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- [00:01]
A
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- [00:27]
B
Hello and welcome to Decoder. I'm John Fort, CNBC journalist, co host of Closing Bell Over Overtime and creator of the Fort Knox streaming series on LinkedIn. This is the last episode. I'll be guest hosting for Nilay while he's out on Parental Eve. We have an exciting crew who will take over for me after that, so stay tuned. Today I'm talking with Richard Robinson, who's the co founder and CEO of Robin AI. Richard has a fascinating resume. He was a corporate lawyer for high profile firms in London before founding Robin in 2019 to bring artificial intelligence tools to the legal profession using a mix of human lawyers and automated software expertise. That means Robin predates the big generative AI boom that kicked off when ChatGPT launched in 2022. As you'll hear Richard say, the tools his company was building early on were based on fairly traditional AI technology, what we would have just called machine learning a few years ago. But as more powerful models and the chatbot explosion have transformed industries of all types, Robin AI expanding its ambitions, it's moving beyond just using AI to parse legal contracts into what Richard is envisioning as an entire AI powered legal services business. AI can be unreliable though, and when you're working in law, unreliable just really doesn't cut it. It's impossible to keep count of how many headlines we've already seen about lawyers using ChatGPT when they shouldn't, citing non existent cases and law in their filings, those attorneys have faced not only scathing rebukes from judges, but also in some cases, even fines and sanctions. So I wanted to ask Richard about hallucinations, how he thinks the industry could move forward here, and how he's working to make sure Robin AI's products don't land any law firms in hot water. But Richard's background includes not just law, but also professional debate. Richard was the head debate coach at Eton College, and so much of his expertise here, right down to how he actually structures his answers to some of my questions can be traced back to just, just how experienced he is at the art of argumentation. So I really wanted to spend time with Richard, talking through his history with debate how it ties into both the AI and legal industries and how these new technologies are making us re evaluate the difference between facts and truth in unprecedented ways. Okay, Robin AI CEO Richard Robinson. Here we go. Richard Robinson Founder and CEO of Robin AI Great to have you here on decoder.
- [03:12]
C
Thanks for having me. I really appreciate it. It's great to be here. I'm a big listener of the show.
- [03:17]
B
I'm going to be all over the place here. But I want to start off on Robin AI. We're talking a lot about AI in a lot of different ways nowadays. I started off my decoder run with Cassie Kozakov, talking to her about decision science. But this is a specific application of artificial intelligence in an industry where there's a lot of thinking going on and there ought to be. So legal industry, tell me, what is Robin AI? What's the latest?
- [03:47]
C
We're building an AI lawyer and we're starting by helping solve problems for businesses. Our goal is to essentially help businesses grow. It's one of the biggest impediments to business growth is it's not just about revenue, it's not just about managing your costs. It's legal complexity. Businesses dealing with legal problems actually slows them down. So we exist to solve that problem. We've built a system that essentially helps a business understand all of the laws, all of the regulations that apply to them, and also all the commitments that they've made, their rights, their obligations, their policies. We use AI to make it easy to understand that information, easy to use that information to ask questions about that information to solve legal problems. We call it legal intelligence. So that's how we're taking the latest AI technologies. We're taking them to law school and we're giving them to the world's biggest businesses to help them grow.
- [04:49]
B
Year and a half ago I talked to you and you're description was a lot heavier on contracts. But you said we're heading in a direction where we're going to be handling more than that. It sounds like maybe you're more firmly in that direction now.
- [05:02]
C
Yeah, that's correct. I mean, we've always been limited by the technology that's available. Before ChatGPT, we had very traditional AI models. Today we have, as you know, much more performant models. And so that's just allowed us to expand our ambition. And you're completely right. It's not just about contracts anymore. It's about policies, it's about regulations, it's about the different laws that apply to a business. We want to help them understand their entire legal landscape.
- [05:30]
B
Give me a scenario here, case study wise on the sorts of things that using your technology, your customers are able to sort through. And then recently, you guys have amped up your presence on AWS Marketplace, so there are a lot more. Different types of companies are going to be able to plug in Robin AI's technology to all kinds of different software and data that they have available. So case study, what's the technology doing now? How is that kind of hyperscaler cloud platform potentially going to open up the possibilities for you?
