiring. Are you hiring?

Marlene Gebauer (08:53)
We’re both hiring, in addition to a bunch of other firms and a bunch of other organizations. So I think it’s great. I mean, yes, I think there is going to be a lot of competition to get people, but that’s not a bad thing. I am elated that the industry is coming to see that these jobs are important, that people who have a deep knowledge of their practice but also understand technology, understand the workflows, understand how to translate between highly technical folks and the attorneys. This is kind of the glue that makes it happen. And so I think it’s great.

Greg Lambert (09:36)
Yeah. I know there was a story run a couple of weeks ago in Bloomberg, especially about the director and up positions, but I think even like entry-level, midlevel, and upper-level roles, this is a new set of skills and we’re looking for new talent. So I know I’m seeing people from outside of legal proper and outside of BigLaw that are applying. So it’s kind of interesting to see where we’re going and again, you know, we’ve made fun of law firms and the legal industry for, well, as long as I’ve been around about being slow

Marlene Gebauer (10:16)
Long time.

Greg Lambert (10:17)
to adopt technology. But I think we’re ahead of our peer industries when it comes to looking at a lot of the technology and beefing up internally. Even if you’re not spending half a billion dollars on infrastructure to help your firm out, you’re still spending a lot of money percentage-wise. I’d say it’s really good.

Marlene Gebauer (10:34)
Yeah. I mean, I do think this is the beginning. You’re starting to really see the beginning of kind of a change in staffing and therefore kind of a change in how the workflow operates because of the impact of Gen AI. So I think

Greg Lambert (10:55)
Yeah. Yeah.

Marlene Gebauer (10:56)
You’re going to see lots more of these kind of interesting new jobs. And I look forward to that.

Greg Lambert (11:02)
Yeah. I think it’s similar to the run that we saw in the early 2000s when Arthur Andersen folded and all of their knowledge management people came into the legal industry and kind of revamped how we looked at our data.

Marlene Gebauer (11:14)
Everything. Mm-hmm.

Greg Lambert (11:17)
I think we’re in that same kind of period of transition. So

Marlene Gebauer (11:22)
Yeah.

Greg Lambert (11:23)
Beef up your resumes, everybody. Send it to me and Marlene if you want.

Marlene Gebauer (11:25)
That’s right. Get tech skills, people skills.

Greg Lambert (11:29)
Yeah. All right, I’ve got one more conference that I’m going to that’s not ILTA. I actually got invited to speak at a conference that I’d never heard of before. Turns out there’s only like 11,000 people that go to this conference, and it’s called

Marlene Gebauer (11:43)
well.

Greg Lambert (11:43)
Ai4, the AI and the number four conference.

Marlene Gebauer (11:45)
In Vegas, baby.

Greg Lambert (11:47)
It’s in Vegas. It’s at the Venetian in Vegas in the best time of the year, early August. So I’m

Marlene Gebauer (11:54)
Only second to July when you fly in.

Greg Lambert (11:57)
Yeah. And again, it’s not a legal conference but AI focused. So it’s going to be interesting. I’m going to be speaking on the legal track. I’ve seen a few other law firm folks that are going to be there as well, hoping to learn a lot from outside the industry as well as what’s going on with some of our peers in legal.

Marlene Gebauer (12:21)
Yeah, I think our industry conferences are fantastic. I would never suggest anything otherwise. They really are second to none. But I think it’s good to go to conferences periodically that are outside of what you normally do. And I love the fact that they’re recognizing that they should bring in some people to hear from them about what they do because this is not something that’s normal for us. I think oftentimes that is the best way to learn about new things and get new ideas and see what’s being applied in other areas and whether we can apply it in our industry. So one of the things I’ve been really interested in is like personas, and you’ve probably seen in the news that AltaClaro now has DepoSim and you know, if you haven’t seen that, that’s an amazing tool. And essentially what it does is train people how to take depositions.

Greg Lambert (13:18)
Didn’t we interview the people on that?

