How AI is Transforming Labor Markets

0:00Now you have software agents that are effectively doing what for 65 years have been human work. Is that going to increase software revenue to X? It could potentially increase at 10X. It's not even on the same kind of playing field. All the data is here in the cloud. All the compute is in the cloud and now you just mix them together. Many of these incumbents aren't going to evolve. Every time there's a new technology shift we have to challenge every investment thesis that we thought was not going to work. and so many of them now are going to work. MOTES still matter and a lot of the MOTES in software today are the same that they've always been.

0:36It's both defense and offense for these companies to figure out what the hell to do. There is a lot of talk and investment in AI. But that opportunity is often framed in software terms. The budgets, the market caps, and the existing companies of the last era. But might this era be fundamentally different? Consider that for centuries, the biggest school of science was Alchemy to turn lead and to gold. And today, AI enables a far more powerful transmutation, turning software into labor. Capital no longer just buys engineers or hardware, it buys code that replaces or augments labor, unlocking completely new markets.

1:17And that's exactly what we discussed in today's episode, together with three A16Z general partners, Alex Rampell, Angela Strange, and David Haber. Together, we trace through the evolution of previous cloud eras and what those tell us about what's to come. Perhaps most importantly, what's really new here? Namely that the $300 billion enterprise software market is just a fraction of the multi -trillion dollar white -colored labor market. These niche markets that weren't that interesting are now potentially very interesting. We'll also cover where we are in the adoption curve, wedges and defensibility for startups, and how pricing is being upended.

1:55If they don't do this right, they could lose all of their revenue or most of it. If they do it really well, they could 10x their revenue. Finally, if you're building in this space, we'd love to hear from you. Just reach out to podpidgesat16z .com and we'll route you to the right place. Alright, let's get started. As a reminder, the content here is for informational purposes only. Should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the company's discussed in this podcast.

2:33For more details including a link to our investments, please see A16Z .com slash Disclosures.

2:44Alex, you wrote an article recently, input coffee, output code. But this idea of turning capital into labor? Hasn't this always been true? What's new here? Well, it certainly has been true for a long time. If you watch some old movie about the Romans, you'd have all these Romans -lave laborers or Roman soldiers rowing in unison on a boat. And then, of course, you got the seam ship, and then you don't need these 50 people rowing anymore. So clearly, there has been this long historical arc of technology is in some cases augmenting labor. But it was always the brawn and not the brains, right? It's I have a bunch of people that are so enclosed and now I have the loom.

3:24But everything that was what we would now call white collar, that hasn't happened before. And what I talked about was the three or four different areas of software, just story information. So for the longest time, if I wanted to keep track of who was on my airplane from the right brothers onward, I would have a filing cabinet with it's like, oh, here's Pan Am flight 192. and here's who's on it, and I'm writing down their name, and then they might call or send a telegram back in the old days of, I don't want to be on that planet anymore than I erase it and then I refile it. And one of the first examples of digitization of the filing cabinet was something called Saber.

4:02And this was developed by American Airlines, I think in 1959 or 1960, in concert with IBM. And this was one of the first examples of taking this filing cabinet that really kept track of who's on Pan Am flight or American Airlines in this case, and putting it in a database. So now, instead of having filing cabinets with lots of erasers and whiteout, you replace the filing cabinet with a computer. And this, in turn, we got travel agencies and travel agencies because they had a thin client, a little computer terminal. I remember booking airline tickets with my mom in the 1980s, you'd go into the travel agent, and they had a green screen computer that connected to a mainframe in Texas, which is where Sabre was.

4:43So the first realm of software was really called from 1960 onwards when computers became a thing of take a filing cabinet. So it could be the HR filing cabinet. It could be the medical filing cabinet. It could be the financial filing cabinet and put that in software. And what was that? It's a database with a front end to actually go enter things. So quick and famously did this in the 1980s for financial statements. There was a company called PeopleSoft that famously did it was the first HR filing cabinet put as software. Sabre obviously did this for airline tickets. Email, I would even argue, did it for mail.

