Co-founder and CEO of Blue J, Benjamin Alaire joins host Bill Bice to discuss the ‘why’ behind Blue J, a predictive tax law software company, and what is driving the future of technology for law:
- Law is fundamentally about predication; technology intersects at the point of prediction to allow for automation and machine learning
- How can we leverage technology to make predictions about how tax situations will be treated?
We’ll see rapid development in the practice of law because of advances in legal tech
- The future of legal disputes will rely on an AI to give a prediction of its outcome
Bill Bice: Hi, this is Bill Bice. I’m here today with Benjamin Alarie. Hey Ben.
Benjamin Alarie: Hey Bill.
Bill Bice: You have a company called Blue J, which we’re going to get to, but I want to start with how did you end up starting a legal tech company and being the CEO? How’d we get here?
Benjamin Alarie: This is an interesting question. Sometimes I ask myself the same question. I kind of feel like it’s the David Burn talking heads kind of question, how did I get here? Well, it’s been an interesting journey. I started off as a lawyer. I was trained at the University of Toronto, at Yale Law School, and then I immediately became a legal academic after clerking at the Supreme Court. Put my head down and wrote a whole bunch of articles, taught my classes, and co-authored some books.
Benjamin Alarie: In the process of serving actually in some administrative capacities at the law school at the University of Toronto, I got completely seized by the ideas relating to the future of law. I got totally consumed by these thoughts about, we can see where things are going. Everybody is taking notice of computational power increasing exponentially, the massive changes in terms of the availability of data. Over the course of my academic career, from the 1990s through to 2015, over that 20 year period massive explosion in the availability of digital legal materials. Then a bunch of things happening in the world, including in Toronto on the machine learning side and the development of deep learning and other technologies at the University of Toronto by folks like Jeffrey Hinton.
Benjamin Alarie: All of those things together really lit me up and got me excited about the future of technology for law. It got me thinking about how should we be teaching law students? How interesting is it going to be able to predict legal outcomes to really evaluate the strength of legal positions using algorithms? I thought, I just have to be part of this. I could stay at the law school clinging to my chalk, as it then was, not even whiteboards, not even dry erase markers, chalk. I could stay clinging to my chalk for the remainder of my career, another 30 plus years, and watch all of this stuff change in front of me. Probably clinging to my chalk would be a disservice to my current students, my future students, or I could do something and throw myself into this and get involved with it.
Benjamin Alarie: As consumed as I was by the ideas, I just had to do something, and so it was out of that tumult that Blue J was founded back in 2015.
Bill Bice: I have to tell you, having worked on a lot of startups, that switch from academic and researcher to a startup, sometimes that’s very tumultuous and a lot of people don’t make that jump successfully. What sort of forced you to do that and how did it play out?
Benjamin Alarie: That’s a good question. I think to underscore your point, it’s difficult to imagine two different careers that are as diametrically opposed as a tenured law professor and a startup tech AI CEO. These are two different circles, two different sets on the Venn diagram and there’s not much of an intersection between the two sets typically.
Benjamin Alarie: I’m not sure. It just seemed like the absolute obvious thing that somebody had to do to bring it into the world. Somebody needed to get working on this. I thought about the different people who might be situated to do that, and it occurred to me it probably wasn’t going to be graduates of computer science departments, young developers because they wouldn’t have the legal context. I’m a tax law professor, and so tax law problems are not the sexiest problems to be working on if you are a 20 something computer scientist software developer, you’re going to be more gravitating towards other problems, typically. You won’t be that interested in doing the hard work necessary to really understand the tax law problems. It wasn’t going to be a tech person, a prototypical kind of tech CEO coming out of an undergraduate computer science program, for example.
Benjamin Alarie: Then it struck me it’s very unlikely to be someone from legal practice, where you’d be potentially a very successful associate or partner of a law firm, or maybe an accounting firm because you have a lot of work to do and it’s a well-worn path to a very nice lifestyle. A hard-working lifestyle, but as hardworking as a startup CEO, but with almost a 100% chance of success staying in that career as compared with starting a tech company, which is a 90% chance of failure.
Benjamin Alarie: I thought, okay, so who really has the expertise in tax law, and second, has an opportunity to do this? Well, academics actually do. I think I just thought that I could figure it out and I could do it. Maybe I come by some naivete on this front. My dad was a business person, an entrepreneur, started an engineering consulting company right after he finished his engineering studies after grad school with some friends, and he grew that business into a successful business before it was acquired by a public company. My mom was an elementary school teacher, a kindergarten teacher, so I kind of have this enthusiasm for ideas and a love of learning from my mom, and kind of this gumption to go get it from my dad and that role model.