Marlene Gebauer (13:21)
We have. Abdi was on, yes, for sure. And so what this does, in case you missed that one, is that it helps train you to take depositions or helps you improve your ability to take depositions. There are a number of personas, both for the deponent as well as opposing counsel. There are different fact patterns. And you can kind of go through the exercises and then you get a grade at the end. That’s a rather simplistic explanation, but you get the idea. But there are a lot of other things coming out. I mean, I attend the CodeX calls, the Stanford CodeX calls, and now I’m seeing a lot of students who are putting these things together. I am aware of former partners in BigLaw that are putting these things together for depositions and hearings. I was on a webinar that Opus 2 sponsored. I attended a couple of days ago and the chief innovation officer at Vorys was on and mentioned as part of that webinar that they have developed personas that they’re using to help people train for drafting, which is amazing. I’m a big fan of this. I think this is going to be a big deal in terms of training and getting people to learn. I’m waiting to see where this goes.

Greg Lambert (14:51)
Yeah. For someone who doesn’t understand when you say personas, do you have an example of what they should expect with a persona?

Marlene Gebauer (15:00)
Yeah, so a persona is basically a character. I mean, it has different, you know, qualities of a person. For example, in a deposition situation, it could be someone who’s very reticent to speak or someone who’s very outspoken and you would have different types of tasks with either one of these people. And that’s a simple example. You can get very sophisticated with some of the personas in terms of someone being a CEO at a small startup and these are their interests. You know, they like to go hiking, they like to ride bicycles, and personal time is important to them. They are concerned about protecting their IP. You can basically build these things to model, you know, whatever it is that you want to test against. Then you would interact with that persona, that model, and they would respond in a way that you would expect. And then you would kind of test yourself accordingly against that. So if you were drafting, then you would, you know, wait to see what their feedback was. You might ask questions. If you were doing a deposition, obviously you’d be asking questions of that persona and sort of dealing with both personas.

Greg Lambert (16:19)
And based on the persona, that’s the difference in the reaction you’re getting is that an outgoing person

Marlene Gebauer (16:24)
Yeah. And I

Greg Lambert (16:26)
may respond differently than an introverted person kind of deal, or

Marlene Gebauer (16:30)
Correct.

Greg Lambert (16:31)
a partner may respond differently than opposing counsel. So yeah, I can see that.

Marlene Gebauer (16:33)
Correct. And I should mention, Flatiron also has worked on this with Stanford and Megan Ma in terms of a negotiator simulator. I mean, very early on.

Greg Lambert (16:45)
Yeah.

Marlene Gebauer (16:46)
We had them on very early on for that.

Greg Lambert (16:48)
I was going to say if you want to know what’s going to happen in two years, talk to Megan Ma now. Well, you pointed one of these to me, but I’ve been seeing more now, you know, I’ve been writing a thing called Beyond the Model on our Substack since January.

Marlene Gebauer (17:05)
I do.

Greg Lambert (17:06)
I’ve been actually writing them since last year, where I’ve taken a fictional law firm and I apply technology to it.

Marlene Gebauer (17:12)
Fame comes slowly sometimes, you know? You just have to be patient.

Greg Lambert (17:16)
I’m the Megan Ma of legal fiction writing. How’s that?

Marlene Gebauer (17:20)
That’s it. That’s it. No.

Greg Lambert (17:24)
Of course, I always claim that I stole it from Anusia Gillespie after our interview with her. But I’ve noticed our friend Richard Tromans has the Innovators fictional story that he’s doing. Jennifer Leonard has a new book published by PLI that uses fictional characters to explain the story. It’s interesting. I think it’s a great way of taking facts or projections of how the technology can work in an actual law firm and applying it in a way that makes sense. In the latest story that I’ve done on our Substack page, I took a nonlegal-industry survey that was given to over 5,000 technology workers on how AI is affecting their job and their view of their importance within their job and applied it to a law firm where we gave our fictional law firm a survey to see how associates, partners and business professionals would take that survey and react to that survey. Again, fictional, but I feel pretty confident in where we were. The interesting thing that was in that survey was whether or not you would recommend your profession to someone who’s starting. In the tech survey, and I think this would apply in legal as well, the further up the chain you were, the more likely you were to suggest that somebody get into the industry to do what you do. So it’s

Marlene Gebauer (19:01)
Makes sense.

