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- [00:00]
Grammarly Representative
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- [00:27]
Meta Representative
Open source AI is available to all, not just the few. Meta's open source is free to use, enabling startups like Xaon to innovate. Here's CEO and co founder Dr. Cal Clark.
- [00:39]
Dr. Cal Clark
We built a tool with Llama Meta's open source AI model to help radiologists.
- [00:44]
Eric Gliman
Double check their diagnoses.
- [00:45]
Tracy Alloway
We're able to collaborate with universities to.
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Dr. Cal Clark
Further radiology education and catch more errors.
- [00:50]
Meta Representative
Learn how others are building with Meta's free open source AI at AI.meta.com Open Bloomberg Audio Studios Podcasts Radio News.
- [01:16]
Joe Weisenthal
Hello and welcome to another episode of the Odd Lots Podcast. I'm Jill Weisenthalm.
- [01:21]
Tracy Alloway
And I'm Tracy Alloway.
- [01:23]
Joe Weisenthal
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 starts to move the dial.
- [01:36]
Tracy Alloway
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.
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Joe Weisenthal
It's like an AI plan.
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Tracy Alloway
Yeah, it's an AI play. And so at some point the hype has to be matched by reality. That is all that investment has to be matched by some sort of revenue. Right? You have to get money out of making this investment.
- [02:07]
Joe Weisenthal
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.
- [02:50]
Tracy Alloway
Joe, are you okay?
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Joe Weisenthal
No, I think those are. Those are like the three. Yeah.
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Tracy Alloway
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.
- [03:13]
Joe Weisenthal
That's right. And we have the perfect guest because he has a great view into what companies are spending money on, including. 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. We're going to talk about all of this stuff. We're going to be speaking with Eric Gliman. 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.
- [03:50]
Tracy Alloway
Do you have complaints?
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Joe Weisenthal
I have headaches about dealing with expenses.
- [03:53]
Tracy Alloway
I think a lot of people do.
- [03:55]
Joe Weisenthal
Yeah. I guess that's why there's a new business to be made. Eric, thank you so much for coming on odd lots.
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Eric Gliman
Joe, Tracy, thanks so much for having me. It's great to be here.
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Joe Weisenthal
Real quickly, why don't you describe a little bit what's Ramp? What's your story here?
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Eric Gliman
You can think about RAMP as a financial operations platform.
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It's a single place where companies can.
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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 we exist is actually to help companies save 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.
- [04:43]
Joe Weisenthal
Got it.
- [04:44]
Tracy Alloway
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.
- [05:08]
Eric Gliman
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, thinking not so much about, I think really the needs of the 2000s, where I think luxury in the 2000s is actually having an hour to yourself at.
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The end of a long work week.
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Versus you have expense reports to do at the end. And so we thought the gap was a few fold. 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, 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.
- [06:08]
Simple and build a company just on.
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Joe Weisenthal
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?
- [06:28]
Eric Gliman
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.
- [06:39]
Some of these are publicly traded, most.
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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. 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.
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Joe Weisenthal
How can you see that? How do you. What do you see? What do you see in your data that backs that up?
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Eric Gliman
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 we'll release data.
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Joe Weisenthal
Wait, sorry, 50% chance you continued it the next.
- [07:54]
Eric Gliman
That's right.
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Joe Weisenthal
Okay, got it, got it.
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Eric Gliman
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 jump was dramatic in 2023 and 2024.
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Tracy Alloway
How granular does your data go? Like can you see people spending on, I don't know, a basic LLM subscription versus something else?
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Eric Gliman
Very much 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 ChatGPT license? And so even among products you're seeing itemized and skew 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 interesting level of insight that hasn't quite been seen in these markets.
- [09:24]
Tracy Alloway
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.
- [09:45]
Eric Gliman
I think you're totally right.
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And there's a variety of.
- [09:48]
I would say 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 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 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.
- [10:29]
Joe Weisenthal
First is a statement. I've never coded in my life. So I've made a goal for 2025 to use AI to 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 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.