Benjamin Alarie: I think it seemed like a natural thing for me to do, although as I tell it, I am hearing the story and like, it doesn’t really sound like it’s the most linear path, but to me it seemed like the most obvious thing to do was to roll up my sleeves and say, “Well, if somebody’s going to do this, it’s going to be really incredible and 10 years from now it’s going to seem absolutely inevitable to everybody else that this is happening, but I kind of feel like I’m in on an open secret about the future that nobody else, or very few others have actually seen. And I’ve got to do something about it. I can’t just have this idea and keep it to myself and not make something happen.”
Bill Bice: You saw that entrepreneurial path work, so you had the benefit of that experience. Because most people attracted to becoming a tenured professor aren’t going to take the kind of risk you did. The problem that you set out to solve, talk more about that.
Benjamin Alarie: Yeah. It all goes back to something that most attorneys will have been exposed to directly or indirectly at some point in their legal studies, and this idea is that the law is really fundamentally about prediction. Oliver Wendell Holmes Jr., wrote a really famous article in 1897, published in the Harvard Law Review, titled The Path of the Law. In that article, Holmes talks about law fundamentally being about prediction and people care … He creates this character, the bad man, who only cares about the results in court. Not driven by morality. Not driven by his own scruples, but instead driven by what are the actual consequences of different alternative courses of action? For the bad man, Holmes says the bad man is going to care about what would happen in court, so what are the chances of detection? If I’m detected, what’s going to happen?
Benjamin Alarie: He does this I think mostly to paint the picture about it, it is fundamentally about prediction. We’ve got all these highfalutin ideas about law that scholars study, that folks try to sketch out what the law should be normative. Ultimately, Holmes was making the point that this should all be about consequences and about probabilities and mathematical, statistical probabilities. What’s really interesting is Holmes talks about the future of the law as being one about prediction, and the law is fundamentally prediction. That’s his argument.
Benjamin Alarie: Now, this has been subject to massive amounts of academic scrutiny and criticism in the subsequent 130 ish years since Holmes made this argument, and I think rightly so. There are a bunch of valid objections that one might make to the Holmesian story, but the fundamental idea that the law is about prediction is one that I think is very normal and very natural to our legal system. We refer to precedents all the time. Judges are expected to treat like cases alike. Different cases may be treated differently, but there’s a lot of learning we can do from past cases that will influence our perception and our prediction about how new cases will get decided.
Benjamin Alarie: I’m coming at this problem from the perspective of a legal academic who’s done a lot of research, a co-author of one of the leading texts on Canadian tax law, so this is a gentle introduction to Canadian income tax law. For those of you who can’t see the image, I’m holding up a book, and it’s 1420 pages long, this is a gentle introduction to Canadian income tax law, and it’s based on this book, which is the Income Tax Act, and so this is many thousands of pages long. The thinnest tissue paper you can find for a published book. The complexity of the materials is just astonishing. There are tons of cases, tons of interpretations. The situation’s even worse in the US with all the different manifold sources of administrative guidance, private letter rulings, and revenue rulings, and the list goes on and on of all the different things you need to take into consideration. Forget about just the complexity of the internal revenue code itself and the regulations, there’s a lot of complexity.
Benjamin Alarie: The animating thought here, the problem is can we leverage technology to make predictions about how tax situations are going to be treated in a way that is highly accurate, incredibly fast, and just is a game-changer for doing tax research, and therefore for providing tax advice? That was the original impetus. Starting in tax seemed like a really intelligent way to begin, not least because the tax system touches everybody, so it really is something that touches everybody. It really is a big industry.
Benjamin Alarie: The IRS collects over $3 trillion in taxes every year. It’s a massive shift of resources in the economy. Folks are engaged with tax lawyers, tax accountants, and with the IRS, with the state and local tax authorities, trying to figure out complex tax questions all the time. It seemed like a natural playground where there’s also a well-organized market, so putting the business hat on, there’s a well-organized market there to assist CPAs, help tax attorneys with the research tasks that they are doing all the time already. It seemed like the right thing to do.
Bill Bice: Yeah there’s a large problem there, and so it seems like the key insight is that you’ve essentially taken the data created by a precedence-based system and you’re using that data to make predictions.
Benjamin Alarie: Fundamentally that’s what we’re doing, yeah.
