How Companies Are Actually Spending Money on AI Now
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Hello and welcome to another episode of the Odd Lots podcast. I'm Jill Wiesenthal. And I'm Tracy Alloway. Tracy, we're recording this January 8th. We ran a good piece in the newsletter this week from Skanda about AI spend as like a sort of like meaningful macro driver or getting close to where it like starts to move the dial. Yeah, that was a really good piece. And I have to say some of it is slightly worrying. But I think...

one of the big things that's happening now is, okay, AI has become such a big pillar of the market, right? Like the entire S&P 500, basically. It's like an AI play. Yeah, it's an AI play. And so at some point, the hype has to be matched by reality, i.e. all that investment has to be matched by some sort of revenue, right? You have to get money out of making this investment. I think there's three ways things could go. I've been thinking about this. There's three ways this could play out. One is...

companies don't get a lot of productivity gains from these tools. They cut back spending. There's a bunch of other projects get delayed, market goes down. Another possibility is there's this great productivity breakthrough. Companies are more efficient than ever, incredible boom.

That's great. And then the other possibility is that none of this matters and they build God at one of these labs. And then everything we know about economics doesn't even make any sense. So to even talk about productivity or the S&P 500 or earnings in that regime is just like it's like a secondary concern to like the way the world has changed when they achieve AGI.

Joe, are you okay? No, I think those are like the three. Yeah, you might as well go big with your scenario analysis. Yeah, but you're right. It could go either way. And obviously, there's a lot of talk and nervousness about a bubble in AI at the moment. So I think it would be a good idea to maybe try to get an understanding of how much companies are actually spending and benefiting from AI. That's right. And we have the perfect guest.

because he has a great view into what companies are spending money on, including AI. And also as the CEO of a company himself in the tech space, but not directly in the AI space, a sort of user of AI tools and maybe could talk about what's being used, what's not being used, where productivity gains are being had, etc.

We're going to talk about all of this stuff. We're going to be speaking with Eric Glyman. He is the founder and CEO of Ramp, which is a New York City-based company. It's a spending management platform for companies, help them deal with expenses. We might also talk a little bit about expense management platforms because I have...

Do you have complaints? I have headaches about dealing with expenses. I think a lot of people do. Yeah. I guess that's why there's a business, a new business to be made. Eric, thank you so much for coming on Odd Laws. Joe, Tracy, thanks so much for having me. It's great to be here. Real quickly, why don't you describe a little bit? What's RAMP? What's your story here?

Well, you can think about Ramp as a financial operations platform. It's a single place where companies can issue cards, make payments of all kinds, and even automate both expense management, which we'll get to, and accounting. But the ethos of the company and why we exist is actually to help companies save money.

time and money. We're the only company in our space that actually reports back to our customers, how much money and time did we save you? Over the past four years, we've saved our customers over $2 billion, 20 million hours, and half of that has actually come in the past year. And so we serve

30,000 plus companies from small, medium to publicly traded. Got it. What was the gap in the market that you saw? Because on the one hand, as Joe just laid out, a lot of people hate expenses and they're clunky and very bureaucratic and they take forever to do. But on the other hand, this is a space that is dominated by some very powerful legacy players, right? I'm thinking about American Express, for instance, and you're basically going up against them.

These are companies that are great in their own right, but I think we're built for a bit of a different era. In the company you mentioned, the founders quite literally wore top hats, you know, thinking not so much about, I think, really the needs of the 2020s, where I think luxury in the 2020s is actually having an hour of

to yourself at the end of a long work week versus you have expense reports to do at the end. And so we thought the gap was a few folds. First, could you actually infuse technology not to make an easier to use expense report that only took an hour instead of two hours, but actually an expense report that does itself, books that keep themselves. And so the difference was we saw an opportunity to create a card where you can tap it, make a purchase,

We pull the receipt from the merchant or your email automatically so your expense report is done for you. Your books and records are done for you. And more for business owners, we found this strange distortion where they were trying to market products like spend more money, earn more points. But every business owner I ever met actually wanted to spend less and be more profitable. So we just try to keep it simple and build a company just on those principles.

I mentioned in the intro, because you have this expense management platform, you have some insight into what companies are spending money on these days on AI. Like, what can you see? You know, what are you able to see about AI spend within large corporations?