- [06:09]
C
We help solve concrete legal problems. So a good example is every day, our customers, organizations, they want to know whether or not they're doing something that's compliant with their company policies. And those policies are uploaded to our platform. And anybody can just ask a question that historically would have gone to the legal team or the compliance team. They can say, I've been offered tickets to the Rangers game. Am I allowed to go under the company policy? And we can use AI to intelligently answer that question. Every day, businesses are signing contracts. That's how they record pretty much all of their commercial transactions. And now they can use AI to look back at their previous contracts and then use that to help them answer questions about the new contract they're being asked to sign. So if you're doing a deal with the Rangers and you've worked with the Mets in the past, you might want to know, what did we negotiate that time? How did we get through this impasse last time? You can use the Robin platform to answer those questions. So I got to go back to.
- [07:15]
B
That Rangers game situation. Please tell me you're going to be able to do away with that annoying corporate training about, you know, can I take these tickets or can I. If that could be just a conversation with an AI instead of having to watch those videos. Oh, my goodness, all the money.
- [07:32]
C
I'm trying. I'm trying my best. This is exact. You're. You're hitting the nail on the head, though. A lot of this stuff caused a lot of pain for a lot of businesses, either through compliance and ethics training, through long, sometimes dull courses, we can make that so much more interesting, so much more interactive, so much more real time now that you can use AI technologies like Robin. So we're really working on it, and we're helping solve a whole vast range of legal use cases that in the past you needed people to do.
- [08:06]
B
Are you kind of taking away the work of the junior lawyers? I mean, is there Any. How is it? It's a little bit of a. A straw man. I'm throwing up there. But how is it changing the work of the entry level, sort of law student, intern, whatever, who would have been doing that tedious stuff that AI can perhaps now do? Is there higher level work or are they just getting used less? What are you seeing your customers do?
- [08:35]
C
I think what we've observed is that if you think about the past, if a business had legal problems, they would either send them to a law firm or they would try and handle them internally with their own legal team. With AI, they can handle more work internally so they don't have to send as much to their law firms as they used to because they essentially have this leverage to tackle what used to be quite difficult pieces of work. And so there's actually more work that they can do themselves now instead of having to send it outside of the house. And then there's some buckets of work where you don't need people at all. You can just rely on systems like Robin to answer those compliance questions. So you're right, the work is shifting. No doubt about it. For the most part, I can't replicate. It's not a whole job yet it's parts of a job, if that makes sense. So we're not seeing anybody cut headcount as a result of using our technologies. But we do think they have a much more efficient way to scale and they're reducing their dependence on their law firms over time because they can do more in house.
- [09:45]
B
But how is it changing the work of the people who are still thinking.
- [09:49]
C
AI goes first, basically? And that's a big transformation. But you see this in the coding space, which I think they got ahead of adoption in the legal space, but we're fast catching up. But a lot of engineers who are using these coding platforms, if you talk to them, they'll tell you for the most part, they want the AI to write all of the code first, but they're not necessarily going to just hit enter and use that code in production. They're going to check, they're going to review, they're going to question and interrogate it and redirect the model where they want it, because these models still make mistakes and so their hands are still on the driving wheel. It's just that they're doing it slightly differently. They're using AI to go first and then people are being used to check. And pretty much everything we do, we make it easy for people to check our work. We include pinpoint citations, references, we Explain where we got our answers from. And so the role of the junior lawyer or the senior lawyer is now, hey, use Robin first and then your job's to make sure that it went correctly, it's been used in the right way.
- [11:00]
B
How are you avoiding the issue of hallucinations? We've seen these mentions in the news of lawyers submitting briefs, arguments to a judge that include stuff that is just completely made up. We hear about the ones that get caught. I imagine we don't hear about the ones that don't get caught. Now, I know those are different kinds of AI uses than what you're doing with Robin AI, but there's still gotta be this concern in a fact based, argument based industry about hallucination.
- [11:35]
C
There is. It's the number one question our customers ask. And I do think it's a big part of why you need specialist models for the legal domain. Because it's a specialist subject area. It's a specialist domain. You need to have applications and people like Robin who are not just taking ChatGPT or Anthropic and, and doing nothing with it. You need to really optimize its capabilities for the domain. So in terms of what we're doing to answer your question directly, I mean, we include citations with very clear links to everything the model does. So every time we give an answer, you can quickly validate the underlying source material. That's the first thing. The second thing is we are working very hard to only rely on external, valid, authoritative data sources. So we connect the model to specific sources of information that are legally verified so that we know that we're referencing things you can rely on. And I'd say the third is that we're educating our customers and reminding them that they're still lawyers. You know, I used to write cases for courts all the time. That was my job before I started Robin and I knew back then it's my responsibility to make sure every source I reference is 100% correct. It doesn't matter which tool you use to get there, it's on you as a legal professional to validate your sources before you send them to a judge, before you even send them to your client. So some of this is about personal responsibility because AI is a tool, you can misuse it no matter what safeguards we put in place. So we have to teach people don't rely exclusively on these things because they can lie confidently. You're going to want to check for yourself right now.