Bill Bice: You have found the very specific application of machine learning that has been quite successful from an appearance standpoint, at least seeing what’s happening with your company. Where’s that going? Where is ML going to take us in the future of legal?
Benjamin Alarie: Well, we hadn’t talked about this, Bill, but I just finished a draft of a book that’s under contract with the University of Toronto Press called The Legal Singularity. This is with a co-author, Abdi Aidid, who is a colleague at the University of Toronto and has, before joining the faculty of law at the University of Toronto, was our vice president of legal research at Blue J. Abdi’s a Yale Law School graduate, a brilliant guy. We’ve been talking about precisely this question, what does the future of machine learning in law look like and where is this going to take us?
Benjamin Alarie: We decided, after many conversations about this, I said, “Abdi, we’re doing all this thinking, we need to write this up. We need to write it into a book.” We’ve got a book contract with the University of Toronto Press. We’ve got a draft manuscript done. We’re aiming to do a revision of that and have it submitted in the next few months to the University of Toronto Press. This book is likely to appear later in 2022. The basic story is we’re going to see a massive acceleration in legal development over the rest of this century. In the coming decades, we’re going to see really rapid development of the law, and it’s going to kind of telescope hundreds of years of legal evolution into the space of a few decades.
Benjamin Alarie: I would posit, and we do posit at length, that it’s going to lead to really profound legal certainty, practical certainty in the law. Not perfect certainty. Not something that’s transcendentally ideal that we’ve got an answer to every single legal question, but for 99 points some percent of the legal disputes that come up, you’re going to be able to rely on a computer system, an AI, a machine learning system to give you a very good prediction about what would happen if something were to go to court. Really cashing out that Holmesian perspective that the law is all about prediction. We’re just going to see an acceleration in this.
Benjamin Alarie: It’s going to also fundamentally change how law gets made, how law gets taught, how lawyers practice, how judges decide cases, and there are far-reaching implications across all these different domains. It could have pretty significant implications for government, for democracy. Things are really big questions that political theorists should be grappling with and will increasingly be grappling with I think. It’s actually really exciting.
Benjamin Alarie: If things go well, if we have a good outcome along these lines, then what we will see is practical certainty in the law, really thoroughgoing access to justice and the system will be much more distributively just. If we have a bad outcome and technology is used for I guess less good purposes, then we could see the rise of authoritarian governments, totalitarian governments, governments that would not be as interested in promoting access to justice and fairness in the legal system. There is a potential dark side too, but these are the times that we live in. The question is, how do we use technology to help promote the more salutary ends and promote human flourishing, and safeguard us against the more negative potential futures that one might imagine?
Bill Bice: Like all tools, you can end up with either the positive or the negative. Would you agree with sort of the traditional view, when you talk about the singularity within legal, you tend to get this pyramid view of the legal profession and really high-value stuff at the very top of the pyramid, and lower value repetitive work at the bottom. A tremendous amount of which is already at risk and is now being handled by other outsourcers, besides law firms. The view is that technology will continue to push that value proposition higher up in the pyramid. Do you see that same thing, or are you talking about a more fundamental shift than that?
Benjamin Alarie: I think we’ll see that. I think a lot of this technology is going to apply to the entire pyramid. There are at least a couple of different mechanisms for that. One is if you think about this pyramid, there are different situations in which you can tell the story of the pyramid. One might be in the court system, where you have lesser tribunals feeding up into full courts, feeding up into the appellate courts, and then the most important disputes are being addressed by national supreme courts, the US Supreme Court, the Supreme Court of Canada, the UK Supreme Court. You can go around the world and name all the constitutional courts that are dealing with those kinds of challenges.
Benjamin Alarie: I think technology is going to affect that entire judicial stack, not least because litigants who have access to these kinds of systems are going to be able to make really good predictions about what would happen in court if we were to go to the court. It’s going to affect trial advocacy strategy. It’s going to affect the nature of the disputes that actually make it onto the dockets of these trial courts, the appellate courts.
Benjamin Alarie: For example, Bill, if you were locked in litigation and a trusted AI told you, you have a 95% chance of winning against your opponent, my prediction would be that your opponent would run a similar analysis on a similar AI, maybe the same AI, and realize that you have a very strong position and just come to a settlement because the cost of litigating that are just too high and it doesn’t make sense. You’re both better off if you just come to good settlement terms. That’s the kind of case that maybe today, with self-serving biases and other things, confirmation bias and looking at the facts, it’s very easy to fool yourself, and it’s very easy for a self-serving attorney to convince him or herself that we’ve got a case here.