Greg Lambert (19:02)
And I think equity partners probably still see a lot of benefits. But I think associates right now, and even though I didn’t talk about law students, I’ve talked to a bunch of folks at law schools and they say the first-years, second-years, and third-years are all freaking out, mostly because of the early job offers that they’re getting, including summer associate gigs before they’ve even taken their first-semester tests and received their grades back. In fact, I heard at AALL that at one of the schools out on the East Coast, a T14 school, they saw earlier this year that an incoming 1L had already accepted a job before they’d even gone to their first class. I see this face. You’re not shocked at all. So it’s insane. The recruiters know it’s insane. The students know it’s insane. But other firms keep doing it.

Marlene Gebauer (20:05)
And yet we just keep doing it.

Greg Lambert (20:07)
Yeah. So I imagine that you know, if you score a 178 on your LSAT, you’ll probably get a job offer before you even apply to school. That’s going to be next. I don’t know. All right, high schoolers, we’re coming for you.

Marlene Gebauer (20:18)
Amazing. No pressure or anything. It’s like that, yeah, I know. It’s pressure enough. It’s like once you’re used to law school and how it’s done and then people are just sort of going in and taking jobs, having no idea. And they have to be

Greg Lambert (20:36)
Yeah. Well

Marlene Gebauer (20:38)
ready that soon without any type of legal prep.

Greg Lambert (20:40)
The other thing that I don’t think a lot of us are thinking about is that the students that don’t get these early offers are feeling like, Well, should I even be here? Is there a chance for me to get into a law firm now?

Marlene Gebauer (20:58)
I think sort of the key here is like you need to kind of have a flexible attitude toward this. I’m sure people went in thinking, okay, this is what it was going to be, but literally in like the last couple of years, the industry has changed remarkably and will continue to do that. And we were just talking about. Look at all these jobs that we’re hunting for. So, okay, it might or might not be an associate job at a law firm, but we’re hiring other types of roles where you can still use your legal smarts as well as any technical smarts you have. I know the big Gen AI companies, you know, are hiring people like this to be researchers, to be people that work on workflows. So I mean, there’s opportunity out there. You just kind of have to open your mind a little bit and the main thing is like, you know, get in there and get that experience. Now, in terms of your storytelling, I wanted to add that it totally makes sense because, look, there’s an entertainment value to fiction that, you know, isn’t present in these guidebooks, which are helpful and good, but you know, people respond to that. I think if you can combine something entertaining with a lesson or with knowledge, I mean that always goes a long way. And I think about how this is writing, but you know, we’ve had the oral tradition for thousands of years to pass on information. I don’t know about you, but you know, fairy tales and parables and Greek mythology, those were things I read when I was younger and I still remember. And I think this is just kind of a continuation of that.

Greg Lambert (22:55)
Yeah, and we’ll talk about this in a few minutes, but I was talking with one of the partners at my firm. There are really kind of two ways I write the story. One is teaching a technology and the reasoning behind it. It started off with why the legal research products suddenly got better. We talked about RAG technology and how the information is indexed, searched, and retrieved. He found that part very interesting. Then there’s a second style of story where I’m applying theory and business models to a law firm, and he’s like, yeah, I don’t like that part. Everyone has their own taste.

Marlene Gebauer (23:39)
Not as entertaining.

Greg Lambert (23:40)
So I don’t want that. I want to learn something. I already know how I’m running the business. I don’t need to know that

Marlene Gebauer (23:46)
Sure, but yeah, I mean, there are going to be other people for whom that’s their learning moment. So that’s, you know, you catch everybody. Okay. All right. So this one I thought was funny and it was kind of cute. So I wanted to bring it up that I was reading that Microsoft’s corporate external legal affairs group selected Harvey as their tool of choice. And I mean, yeah, it does make sense.

Greg Lambert (24:09)
Makes sense to me.

Marlene Gebauer (24:12)
It kind of makes sense and I’m thinking about again, I was reading that there’s sort of a deeper relationship now between Harvey and Microsoft and so it’s like, okay, well that totally makes sense, even though they have Copilot, but why not?

Greg Lambert (24:27)
I don’t know if I wanna say anything. Maybe they used Copilot and found out just how well it works.

Marlene Gebauer (24:35)
Well, I mean, you know, I think if you’re using it for legal, but I mean using it for other stuff is fine.