- [13:25]
B
All kinds of relationships, all kinds of arrangements are getting renegotiated globally, internationally, Deals that made sense a couple years ago perhaps don't because of tariffs or expected tariffs or frayed relationships. And I imagine certain companies are having to look back at the fine print and say, what exactly are our rights here? What exactly? What's our wiggle room? What can we do? Is that a major AI use case? How are you seeing language getting combed through, getting compared from perhaps how it was phrased 20 years ago to how it needs to be phrased now?
- [14:06]
C
That's exactly right. Any type of change in the world triggers people to want to look back at what they've signed up for. And you're right, the most topical is this, the tariff reform, which is affecting every global business, for sure. And people want to look back at their agreements. They want to know, can I get out of this deal? Is there a way I can exit this transaction? Because we entered into it with an assumption about what it was going to cost, and those assumptions have changed. And that's very similar to what we saw during COVID when people wanted to know, hey, can I get out of these agreements? Given that there's an unexpected, huge pandemic happening, we're seeing the same thing now that this time we have AI to help us. And so, yeah, people are looking back at historic agreements. I think they're realizing that they don't always know where all their contracts even are. They don't always know what's inside them. They don't know who's responsible for them. And so there is work to do to enable AI to be effective. But we are absolutely seeing customers who are global businesses trying to understand what the regulatory landscape means for them. And that's going to keep happening. Every time there's regulatory change, every time there's new laws passed, it causes business and even government to look back and think about what they signed up for. I'll give you another quick example. When Trump introduced his executive order relating to DEI universities, a lot of universities in the United States needed to look back at, okay, what if we agreed, what's in some of our grant proposals? What's in some of our legal documents, what's in some of our employment contracts? Who are we engaging as consultants? And is that in danger given these executive orders? And we saw that as a big use case, too. So I think it's permanent. Change is a reality for business, and AI is going to help us to navigate that.
- [15:59]
B
What does the AWS marketplace do for you?
- [16:02]
C
Well, I think it helps give customers confidence that they can trust us. I think when we had the cloud and businesses started to adopt. The biggest reason that it took time for that adoption, as well as change management, was concern about security. For business, keeping their data secure is probably their single most important thing. It's a never event. You can't ever let your data go insecure. But for business to really get the benefit of AI, they're not going to be able to build everything themselves. They are going to have to partner with experts and with startups like Robin AI. But they need confidence that when they do that, especially with their most sensitive documents, that they're gonna be secure, they're gonna be protected. So the AWS marketplace, first and foremost, it gives us a way of giving our customers confidence that what we've done is robust, that our application is secure, because AWS security vet all the applications that are hosted in the marketplace. So it gives.
- [17:01]
B
So it's like trust. It's like Costco. It sounds like I'm not a business vendor or software company like you are, but this sounds to me like I know if I go shopping at Costco, there are certain guarantees. I know their reputation because I'm a member, right? They curate what it is that they carry on their shelves. They sort of stand behind it. And so if I have a problem, I can just take my receipt and go to the front desk and say, hey, I bought this here, and blah, blah, blah, blah, blah. And you're saying it's the same thing with these AI driven capabilities in a cloud marketplace.
- [17:37]
C
Yeah, that's right. That's right. You get to leverage the brand and the reputation of aws, who are the biggest cloud provider in the world. And that second thing you get, you mentioned it there too, is we get basically a seat at the table for the biggest grocery store in the world. Right? They have lots of customers and a lot of businesses make commitments to spend with aws. And they will first and foremost choose vendors who are hosted on the AWS marketplace. They'll go to them first. And so it gives us a position in the shop window to help us advertise to customers. That's really what the marketplace gives to Robin AI.
- [18:18]
B
We have to take a quick break. We'll be right back.
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C
Foreign.
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D
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- [19:44]
B
We're back with Robin AI CEO Richard Robinson discussing the landscape for legal AI tools. Now, I want to take a step back, get a little philosophical about this. We got a little in the weeds with the enterprise stuff, but part of what's happening here with AI and I think, in a way with legal, is we're having to think differently about just how we navigate the world. And it seems to me that the two steps at the core of this are, first, how do we figure out what's true? And then two, how do we figure out what's fair? And you're. You are a student of. A practitioner of debate. We'll get a little bit into that in a bit, too. But figuring out what's true. I'm not a professional debater, though I have been known to play one on tv. Figuring out what's true is step one, right?