I mean, it's real. It is dramatically increasing, but in actually interesting ways. And to give you a sense of the panel data that we're looking at to get these insights, we see over 50 billion a year in spend by companies. Some of these are publicly traded. Most of these are private. And often these tend to be on the bleeding edge. So these can be from AI research labs themselves to farms to nonprofits, mom and pop shops.

And this is across both credit card data as well as bill payment data. So it's a pretty good subset of it. And what we've seen is maybe twofold. First, just in terms of raw and aggregate numbers, an average customer on ramp from the start of 2023 to the end was spending about four times the number of raw dollars on AI-based products. And so there's real budget that's starting to go to this in increased ways.

And next, the products themselves are starting to actually go from experimental to operational. - How can you see that? What do you see in your data that backs that up? - So the best way we think to know this is that if you looked at an average AI purchase, maybe you purchased some software seat in 2022, there was a 50% chance that within the next month,

A customer that bought it would no longer be a customer. They were experimenting with it. In 2023, that had jumped to a 70% chance. In 2024, it's continuing to go higher and will release data. Wait, sorry, 50% chance you continued it the next. That's right. Okay, got it, got it. And so there was a radically higher chance that you were keeping this around. And so it went from tinkering to this is starting to become a real part of engineering processes.

sales tools that teams were using to be more productive, to even back office tools to manage accounting, manage expenses. And so I think we're still on the trajectory best in class of products are going to be in the 90s of percent. But the jump was dramatic in 2023 and 4. That's interesting.

How granular does your data go? Like, can you see people spending on, I don't know, a basic LLM subscription versus something else? Very much so. So the interesting part about what we do is because we automate the expense report process, we can see not just that a company spent on OpenAI, but specifically was it an API call?

Was it a chat GPT license? And so even among products, you're seeing itemized and SKU level data. And so you can start to get really interesting insights of even in terms of sub markets. One of the emergent themes that people are talking about now was in 2023, there was only one name in AI that mattered, and that was OpenAI. In 2024, suddenly 20% of developer market share was going to Anthropic, which was

I think at 3% in data in 2023. And so you can start to get very granular of how is this even being used across which models are being called. And so it's actually this interesting level of insight that hasn't quite been seen in these markets.

And then, sorry to focus so much on the data, but how do you actually classify an AI use versus something else? Because I imagine there's a lot of software, for instance, out there now that incorporates some sort of AI component, right? It feels like the Venn diagram of AI and basic tech spending is kind of starting to come together. I think you're totally right. And there's a variety of

I would say it was an easier question in 2023. There was only a few strange companies calling themselves AI. Now you see kind of AI washing of companies that are not. We love their stock to pop. But I would say that we tend to classify these based off of kind of self-identification of the companies. These tend to be large language models, labs. These tend to be companies that are pure play AI products, maybe in 11 labs if you want to generate an AI digital voice.

cognition or Devin. You can hire an AI developer and these type of tools. I think you're right, though. My sense, and if you talk to too many people in the Valley, they'll tell you there will be no company that sells technology in five years that isn't an AI company. And we'll see how the jury goes there. But it's this basis.

Two things. First is a statement. I've never coded in my life. So I've made a goal for 2025 to like use AI to like build an app. And I actually built a really rudimentary app, but it wasn't really doing what I wanted it to do. I'm not going to talk about what it is. It wasn't really doing what I wanted to do. And then I like fixed the code and I tried to re-upload it and I broke it. So I had an app for about five minutes and

and then it died. But I'm going to, this is like my goal. I really want to learn to use technology. I want to talk more though about, you know, me when I use, quote, use AI, it's just me like going to chat.openai.com, just like the most rudimentary user interface. Talk to us more about what you can see

on the gap between just someone subscribing to the website versus someone paying for API calls, which I imagine is sort of a deeper level of sophistication building these models into a workflow in some way. Oh, for sure. And I actually think this is

the most interesting development that if this happens successfully, it unlocks what many in the Valley are talking about of historically technology with software as a service. You know, you could sell seats to people and it would do it. And there's this growing idea of service as software where suddenly there are workflows where if AI is not just a window you chat into and get a response, but actually at every step of it,

becomes very, very interesting where you have kind of end-to-end products, videos being created, books being done automatically for finance teams, even art kind of getting created. And so what I would say is the way you can see it mechanically is often the type of license. Usually these are consumer licenses if you're buying like a ChatGPT Pro subscription versus there are, let's say, enterprise or even kind of developer plans where at the end of the month you get an invoice from one of these vendors