Greg Lambert (24:40)
Yeah. Copilot’s great because it connects to the M365 network and platform. So, yeah.

Marlene Gebauer (24:47)
Yeah, it connects to the stuff that you have and that is helpful. Very helpful.

Greg Lambert (24:52)
No, I use it every day, especially when

Marlene Gebauer (24:54)
Me too.

Greg Lambert (24:55)
I can’t find that stupid email I sent three weeks ago and I need to get it.

Marlene Gebauer (24:58)
How many would you say you use a day? Like different ones?

Greg Lambert (25:01)
Different ones?

Marlene Gebauer (25:02)
And for different things, I guess.

Greg Lambert (25:03)
Let’s see. I definitely use Harvey. Definitely use Claude every day. Okay, well let me tell you what I subscribe to.

Marlene Gebauer (25:13)
I use a lot.

Greg Lambert (25:15)
So I’ve got a $200 subscription to Claude,

Marlene Gebauer (25:18)
Crazy.

Greg Lambert (25:19)
and I’ve got a $20 subscription to Gemini. I’ve got a $20 subscription to OpenAI’s

Marlene Gebauer (25:26)
You’re an addict.

Greg Lambert (25:26)
ChatGPT. We have Harvey,

Marlene Gebauer (25:29)
You’re an LLM addict.

Greg Lambert (25:31)
we have Microsoft M365, so I have Copilot. So I use them all.

Marlene Gebauer (25:36)
Yeah, I mean I have OpenAI’s ChatGPT, I have the $20 plan. I have Claude, I have Copilot, I have Legora. There’s something else I have. What do I have? I feel like I’m missing one. I can’t remember right now, but not quite as many as you. Mm-mm, mm-mm. I refuse. I refuse.

Greg Lambert (25:54)
Just give your bank statement to Claude and it will tell you how many you have. Yeah. I actually had someone say on a LinkedIn post and I thought he was joking. He was like, “If you give Claude all of your data on your attorneys, their billing, their time entry, their forms, paperwork and emails that it does great for planning out these seven-figure deals.” And I know the person and I was going to do like a laughing emoji back to them and then I thought, nope, they’re actually being very serious about this.

Marlene Gebauer (26:28)
They’re serious.

Greg Lambert (26:30)
I’m like, if you want to watch my security ops team break into my office and tackle me away from my keyboard, that’s what I will start doing. Yeah.

Marlene Gebauer (26:37)
Mm. Wait, we’ll get the camera set up. Yep.

Greg Lambert (26:42)
Well, speaking of OpenAI, I don’t know if you saw the story since you were traveling, but apparently Hugging Face, the open-source platform, found that they had been hacked and they knew that whatever was hacking them must have been an agentic AI system because of the speed of what it was doing. A person couldn’t be doing this, so it had to be an automated attack. It turned out it was OpenAI doing a test on a benchmarking test that used a model and it was a combination of their new GPT-5.6 Sol and an unreleased model where it was given a test, put in a sandbox, and given limited access, supposedly. And like a resourceful college student, it found that rather than doing the work and doing the test as assigned, it was actually easier to go and break into the professor’s office and steal the key to the test and use it that way. So,

Marlene Gebauer (27:51)
Path of least resistance.

Greg Lambert (27:55)
It found a way through a multilevel hack to get internet access, went to Hugging Face, broke into Hugging Face using some stolen passcodes that it found, then found the answer key.

Marlene Gebauer (28:08)
Hugging Face had no idea what was going on.

Greg Lambert (28:10)
Found the answer key. So, I mean, it was interesting that it broke in and had access to all this, but was still on the mission of finishing the quest. It found the answer key, came back and answered the benchmarking test and apparently scored very high. But I will say that, you know, we’re joking around on this, but this is, I would say, this is a turning point in AI right now because the U.S. government as it’s set up today is very hands-off. There’s very little regulation. The federal government is actually stepping in and trying to keep states from regulating it. Now we’ve got this, you know, yeah, it’s kind of funny. But it’s also not funny. It’s not funny because

Marlene Gebauer (28:58)
This time it’s funny and

Greg Lambert (29:01)
Well, most of the podcast and writings I’ve seen on this said that if a human did this, it would be a crime. The FBI would be coming in and arresting people and instead you see Hugging Face’s and OpenAI’s leadership laughing it off and acting like this was a great thing that happened and if you’ve ever watched The Terminator, this is how it begins, people. This is how it begins.