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Eric Gliman
Oh for sure. And I actually think this is the most interesting development that if this happened successfully it unlocks what many in the Valley are talking about of historically technology was software as a service. You know, you could sell seats to people and would do it.
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And there's this growing idea of service.
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As software where suddenly there are workflows where if AI is not just a window you chat into, you get a.
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Response but actually at every step of.
- [11:47]
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 I, what I would say is the, 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 of developer plans where at the end of the month you get an invoice from one of these vendors and you see okay, there were this many calls to this endpoint, these many tokens were ultimately used. And while the specific use we aggregate, 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 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, OpenAI see which one's the best and the winning call starts getting more calls. And so things are just the markets are moving faster.
- [13:41]
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- [14:40]
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- [15:38]
Eric Gliman
Small businesses everywhere so the specific companies.
- [15:41]
Tracy Alloway
Are anonymized, but can you see stuff on a sectoral level?
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Joe Weisenthal
We both went to the same that's.
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Tracy Alloway
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?
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Eric Gliman
We can and I think that there's.
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A few things that were obvious and.
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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 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 healthcare, which tends to be late adopters, the rate 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 two people these are some interesting emerging industries, but it's still quite early. I would say the big question that.
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People have out there about the valuations of these companies is still very present. The relative increase is dramatic.
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Forex year over year in raw dollar.
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Spent per company is very very large. But to start to pay back some of these large capex cycles it's going to be early.
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And so the bet that needs to happen from where we sit is AI can't Just be a product skew that people buy. But I think to pay this back, you probably start to need everyone to use it.
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Tracy Alloway
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?
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Eric Gliman
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.
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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.
- [18:37]
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.
- [19:01]
Joe Weisenthal
I need that end to end agentic AI for this app that I'm building. Because like, what's really annoying is it'll be because, like, I'll like write some code in like Google Collab and then it'll be like, I'll try to push it to GitHub and then I have to like go find my token and I don't want to like 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 go find.
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Tracy Alloway
Does that count as coding if the AI is doing the ending? No, if you're literally just typing in design and taking the wheel, I have.
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Joe Weisenthal
Like, you know, I have some ideas I'm like trying, but it's like, it's Still a little bit annoying. Like all these different windows and everything. Okay. But speaking of all that, let's talk about your. 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, like, 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?
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Eric Gliman
So the three big places, and you nailed it, number one is in engineering.
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It is one of the most digital jobs.
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All code is digital.
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And so in a strange way, that.
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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 the next closest competitor.
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A lot of that is a heavy.
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Use of AI to automate aspects and low specifically.
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Joe Weisenthal
Let's talk specifically. What do they do? Yeah, someone makes a sales call or so what are they using AI for?
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Eric Gliman
So one of the most important functions.
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And the first rule for anyone going into sales is this role called a.
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Sales development rep. And the job of that person is book meetings.
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Find people out there who maybe are.
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Doing expense reports the old way, and.
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Let'S bring them in. And if you kind of decompose what.
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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 and some needs. There are these signals out there in the world.
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Then you notice, okay, you've got your lead list assembled, you have to go write them.
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Maybe you have a junior person just had a 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?
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Then they write kind of the message, what's going to be compelling. There's all these little steps. What makes a human seller great and.
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What makes sales interesting is the genuine human connection. Someone who can go deep, understand all.
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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 these signals for intent, sending the message.
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And the job of an entry level.
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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.
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Tracy Alloway
I'm getting flashbacks to Glengarry Glen Ross. Didn't companies used to buy lead lists as well?
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Joe Weisenthal
They still sell them.
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Eric Gliman
They still do, yeah.
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Tracy Alloway
Right. So I imagine if you can basically build your own lead generator, you would save some money as well.
- [22:53]
Eric Gliman
That's exactly right. But the interesting thing is just the speed moves faster. There's suddenly signals of intent, of 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, 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 from 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 1000 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 100 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.