5:20It's like you have files of mail and now you just have a filing cabinet if you want on your computer. But the actions that were done on the software were really the same. So imagine an HR department that has 50 people working in an HR, they might have one person that's in charge of the filing cabinets. And the HR person says, hey, get me David's file because I want to talk to him about something, which is very scary. Don't worry. But get me David's file. So the filing cabinet person, the gofer, this is what the filing cabinet person would offer to be called. The go for something. That's what it means.

5:49There's the go for that obviously Bill Murray tries to kill in catty shack. But then there's the go for of the person who goes for something. If you are a go for at Creative Artists Agency, you are going and getting files. So that person went away. The filing cabinet went away. It was more efficient from a space perspective. But the 50 HR people are still 50 HR people today. So, round one of software was really take the filing cabinet and put it not as physical files, but as a database with a front end. Round two, which started arguably 1998, 1999, and this is what Salesforce did. The idea of a customer relationship management product has been around for a long time.

6:28The role of X is an actual physical thing where you put every business card, organize them alphabetically, and you'd find the person that you want to contact that way. Sales force put that not in software, but in the cloud. So what QuickBooks had done for a long time or something called Great Plane Software had done for a long time, net suite did in the cloud. They put financial statements in the cloud, Zendesk put email support in the cloud. So it was still software, but instead of having a giant mainframe in your office somewhere, you now had it in the cloud. But again, going back to the HR example.

7:00So people saw first did this with on -premise mainframe computer, HR filing cabinet now is software, and then Workday came along to the exact same thing, and now it's in the cloud. It's just much, much easier. You don't have to have to dedicate the IT team to go like worry about your server, like exploding in flames if your office building burns down or something. So it's more secure. So it was kind of software 1 .0 then became software 2 .0, which is in the cloud. And that played out from call it 1998 to maybe 2010. And then that grew a a little bit with the insertion of financial services because the way that I like to think about this is how many restaurants need and will pay tens of thousands of dollars a year for software.

7:42Like Pan Am needed this in 1960, but does a restaurant with one location, are they going to spend $100 ,000 on a server and pay for software, no in 1960, no in 2000? But when the idea of bundling and payment processing became a thing and other financial services, is now the market became big enough for restaurant software to exist. And this is where toast came from. Toast is a $15 billion company that does this or service Titan. Again, what's the software market in 1965 for HVAC Contractor Zero? What's the cloud software market for HVAC Contractor Zero? But once you bundle on these other things, it becomes big enough.

8:21But the point that I'm getting to is that the same 50 HR people that worked in 1960 are the same 58 HR people in 2024. The same email support team in 2024 was the phone support bank in 1985, was the letter writing typewriter support team in 1965. What's exciting about AI is that it's taking this filing cabinet and now allowing actions on the filing cabinet. And that's what I think is really revolutionary because you can actually ask the software filing cabinet application like Workday, hey, I want to add a dependent and now Workday will do all the work involved with that and Workday can charge a premium for that.

9:03So why is it input, coffee, and output code? The whole idea is that you now have software engineers that can build products on top of these cloud -based filing cabinets that now do the job that the end user of that software or product in 1960, 1970, 1990, 2000, 2010, 2023, 2024, but 2024, 2025, onward. Now you have software agents that are effectively doing what for 65 years have been human work. Yeah, and that sounds really important. So I want to underscore that. You kind of went through the areas. You have the non software era. Then you have software. Then you moved into cloud. Then you have this financial services enabled cloud era.

9:47And now we're in this new era. Can you talk about how, as we chart through these different eras, the scale maybe in this new era is fundamentally different? Yeah, it's completely different because it's really comparing wages to software and to take an example from a completely different field that also has no software market. There are about 4 .7 million registered nurses in the U .S. Average wage for a nurse is a little over $120 ,000 a year. So it's a high paying profession. and that means that the annual nurse, not software market, but wage market is over $600 billion a year, which is a lot.