- [20:38]
C
Yeah, I think it is. It's increasingly difficult because there are so many competing facts and there are so many different communities where people will selectively choose their facts. But you're right. You need to establish the reality, the core facts, before you can really start to make decisions and start to debate what you should be doing and what should happen next. And I do think AI helps with all of these things, but it can also make it more difficult. These technologies, they can be used for good and bad. And so it's not obvious to me that we're going to get closer to establishing truth now that we have AI.
- [21:15]
B
I think you're touching on something here that's interesting right off the bat. The difference between facts and truth.
- [21:20]
C
Yes, that's right. It's very difficult to really get to the truth. I mean, facts can be selectively chosen. I've seen spreadsheets and graphs that technically are facts, but they don't really tell the truth. So there's a big gap there.
- [21:39]
B
How does that play into the way we as a society should think about what AI does? AI systems are going out training on data points that might be facts, but the way those facts or those details, those data points get arranged ends up determining whether they're telling us something true, right?
- [22:06]
C
I think that's right. I think that as a society, we need to use technology to enhance our collective goals. We shouldn't let the technology just run wild. Now, that's not to say that we should regulate these things, because I'm generally quite against that. I think we should let innovation happen to the greatest extent reasonably possible, but we should have a say as consumers. We do have a say in how these systems work and how they're designed and how they're deployed. I think as it relates to the search for truth, the people who own these systems and who use them, they've grappled with these questions in the past. So if you want to Google search certain questions, like the racial disparity in IQ in the United States, you're going to get a fairly curated answer from the Google search. And I think that that is in itself a very dangerous, polarizing set of topics. And I think we need to certainly ask ourselves the same questions that we've asked with the last generation of technologies to this new technology, because that's what it is. AI is just a new way of delivering a lot of that information. In some ways, it's a more effective way. It's going to do it in a more convincing and powerful way. So it's even more important that we ask ourselves, how do we want information to be presented? How do we want to steer these systems so that they deliver truth, so that they avoid bias? It's a big reason why Elon with Grok has taken such a different approach than Google took with Gemini. If you remember, the Gemini model famously had black Nazis, and it refused to answer certain questions. It was alleged to have some political bias. It was because I think Google was struggling to answer and resolve some of these difficult questions about how do you make the models deliver truth, not just facts, and they maybe hadn't spent enough time passing through how they want to do that.
- [24:16]
B
Well, I mean, yeah, Grok seems to be having its own issues, right? It's very much like people, right? Like somebody who swings in one way, you know, has trouble with certain things, and somebody who swings in another way has trouble with some other things. There's the matter of facts, and then there's what people are inclined to believe. And I'm getting closer to the debate issue here, but sometimes you have facts that you string together in a certain way, and it's not exactly true, but people do really want to believe it, right? They embrace it. And then sometimes you have truths and people just completely want to dismiss it. The quality of the information, the quality of the truth or confusion doesn't necessarily correlate with how likely your audience is to go, yeah, Richard's right. How do we deal with that at a time when these models are designed to be convincing, regardless of whether they're stringing together the facts to create truth or whether they're stringing together the facts to create something else?
- [25:28]
C
Well, I think that you observe confirmation bias with or without AI throughout society. People in search of facts that confirm their prior beliefs. There's something comforting to people about being told and validated that they were right. And whether you've got whatever technology you might use, that's just a baseline for all human beings, I think, is this desire to feel like they're correct. And so I think if you want to shape how people think, you want to convince people of something that you know to be true, you have to start from the position that they're not going to want to hear it if it. If it's inconsistent with or incongruent with their prior beliefs. And I think what AI can do is it can make these things better and it can make these things worse, right? It can. For people who are looking for facts that just back them up and validate what they already believe, it's going to make that much easier. It's going to give you the world's most efficient mechanism for delivering information of the type that you choose. But I don't think all is lost, because I think as well, people who are trying to provide truth or help change people's perspective show them a new. A new way. We have a new tool in our armory to do that, right? We have this incredible research assistant called deep research that we never had before, which means we can start to deliver more compelling facts. We can get a better sense of what types of facts or examples are going to convince people. Using technology, we can build better ads, we can make more convincing statements, we can road test buzzwords, we can be more creative because we have AI, and fundamentally, we've got a sparring partner that helps us to craft our message. So I think AI is basically going to make these things better and worse all at the same time. My hope is that the right side wins. People in search of truth are able to be more compelling now that they've got a whole host of new tools available to them if they use them and they learn how to use them. And it's not guaranteed that people will learn these new systems unless people like me and others, people like you, are out there helping to proselytize on the benefits and capabilities these things bring.