And you see, OK, there were this many calls to this endpoint. These many tokens were ultimately used. And will the specific use we aggregate and anonymize and don't report down to that level, you can start to see suddenly this is much more similar to how maybe a company would

call Amazon Web Services or Microsoft Azure. And this is core compute for some service that is reliant. And the different things about these graphs is done right. And what all of these companies are betting on is that they're going to grow exponentially and that it's going to be deeply embedded. There are some distinctions. One thing that I think makes this market very different and in some sense more vicious than anything I've ever seen is

is usually there's this idea of lock-in. You host all your cloud services on one provider, and you can't change from one cloud to another only for small use cases. But in AI, there's this practice of kind of multiplexing. And so what developers are often doing and sort of why Anthropic came out of nowhere, seemingly, in a way that wouldn't be possible was...

People would try some knowledge work or some response on multiple libraries, open source, open AI, see which one's the best. And the winning call starts getting more calls. And so things are just, the markets are moving faster. 89% of business leaders say AI is a top priority, according to research by Boston Consulting Group.

But with AI tools popping up everywhere, how do you separate the helpful from the hype? The right choice is crucial, which is why teams at Fortune 500 companies use Grammarly. With over 15 years of experience building responsible, secure AI, Grammarly isn't just another AI communication assistant. It's how companies like yours increase productivity while keeping data protected and private.

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So the specific companies are anonymized, but can you see stuff on a sectoral level? We both went to the same, but that's what I was thinking. Like, for instance, can you see which industries seem to be ahead in AI and which ones are perhaps lagging behind?

We can. And I think that there's a few things that were obvious and others that have started to jump out in interesting and unexpected ways. The obvious ones of certainly the earliest adopters are technologists like technology. These are engineering offices, startups. A lot of folks doing training are transmedia.

trying and adopting very, very quickly and are nimble to use this. The interesting thing there is that have actually most surprised us, maybe correlate to what you might see and experience. A lot of newsrooms are actually using kind of recording tools. So if you're having a call, notes are taken for you. A lot of sales teams are using this tools that listen to sales calls, take down the notes, suggest the next steps and start to go into this.

But you also see, I would say, historically, if companies wanted to cut costs and sort of focus on efficiency, their choice was higher, lower cost labor. Often in spaces where some of this can be, if you can get the rules right, things like health care, which tends to be late adopters.

the rates of use of increase is actually starting to be much faster because when costs and margins are very low, if you can start doing network calls to do more work when there's few to people, these are some interesting emerging industries. But it's still quite early. I would say the the big question that people have out there about the valuations of these companies is still very present. The relative increase is dramatic. Forex year over year in raw dollar spend per company is very, very large.

But to start to pay back some of these large capex cycles, it's going to be early. And so the bet that needs to happen from where we sit is AI can't just be a product SKU that people buy. But I think to pay this back, you probably start to need everyone to use it. Actually, this was going to be my other question. So we're talking about AI spend. Can you see the other side of it in the form of, I guess, savings, either by being more efficient or perhaps cutting jobs? Yeah.

I would say it's very interesting questions. And I would say generally as AI has taken place, unemployment has started to come down. And so I think these are very real questions for the long term. And in fact, I actually think one of the biggest misses in 2024 for those promising AI was it was going to be the year of the AI agent.

was what everyone pitched. You'd have the AI CFO, the AI engineer, the full kind of jobs. And I think that what you start to see is you see slices of things happening. For example, I hope it's not anyone's job in 2024 to do just expense reports, but actually an AI can do your expense reports. It can kind of look at your invoices. It can do kind of the lowest value tasks

where that's starting to become a present thing in these tools. And so I think in the short run, I actually find generally we hear from customers work is getting more interesting in some sense when AI is being adopted. I think long term there are real questions of might it actually be able to take a workflow end to end. I think practically speaking, AI often has very limited context. It gets a question, it can prompt out a response, but doesn't see the rest of the knowledge work. But as it starts being everywhere,

It might be possible. I need that end-to-end agentic AI for this app that I'm building because what's really annoying is it'll be... Because I'll write some code in Google Collab and then it'll be like, I'll try to push it to GitHub and then I have to go find my token. And I don't want to do... And then I'm like, wait, where do I find the token in GitHub so that I can put it in here? And that's the stuff that I don't want to... I mean, I guess I have to do it, but I need the AI to just like...

