a16z Podcast - Why AI Agents Can Beat the Incumbents

Episode Date: October 2, 2026

a16z’s Seema Amble and Elena Burger sit down with Lio co-founder and CEO Vladimir Keil to ask where AI-native startups have an advantage when incumbent software companies already own the customer, the data, and the system of record.Their answer comes down to the work that happens outside those systems. In procurement, a final price in an ERP can hide hundreds of emails, spreadsheets, supplier conversations, engineering analyses, and decisions across legal, finance, and operations. Vlad explains how Lio uses multi-agent systems to take on more of that end-to-end work, from sourcing and RFQs to negotiation, shipment tracking, and invoices.They also discuss how enterprises learn to trust agents with increasingly consequential decisions, why the last 20% of an internal AI build can require most of the effort, and what happens when both buyers and suppliers have agents working on their behalf.Resources:Follow Vladimir Keil on X: https://x.com/askvladi?lang=en Follow Vladimir Keil on LinkedIn: https://www.linkedin.com/in/vladimir-keil/Follow Seema Amble on X: https://x.com/seema_amble Learn more about Lio: https://www.lio.ai/ Seema Amble’s “Investing in Lio” article: https://a16z.com/announcement/investing-in-lio/ Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

If you want to build an aircraft, you need to procure thousands of suppliers. Someone sends a confirmation of like, hey, sorry, like this part is going to arrive two weeks later. And if they missed this email, hundreds of millions of them. Procurement historically may have been more in a box. And now it's like, okay, it's touching legal, it's touching finance. It's touching a bunch of different software systems and people. The opportunity for the AI native startup is to say, we're going to own that entire end-end arc. No company and no enterprise starts with fully autonomous negotiation agents from day one.

Why? Because they don't trust us and they don't trust the technology from day one. And by having this UN in the loop approach, we are feeding our agent with all the feedback and all the learnings. And then they suddenly trust us for like 10K negotiations, 20K negotiations, 100K negotiations. When you think about what a durable vertical AI company looks like, what are the qualities that you look for? It's really, really hard to forecast your mode going forward. If you look back at all the best businesses, at the early stages, they were... If the incumbent already owns the customer, the data, and the system of record, where does an AI-native startup have an advantage?

In this episode, Elena Berger sits down with A16Z partner Seema Amble and Leo co-founder and CEO Vlad Kyle to answer that question through one of the most complex parts of the enterprise, procurement. They get into why so much of the actual work happens outside the system of record across emails, spreadsheets, contracts, engineering data, and conversations with suppliers. And Vlad explains how Leo is building agents that can coordinate that context and increasingly take on the job end to end. They also discuss how you earn enough trust to let an agent negotiate on your behalf,

by building an internal AI tool can be deceptively easy until you hit the exceptions. And what changes when agents eventually sit on both sides of a transaction? Welcome back to the A16Z podcast. I'm Elena Berger, and today I'm joined by Seema Amble, a partner at A16Z. and Vlad Kyle, co-founder and CEO of Leo, which builds AI agents for enterprise procurement. Seema, you recently wrote a piece called The Incumbents Are Coming, and then asked a question facing almost every AI application company. If an established software vendor already has the customer and the data

and the model can work across its tools, where does a startup have an advantage? And we have Vlad here, and Vlad can really help us understand where a startup has an advantage. So I think a good place to start is the cases for and against the incumbents. So if a company can connect a capable AI agent to the software it already uses, where does another application actually have a use case and an advantage there? Yeah, let me back up and frame it up a little bit. So historically, we thought about there's the incumbent and then there's a startup,

and it's a fight between distribution and innovation, which to take my partner, Alex Rampel's phrase. However, now there's like this third piece, which you're pointing at, which is the incumbent can layer on one of the models on top, and then there's a much more formidable competitor in the market. So why do you need an AI-native startup if you've got Cloudforce,

which is taking Claude plus Salesforce and putting the two together, and then you've already got all your data and your employees are all used to using the product. So why another product? I absolutely still think there's obviously still a case for the AI native startup. And it really centers around the fact that the legacy incumbent is limited to their system of record

and that record they have. And they're not completing the end-to-end job. So let me put that more concretely in an example. So say you're a customer and the customer calls and says they got charged after they got canceled. Resolving that cancellation history, isn't just the, you know, customer going into the chat and saying, hey, I got overcharged.

