The last wave of automation in the 1970s‐80s was industrial as robots replaced manufacturing line workers. The economic dislocation fell hardest on those least able to afford it, blue collar workers without formal education and comparable alternate career paths.
But today, automation is coming for white collar workers as well. There are jobs, that despite requiring education and advanced training, involve what is essentially pattern recognition and processing speed, things that artificial intelligence can do more quickly and efficiently than human beings. Jobs in law, analytics, and finance are on the cusp of mass automation, leaving those newly entering those fields with massive student debt and limited job prospects.
Today we talk to two startups, one which is bringing that automation to law firms, the other which is trying to mitigate worker dislocation by helping students find alternative career paths requiring irreplaceable‐by‐AI social skills.
When was the first wave of automization? Is the automation apocalypse upon us? Can AI streamline the legal process, specifically in documentation review? How can AI compliment the legal process? What value do you want to get out of hiring a lawyer?
00:05 Paul Matzko: Welcome back to Building Tomorrow, a show that is, among other things, about cool new tech and innovations that could drastically change our daily lives in the near future. I’m your regular host, Paul Matsko. And there’s no better place to get a sneak peek into the near future than at TechCrunch Disrupt, an annual conference in San Francisco that I attended the first week of September. There are other major tech conferences, of course. At MIT, there’s a really cool one with tech in the medium‐term horizon. But what sets TechCrunch apart is that the goal of the conference is to match up Silicon Valley investment firms with tech startups, which means that much of the tech on display is very close to being ready for mass market. So, as I talk about some of the interesting startups I saw and some of the founders I spoke to in San Francisco, I’m not just talking about hypothetical distant future applications of technology. This is stuff for which there’s a high probability that you’ll see the tech implemented in a grocery store, on a road, or even in your ear over the next year or two.
01:12 Paul Matzko: Now, this is a departure from our usual format for Building Tomorrow episodes. I recorded these interviews with startup founders in person on the floor of the startup alley exhibition hall, so you’ll hear more background noise than usual. But I hope you get from that a taste of how exciting it is to go to a conference like TechCrunch. Each of the next three episodes features one or two interviews with a startup founder, is organized around a different theme or cluster of technology. One good sign the tech is nearly ready for mass adoption is when multiple startups are competing in the same space and attempting slightly different solutions to a similar problem.
01:51 Paul Matzko: Our theme for today is job automation. The robots are coming. Well, they’re already here, of course, as anybody who works in manufacturing will tell you. But whereas the wave of robotization in the 1980s, ‘90s and ‘00s primarily displaced blue collar jobs in manufacturing‐based industries, this next wave is coming for folks with traditionally white collar jobs that we thought were immune from displacement And instead of robots, this next wave of automation is going to be a function of artificial intelligences. A robot can build things more quickly and precisely than a human ever could, but an AI can think through things more quickly and precisely than a human can. Now, our first interview is with the founder of a startup that is applying AI technology to the drudge work previously done by young bright‐eyed, bushy‐tailed lawyers fresh out of law school who spend many, many, many, many hours engaged in document review, which is the bane of their existence. So, listen in.
02:55 Paul Matzko: I’m standing here with Nick Whitehouse, who’s the co‐founder of McCarthyFinch, which is coming to take your legal job away from you, future lawyers. Oh, no. [chuckle] The automation apocalypse is upon us. Tell me why that’s not true, Nick.
03:10 Nick Whitehouse: Hi, Paul. It’s not true because, I think, AI isn’t at this point where it’s this major threat to us as people. I think it’s much more on the augmentation space. And so what we’ve found is you’re always going to need a lawyer. I give the example of Brexit. I give the example of privacy in the United States at the moment. And law is an incredibly topical subject in America at the moment. I don’t think you can go to any newspaper without seeing something about the law on the front page. And these are really, really complex situations that are really perfect for people based on the relationships that they have, based on the really creative thinking that they have to go into to navigate through laws.
03:58 Nick Whitehouse: But where AI plays a really big part is in the drudgery, the work that is really, really repetitive, that we don’t we don’t… As consumers of law, don’t necessarily value. And that also goes for in business, it’s massively big in business in the sense that all of these changes have really pushed the pace of business further, faster, faster, faster, and the risk appetite of business has dramatically changed because of that. And so the value that they place on this mundane legal stuff has decreased. And so where AI plays that part is picking that up and really helping lawyers get through that quicker and focus on valuable things. So, I think it’s a really complementary approach and a complementary future at the moment for lawyers in AI.