- [27:53]
B
But it feels like we're at a magic show, right, where the reason why so many illusions work is because the audience gets primed to think one thing right, and then a different thing happens. Being conditioned. And AI can be used to convince people of truth by understanding what they already believe and building a pathway. So. But they could. It can also be used to lead people astray by understanding what they already believe and then adding little breadcrumbs to make them think. Believe whatever conspiracy theory may or may not be true, how is it swinging right now? And then how does a product like the ones Rob and AI are putting out lead society, lead all this in a better direction?
- [28:44]
C
I think a lot of this comes down to validation. Sam Altman said something that I thought was really insightful. He said that the algorithms that power most of our social media platforms, x, Facebook, Instagram, they're the first example of what AI practitioners call misaligned AI at scale. And what we mean by that is systems where the AI models are not actually helping achieve goals that are good for humanity. So the algorithms in these systems, this is before ChatGPT, but they are using machine learning to work out what kind of content to surface for people. And it. So it turns out people are entertained by really outrageous, really extreme content. It just keeps their attention. I don't think anybody, though, would say that that's good for people. It makes them better. It's not nourishing. There's no. There are no nutrients in a lot of the content we're getting served to us on these social media platforms. Whether it's politics, whether it's people squabbling, whether it's culture wars, these systems have been giving us information that's designed to get our attention. And it seems like that's just not good for us. It's not nutritious. So I think that on the whole to date, we're not doing very well in the battle to search for truth because the models haven't been optimized to do that. Actually, they've been optimized to get our attention. And so I think you need platforms that find ways to combat that. So to the question of how do systems like us but also, more broadly, how do AI applications help combat this? I think it is by creating tools that help people validate the truth of something. The most interesting example of this, at least in this popular social paradigm, is Community Notes, because Community Notes is a way for someone to say this isn't true or this is false, or you're not getting the whole picture here. And it's not edited by a shadowy editorial board. It's generally crowdsourced. Wikipedia is another good example. These are systems where you're basically using the wisdom of the crowds to validate or dis. Validate information. In our context, we use citations. So we're saying, don't trust the model, test it. It's going to give you an answer, but it's also going to give you an easy way to check for yourself if we're right or if we're wrong. And I think for me, this is the most interesting part of AI applications. It's all well and good, having capabilities, but as long as we know that they can be used for bad ends or they can be inaccurate, we're going to have to build countermeasures that make it easy for society to basically get what we want from them. And I think Community Notes, citations. These are all children in the same family for trying to understand how these models truly work and are affecting us.
- [31:44]
B
Another child in that family. Leading me right to where I was hoping to get is debate, because to me, debate is gamified truth search. You're searching for truth and you create these sort of warring tribes and they assemble facts and they fight with each other. And it's like, here's my set of facts and here's my argument that I'm making based on that. And okay, well, here's mine. Well, here's why yours are wrong, or why you're using yours wrong, or you forgot about this and it happens out in the public square and then people can see, and then people decide who wins. And it's fun, but the payoff is that we're smarter. At the end, we should be right. We've gotten to sift through and pick apart these things, hopefully on the right basis if the teams have done their work. Do we need a new model of debate? In the era of AI, should these models be debating each other? Should there be debates within them? Do they get scored in a way that helps us understand either the quality of the facts, the quality of the logic in which those facts have been strung together to come to a conclusion, the quality of the analysis that was developed from that Conclusion is that really part of what we're trying to claw toward right now is how to gamify a search for truth and vetted analysis in this sea of data we've got now?
- [33:14]
C
Well, I think that's what we should be doing. I don't know that confident we're yet seeing that. But if you go back to what we said earlier, I actually think what we've observed over the last five or six years at least is people becoming. There's less and less debate actually. People are in their communities, whether they're real or digital. They're getting their own facts and they're actually not engaging with the other side. They're not seeing the other side's point of view. They're getting the information that's serve to them. So almost the opposite of debate. I think what we need is we need to use these systems to do a really robust job, like you say, of surfacing all of the information that's relevant of characterizing both sides. And I think that's really possible, for instance, in a political debate. I watched some of the presidential debates and I watched the New York mayoral debate recently, which was, it was really interesting. But we now have AI systems that could give you a live fact check during the debate or it could give you a live alternative perspective during the conversation. Wouldn't that be great for society? Wouldn't it be good if we could use AI to have more robust conversations? And like you say, the gamified search for truth, I think it can be done in a way that's entertaining, that's engaging and that ultimately drives more engagement than what we've had in the past.