10:23And the worldwide software market is under $600 billion. Not just America, by the way, that nurse figure is just for the US. Of course, there are nurses in the UK and France and go like every country around them, right? Probably Antarctica has nurses on the first. So, almost certainly. They're probably even higher paid down there. So, the labor market is enormous, miss, but what is the dedicated software market for nurses, like probably zero, because nobody took the time to develop software? And you could try developing software, but it's just there's no budget. That economics didn't make sense.

10:53Exactly. But if every hospital in the US currently has a nursing shortage, or maybe you're in Minneapolis where there's a giant Somalian expat community, and you need nurses that speak that language, how do you find them? And it takes three to four years to actually get trained as a nurse. Now you have a software product that can deliver not everything that a nurse does. Of course, they can't be a phlebotomist. You can't perform CPR, but it can call you the night before you're colonoscopy and say, don't eat food. And it can say that in 45 languages. And it can actually have a conversation with you.

11:26So that's a labor example or going back to financial services land, net suite is used by I think it's 70 % of companies that go public are on net suite. It's some very very important. I'm sure I've heard a podcast ad with that exact. Yeah, it's maybe it's higher. Something on that order of magnitude. If I'm wrong by 10 points, it doesn't belittle the point. Yeah. But the key thing is you always have people that are paying you late. Like you'll look at your accounts receivable and you'll see, wow, a bunch of customers owe me millions of dollars. And that's what you'll see when you look at your financial statements.

11:54And then again, this is where humans take the operation on that information. I have teams that are in collections that will go call you and remind you to pay or I'm going to cut off the product. That's an operation that can now be done within NetSuite. They haven't done this yet, but I'm sure they will. And versus charging for the filing cabinet, now they could say, well, we know that you wanted to hire five collections people. We know that you pay those collections people $80 ,000 a year with benefits and everything else. We know that it takes a year to train them. Now our software product can do that not for $80 ,000 a year, but for $2 ,000 a year.

12:28And that's incredible. So the question is actually, how will the customer think about this? Because once they start paying net suite 10 times more than they paid net suite last year, they're like, wow, my software budget has ballooned. I got to cut my software spend. Or will they say, wow, I'm saving so much money because as opposed to these five job openings that I'm waiting to pay $400 ,000 a year for these five collections people, now I can pay $10 ,000 a year to net suite. And I'm paying more for software but less for labor. And that part, it's very, very new. But a lot of the hyper growth that we're seeing in this category of company is because because they are really moving into the labor market, unless the software market.

13:07And that nurse example is a prime example of that. Definitely. It'll be very interesting to see the appetite. If you think back to the app store, early days, when people were so reticent to pay $0 .99 for an app, for arbitrary reasons, but just because it was what they were used to paying. So as we round out these areas of clouds, you mentioned people's off or quick in or zen desk. And all these companies that are capturing data, How are we uniquely set up now because of the previous errors? Yeah, so I would argue that if we just went straight to AI in 1960, this just wouldn't have worked, because you still need human input to go collect the customer information.

13:45We have everything built out where right now this isn't an API, but if I want to go answer a customer question, the question's already on the internet. It's already in a database. Like all of these things have set this up to be the ultimate platform. So this is why we love investing in what I would call systems of record. And a system of record is just something that has every single piece of minutia that runs a business. And it could be the strangest business that you can imagine. If I'm running a laundromat, there is actually dedicated laundromat management software. All of these systems of record that have popped up for all sorts of different businesses and all sorts of consumer use cases, like the fact that now the mainstream form of communication for many adults and children is texting and email.

14:30Like, it's not voice, it's all in the cloud. Now you can perform these operations. So I would almost say there's been a 60 -year period of digitization of physical things, putting them in the cloud. Why is the cloud part important? Well, again, if this were the mainframe era of the 1970s, how would you get the AI that's in some Google server somewhere? where how would it actually have access to all the information that's in some server basement in Indiana? Like that's really hard to do. So all the data's here in the cloud. All the compute is in the cloud and now you just mix them together. So the fact that systems of record for so many different types of businesses and so many different kinds of consumer use cases are now widespread and hundreds of billions of dollars of company market cap has been created from these systems of record either horizontal or vertical.