Benjamin Alarie: Your opponent might have an attorney that’s trying to foment the adversarial litigation here, but in the future, it’s going to be very difficult to pull that off in the same way. You’re going to see a bunch of cases settling, even more so than they are today, and the cases that end up before the courts are going to be more challenging. They’re going to be normatively challenging, in that there are contested principles at stake that are well-founded in precedent, so ample reason to think that those are real conflicts in terms of normative principles, or there will be a big dispute about what actually happened, so there’ll be factual uncertainty driving the results. It could be both in some cases.
Benjamin Alarie: The really interesting thing is the judges who are left deciding these things are going to be really challenged. They’re going to need AI to help them situate those cases because the litigants are already well advised and the judges are going to want to make sure that they’re not going to be stepping out of line. The way to get to the best possible judgment is going to be by leveraging these kinds of systems. Even at the trial court level, judges are going to be relying on these things not because their job requires them to rely on it necessarily, but because they don’t want to be embarrassed. They want to produce the best possible judgments.
Benjamin Alarie: The cases are going to tend to be those cases that are 50/50, 60/40, somewhere in the middle, so the judge still has some opportunity to use their judgment, this is institutionally why we have judges, they’re exercising their discretion, they’re leveraging their human experience. Increasingly, judging is going to be a full-body exercise. It’s going to use your head, your heart, your gut, and also your algorithm. You’re going to be relying on these systems to help guide you as a judge to figure out what should I be doing here? If you make a decision that goes contrary to the best prediction of the algorithm, you’re going to understand that you kind of has a heightened obligation to provide reasons. I’m going to go with the 40% likely outcome in this one, but here’s why I think the law is moving in this direction. Explain that case.
Benjamin Alarie: I think we’re going to see it affect the entire stack. Now, as you move up the appellate hierarchy, those pressures just become even more significant for those judges. Yes, it’s going to happen at the trial level, but yes, it’s going to happen at the appellate level, all the way up to our supreme courts. Law clerks are going to be relying on these things, and judges will be asking their law clerks to be running it through this AI or that AI, whatever it is.
Benjamin Alarie: That’s one place where we might see this pyramid. The other place we might see the pyramid is in legal practice. Just think about the typical law firm. You’re right, it is a hierarchical place. You’ve got juniors, and you’ve got the associates, and then junior partners, senior partners, all the way up. I think it’s going to have a similar role to play, but clearly, the uptake is going to be more so at the lower levels, I think, of those practices as it becomes the new way to get up to speed, to engage in training as a junior attorney. These tools are indispensable.
Benjamin Alarie: If you think of a senior partner in a law firm as kind of like a chess master, someone who has played chess for a long time, has really proven themselves, understands different positions, is a master of the opening book, really understands how to play chess. They probably would still benefit from using a chess engine and analyzing what’s the best next move in this position, but you know who really benefits? Somebody who’s pretty new to the game of chess and would benefit from getting an evaluation of a position from an algorithm, and then can train up and get better faster by getting that feedback from some kind of software system, some kind of AI. It becomes a new way to be smart, and it’s a new way for everyone to be smart.
Benjamin Alarie: I know grandmasters now make extensive reliance, even at kind of the world championship level, of different algorithms and different software, chess engines, to inform different lines of strategy and figure out what’s the best move. Pundits watching the world chess championships are tracking in real-time on their own favorite chess engine what the right evaluation is of a given position, who’s winning, who’s losing, who just mucked up a move, who blundered in which position. I think that’s going to be extremely common across the stack of individuals in those law firm pyramids, but more so at the junior level. I think it’s going to be really indispensable at the junior level.
Benjamin Alarie: There are good anthropological reasons for that too. In that kind of system, it’s easier for new folks to adopt new tools. They’ll be exposed to these tools in law school. They’ll come into the firm with an expectation that the firm has access to these tools, and that expectation is a self-fulfilling prophecy because the firms will see that the students want these things because they were using them in law school and then they’ll acquire the tools. The older folks will kind of have, of course, some are going to be eager adopters, but most will be a little bit hesitant and not really know how to use these tools, and so they might be a little bit slower in adopting them, but they’ll see the value of having them for the firm and they’ll just rely on those juniors to do a lot of the work with those tools. They’ll know that they need to be used.