GoFundMe. Does that count as coding if the AI is doing the end-to-end? No, I don't care which one. If you're literally just typing in design an app. It's fine. I have some ideas. I'm trying, but it's still a little bit annoying, like all these different windows and everything. Okay, but speaking of all that,

Let's talk about what you're seeing in your company. I imagine every engineer that I talk to is like, yes, in 2024 or 2025, they have a window open with their AI and they have their coding window and it's improved their productivity. So I assume that's happening in your company that a lot of code is being written either directly or indirectly or with assistance from AI. Where else besides engineering, what actually are you spending money on in terms of AI resources?

So the three big places, and you nailed it. Number one is in engineering. It is one of the most digital jobs. All code is digital. And so in a strange way, that is actually arguably maybe the first industry that is closest to AI. Second is sales and growth.

Ramp, beyond having a large amount of spend data, is one of the fastest growing companies or startups in history. And part of what's allowed us to do this is the average salesperson at Ramp is about four times as productive as an ex-closest competitor. A lot of that is a heavy use of AI to automate aspects. Specifically, let's talk specifically. What do they do? Yeah. Someone who makes a sales call or something, what are they using AI for?

So one of the most important functions in the first rule for anyone going into sales is this rule called a sales development rep. And the job of that person is book meetings. Find people out there who maybe are doing expense reports the old way, and let's bring them in. And if you kind of decompose what that task actually is, it's first asking, who are these businesses?

What is a relevant moment? Have they just raised funds? Have they just hired someone on their finance team? Maybe they're posting an open role in some needs. There are these signals out there in the world. Then you notice, okay, you've got your lead list assembled. You have to go write them. Maybe you have a junior person just out of college going and trying to guess, what's this person's email? What's this person's phone number? How do I get a mailer in front or how do I knock on their door? Then they write kind of the message, what's gonna be compelling. There's all these little steps involved

What makes a human seller great and what makes sales interesting is the genuine human connection, someone who can go deep, understand all the context and actually close that great sale. And yet, if you looked at the task in 2022, how most people were spending their time was things that algorithms are great at.

finding people's email address, testing which copy will ultimately work better, detecting across vast swaths of data, what's the signal and the noise. And so effectively, part of what's powering this level of growth is a broad set of AI tools, which do exactly that, where AI is finding the person's email, AI is detecting the

these signals for intent, sending the message in the job of an entry-level salesperson now is the majority to respond to interest, to close people on the call. And so that's one example. We can go through several kind of throughout the sales cycle, but it's changed the role to be much more interesting.

I'm getting flashbacks to Glenn Gary, Glenn Ross. Didn't companies used to buy lead lists as well? They still sell them. They still do. Yeah, right. So I imagine if you can basically build your own lead generator, you would save some money as well.

That's exactly right. But the interesting thing is just the speed moves faster. There's suddenly signals of intent. Maybe there's an IP that you can back into. This is a company has gone to your site five times today. Maybe that's a low value list. It's the C list. It's not the A grade list. But with kind of a modern stack that's sifting across these signals, you can get more interesting. And so I think there's things like that. The other big thing that has just transformed the job is

There's just so much more noise than there is signal out there in the world. So if you're a manager and you're trying to help a 22-year-old new in their career get better at sales, it's just too many hours of calls to listen to across your whole team to do that. The large language model has no problem listening

to a thousand years of calls in a single day more than any human can and so suddenly i actually think one of the more interesting stories and lessons i learned about this actually came from a hundred plus year old credit card company where i was first skeptical when i heard the story the executive was explaining how they stopped checking net promoter scores

And I'm like, wow, they must have gotten that bad. But the truth was something more interesting. What's a net promoter score? So a net promoter score is this process of often you sample. So let's say there's 10,000 people who call in with customer support and you ask them at the end of the call from zero to 10, how happy are you with the service? And often in these surveys, you ask a small sample and you can see an aggregate, how good are your agents?

The response was much more interesting. What they started doing was applying a large language model to listen to all calls simultaneously, and they could apply sentiment analysis from the tone of a voice, how happy were customers or not. And what the algorithm started doing was routing calls from customers increasingly to people who did a great job that made customers happy. This is always the case. If you're good at your job, you get more. That's the reward. Yeah.