Marlene Gebauer (29:25)
You know, I mean I know this isn’t human, but when you look at the whole picture, I mean it’s a very human experience. I mean, this is exactly what someone could do. A gray-hat or black-hat type of actor who says, okay, let’s see if I can do this and then does it. And what’s interesting here is that it basically assembled a bunch of actions that weren’t part of that environment and went with it and was very successful. Hugging Face was like, Okay, something’s happening but we don’t know which model is doing it. Then the model actually reveals later.

Greg Lambert (30:08)
what was even

Marlene Gebauer (30:09)
it’s like, it was me.

Greg Lambert (30:10)
What was even funnier was they tried to use both OpenAI’s foundational models and Claude’s foundational models to help fight off the

Marlene Gebauer (30:22)
Ha ha

Greg Lambert (30:22)
attack. But it wouldn’t let them because they had guardrails that wouldn’t allow them to use

Marlene Gebauer (30:27)
It’s like, sorry, we can’t do that.

Greg Lambert (30:29)
their tool to help because they could be going the other way. So they had to use the Chinese models. They had to use the Chinese models to fight it.

Marlene Gebauer (30:30)
So they were the white hats. It’s like we follow the rules. Yes, yes, I read that. It’s like, hey, can we borrow your models? Crazy. Mm-hmm. Mm-hmm.

Greg Lambert (30:42)
Yeah. It’s a great time to be alive. I’m going to end with that.

Marlene Gebauer (30:48)
Yeah. You know, I think KM is having a moment again. I’m very happy to see that. And I think you’re starting to see how, we had looked at, you know, Gen AI in terms of like efficiencies and doing things faster and better. And now I think the industry is turning towards how we can use AI to kind of harness knowledge that we have and surface knowledge that we have and combine knowledge that might be out there. So your traditional KM, you know, document-based, it’s like, okay, well, we’ll have things like that. But, you know, we may also have personas. We may also have financial information. We may also have information from the outside, you know, public information. Being able to synthesize all of this and surface all of it for different types of work is becoming important. So I know NetDocuments is rolling out some new AI-enabled capabilities. I know iManage is as well. I’m wondering if everybody was saving their announcements for ILTA. So we might hear something

Greg Lambert (31:59)
They are. They are.

Marlene Gebauer (32:01)
Something there. We’ve talked to you know a number of people that have products, like ClearPeople and their product, Atlas, and then Atlas AI, who we just recently had on the show. There are a number of these vendors that are looking at how to do this. Microsoft is also doing this as well. And I think the trick is

Greg Lambert (32:24)
But are they doing it through Harvey? That’s what I wanna know.

Marlene Gebauer (32:29)
I should say Harvey also has something, too. And so I think the trick at this point is, you know, going back to what you were saying about security and sort of what we’re comfortable letting these tools access because, you know, we’re still in a situation where certain clients are saying, you know, I don’t want Gen AI to touch any of my stuff, or I don’t want my work to be combined with your other work. There’s also movement towards a need-to-know model in your DMS. Given all of these factors, how do we make these tools work effectively for people? And I mean, it’s going to be done because, I mean, some of these tools already follow the rules that your DMS already has in terms of who can access what. So you know, I think it’s really just a finesse question.

Greg Lambert (33:23)
Well, you know, giving the AI rules works every time, right, OpenAI. So, yeah. Now I do

Marlene Gebauer (33:29)
Yeah, why did I have this story like right after that one? It’s like, never mind. Never mind.

Greg Lambert (33:36)
I do want to say that in the OpenAI test, they actually took the guardrails off, which explains part of this. But they thought because it was in a sandbox environment that would protect it. But yeah, of course, yeah.

Marlene Gebauer (33:45)
They’d be okay. I feel much better now. Of course.

Greg Lambert (33:52)
I watched a video last week from Claire Vo, who hosts How I AI. It’s great, and although it isn’t legal, I think very applicable to what we’re doing. Last week she was showing how she makes AI harnesses. And after

Marlene Gebauer (34:13)
Tell us what a harness is.