04:50 Paul Matzko: Now, just for nitty‐gritty, for those of our listeners who don’t know about the world of document review, this is… I guess, if you’ve ever seen an episode of Better Call Saul, and they’re down in a windowless basement room and there’s…
05:04 Nick Whitehouse: Oh, yeah. And behind the nail salon.
05:07 Paul Matzko: Yeah. Behind the nail salon, there’s boxes of documents that they… You’re fresh out of law school, you get hired by a big law firm, and you just spend hours and hours and hours for your first couple of years combing through basic legal documents, looking for errors in wording, a missing clause here, a missing article in the contract. And it has to be done, but it’s incredibly boring and tedious work. Is that what you’re focusing on at McCarthyFinch, to try to replace that work with…
05:40 Nick Whitehouse: Yeah. It’s a little bit broader. In that scenario, discovery in litigation is massive. It’s really hard work. If you’re a junior lawyer, you’re coming in to law firms, you’re not trusted, you’re not able to do stuff and be creative. You’re told to do research. You’re told to find things. You’re told to print things, and you’re charged out for that. But as a human, you don’t necessarily understand how you fit into that process, and you don’t necessarily learn that fast, either, because it’s not an environment to learn in. Where we see McCarthyFinch playing a role is we’re virtualizing that lawyer with cognitive services, and that goes right across from a law firm to an in‐house team through to consumers, through different legal tech providers, to take those repetitive, highly transactional legal tasks and apply AI to that, so that they happen fast, instantaneous.
06:38 Nick Whitehouse: And a good example is, we showed on the battlefield stage, we completed a contract approval in under two minutes. When we gave that to lawyers to do, it took them 75 minutes just to read the contract, not to go through all the different points. And so you can understand how that’s a massive shift in how you’d actually approach transactional legal services. As a client, I just want the outcome, and I need the outcome as fast as I possibly can, as cheaply as… As cost efficiently as I possibly can, and this is a real paradigm shift for me as a consumer.
07:20 Paul Matzko: So, is this, in part, a tool meant, you’re a lawyer’s office, this is a complementary tool that you can pay for that will allow you to move through this material more quickly? If you’re, I don’t know, a real estate attorney and you’re used to combing through this real estate contract, it’ll take you an hour or two hours, you can put it through the system, it’ll spit out, “Here’s areas you should maybe review more closely, here are some conflicts you need to resolve.” It’ll speed up your workflow, meaning you’re spending less time per document, which, in theory, will lead to cost savings that you can pass on to consumers as well.
08:00 Nick Whitehouse: Yeah. I think, at a base level, that’s where people are focusing on, is reducing cost. I’m not a big believer in commoditizing services. I’m a big believer in creating value. And so, yeah, we can absolutely do that. But I think about healthcare. When I think about how do you disrupt an industry, and I look at healthcare and wouldn’t it be great if you and I, if we have a health problem we can self‐diagnose and heal ourselves? That’s utopia. And when I look at law, that’s the same sort of thing. And a law firm shouldn’t feel like they’re excluded from that, or a lawyer shouldn’t feel like they’re excluded from that. What a lawyer does is transfer their expertise, transfer their knowledge to you as a consumer of that. At the moment they do that through billable hours predominantly, and everything that’s in it has to be inefficient because that’s how they get their money.
08:49 Nick Whitehouse: But if they can provide services… We have a service called Author. They can create… They can take that Author service and they can sell that, and they can transfer that expertise and enable you to do what you need to do without having to play a part. But it’s a service. They’re still transferring their expertise, a completely new way of serving clients. So, if that’s a conversation or AI that you can go to on a website, if it’s built into your contract so you can ask your contract your question without having to go to the lawyer, doing all of that sort of stuff is an amazing new experience for clients that clients value, because you’re getting what you want out of it. And a lawyer is still transferring their expertise. It’s just a different way of doing it. And I think that’s where you create value, that’s where you drive much more empowerment of the consumers of law, and I think that’s a really good thing. The more we’re empowered to consume law, the more we’ll protect our rights and the more we’ll want to consume law more, because ultimately you’re being pulled into to understand there’s a value and benefit of doing it.