But it's a real way. Suddenly, like you can actually tell from every customer how are they doing and actually get more output per unit of input. It's a real use case. I don't know how you do a few years ago. And I'm sorry, you were talking about where you're deploying AI and you had engineering and sales and we didn't get to the third one.

Oh, sure. Yeah, we'll see if it works. Maybe most avant-garde would actually be kind of in growth and marketing. Of course, there's customer support and other areas that people have talked about. But I actually think there's a real case that marketing and growth is becoming a technical function. People thought that art would be one of the last use cases and suddenly you can create images

videos, interesting things. And now in a world where you can go and start to do this, you can see based on intent, on conversion, can you start to combine the mathematical function of what really works with people to creating beautiful, striking, interesting images on demand? And so the jury is still most out with that one.

But it's been real. I think early tests are promising. And I think that the early example of this that to me in some ways kind of inspired this was maybe twofold. One, Amazon, they had kind of the editorial and the personalization team. It used to be that folks at Amazon actually wrote and recommended you bought, you know, here's a newsletter of all the things that we have. And it was like the Sears Robux catalog. Eventually it became you bought this, you might be interested in that. And eventually things went out. And so you may be able to do that not just with

clustering of items, but actually understanding people and making beautiful things. And I think of inspirations in New York, like Andy Warhol, his factory where every day they'd make something new. Things were incredibly striking and had this method of new levels of art, thinking about being incredibly generative. And I think that every industry in some sense can be improved and augmented and made more interesting actually through kind of just the leverage that these tools can bring.

I'm going to be cynical for a second, and I'm going to press you a little bit further on the question of AI and sales, because I certainly take your point about searching for signal, what companies might be in a position suddenly where they're looking to upgrade their expense management platform, or what might be the person who best to call. Some of that, I imagine, is stuff that someone was selling via Salesforce for a while and calling it five years ago machine learning or 10 years ago machine learning, but

Obviously, what is technically AI is one of these things that people argue over to get a better multiple, etc. But for the purposes of what many people in the stock market are excited about, a lot of it's the sort of, you know, the post-2022 generative AI that somehow comes in the lab and was inferred on an NVIDIA chip or something like that. Say more about sales and what are other things that you can do in sales today in 2025? Yeah.

that you could not have done in 2021? I think that probably the most exciting area has to do with reasoning. When I think about a lot of classic machine learning, which is still deeply important, it's often around correlation of certain variables and prediction in a very narrow sense. And so maybe the first phases of machine learning is prediction of what comes next.

You know, my frame around a lot of this is now there's generation based on what connects. What do you create? And I think the most interesting when you think about kind of the 01 models, 03 and the new reasoning models where it's thinking and can think multiple steps ahead, some of the tech

that made AlphaGo so good at what it's doing. It's those types of work. And so you're exactly right. Some of this is actually just good data infrastructure and prediction. But when you start to fuse that with based off of these signals, what maybe should we write based off of the context of the calls and the usage of this data? How do we follow up and orchestrate it across multiple steps?

That's where I think generative AI starts to get really interesting in these boring use cases like expense management and saving people time and money where it starts to get very useful. 89% of business leaders say AI is a top priority, according to research by Boston Consulting Group.

But with AI tools popping up everywhere, how do you separate the helpful from the hype? The right choice is crucial, which is why teams at Fortune 500 companies use Grammarly. With over 15 years of experience building responsible, secure AI, Grammarly isn't just another AI communication assistant. It's how companies like yours increase productivity while keeping data protected and private.

Designed to fit the needs of business, Grammarly is backed by a user-first privacy policy and industry-leading security credentials. This means you won't have to worry about the safety of your company information. Grammarly also emphasizes responsible AI so your company can avoid harmful bias.

See why 70,000 teams and 30 million people trust Grammarly at grammarly.com slash enterprise. That's Grammarly at grammarly.com slash enterprise. Grammarly. Enterprise-ready AI.

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Can you talk a little bit more about what data exactly you're scraping to infer those specific signals? Like, what do you have access to and what do you find most useful? Yeah. So first, some of this is just let's take a use case of, let's say, an account manager. They might be overseeing hundreds of individual accounts at ramp and their goal is to get back on the phone and make sure people are getting value out of it.