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But the truth was something more interesting.
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Joe Weisenthal
So what's a net Promoter score?
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Eric Gliman
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 0 to 10, how happy are you with the service? And often in these Surveys.
- [24:16]
You ask a small sample and you.
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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.
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The tone of a voice, how happy.
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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.
- [24:42]
Tracy Alloway
This is always the case. If you're good at your job, you get more.
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Eric Gliman
That's the reward. But it's a real way. Suddenly you can actually tell from every customer how are they doing and actually get more output per unit of input.
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It's a real use case.
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I don't know how you do a.
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Tracy Alloway
Few years ago, and I'm sorry, you were talking about where you're deploying AI and you had engineering and we didn't get to the third one.
- [25:07]
Eric Gliman
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.
- [25:54]
It used to be that folks at.
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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 Roebucks 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. 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.
- [26:32]
Of just the leverage that these tools can bring.
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Joe Weisenthal
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 accommodates. 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. Obviously what is technically AI is one of these things that people argue over to get a better multiple, et cetera. 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 say more about sales and what are what other things that you can do in sales today in 2025 that you could not have done in 2021?
- [27:36]
Eric Gliman
I think that probably the most exciting.
- [27:38]
Area has to do with reasoning. When I think about a lot of classic machine learning, which is still deeply important, of course, it's often around correlation of certain variables and prediction in a very narrow sense.
- [27:51]
And so maybe the first phases of.
- [27:53]
Machine learning is prediction of what comes next.
- [27:55]
My frame around a lot of this.
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Is now there's generation based on what comes next, what do you create? And I think the most interesting when you think about the O1 models, O3 and the new reasoning models where it's thinking and can think multiple steps ahead, some of the techniques 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.
- [28:56]
Grammarly Representative
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- [30:54]
Eric Gliman
Everywhere can you talk a little bit.
- [30:57]
Tracy Alloway
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?
- [31:05]
Eric Gliman
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 meet 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 you actually spent. How is that going? Call log data Large language models can remember all calls, all notes, what people.
- [31:36]
Kind of committed to by what dates and do that.
- [31:38]
And so when an account manager gets on the phone they can go and have all the right context 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.
- [32:03]
Tracy Alloway
So you're tracking like actual physical movements on the website through beacons, I assume.
- [32:08]
Eric Gliman
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, you know, 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 our 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?
- [32:41]
Tracy Alloway
It does. Although I have maybe this is a weird question but like how does the system actually 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?
- [33:02]
Eric Gliman
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.
- [33:10]
Technology now is you have different people with different aspects.
- [33:13]
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 Rox. I think it's like Rox.com they're 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 from the website itself.
- [34:00]
Here's more data to go see it.
- [34:02]
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 Devin have different bets. Cursor is like GitHub Copilot. Sort of won the love of many developers where as you're coding, it's like.
- [34:19]
A better autocomplete that you just say I want to build this app and it can go ahead, it can audit.
- [34:24]
Lines of codes and knows your repo to. There's stranger bets like Devin and Cognition where the form factor of that is it is meant to be a digital AI engineer.
- [34:34]
And you can tell Devin, I want.
- [34:36]
To build this app. Go research these websites, make it look like this. Come back to me when you have.
- [34:41]
Questions, should I do this, should I.
- [34:43]
Joe Weisenthal
Do that for my app?
- [34:44]
Eric Gliman
Try it.
- [34:45]
Joe Weisenthal
Because like 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 like different approaches to some of them stuff including ones where like the AI basically controlled them out, 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.
- [35:07]
Eric Gliman
You are exactly right. And this is one of the strangest.
- [35:10]
And most interesting things about the time that we live in and that computers can kind of think, they can kind.
- [35:15]
Of see, they can kind of hear.
- [35:17]
And process different levels of data.
- [35:19]
And I think that the general story.
- [35:21]
Of computing is increasing levels of abstraction. Back 80 years ago, people were writing machine codes, it was 1:0 binaries and over time it went to C, to Python, which were increasingly higher order compromises between the language you and I speak.