- [34:48]
B
We have to take another quick break. We'll be right back.
- [35:02]
E
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- [35:32]
B
We'Re back with Robin AI CEO Richard Robinson. Before the break, we were exploring how AI systems can be very convincing even when what they're saying is blatantly false and what that means at a time when society seems to disagree about what's even true. But now I wanted to ask Richard about his debate background and how that informs his views on the future of AI. So let's talk about how you got into debate. You grew up in an immigrant household where there were lots of arguments all the time. And my sense is that debate paved your way into law. But tell me about the environment of debate that you sort of grew up in and what that did for you intellectually.
- [36:26]
C
Like you say, my family were arguing all the time. It was gather round, watch the news together and argue about every story. It really helped me to develop a level of independent thinking because there was no credit for just agreeing with someone else. You really had to have your own perspective. It encouraged me to think about what I was saying more than anything else, because you could get torn apart if you haven't really thought through what you have to say. And it made me value debate to help you change your mind as well, to help you find the right answer. Coming to a conversation, wanting to know the truth, not just wanting to win the argument. And so for me, those are all skills that you observe in the law. Law is ambiguous. I think people think of the legal industry as being black and white, but the truth is almost all of the law is heavily debated. That's what the Supreme Court is for. Basically. It's to resolve ambiguity and debate. If there was no debate, we wouldn't need all these judges and these court systems. And so for me, it's really shaped a lot of my. The way I think in a lot of my life. It's why I think free speech, it's why I think how AI is used in social media is such an important issue for society. Because I can see very easily how it's going to shape the way people think, the way people argue or don't argue. And I can see the implications of that.
- [37:58]
B
You coached an England Debate Team 7, eight years ago. How do you do that? Particularly at the individual level, when you see the strengths and weaknesses of a person, how do you coach them to debate more effectively? And are there ways that you translate that into how you direct a team to build software?
- [38:24]
C
Yes, all the time. I see the similarities between coaching the England team and running my business. It still surprises me, to be honest, but I think that when you're coaching debate, the number one thing you're trying to do is to help people learn how to think. Because in the end, they're going to have to be the ones who stand up and give a five or seven minute speech in front of a room full of people with not a lot of time to prepare. And when you do that, you're going to have to think on your feet. You're going to have to really find a way to come up with arguments that you think are going to convince the people in the room. So for me, it was all about helping teach them that there's two sides to every story. That beneath all of the information and facts, there's normally some valuable principle at stake in every clash or every issue that's important, and you want to try and tap into that emotion and that conflict. When you're debating, you want to find a way to understand both sides because then you'll be able to position your side best. You'll know the strengths and weaknesses of what you want to say. And I'd say as well, final thing, it was all about coaching individuals. Each person had a different challenge or different strengths, different things they needed to work on. Some people would maybe speak too quickly. Some people were not confident speaking in big crowds. Some people were not good when they had too much time to think. And you have to find a way to coach each individual to manage their weaknesses and bring the team together so that they're more than the sum of their parts. And so when we're building software, I see this challenge all the time, right? We, number one, we're dealing with systems that require different expertise. No one is good at everything that we do. We've got legal experts, we've got researchers, we've got engineers. And they all need to work together using their strengths, managing their weaknesses so that they're more than the sum of their parts. And so that's been a huge lesson that I apply today to helping build. Robin AI, I would say, as well, just focusing on individuals, because at any given time, especially in a startup, you really need to find a way to put people in the position where they can be in their flow state and do their best work. Because it's really hard being in a startup where you don't have all the resources and you're going up against people with way more resources than you. You basically need everybody at the top of their game. And that means you're going to have to coach individuals, not just collectively. And that was a big learning I took from, from working on debate.
- [41:10]
B
Are people the wild card? When I, you know, see the procedural dramas or see the movies and the closing arguments and the things that lawyers do very often, understanding your own strengths as a communicator, your own impact in a room, understanding people's mindsets, their body language can be very important. And right now, I'm not sure, at least at this stage, we're close to a time when AI is going to Help us get that much better at dealing with people. Yeah, at dealing with facts, at dealing with huge unstructured data sets, with analyzing tons of video or images to identify faces. But to know how to respond, what to say, how to adjust our tone to reassure or convince someone. I'm not sure we're anywhere near that, are we?