We want to save your business time and money. We want to make automated accounting, but is it set up that way? Are you seeing these terms of use cases? And so some of this is going to be internal use cases based off of spend that you wanted to bring over versus which would you actually spent. How is that going? Call log data, large language models can remember all calls, all notes.

what people kind of committed to by what dates and do that. And so when an account manager gets on the phone, they can go and have all the right contacts in front of them that they didn't need to go and spend hours the night before kind of creating, but it's pulled up and pulled in terms of what's most useful. You next may have interactive data, can pull through based on the website itself,

Were there flows where it appeared people just got stuck and confused? Maybe they wanted to go close the books and it seemed like too many steps and they kind of paused. We can kind of- So you're tracking like actual physical movements on the website through beacons, I assume. Yeah.

Yeah. So you can do things like that coupled with even external data as well. Sometimes a company could be doing really well, announce a fundraise, need to expand and make sure, hey, we've seen this good news. We want to make sure that we're expanding with you. And so I think that...

Any one individual point a person can do, but often the fullness of what does it take to really understand and be a partner with how are one to thousands of employees at a single individual customer, how are they actually doing so we can be a more useful partner is often where this comes together. Does that make sense? It does. Although I have, maybe this is a weird question, but like how does the system actually work?

generate its suggestions. So if I'm a salesperson and something like the system spots a lead of some sort and it thinks you should get in touch with this person because of whatever reason, like how does that message actually get to me? Yeah.

And I think that's exactly, in some sense, probably the million dollar question of which SaaS companies are going to do really well. And I think even the story of technology now is you have different people with different aspects. Some are in the browser, some have this sales tool, some have this data tool, some are a data warehouse, some are a training tool.

Where does it show up? And I can tell you, we leave it ultimately in the hands of our sales team and the growth team building for them of whatever works, you are free to pick given the period of change. But for them often, like I'll tell you one of the more useful tools for that use case for account managers is a company called Rocks. I think it's like r-o-x.com.

They're a less than a year old company, but effectively are pulling data from Salesforce, usage data, internal level data, analytics, and appends notes for an account manager's calendar prior to meeting. Here's the core things to know. Here's links. And if you want to pull so you can get things at a glance in your calendar and you can actually pull things.

from the website itself. Here's more data to go see it. So it's, they're trying to become a bit of a mission command for sales. Of course, Salesforce is trying to do these things too. In engineering, there's tools like Cursor and Devon have different bets.

Cursor has, like GitHub Copilot, sort of won the love of many developers, where as you're coding, it's like a better autocomplete that you just say, I want to build this app, and it can go ahead, it can audit lines of codes and knows your repo. There's stranger bets like Devin and Cognition, where the form factor of that is it is meant to be a digital AI engineer. And you can tell Devin, I want to build this app, go research these websites, make it look like this, come back to me when you have questions.

questions. Should I do that for my app? Try it. I was talking to someone, I forget who it was, maybe it was even someone from one of these companies, and they were talking about different approaches to some of them, including ones where the AI basically controlled the mouse, controlled the cursor, and clicked on websites and read websites. So we think of API calls, but

There's a different model where it's just like you're scanning the website like a human is, right? You are exactly right. And this is one of the strangest and most interesting things about the time that we live in, in that computers can kind of think, they can kind of see, they can kind of hear and process different levels of data. And I think that the general story of computing is increasing levels of abstraction.

Back 80 years ago, people were writing machine codes. It was one, zero binaries. And over time, it went to seed a Python, which were increasingly higher order compromises between the language you and I speak. Separate from binary, like layers and layers above binary. Yeah. And there are many people who would make the argument, I think convincingly so, that the next programming language, in fact, is

is English and what you see. And so I even think of now what many people talk about, one of these coming battlegrounds is actually what are you seeing on your computer? And the best interface is not chat, but actually it's just a large language model that sees everything you're doing on your computer and can predict what's next. Who knows? It's nice. Well, when I keep getting error messages in my Google collab, I just take screenshots and then I upload them to ChatGPT and I say, what's this error message mean? Yeah.