09:54 Paul Matzko: Let’s say I’m just an ordinary consumer, I’m looking for a will, I wanna write a will. I’m starting to realize, “Oh, I don’t have anything in place in case myself or my partner dies, someone has to look after the kids.” How would this software make it easier for me to interact with a lawyer’s legal expertise to ease that process? How does this look on the ground?
10:21 Nick Whitehouse: On the ground, I think wills is a really good example, because when you talk to anybody who is just a consumer of legal services, you go to a lawyer three times in your life: When you get married, when you got a will and when you’re buying a property, or maybe when you die as well… [laughter] But you’re not…
10:38 Nick Whitehouse: And so from the will perspective, it is really about taking… That’s very much a conversational AI approach, understanding all those really complex different ways that a will could best suit you, and allowing the AI to do the interaction to find those concepts and connect you to the right things that should be in a will. And so where we see our product helping in that space is with those providers who are providing those services around wills. And that could be a LegalZoom or a Rocket Lawyer, that could be the local legal office, or it could be another legal tech product that comes out. But really shifting their focus. Absolutely the lawyer needs to be involved in this at some point, but there’s a whole bunch of work that you don’t have to pay $400 an hour to do to inform them to help make that will better for you. And I think that’s where the real power is, is that you can go on on that journey in your own time, at your own pace, to understand what options are available to you, to be informed so when you are paying $400, $500 an hour for a lawyer, you’re getting the best value out of that time.
11:49 Paul Matzko: Well, in theory, I can imagine this should increase the pool of people who right now are intimidated by the prospect.
11:56 Nick Whitehouse: Oh, yeah.
11:56 Paul Matzko: Right? Like, I’m in my mid 30s, I’m starting to think about this. I mean this literally, not just metaphorically. I don’t have a will in place. I should have one. But do I wanna go talk to a lawyer and go to an office and pay lots of money? But if there was a way for me to start exploring the options that could then later be validated by an actual attorney. Well, there are people who want legal services but find that barrier intimidating or too expensive or just for whatever reason, they’re not taking that plunge.
12:27 Nick Whitehouse: Absolutely.
12:28 Paul Matzko: We should expand that pool, right?
12:30 Nick Whitehouse: Yeah. And so when you look at the numbers, America spends about $437 billion on legal services a year. 49% of the global’s legal spend is in America.
12:42 Paul Matzko: Wow.
12:42 Nick Whitehouse: When you… We’ve done market research. When we go to consumers of law, we see that 88% of people would rather go to Google or talk to a friend than ever go to a lawyer if they have a legal problem. Not just exploring. I have a legal problem. I don’t want to go to a lawyer.
13:00 Paul Matzko: Trust Google instead. Yeah.
13:00 Nick Whitehouse: Right. People often find law is expensive, intimidating and really, really kind of slow. And that’s one of the things that we see. And by augmenting lawyers, you actually create, you scale the legal services. And I, as a consumer of law have experiences. I bought a house. I had a terrible experience with my lawyer, hated it. They were really, really slow. I had to chase them up for settlement. It was like a really bad experience. And I walked away from that thinking, “What’s the point of a lawyer? I pay $1,000 an hour for somebody to do what?” And then I bought my next property, and that was with… Not that I’m a really rich guy or anything, but New Zealand’s residential market is great. But when I went down that path, I went with the law firm that I was working with at the time. And the experience was entirely different. They negotiated the contract for me really hard. I got a bunch of stuff on that that I just didn’t think was possible.
14:00 Nick Whitehouse: And that was where I found what the real value was, it wasn’t the reading of the contract. It was understanding what I wanted and then going into battle for me to get what I wanted. And that is where the value of a lawyer really lies, is that negotiation. And if I can consume and understand and be empowered and then know what I should be getting from my lawyer and where a lawyer plays and really serves me with value, that’s an entirely different buying experience that I think actually brings lawyers back into relevance in everyday life.