- [42:09]
C
No, I think you're right. The in the moment interpersonal communication that is at least today something that is very human and it only comes. You only get better at these things through practice and because they're so real time. I mean, knowing how to respond, knowing how to react, knowing how to adjust your tone, knowing how to read the room and to maybe change course. I don't see how at least today AI is helping with that. But I think you can maybe think about that. As in game, but before and after the game, AI can be really powerful. So people in my company will often use AI in advance of a 1 to 1 or in advance of a meeting where they know they want to bring something up and they want some coaching on how they can land the point as well as possible. Or maybe they're concerned about something but they feel like they don't know enough about the point and they don't want to come to the meeting ignorant. Well, they'll do their research in advance. And so I think AI is helping before the fact and then after the fact. We're definitely seeing people basically look at the game tape. All the meetings at Robin are recorded. We use AI systems to record all our meetings. The transcripts are produced, action items are produced, summaries are produced, and people are asking themselves, how could I have run that meeting better? I feel like the conflict I had with this person didn't go the way I wanted. What could I have done differently? And so I think AI is helping there. I'd say final point, we have seen systems and not much is written about these systems, but we've seen technologies that are extremely convincing one on one. So there was a company called Character AI which was acquired by Google and really what it did is build AI avatars that people could interact with and it would sometimes license those avatars to different companies. And we saw a huge surge in AI girlfriends. We saw a huge surge in AI for therapy. We're seeing people have private, intimate conversations with AI and what Character were really good at was learning from those interactions. What would convince you? What is it I need to say to you to make you change your mind or to make you do something I want. And I think that's A growing area of AI research that could easily go badly if it's not managed.
- [44:41]
B
I don't know if you know the answer to this, but are AI boyfriends a thing? I haven't.
- [44:47]
C
Don't know the answer.
- [44:48]
B
I haven't heard anything about AI heard.
- [44:51]
C
Anybody say AI boyfriend.
- [44:52]
B
I've never heard anything. And it makes me wonder why is it always an AI girlfriend? Why?
- [44:59]
C
I don't know.
- [45:00]
B
What is it?
- [45:02]
C
I've never heard that phrase. You're right, right.
- [45:05]
B
Like I'm a little disturbed that I never asked this question before. It was always like, oh yeah, you know, there's people out there getting AI girlfriends and there's a movie her. There's no movie him, is it?
- [45:15]
C
No.
- [45:15]
B
Do they just not want to talk to us? Is it. Do they just not need that kind of validation? That's. Huh. There's something there. There's something there, Richard.
- [45:28]
C
There absolutely is. I mean, it's a reminder that these systems reflect their creators to some extent. It's why a lot of the. Like you said, why there's a movie her. It's why there's a lot of AI voices are female. It's partly because they were made by men. I don't say that to criticize them, but it's a reflection of some of the bias involved in building these systems as well as lots of other complex social problems, I guess. But that explain why we have a prominent sort of AI girlfriends. But not. At least not yet. I haven't heard about many AI boyfriends, although there was a wife in a story in the New York Times, I think, who. Who developed a relationship with ChatGPT that broke apart her marriage. So I think similar things do happen.
- [46:12]
B
Do we just not find those stories interesting or is there just not demand to really talk to a man? And that kind of. And a little off topic, but that's. That's on brand. Off topic, but on brand for me to go down that rabbit hole. All right, let me try to bring this all together with you then. What problems are we creating that you can see already perhaps with the solutions that we're bringing to bear? So we've got this capability to analyze unstructured data, to come up with some answers more quickly, to give humans perhaps higher order work to do. But I think we've talked about how there's this whole human interaction realm that right now isn't getting addressed as deeply by AI systems. And my observation as the father of a couple, what gen is it Z now if you're under 20, like they're not getting as much of that high quality, high volume human interaction in their formative years as some previous generations did, because there are so many different screens that have the opportunity to intercept that interaction and they're hungry for it. But I wonder if, you know, if they were models getting trained, they're getting less data in the very area where humans need to be. It sounds like from our conversation, even sharper, because the AI systems aren't going to help us. Are we perhaps creating a new class of problems or overlooking some areas even as these brilliant systems are coming online?