And then it tells me one thing I've wondered about. And, you know, people are very anxious about what are the jobs of the future and all these things and what professions are going to get disrupted away. And, you know, people could speculate on this forever. But one thing I've like wondered about is like there are certain jobs where to be good at them,

you have to have probably like sacrificed years of your life and like not gone to parties and not had friends and, you know, not had sleep. Some mean jokes, right? Because like there's so- The true artists. Right? No, or the technical skill to acquire just took years and years and years, et cetera. And so you had to be the type of person that was willing to sacrifice a lot to get good at them or to build up that technical skill, right? Those hours. And I'm wondering like if AI is going to sort of

cut into the jobs where a major part of getting good at it was this sort of being willing to like sacrifice being a normal person because the AI could just do thousand doesn't need to sacrifice anything it's just a computer and that it would benefit the people like you know the low-rounded people who I bring people they want to have yeah you bring you bring some IQ to work but you also bring some people that have like emotional IQ yeah yeah exactly this is what I'm wondering about

I think this is exactly the right line of questions and I think is going to be a real one to confront. And I think in some sense, I think even probably listeners of this podcast fall into this group of like very curious people and people who know how to ask interesting questions.

keep going down it and create things, I think we'll do very well in the future. Thank you. Yeah. It's all going to be okay. Don't worry. No, but I think things are going to change a lot. Like even in thinking about coming here today, like I was curious of at the turn of the century in 1900, I think in the United States,

40% of all jobs were in farming. You know, today, less than 1% of all jobs are in farming. And I think things are okay currently, but I think that the nature of jobs is going to change probably in ways that will be very hard for people just as in 1900 to now to predict. It's probably the same for us and what it may look like in 50 to 100 years.

How are you or your clients handling privacy and legal slash copyright concerns with some of these platforms? Yeah. Well, first of all, like we have a bit of a simpler time on at least copyright in that we're not in the business of generation of how do you go and create new art and images. And I think those are real and present questions, especially for

content generation, a lot of what we're doing today is you've made this card transaction, you've texted back 30 seconds later, here's a photo of the receipt, and then we can match, okay, here's what the memo should be, here's what the accounting category should be, and you're done. And so a lot of this is process automation and workflow automation. And there's an interesting value we can bring, and probably the most interesting part of our business, we need to think a lot about this, is on this area of price intelligence. Mm-hmm.

Now, as a consumer, you can go onto Zillow and know here's what your home maybe is worth. You could go on Truecar and get a sense of what should you pay for this car based on lots of aggregate anonymized data. And for businesses, it's very useful to know what is this business paying down the street

for this supply or for this Salesforce license? What are people paying? And so in that part of our business, we actually do want to be able to go to customers and say, you know, here's where your pricing compares to the rest of the market. And we want to help you negotiate for lower prices. And we think it's really good the way you get there. And I think probably the core of our strategy around this is aggregated, anonymized.

is really the part that needs to come or if it's individual data that really sits on ramp and is for the purpose of providing service to you. And if we share things broadly, it's give get. If you want to see pricing data, you need to share in it. But we need to have enough data to effectively anonymize like what's coming out there. And so we think it's very useful for finance teams, for business owners to help them pay less. It's part of what helps an average customer cut expenses by about 5% per year.

but maybe less good news for people who try to discriminate against our customers. We are in an age of cracking down on spending and cracking down on wasteful spending in D.C., for example. And people are talking about Doge, etc. And if we're going to really move the dial on spending, it's not going to be wasteful spending. It's going to be we're going to have to actually have different priorities. Nonetheless, cracking down on waste seems good from the perspective of an expense management platform.

What are some fingerprints of wasteful spending that you see? Can you see into companies and like,

see the fingerprints of waste? Do you have any advice or things that you look for when you if you wanted to hunt waste and spending? I'm happy that people are thinking about this in a real way, because often there's an obsession of how do you spend more? But if you want to make a change, it actually comes. Other than putting the entire government on rent. So other than that, I assume you support that. But what's the next thing you have to do?

Look, maybe one of the founding fathers, Ben Franklin, I think was known for saying a penny saved is a penny earned. And if you look at reasonably efficient organizations like an average American company, they have a profit margin of about 8.5%. So mathematically, a penny saved is actually 12 earned. Yeah.

is the same. And I think about organizations like the government, which, look, I have a lot of empathy. They have a lot more complex constituencies and needs than, you know, a profit-making entity. But the exercise of cutting costs has not been taken seriously for a very long time. And it's led to very different behaviors.