- [35:37]
Joe Weisenthal
Separate from binary, like layers and layers above binary. Yeah.
- [35:41]
Eric Gliman
And there are many people who would make the argument, I think convincingly so that the next programming language in fact is English language.
- [35:48]
Joe Weisenthal
Yeah.
- [35:49]
Eric Gliman
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.
- [36:05]
Your computer and it can predict what's next. Who knows?
- [36:07]
Joe Weisenthal
It's nice. Well, when I keep getting error messages in my Google Cloud, 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 people are very anxious about what are the jobs of the future and all these things and what professions are gonna get disrupted away. And people could speculate on this forever. But one thing I've wondered about is there are certain jobs where to be good at them, you have to have probably sacrificed years of your life and not gone to parties and not had friends and not. Right. No. Or the technical skill to acquire just took years and years and years, etc. 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 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 sacrifice being a normal person because the AI could just do thousands. It doesn't need to sacrifice anything. It's just a computer. And that it would benefit the people. Like, you know, the well rounded people who I bring.
- [37:20]
Tracy Alloway
People that want to have fun.
- [37:21]
Joe Weisenthal
Yeah, you bring some IQ to work.
- [37:22]
Tracy Alloway
But you also bring somebody have like emotional iq.
- [37:25]
Joe Weisenthal
Yeah, yeah, exactly. This is what I'm wondering about.
- [37:27]
Eric Gliman
I think this is exactly the right.
- [37:30]
Line of questions and I think is going to be a real one to confront.
- [37:32]
And I think in some sense, I.
- [37:34]
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 will.
- [37:44]
Do very well in the future.
- [37:46]
Joe Weisenthal
Thank you.
- [37:47]
Eric Gliman
Yeah, it's all going to be okay, don't worry. No, but I think things are going.
- [37:52]
To change a lot. Even in thinking about coming here today, I was curious at the turn of.
- [37:57]
The century, in 1900, I think in the United States, 40% of all jobs were in farming. Today, less than 1% of all jobs are in farming. And I think things are okay currently.
- [38:09]
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.
- [38:16]
Probably the same for us and what.
- [38:18]
It may look like in 50 to 100 years.
- [38:20]
Tracy Alloway
How are you or your clients handling privacy and legal copyright concerns with some of these platforms?
- [38:30]
Eric Gliman
Yeah, well, first of all, 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.
- [38:57]
Process automation and workflow automation.
- [39:00]
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. Now, as a consumer, you can go on to 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, 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 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.
- [40:18]
Joe Weisenthal
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, et cetera. 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, like, 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 if you wanted to hunt waste in spending?
- [40:55]
Eric Gliman
I'm happy that people are thinking about.
- [40:56]
This in a real way, because often.
- [40:58]
There'S an obsession of how do you spend more. But if you want to make a.
- [41:02]
Change, it actually comes other than putting.
- [41:05]
Joe Weisenthal
The entire government on ramp. So other than that I assume you support that, but what's the next thing you have to do?
- [41:11]
Eric Gliman
Look, maybe one of the founding fathers.
- [41:13]
Ben Franklin I think was known for saying a penny saved is a penny earned.
- [41:17]
And if you look at reasonably eff.
- [41:19]
Organizations like an average American company, they have a profit margin of about 8 and a half percent. So mathematically a penny saved is actually.
- [41:26]
12 earned is the same. And I think about organizations like the government which look, I have a lot of empathy, they have a lot more.
- [41:34]
Complex constituencies and needs than a profit making entity.
- [41:38]
But the exercise of cutting costs has not been taken seriously for a very.
- [41:42]
Long time and it's led to very different behaviors. If you want to sell to the government today, often the selection criter is can you last through a one to two year request for proposal, an RFP.
- [41:57]
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 if it's done next?
- [42:07]
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.
- [42:33]
The same sets of goods, but you.