- [48:00]
C
Well, we're definitely creating new problems and this is true of all technology that's significant. It's going to solve a lot of problems, but it's going to create new ones. I certainly think that with AI, one thing I'd say point to three things. Number one, we are creating more text and a lot of it is not that useful. So we're generating a lot more content. For better or for worse, right? So you're seeing more blogs because it's easy to write a blog now. You're seeing more articles, more, more LinkedIn status updates and more content online. Whether that's good or bad, we are generating more things for people to read. And what may happen is people may just read less because it's harder to sift through the noise to find the signal, or they may rely more on the systems of information they're used to to get that confirmation bias. So I think that's one area AI, at least today has not solved. It's a new problem is generating incremental text has gotten dramatically cheaper and easier than it ever was, I'd say another thing I observe. Second thing is people are losing the skill of writing because you don't have to write anymore, really. You can just tell ChatGPT. You don't even need to tell ChatGPT in proper English. You can. Your prompts can be quite badly constructed and it kind of works out what you're trying to say. And I think what I observe is that people's ability to sit down and write something coherent that takes you on a journey is actually getting worse because of their dependence on these external systems. And I think that's very, very bad because writing to me is deeply linked to thinking in some ways. If you can't write a cogent sequential explanation of your thoughts, that tells me that your thinking might be quite muddled. And Bezos had a similar principle. He would ban slide decks and insist on a six page memo because you can hide things in A slide deck. But in a six page memo, you have to know what you're talking about. I think that's a gap that's emerging because you can depend on AI systems to write. I think it can excuse people from thinking. And then I think the final thing, at least that I would point to, is that we are creating this crisis of validation so it's no longer possible. When you see something extraordinary online, I by default don't necessarily believe it. Whatever it is, I just assume it might be fake. So I'm not going to believe it until I've seen more corroboration, more validation. By default, I assume things aren't true, and that's pretty bad. Actually. It used to be that if I saw something, I would assume it's true and it's kind of flipped the other way over the last five years. So I think AI has definitely created that new problem. But like we talked about earlier, I think there are ways you can use technology to help combat that, to fight back. I'm just not yet seeing too many of those capabilities at scale in the world.
- [51:12]
B
I gotta ask, you're kind of a news podcaster's dream interview because. And I want to know if this is conscious or trained, you tend to answer in three point answers that are highly organized, where you'll give the headline and then you'll give the facts and then you'll analyze the facts as that's point one and then point two. And then finally what I would say is like, it's very well structured and you're not too wordy or lengthy in it. Is that the debater?
- [51:38]
C
What is that?
- [51:40]
B
Do you even have to think about it? Do you have to think about it anymore or do the answers just come through that way for you?
- [51:48]
C
I do have to think about it, but if you do it enough, it does become second nature. I would say that whenever I'm speaking. Someone like you who in these types of settings, I think a lot more, the pressure's on and you get very nervous. But it does help you. And it goes back to what I was saying about writing. It's a way of thinking. You gotta have structured thoughts and to take all the ideas in your mind, but to hopefully communicate them in an organized way so it's easy for the audience to learn it. That's a big part of what debate teaches.
- [52:22]
B
You're a master at it. Love that. I almost didn't pick up on it. You don't want them to feel like you're writing them a book report in every answer. And you're very good at answering naturally at the same time. But I was like, man, this is well organized. He always knows what his final point is. And I like that. I love that. I'm kind of like a drunken master in my yes, yeah, I'm not.
- [52:48]
C
I know exactly what you mean.
- [52:50]
B
Yeah, I'm so There's not a lot of obvious form there, so I appreciate it when I see it. Richard Robinson, Founder CEO of Robin AI Using AI to really ramp up productivity in the legal industry and hopefully get us to more facts and fairness. We'll see if we reach a new era of gamified debate, which you know well. I appreciate you joining me for this episode of Decoder.
- [53:22]
C
Thank you very, very much for having me.
- [53:27]
B
I'd like to thank Richard for taking the time to speak with me and thank you for tuning in. I hope you enjoyed it. If you'd like to let us know what you thought about this show or what else you'd like us to cover, drop us a line. You can email us at Decoder at the Verge. They really do read every email or hit me up directly on X or LinkedIn. I'm on Fort on all platforms. Decoder also has a TikTok and an Instagram. Check those out. DecoderPod. They're a lot of fun. If you like Decoder, please share it with your friends and subscribe wherever you get your podcasts. Decoder is a production of the Verge and is part of the Vox Media Podcast Network. Decoder is produced by Kate Cox and Nick Stadt. The show is edited by Ursa Wright. The Decoder music is by Breakmaster Cylinder. See you next time.