14:32 Paul Matzko: If you like imagine if in healthcare, we require doctors to do everything that takes place in the doctor’s office. They had them do the blood draws, they had to fill out the paperwork, they had to… Well, that would be a crazy way of constructing a system, because what makes a doctor valuable is the specialized expert service they provide, which is diagnosing what’s wrong with the patient. And that takes a lot of training and whatnot. It would be wasted if we made them do something that could be done by someone else, just as, if not more effectively.
15:01 Nick Whitehouse: Right, absolutely.
15:02 Paul Matzko: Same thing kind of in law. Why should we be having people go through three years of training, internships, etcetera, just to read documents that could be done by an AI more quickly, more effectively, and allow lawyers to spend their time on the real value‐added service.
15:20 Nick Whitehouse: Yeah. And when was the last time you went to a doctor and you hadn’t gone to WebMD and figured out what was wrong with you before you went to the doctor?
15:26 Paul Matzko: That’s right. Yeah.
15:28 Nick Whitehouse: And over the last 15–20 years, we’ve all become far more empowered when seeing medical professionals, right? We’re not empowered to see lawyers. And you’re absolutely right. You’ve got these incredibly smart people and they are very, very smart people who come out of law schools and go into law firms. And some of them do move up fast, but they are not necessarily given the hard tasks. And we pay a lot of money for them. And so we talked a lot about the consumer side of things and we definitely are working with legal tech providers to change that space. But we also working with the businesses. And those businesses have just as many of these problems, if not more, trying to navigate their space as well. And that’s a big cost to us as… It costs in different ways. One is, obviously the cost of regulation. All that does get passed onto us as consumers. But risk and fear slows down innovation, slows down how quickly products get to market, slows down businesses in general. And so this is kind of a malaise that you can cut through by creating clarity and getting through the mundane legal stuff so that lawyers can focus on being creative and actually moving these businesses forward quickly as well.
16:43 Paul Matzko: Now, have you gotten any pushback from, or I guess any reaction at all from like the American Bar Association or any of the…
16:51 Nick Whitehouse: No, no, no. We play in a specific space. So we don’t believe that the AI should be giving legal advice. We believe it should be empowering people, much like Google would, but in a much smarter and more specific way. And so, from the legal side of this stuff, we talk about augmentation. We don’t talk about replacement of lawyers. And no way would I ever advocate that people should go down the path of when they have a legal problem, just relying on an AI. AI is not at that point. You should definitely always if you believe you have a problem, seek advice from an actual lawyer. But what we aim to do is empower people though that process.
17:36 Paul Matzko: Yep. You’re kind of the WebMD of the legal profession.
17:39 Nick Whitehouse: Yeah. Absolutely.
17:40 Paul Matzko: Just this statistical, not a replacement, but an augmentation that…
17:43 Nick Whitehouse: Yeah, absolutely. So you, as a consumer, are empowered so that you’re not spending your money unwisely. So you spend your money on the right things. You’re getting the right results. And you know what you should be pushing for.
17:56 Paul Matzko: So you come from New Zealand. How did you end up in this place, in the US, with a startup in Silicon Valley? Like how did this happen for you, Nick?
18:07 Nick Whitehouse: Yeah, so it’s been a really fun journey. So I was the chief digital officer of a very large law firm in Australasia, in New Zealand, the New Zealand arm. About 3,000 lawyers in the legal group, so between Australia and myself, we had teams and I was tasked with looking at innovation from the New Zealand perspective, and we looked at both sustaining and disruptive innovations and this really was an idea that came out of this disruptive innovation space. How do you serve the world differently in law? And so, that ended up, I was on an innovation mission to Silicon Wadi in Israel, and I met a couple of people who were actually from New Zealand who were like, “This sounds a really cool idea, why the hell is a law firm sending somebody to think about innovation?” And so we actually ended up meeting with a VC and the two, the law firm and the VC invested and we found a bunch of PhDs and actually I’ve been traveling the world for the last year, talking to large law firms, talking to consumers, talking to large businesses, legal tech providers, really understanding the market.
19:12 Nick Whitehouse: And the reason we’re in the US is because from a… US is an incredibly innovative place. As I said it’s 49% of the world’s legal spend. It’s in a highly… It’s the most litigious space in the world, and I think there’s a real need here with the cultural trade of innovation and trying new things and taking risks, and so it’s perfect for us to be here.
19:35 Paul Matzko: Yes, American litigiousness for the win.