15:20and a vertical one would be something like a toast that vertically runs a restaurant. And a horizontal one would be like a zendesk that just does customer support software in the cloud for every different type of company. Building on that, if you take the toast example, start with the cloud wave, move to the financial services wave, it was initially a hypothesis that these vertical SaaS companies would make a lot more from financial services. Fast forward to today, 80 % of toast revenue is payments, insurance, all sorts of financial services versus software. And so if you're operating toast or one of my favorite examples is mind body which runs fitness studio software, it does scheduling for employees, it's a CRM, they also make a lot of money from financial services, but they still need a lot of people beyond the yoga instructor.

16:05You've got your financial back office, you've got people answering the phone to answer very basic questions. Like all of that can start to be done with AI. And I think the most bullish version of that is All right, you then don't need to hire people to do the tasks that are not human facing that AI can do better. Is that going to increase software revenue to X? It could potentially increase it 10X depending on how the customer views it and how much they're willing to let their software budget bleed into their labor budget. I think part of the challenge or the potential for disruption is that the pricing model may need to change pretty significantly, right?

16:37You talk a bit about this in your piece, Alex, where Sendesk today is charging on a per seat basis. But if you're actually eating away at some of the laborer, you can charge for the output of work. How does that create the potential, both for increased ACV, but again, creates potential disruption for a lot of these larger incumbent players, given their existing pricing structure? Can we actually talk about that example from Zendesk? What is the difference between the software component and the human component? Well, it shows how stark the difference is. Most companies like Salesforce charges per seat.

17:11Zendesk at the time that I wrote my piece, they might have changed their pricing a little but it was $115 per seat per month. So imagine that you have a thousand people that work in an email -based support center and they use Zendesk. And Zendesk then profits as you hire more people because the thousand is better than 100. So $115 ,000 a month is about $1 .4 million a year in spend on software for Zendesk. And Zendesk I think has about $2 billion in revenue, something on that order of magnitude of annual recurring revenue from all these seeds that are paying every single month. And of course, they want their customers to grow seeds.

17:48Now, if you assume that each seat, how much is that person paid? How much does their healthcare cost? How much does their stipend for commuting and yoga benefit? And all these other things that the company throws in, maybe it's $50 ,000 a year per person. So what's $50 ,000 times a thousand? Again, a thousand seats, that's $50 million. So you're spending $50 million on people. And by the way, it's very hard to hire and train these people. So I think one misnomer is like, oh, AI is terrible. It's going to end all employment and it's going to be chaos. I always like to point out that when the United States was founded 97 % of people in the US were farmers.

18:26And most of them were put out of jobs by things like the tractor, but that's actually fine because they moved on to other kind of productive parts of labor. And by the way, the average life expectancy was a little over 30 back then. So I feel like these again, things have gotten better. I like penicillin and all the other benefits that we got and fertilizer and all these things that have made our lives better So you have 50 million dollars a year for people and then you have 1 .4 million dollars a year for software Which one's bigger and this is the concern is that on the intermediate basis and Zendesk is actually very lucky because they were a public company And they got taken private so two big private equity firms bought it and They're actually working on this right now because they're like uh -oh If we make AI really good, then the customer that has a thousand seats might cut down to ten seats.

19:12Because there are two forms of AI tools. There's more than two. But the common example that we talk about is there's autopilot and then there's copilot. So copilot is the productivity enhancer. So I'm trying to figure out how do I answer Angela's query. I just got hired yesterday. I don't even know where the bathroom is, what do I do? And then it's like, hey, we think this is the right answer. So it makes Angela so much more productive in her job. And that's great. Autopilot is like, Angela quit yesterday and we need somebody else to go answer emails because it's Black Friday. What do we do? What do we do?