Greg Lambert (34:14)
So a harness is essentially software you put on top of the AI to help guide the AI on what you’re asking it to do, the rules that you set up, and the output. And one of the things that stood out as she was talking about how she develops the AI harnesses for the tasks she performs, mostly for evaluating code she uses for her company and its website. If you need something to be the same every single time regardless of the AI model that’s underneath, the harness, she explained, is how you get there. So you set up the parameters of this is how you handle the information, this is what you do, and then this is the output and every single time it’s the exact same thing. And there’s lots of stuff that we do that falls into those parameters, those guidelines of making sure that if we’re processing data that it processes in a certain way and the output is in a certain format. So I’m going to make the projection. You know how we’re talking about AI agents in 2026 and turning the agents loose. I think for 2027 we’re going to be talking about AI harnesses. We’ve got the power. Now we need to set the direction of how these very powerful models actually work in a predictable and consistent way. So if you’re not looking into harnesses now, I suggest you do that. Coincidentally, there’s a three-part story that I’m writing on Substack this week that talks about AI harnesses. So if you haven’t subscribed to the Substack page, do so now.

Marlene Gebauer (35:57)
And in case you wanted to hear more, are you aware of any vendors that are using these or promoting them?

Greg Lambert (36:11)
Well, they all have harnesses. So, when you’re using the interface on Harvey, that is a harness. It’s a layer on top of the AI company’s models.

Marlene Gebauer (36:21)
But this harness apparently produces the same results every time. So

Greg Lambert (36:24)
Yeah. You set it up, you tell it what to do. You program it or vibe code it, and this is the interface that you use or this is the tool that uses the AI and then guides the AI and its output. So, lots of things to learn. Like I said, three-part story coming out this week, so stay tuned. Yeah.

Marlene Gebauer (36:44)
Very good. We’ll be looking for it. I wanted to put in one thing about small firms and I saw something in the news. It’s an AI legal receptionist called LexiDesk. What I thought was interesting about it is that it captures quality business during intake. So it’s not just strictly a receptionist, it’s trying to figure out whether this is the correct business for you. What I thought was interesting about it was what counts as quality business how You program that, how do you prompt that and how do you make sure that’s happening? Can it recognize urgency or vulnerability? Because, again, we have certain obligations as attorneys to address things. So, can it figure that out? How is confidentiality handled? I mean, it’s one thing to record that a call came in and schedule something on your calendar and it’s another thing to really do an assessment of the call and what your next steps are. So I thought it was an interesting one.

Greg Lambert (37:50)
Yeah. Speaking of interesting, this is either going to be really interesting or not interesting at all. You know how we’ve been asking folks to tell us about what resources they’re using to read or keep up with the industry. I heard a very interesting question this week from Simon Willison when he was talking with three engineers at Anthropic and one of the questions he asked was, what is true today that wasn’t true a year ago for you? And of course these were engineers talking about Claude Code and the harness and everything that they’re doing. But we may swap out the question. We ask a crystal ball question about looking into the future. I think this would be a good setup to, you know, take a peek back at the past and look at today and say, you know, what’s true today that wasn’t true a year ago? And so, Marlene, you get to be my guinea pig on this. What’s something that’s true today that wasn’t true a year ago?

Marlene Gebauer (38:55)
Well, a year ago I think there was a lot more emphasis on adoption and just using the tools. And I think now there is much more focus on incorporating this into workflows. We need to change the workflows. It’s not just making an existing workflow more efficient. It’s basically changing the workflow completely. And that includes staffing, that includes pricing, and so it’s I mean, the whole ride has been interesting, but it’s super interesting now because you’re bringing in a lot of different experts. You know, there’s a lot of different departments this touches. You have these groups kind of working together, which I think is, you know, absolutely incredible and interesting. Everybody’s bringing their expertise to the table to make these changes in a positive way for businesses. The other thing that is quite different, and this is very recent, is now everybody’s sort of talking about the cost of generative AI. Because I think, you know, we got everybody on board and now all of a sudden the reality hits I know, I know. And it’s

Greg Lambert (40:12)
Wait, wait, I can’t burn every token I can get my hands on? Yeah.