19:40 Paul Matzko: Well, that’s great. Well, thanks for taking the time to talk to me, Nick, I appreciate it. I think our listeners will enjoy hearing what McCarthyFinch is doing, and we’ll be sure to put a link to y’alls website…
19:48 Nick Whitehouse: Awesome. Thank you very much Paul.
19:49 Paul Matzko: In our show that’s… Alright.
19:49 Nick Whitehouse: Right.
19:51 Paul Matzko: While Nick says that McCarthyFinch’s goal isn’t to replace lawyers with artificial intelligences, rather they want to provide lawyers with an additional tool for their legal toolbox. The net effect of this tech, if it works well will be to depress the hiring rate for new lawyers. However, even the smartest AIs aren’t anywhere near capable of replacing the entire legal profession. Much of what lawyers do is customer service and human relations, the human‐facing side of the profession, dealing with the unique demands of clients and persuading recalcitrant juries that can’t be automated or at least not anytime soon. That work is safe, which means that established lawyers with dense relational networks between clients and other attorneys, they’re not gonna be hurt by this new tech. Indeed, if anything, they’ll be helped since it gives them a tool to lower labor costs at their firms and cut some of the drudgery out of their own work routines. Who this tech potentially harms the most are those currently in or freshly out of or at least even considering going into law school.
20:56 Paul Matzko: Imagine that you’ve spent three years in law school, accumulated say $200,000 of debt, you go on the job market and you can’t even find an entry level position at a major law firm ’cause they’re only hiring a fraction of the new JDs to do the basic document review that they used to. You hunt, you hunt, you hunt, finally you have to settle for something temporary in an adjacent job field that pays a fraction of what your legal job would; it doesn’t have really the same future career or career earning potential, given that your student loan debt is, thanks to the George W. Bush administration, not dischargeable in bankruptcy, it’s gonna follow you the rest of your life, you can just barely meet the basic interest payments, you may never pay off those loans in your lifetime. And so, this basic career miscalculation as the future supply and demand of legal labor impacts how soon you’ll be able to afford a home, how many kids you can afford to have, whether you can afford to take any financial risks to start a new business or to move to a new region, and how soon you can retire.
22:00 Paul Matzko: But what if you could avoid that fate, if there was a better way of assessing the risk that the career you are considering will be automated in the next five years or so? That’s the promise of our next startup interview, listen in.
22:11 Paul Matzko: I’m here with VC who’s the founder of a startup called 6figr.com and we’re gonna be talking here about a new trend. It’s actually a website and a program that helps deal with something that’s an increasing issue here over the next decade, which is white‐collar job loss. Jobs like analysts and lawyers and even some doctor or medical related fields are being taken by AI. I mean, they’re being automated, they’re being routinized in the modern economy which means job loss for people who were previously thought they had a job guarantee lined up for them for the rest of their careers. And 6figr is designed to help mitigate some of that problem, to help catch it earlier for students. So, VC, why don’t you explain a little bit about your website for our listeners and describe how the system works?
23:09 Vinod Chandrashekar: Right. So, thanks for having me. At 6figr we work on future of work. There is an Oxford study said 47% of the jobs, existing white‐collar jobs, are gonna be taken over by AI by 2024, and it’s coming. So what we had… What we help with the users is, any person who logs into our website he knows what is… We calculate what is the job risk associated with any title, and then we say, “What are the skills you should be learning to make a transition to a safe zone where the person will still be employed?”
23:50 Paul Matzko: Right, right.
23:52 Vinod Chandrashekar: And what are the variables which we use in calculating the risk associated with the job is to find out whether the job involves negotiation, perseveration, is there a finger dexterity, is there a creative intelligence to it, or a social intelligence to it. So on a research back study we are able to find out what are the jobs that are at high risk and what are the skills you should be learning to move to low‐risk jobs and still be employed. For example, when you’re talking about, we might think like, okay, the lawyers and the doctors they were like… The automation is only coming in tech, but that’s not true. The automation is coming even for something as complicated as medical, and the automation risk for the radiologist is like 98%, 98% of the jobs are not gonna be there for the radiologist. Imagine people are paying 500K to go get a medical degree and they’re spending 10 years and at the end of it, there’s not gonna be a job. Or there’s not gonna be a job which is gonna pay as much in which they can recover whatever the investment has just been made.