- [42:35]
Don'T see normal common sense things of.
- [42:37]
Hey, we should have a group discount for people in buying to last a.
- [42:41]
Lot of the tools because procurement cycles.
- [42:44]
Are 15 years, you can't break contracts, you're paying full rate.
- [42:48]
So these companies are 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.
- [43:02]
And so whereas the private market today can tap a card, their expense report is done for them, their books are kept for them.
- [43:09]
The result is that you have an incredible amount of waste of people's time.
- [43:12]
Of really hardworking people and in some cases actually spending most of their time.
- [43:16]
Trudging through the bureaucracy of old tools.
- [43:18]
That don't work to each other to.
- [43:20]
The most shocking, I would say is.
- [43:22]
Like you don't have to look hard.
- [43:23]
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.
- [43:35]
It's crazy, right?
- [43:37]
Joe Weisenthal
I've seen that government websites have, like, a time.
- [43:40]
Eric Gliman
That's exactly right because they're using old private servers contracts from 30 years ago. And so if you want to talk.
- [43:48]
About true efficiency, it's nowhere close to the leading edge.
- [43:52]
And. Well, I think you're exactly right.
- [43:54]
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.
- [44:00]
But if you want to go to.
- [44:01]
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 keep. We can't track where spending is going, but actually, it's all digital. It's all tied to it.
- [44:14]
I think you need to get serious.
- [44:15]
About allowing people to pick best tools for the problems.
- [44:19]
Joe Weisenthal
Eric Gliman, thank you so much for coming on Odd Lots. I'm really glad we made this happen.
- [44:23]
Eric Gliman
I really appreciate.
- [44:36]
Joe Weisenthal
Tracy. I really like that episode. There's a. There's all kinds of AI tools I need to now dive into. Because, like, now I'm going to be the person who subscribed to nine different things. Yeah, I know. Like, what am I subscribed? I'm like. I'm subscribed to at least like, three. Probably more right now. Now that I got to, like, dive into these, like, specialized coding tools. But, no, that was really fun.
- [44:58]
Tracy Alloway
Have I told you the story before about my coding class in high school?
- [45:03]
Joe Weisenthal
Seymour?
- [45:04]
Tracy Alloway
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 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.
- [45:56]
Joe Weisenthal
That's extremely funny. But no, there was a bunch in there that I thought was interesting. So one, the idea that you can just track what percentage of people pay for something one month, month, then also the next month also this idea. And we got to keep 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 all this money going to models like A where it's 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.
- [46:35]
Tracy Alloway
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? I think that's interesting. We talked a little bit about the importance of interfaces.
- [46:56]
Joe Weisenthal
Totally.
- [46:57]
Tracy Alloway
Anyway, shall we leave it there?
- [46:58]
Joe Weisenthal
Let's leave it there.
- [46:59]
Tracy Alloway
This has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.
- [47:05]
Joe Weisenthal
And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow our guest Eric Gleiman. He's E. Gleiman. Follow our producers Carmen Rodriguez, Ermenarman, dashiell Bennett at Dashbot and Kell Brooks at Kellbrooks.
- [47:19]
Grammarly Representative
For more Odd Lots content go to.
- [47:20]
Joe Weisenthal
Bloomberg.Comoddlots 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 oddlots.
- [47:31]
Tracy Alloway
And if you enjoy Oddlots. If you like it when we talk about how much companies are actually spending on AI, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad free. All you need to do is find the Bloomberg Channel on Apple Podcasts and follow the instructions there. Thanks for listening. Sing.
- [48:25]
Dr. Cal Clark
Join Bloomberg in Chicago or via livestream on March 11th for the Future Investor Finding the opportunities this 2025 event series will examine how companies are investing in their businesses to create efficiencies, innovating their products and services and improving the customer experience. This series is proudly sponsored by by Invesco. Q. Q. Q. Register@Bloomberglive.com FutureInvestorChicago that's Bloomberglive.com FutureInvestorChicago.