19:47Okay, now we just throw the tool at the customer directly and have them answer the questions. And that's the big danger. Copilot is actually a danger for revenue as well. Because why do I have a thousand support reps? because they can only answer 10 questions a day. And I get 10 ,000 queries a day, so it's just basic math. Now with co -pilot, each one of my reps can answer 100 questions a day. I only need 100 reps. So now Zendesk lost 90 % of his revenue. And then by the way, if autopilot becomes a thing and it actually works very, very well, then I need nobody and therefore I sell no seats if I'm Zendesk.

20:21So it's both defense and offense for these companies to figure out what the hell to do. because if they play offense, they could maybe 10x the revenue because of the example. Like it's $50 million for people or 1 .4 million for some, I'd rather get 50 million. If I'm Zendes, then 1 .4 million, but they're probably not gonna be able to take all 50 million, right? Because ultimately, like the cost of delivering these services is very low, the moat is not that high. It's higher for companies that have the system of record, I would argue, because all of your past correspondence with all of your customers is in Zendesk.

20:54Or if I'm Salesforce, like every single communication that I've ever had with any of my customers, my pipeline, everything. It's in sales for us. It's hard to yank that stuff out because everybody's using it. And it's not like this binary thing tomorrow we're all autopilot. We're going to see a lot of these co -pilot tools. They will run on the systems of record. But even as they're running on the systems of record, now I need less seats. That's why sales for us, it's a $200 billion plus public company. If they don't do this right, they could lose all of their revenue or most of it. If they do it really well, they could 10X their revenue.

21:25It's like, where is it going to go? As early as the VCs were very excited about this. Yes, yes. I saw a lot of them. This is a question, right? More about finding the next one for those versus the end of the episode. It's an interesting moment in time for enterprising young founders to reinvent the model and charge radically different because many of these incumbents aren't going to evolve. They're not going to change their perceived pricing and they risk disruption. And is that the wedge, right? If you're a startup and you're trying to figure out how do I enter the market, especially when you have companies like Salesforce, which have the system of record, they have all the data, they have all the customers, is that the wedge where you say, I'm going to undercut and I'm going to charge a tenth of the price even though I still have great margins because I'm entering the labor part of the equation.

22:04I think so, yeah. I wrote a piece recently that I called the Messian Box Problem, which is my way of describing a wedge strategy that we're seeing across lots of different industries. And the idea is basically that there's a classic founders that are building software products to solve a lot of the, what was historically judgment -intensive work in lots of different industries, there is some sort of human administrators job. It is to basically extract information from a wave of unstructured information, whether it's emails, faxes, transcribing phone calls, and then put that information into one of these downstream systems of record.

22:37It could be an EMR, it could be an ERP, it could be a CRM system, and historically that work lived upstream of any of that software, right, because it was the humans job, and software couldn't do that. Now we're seeing companies sort of wedge in and replace, again, that messy inbox problem with software and slowly begin to eat away at all the kind of downstream workflows. And over time, I think that thesis is that while that initial wedge is highly differentiated against the human, it really is the opportunity to eat away at everything else and become the kind of new AI native system of record.

23:09We have a company, it's an example called Tenor that is doing this in a healthcare context. So the problem that they're solving specifically is around patient referrals. So you go to your general practitioner, they're referring you to a specialist, could be a dermatologist or an imaging center. Today, they're often faxing your medical records and it is somebody's job to go physically to the fax procedure, re -enter that information into the MR system. Tenor has trained a model against, I think, 4 million healthcare specific documents and now can basically extract all that information about the patient programmatically and have effectively begin to solve this patient intake problem.

23:47And they're able to reduce now about 90 % of the admin costs of that patient intake before the patient's actually seeing the clinician. So they again, wedged in with the messy inbox problem. And over time, they're now eating away at things like scheduling and eligibility and benefits. And over time, we'll see if they become the kind of core AI native system record. And something you're pointing to there is also the defensibility of it all, right? So you can get the wedge maybe through pricing. How do you actually protect your customers? I think one place people jump to is, okay, you need your own models or you need some sort of proprietary data.