24:54 Paul Matzko: Yeah. You’ve got six figures of debt now, and there’s no job at the end, because instead of a radiologist looking at a cancer skin, now an AI can identify whether that’s a tumor or a cancer just as effectively and a whole lot more cheaply for the hospital. Right?
25:10 Vinod Chandrashekar: Exactly. So what the radiologist is doing is nothing but an image recognition problem. And there’s a ton of data out there, the hospitals are opening up the data and there’s open source of the data and it’s a computer science problem and once… And the automation is gonna come after the jobs which are very, very, highly, highly paid. A radiologist gets paid somewhere around like 300 to 500K, so an employer at the hospital will be like, “Okay, I’m gonna just use this automation technology. And I’m gonna save like 300K, thank you.”
25:46 Paul Matzko: And I think the range of fields is quite fascinating, we’ve touched a few of them. There’s fresh out of law school graduates doing document review, while there’s… And again, there was actually a presentation here at TechCrunch Disrupt that was gonna automate document review; radiologists, healthcare professionals… I think one of the examples you showed on your system to me was a financial analyst, or credit analyst. Again, it’s a data crunching kind of story and it doesn’t require necessarily, especially if you’re a radiologist looking at an X‐ray or looking at some data or a credit analyst looking at numbers, you’re not actually interacting with the person which requires a level of social intelligence, that’s kind of a bulwark against automation, for now at least, or at least during our lifetimes. So those are fields that are really vulnerable. So where do you see 6figr being used? Who’s paying for this service?
26:43 Vinod Chandrashekar: Right. So, one thing we wanna get it clear is the employer is not gonna care about your job loss.
26:51 Paul Matzko: Right, right.
26:52 Vinod Chandrashekar: So the employers do not pay for this. Who pays for this is the schools, so we partner with the schools to say, like, “Okay, these are the job titles which is not gonna be there, and these are the job titles which is possibly gonna exist. And how you should be designing your curriculum, what are the courses you would be teaching,” and to help schools think about what is it that they should be teaching to the students and for the students to democratize opportunities. So if you look at right now, we all pursue career paths which are known to us, which is very limited to our friends and family, what is the path which they have gone through and we try to retrace the path, but that’s very limiting.
27:39 Vinod Chandrashekar: So that’s why we plug in our big data engine in which we say, “What is the best possible move, given the choices I’ve made ’til date, what is the best possible move from here?” And you can visualize your career… A career is like a 40‐year marathon, so you can visualize it like a chess game, and look at what is my end game, and make moves according to it. And we calculate in terms of odds and seeing like, “Okay, this is a dead‐end job, if you go here, you can never move out to any of the other jobs.” And we really look at how can you make the moves in which your opportunities are wide, or you can transition better or you can be in titles which is not gonna be automated.
28:16 Paul Matzko: Yeah. Then that’s relying on, I think your database of career changes that professionals have made in the last couple of years, is that where you’re getting that transition information from?
28:28 Vinod Chandrashekar: Right. So the data is actually for the last 12 years, the data. So we have been live for the last two years, but we also partner with job sites who also provide us with the data, and using the data we add the intelligence on top of it.
28:41 Paul Matzko: Ah. Okay. So you know what, for whatever reason people had to make a job transition. Maybe 12 years ago, they were moving from radiologists to something else for… Well, who knows why? So, it doesn’t really matter why they’re moving, it’s what they were able to most easily move to, those alternate career paths. And I think that’s interesting. I can see the use case for a career advising office or a professional advising office. As it happens, my partner works in a health professions advising office, and this is the sort of information that would be… It’s a tool kind of in the toolbox that would be useful for those offices.
29:17 Vinod Chandrashekar: Right. What we also take into consideration is when you’re making the career transition, we also take into account, does it pay as much as… Income, as much of a salary which you were getting before.
29:29 Paul Matzko: Right, right.
29:30 Vinod Chandrashekar: So that is an important aspect of it in which, we’re not suggesting career paths where you’ll still remain employed, which is far less so. Often, that comes into the ego of the person, of like, “I was getting paid 300K, I don’t wanna transition to a job which is like 120K, in which I’ll just be employed.” So that’s why we plug in of saying that, “Okay, here are the real transitions which people have made,” and we also try to connect, like, “Who’s that person, can he refer to you and pull you out of that abyss?”