If you want to sell to the government today, often the selection criteria is, can you last through a one to two year request for proposal, an RFP and process, which is very different than how most companies and people select for things. What has the most value? What's the lowest cost? What can I try and see it is done? Next, the other very counterintuitive thing that's interesting about the government is

You know, you would think that as one of the largest buyers of anything in the world, we would get that discount for all taxpayers if you're buying a million licenses for a piece of software. Volume license. But it's not that way. The typical way that government buys is to pay...

sticker price and to not have discounts. And so you see not only are different agencies paying very different prices for the same sets of goods, but you don't see normal common sense things of, hey, we should have a group discount for people in buying to last. A lot of the tools, because procurement cycles are 15 years

You can't break contracts. You're paying full rates. So these companies are going to sue you if you try to go and do this. You start seeing some crazy things in terms of the actual tools themselves. The spend management architecture that the government is using was primarily selected in the early 2000s.

And so whereas the private market today can tap a card, their expense report is done for them, their books are kept for them. The result is that you have an incredible amount of waste of people's time, of really hardworking people, in some cases, actually spending most of their time trudging through the bureaucracy of old tools that don't work to each other. To the most shocking, I would say, is like, you don't have to look hard. There's several friends at different agencies who...

in trying to learn and understand some of these Doge efforts, a shocking thing I learned is that at multiple agencies, four hours per night, email is just shut down. You can't send, you can't receive. It's crazy, right? I've seen the government websites have like a time of... That's exactly right. Because they're using old private servers, contracts from 30 years ago. And so if you want to talk about...

you know, true efficiency, it's nowhere close to the leading edge. And while I think you're exactly right, if you want to really make a dent in the budget, you have to talk entitlements, you have to talk debt service and what that's going to look like. But if you want to go to this next level, great tools that prevent wasting of time, that automate auditing of records. So you don't have a department of defense that's failing seven audits in a row and can't track

we're spending is going, but actually it's all digital. It's all tied to it. I think you need to get serious about allowing people to pick best tools for the problems. Eric Gleiman, thank you so much for coming on Odd Lots. I'm really glad we made this happen. I really appreciate it.

Tracy, I really liked that episode. There's all kinds of AI tools I need to now dive into because like now I'm going to be the person who's subscribed to nine different things. Well, that's everyone, right? Yeah, I know. Like what am I subscribed? I'm like I'm subscribed to at least like three, probably more right now. You know, now that I got to like dive into these like specialized coding tools. But no, that was really fun.

Have I told you the story before about my coding class in high school? Say more. So actually, it was just a basic like IT class. But as part of the class, we had to program our own little application. And this was like in the early 2000s. So it was all very rudimentary.

But our teacher commissioned us to do this and I built a fortune cookie program where you like clicked a button. Well, the button looked like a cookie and it gave you your fortune, blah, blah, blah, blah. And,

And at the end of the assignment, everyone turns in their program to the teacher and he made everyone sign a contract giving away the rights, the licensing rights to him. And he said he did it to teach us all a lesson about copyright and how your work is rarely your own if you're working for a big corporation, which is why I asked that copyright question because I still think about that to this day. So it was a good lesson. That's extremely funny.

But no, there was a bunch in there that I thought was interesting. So one, you know, the idea that like you can just like track like what percentage of people like pay for something one month and also the next month. Also this idea and like we got to keep like coming back to it. The lack of lock in for some of these models. Right. We're really used to there just being one winner in search, one winner in social networking, one winner in e-commerce.

so forth, one winner in photo sharing. So it's really interesting to think about like all this money going to models like A, where it's like really easy to move from one to the other, a simple project. B, where there's open source competitors that maybe are just as good. That seems like a pretty big deal right there. Yeah. And I guess the big question is, will there eventually be some sort of winner that turns out to be

better at it than everyone else or is there going to be space for that sort of specialized either you know specialized use case or the specialized like actual interface for different jobs yeah that's interesting we talked a little bit about the importance of interfaces totally anyway shall we leave it there

Let's leave it there. This has been another episode of the All Thoughts Podcast. I'm Traci Allaway. You can follow me at Traci Allaway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our guest, Eric Gleiman. He's at E. Gleiman. Follow our producers, Carmen Rodriguez at Carmen Arman, Dashiell Bennett at Dashbot, and Cale Brooks at Cale Brooks.

For more OddLots content, go to Bloomberg.com slash OddLots, where we have transcripts, a blog, and a newsletter. And you can chat about all of these topics 24-7 in our Discord, discord.gg slash OddLots.

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