Benjamin Alarie: I see parallels like this if you go back 20 years ago, 25 years ago, when all law students started to get access to Westlaw as a routine part of legal education. A lot of folks in practice weren’t up on the latest online legal research tools, but students coming out of law schools were expecting those tools, that’s how they knew how to find the materials that they were looking for. Now of course-
Bill Bice: It’s like expecting librarians to do the research work for them, and that associate wanted direct access to the tools.
Benjamin Alarie: 100%.
Bill Bice: That’s one of the biggest trends in legal tech right now, which is associates really want direct access to all of the tools because that’s what they grew up with, that’s what they’re accustomed to.
Benjamin Alarie: Yeah. 100%.
Bill Bice: What you’re talking about is taking that first year, the second year, third-year associate and making them much more valuable because they have this toolset available to them. How do you deal with the common concern with AI around not really understanding how the decision was made, or the unintended bias that comes into the process? Does that change how the application, how it’s being applied here?
Benjamin Alarie: Yeah. I can speak specifically about how Blue J handles that. In Blue J solutions, we know that people care about the overall evaluation of a position, but they also care at least as much about the reasons for that. What are the germane precedents that this is drawing on, and can you help us find the form of argument in these precedents? What is it about these precedents that make this the most likely outcome? I think about it as kind of a stack of resources that you get when you’re running an evaluation of a position.
Benjamin Alarie: You’ll get, yes, overall this has a 95% chance of success based on the facts and circumstances as you dictate them to the system. Here’s why. It’ll find the most similar cases based on the facts and circumstances substantively of the position that you’re testing. It’ll provide links to all of those precedents. It provides a plain language explanation about why that’s the most likely outcome. It’s kind of like a really good exam answer on a law school exam. It’s based on a proper representation of the facts, an appreciation of the relevant precedent applies those precedents in a commonsensical way, and distinguishes things that aren’t likely to apply. You get all of that when you get to a result screen, kind of giving you the evaluation of the position.
Benjamin Alarie: It’s not just an oracle, like a magic eight ball saying, “Hey Bill, good news, 95% chance that you’re going to be successful in your litigation.” It’s like, “Bill, good news, 95% chance that you’re going to be successful in your litigation. Here are the 10 most similar cases, they all went in your direction, and here’s a plain-language explanation, a one and a half-page explanation, a proto memo here about why that’s the most likely answer.” Then you’re invited to try out different variations of the facts and circumstances, respond to those questions in different ways, and run different hypothetical variants on your situation just to see how robust is that very strong result in your favor?
Benjamin Alarie: All of those things, the true kind of ground truth legal materials, those precedents, the machine learning generated explanation of it, but then also the opportunity to play with the facts and see just how robust is this. All those things contribute to folks feeling more comfortable with these kinds of systems because we know that the facts are often contestable. Yes, you may have your version of the facts, but the other side is likely going to have a slightly different version of the facts. The beauty of these kinds of machine learning models is you can run both sets of facts through the system and see what the differences are in terms of the likely outcome.
Bill Bice: If you’re smart, you’re going to pretend to be the opposing counsel and put the other view in at the same time.
Benjamin Alarie: Yeah, for sure.
Bill Bice: What’s really interesting about this perspective is that it’s not just how it changes the future of litigation, it’s how you would prevent that litigation, right? Because if you can game these scenarios out, then the counsel you’re going to give, the way you write up a contract, the way you structure a deal is going to change because you know of the potential challenges in the future.
Benjamin Alarie: Yeah, you’re exactly right. This is really interesting in the tax context because there’s so much work that goes into tax planning. If you can avoid a fight with the IRS, you’re best to avoid that fight with the IRS. They have a lot of powers to reassess and challenge tax positions, and they can kind of come up with their own stipulated facts and kind of impose those on you, and then you’re put in the position to displace those assumptions about the facts. If you can do the work upfront and say, “Okay, if I’m going to be really smart here, I’m going to make sure that I’m going to document all the facts and circumstances that secure for me the right tax result upfront. And I’m going to document all of that right now, before I even lift a finger and put any of this into place in my business, and make sure I do my homework here and make sure I get this right.”
Benjamin Alarie: You can save yourself a massive amount of uncertainty and pain later in just being able to head that off at the pass and say, “Yeah, we did our work.” Not only do I have a tax opinion that said this, I also am really confident in this tax opinion because look, but it’s also rooted in all of these things. It’s based on an assessment by this independent machine learning algorithm of all of the relevant materials. It’s not some tax attorney who was essentially lured by the prospect of some opinion revenue to provide me a very friendly tax opinion. This is really rooted in the case law, which makes it a lot more credible. I think it allows you to be smart upfront and with a ton of lead time, years before you might have an issue.