30:02 Paul Matzko: So how about we plug one in, and see how it works? Let’s see, what profession should we choose? We did analyst before, we could look at that. So you plug in analyst, it gives a little job description here of what an analyst does, prepares reports with credit information, etcetera, etcetera. And then as you scroll down, it gives a job risk number, which is… What’s that there?
30:27 Vinod Chandrashekar: It’s the probability of AI taking over this job title.
30:30 Paul Matzko: In the next six years. And sorry, analysts, your risk is 98%. Does that mean 98% of the jobs or 98% likelihood that your job will be gone?
30:41 Vinod Chandrashekar: 98% of your job will be gone.
30:43 Paul Matzko: Of your job being gone, okay. So you’ve got a 2% chance of keeping this six years from now, so maybe it’s time to make a career change. Oh no, what do I do? I am doomed. It tells me I’m doomed in no uncertain terms. I like that automation risk: Doomed. Okay, what was your median salary before as an analyst? $82,000 almost a year. Safe designation switches, so it gives some suggested career options that are relatively similar, require relatively similar career sets or skill sets, but have a much lower risk. So number one, here is account manager in sales.
31:22 Vinod Chandrashekar: That’s because the sales engineers involves a social aspect of it, involves a perseveration, it involves negotiation. These are the ones where the AI is still not good.
31:33 Paul Matzko: Right, right, and so your odds then…
31:34 Vinod Chandrashekar: It could come in the next 50 years but right now…
31:37 Paul Matzko: By then you’re retired, so it doesn’t matter. And there are your odds of losing your job in the next six years to automation is 0.41%, so now you’re safe.
31:46 Vinod Chandrashekar: Now you’re safe, now you’re very, very safe. For your lifetime you’re safe. You don’t have to worry about it. A career is 40 years, so no automation.
31:54 Paul Matzko: Yeah, that’s great. If you know that in advance as a student, you’re not having all the transition costs of… It helps you tailor what you’re doing, the internships you’re taking, the initial jobs you accept, all of that then can be tailored because of this kind of prediction engine.
32:10 Vinod Chandrashekar: Right. So one other thing, aspect which I wanted to highlight about is, because we are taking the analogy of a chess game, in the chess, as soon as you’re able to seize the middle, your opportunity, your chances of winning goes very high. So that’s what we try to do here with the careers, of saying, “Which is that company or which is the job title where you should be transitioning as soon as possible, so that your opportunities are unlimited?”
32:39 Paul Matzko: Yeah, yeah. Well, that’s just fascinating. VC, I wanna thank you for coming on and telling our listeners about 6figr. I will put a link to the website in our show notes when we air this. So thank you so much for your time.
32:52 Vinod Chandrashekar: Thank you so much. It was a pleasure.
32:54 Paul Matzko: I appreciate VC’s attitude. He combines a realistic sense of inevitability about the coming wave of white‐collar automation with an optimistic sense that he and his company can help lessen the pain and suffering of that transition. Indeed, that’s very much the spirit of TechCrunch Disrupt as a whole. Disruption is there, it’s built right into the name of course, but it’s inspiring being around the very bright, very competent people who are rolling up their sleeves, and trying to solve social problems, and make human lives better, and they’re doing so without having to appeal to government to do it for them. There was no talk of, “Here’s why we need $100 million and job retraining funds to transfer people from one career to another.” No, these are people who are anticipating in advance that there will be a social problem, there will be economic dislocation and are doing something now to try to fix that, in order to try to mitigate that damage.
33:47 Paul Matzko: And they’re doing so through private means with private funds and trying to do so to turn a profit, to make a career and a living for themselves. So I think it’s a lesson about the power of the private sector, the power of free enterprise in addressing and anticipating future crises. And that’s all for this episode. Tune in next week for the next in our series of interviews from TechCrunch Disrupt. Until then, be well. Building Tomorrow is produced by Tess Terrible. If you enjoy our show please rate, review and subscribe to us on iTunes, or wherever you get your podcasts. To learn about Building Tomorrow or to discover other great podcasts, visit us on the web at libertarianism.org.