Marlene Gebauer (40:18)
Again, it’s intellectually interesting. It’s also very stressful. I mean, from a business perspective in terms of like what this is going to cost and how is it going to get paid for? And everybody has seen stuff in the news about paying per token, but I don’t know whether that is going to end up being the right model. And I do think vendors are trying to be thoughtful about this. I mean, I’ve had some discussions with some of them and you know, they are trying to be very thoughtful about how to do this because straight-up token costs or use costs. I mean, we’ve seen that model before, like you and I have seen that model before. And it’s the old Lexis Westlaw model from years and years ago. And

Greg Lambert (41:02)
Yeah. We’ll just find a way to track the cost and then pass that on to the client, right? Then

Marlene Gebauer (41:09)
Right. And I mean, look, in the beginning that made very good sense, but you know, in time it became a situation where clients said, look, that’s the cost of doing business and we’re not gonna pay for that. So a lot of times that is not paid for. And I think now it’s even more imperative to think about it because everybody has access to these tools now. I think there’s a fine balance between saying it’s like, okay, we’re gonna pass on, you know, token costs to people, to clients, and clients are like, well, you know, we could do that. Like we can do it ourselves. I think there’s a lot more thought that has to go into sort of what you’re doing with the AI and you know, the value that provides to the clients. And whether you’re kind of working on these things together or if it’s something that’s just more internal that helps you.

Greg Lambert (42:08)
Yeah. I’m going to kind of spin this into our crystal ball question when it comes to the tokens because I can’t remember, I think it’s Taleb’s Law where it’s the saying, “I’ve seen gluts not followed by shortages, but I’ve never seen a shortage not followed by a glut.” I don’t think it’s going to be next year, but I think in two or three years this whole, you know, looking at the cost of tokens is going to be a nonstory anymore because

Marlene Gebauer (42:43)
Agreed.

Greg Lambert (42:44)
efficiencies are going to catch up. Hopefully the latest model doesn’t burn fuel like a rocket. We’ll find efficiencies because we’re gonna have to. And I think competition will help. I think we’re gonna see more competition from the Chinese models. We’ll probably see competition from open-source models from Europe. We’ll probably see some regulation come in after 2028 perhaps. But in the meantime this is a wave we’re gonna have to ride till it breaks, right? So

Marlene Gebauer (43:16)
Yeah, I mean, I agree that it’s not a long-term issue, but it is a significant

Greg Lambert (43:21)
But it’s an issue.

Marlene Gebauer (43:22)
It’s a significant short-term issue. I mean, in terms of cost, but I agree with you and I mean, I think competition is going to force people, I think, to become better users. I mean, it’s sort of a painful way to do it, but

Greg Lambert (43:39)
Yeah.

Marlene Gebauer (43:40)
it will do it. So that is also not a bad thing.

Greg Lambert (43:45)
Yeah. Well, it was great catching up with you this week.

Marlene Gebauer (43:50)
Yeah, you too.

Greg Lambert (43:52)
It’s always nice to have these and just kind of talk about what’s going on in the industry ’cause man, there’s so much. So much going

Marlene Gebauer (43:57)
So much. So much.

Greg Lambert (44:00)
on. So thanks for sitting down in the morning and for letting me pull out my really cool coffee mug.

Marlene Gebauer (44:08)
Just got to rub it in. You just got to rub it in.

Greg Lambert (44:11)
And show everybody your Starbucks cup while I show my Wolverine claws.

Marlene Gebauer (44:13)
Yeah. No, they saw it already. It’s okay. I’ll bring a good one next time. Thanks to all of you for listening to The Geek in Review. If you enjoyed the show, please share it with a colleague. You know, we’d love to hear from you on LinkedIn and Substack. And as always,

Greg Lambert (44:27)
Yeah. Subscribe to that Substack. Watch those stories roll in.

Marlene Gebauer (44:31)
Greg needs likes. Greg needs likes.

Greg Lambert (44:34)
I do!

Marlene Gebauer (44:34)
And as always, the music you hear is from Jerry David DeCicca. Thank you, Jerry, and goodbye everybody.