Bill Bice: Yeah, it’s a fascinating view. What is it about Canadian legal tech companies? I mean, you’ve got your friends down the road at Clio that have had such a huge impact in small law. You’re doing really interesting work. I mean, the Canadian market’s about roughly 10% the size of the US market, and yet you’ve got some really fascinating technology coming out of the market.
Benjamin Alarie: Yeah. I’m not really sure, Bill. I don’t know. I do know that our universities are very strong in AI research. There’s been a long tradition in Canadian universities, at the University of Alberta, at the University of Toronto, at the University of Montreal. There’s been very strong public investment through CIFAR, the Canadian Institute for Advanced Research, in financing a lot of AI research, including in the 1980s and the 1990s, when a lot of funding for AI kind of dried up around the world. There was government interest in supporting AI research, so that might be part of it.
Benjamin Alarie: Part of it I think is the fact that North America is the market where Canadian companies are selling into, and so being adjacent to the US market makes it, we’re kind of indistinguishable in a lot of ways to American companies. We speak the same language. We share a ton of cultural values and we know how to talk about the law. We also I guess we benefit to some extent by being a little bit outside the system. Sometimes when you’re just a little bit outside the system it allows you to see things a little bit differently, and that can identify opportunities for you.
Benjamin Alarie: Maybe an analog here is the success of Canadian comedians in the United States. This might be something that actually seems like a weird connection to make here, but actually, Canadian comedians actually punch far above their weight in North American entertainment than you would expect just by raw population numbers. Maybe it is a little bit the same phenomenon, just being a little bit outside the system, a lot of the same cultural references, but just seeing things a little bit differently allows for comedians to really flourish from Canada. Maybe there’s something to that. Not sure.
Bill Bice: Basis of the consulting profession, right? It’s just a slightly different perspective that adds value. It’s so much easier to see things if you’re not right in the middle of it. The market is so different today. I mean, compared to when I was building my first legal tech company when the concept of professional investment in legal tech was almost nonexistent, and today we’re in one of the hottest sectors where you have just a ridiculous amount of money, private equity, VC that’s flowing into it. There’s a lot of innovation that is coming out of that. It’s going to be a lot of wasted money also, but there is going to be a lot of really good innovation that comes from that investment.
Benjamin Alarie: Yeah, absolutely. I’m really excited about where things are going. I think we’re probably looking at a lot of the same data and a lot of the same developments in the market. I think there’s a new receptivity among law firms and professional services firms more generally. People have really started to see that adopting technology is really a wise thing to do, and those investments truly are investments. They’re not reductions in partner draws. They’re actually investments that really pay off over time for professional services firms. It’s nice to see that learning and that increased sophistication on the part of the firms. I suspect that’s been your experience, but it’s certainly been our experience at Blue J.
Bill Bice: Yeah. It’s been one of the silver linings of the pandemic, which is an appreciation for technology, even at a very basic level. Because the ability for firms to adjust and be able to deal with the pandemic was driven by their investments in technology, I’ve seen an appreciation for that in a way that really didn’t exist before. I think there are a lot more partners that get that now.
Benjamin Alarie: The courts too. The courts are understanding this, and the government is understanding it in ways that were really challenging before. Universities too. I can say this as an academic as well, teaching classes. Suddenly in March 2020, over the course of a weekend, there was a decision made to turn all of these bricks and mortar classes into virtual online classes, and we didn’t miss a beat. Monday morning, I had a class, I guess it was Monday, March 16th, is that the right date? 2020, at 8:30 AM, every student was in that virtual classroom, and I was there with my colleague, we were co-teaching the class and we didn’t miss a beat. It was pretty incredible. Courts have taught themselves largely the same tricks over the course of the pandemic. Law firms are just absurdly better positioned than universities even or the courts to embrace technology and do this, just because they have more resources available at their disposal to make that happen. The clients are clearly demanding it too, and that helps a lot.
Bill Bice: Yeah. Clients are requiring it. We often talk about the need to force function sometimes to move legal forward. That’s even more true in the courts, who have seen really a dramatic increase in access to justice because of this. It’s not all been positive, but some of it has been amazingly positive.
Benjamin Alarie: Agreed.
Bill Bice: Ben, this has been a lot of fun talking with you. It was great seeing your vision and your success. Thanks for joining me.
Benjamin Alarie: Well, thanks for having me on, Bill. It’s been a pleasure.