And the response there, it has to hit billing, it has to look at all the chat history, it has to look at the contract. That's not one system of record. That's the knowledge around that customer and everything it touched. And that's something that one system of record wouldn't touch. However, the opportunity for the AI native startup is to say, we're going to own that entire end-to-end arc. So that could be illegal.

So owning everything from brief all the way through trial, well, I can talk more about procurement, but it's really the concept of owning the end-end work. Yeah. Vlad, do you want to talk about where that does show up in procurement, like where existing incumbent plus a model just as insufficient? And what have you seen just for the companies that you work with? Sure.

So when we think about procurement, I would assume that most people think about like prices, right? So what is the end price that we negotiated on? And surprisingly, the record looks always very, very simple and very easy. It's just 8K. That's the result, for example. And I mean, it's the same for sales, right? So even if I come to see my own teller, like, hey, we now partner up with another enterprise,

look at the signature here for like this contract. It looks like very easy. But like Seema doesn't see like all the work behind it. Right. So like there are probably like 30 stakeholder meetings happened, 500 email. 20 axle sheets and that's the same for the counterpart procurement right so you see in your ERP system

8K for aluminum but you don't see that maybe the supplier did like a pushback and asked for like 10K you don't see that like a cost engineer had run three weeks of excel sheets and 3D modeling to find out the prices of the part and everything else

so that's what we see so like most of the work in procurement actually happens outside of this ERP or any system of record Yeah. And I'm sure in the workflow that you described, an agent can do a huge number of things and different kinds of agents can do a large number of things too. And I actually think that's a good bridge into the next question, which is, you know, a year ago, I think a lot of the incumbents were releasing chat bots. And that was kind of the extent of what you would see. But Seema, in this piece that you wrote, you lay out four different kinds of agents, retrieval agents, which are kind of the chatbots that were familiar with, processing. agents, policy agents, and principal agents. So can you just walk us through all of them and explain kind of what changes in the kind of judgment that's necessary across all of them? Yeah. So a year ago, I made this meme, which was the slap on a chat bot strategy, which is essentially like all

the incumbents effectively had a chat bot that sat on top of the system record, which you could chat with to retrieve information, maybe do some like analytics. And that really was in that first bucket of what the retrieval agent is. And maybe you're going to be a lot of the retrieval agent is. And maybe Let me walk you through an example of what each of like the retrieval, the process, the policy, and the principal, back to the customer support example just because it's very, it's easier to understand. So imagine if you're a customer and there's a service outage and you're calling in to say, hey, I want to get compensated for the service outage. The retrieval assistant, which the incumbent may have, is going to be able to pull up, yes, this was what the contract term said. And yes, there was an outage and just verify that information. It's pulling up information about the customer.

It's in the database, and it's just sharing it back and maybe synthesizing. The second step in the agent sequence is the process agent. So that process agent may be able to pull through an approval of credit and say, okay, based on our policy handbook, it was out from these dates. Therefore, we say you are entitled to money and it can just apply the bill and just do process. There's no judgment involved. Then as you keep going to the policy agent, so the policy agent isn't just going to apply the process, but it's going to say, okay, in this situation, it was out for 20 minutes.

That's enough to be considered a significant outage. And they're applying that judgment because there isn't really a strict definition around it. And then the last stage, which is a principal agent, you're actually weighing, okay, should we offer more compensation? Because that was a pretty terrible outage. and we want to reserve the relationship, and it's worth doing more beyond even what a process or policy. But the importance of these four things is that most incumbents started out in, if at all, in bucket one.

Now, at least they're marketing that they are moving towards process and policy, meaning they're able to apply more judgment. And, you know, if you look at what they've launched, these are workflow agents that will help you get a document signed or input information from a transcription or, you know, things like that. They're very much still limited to, I would say, retrieval and a little bit of process. They've not gotten into more judgment. And I can get into why there are all sorts of incentives that are preventing them from that. But the incumbents are trying. And I think what they're not able to do, they're like, some of them are saying, okay, let me partner with Open AI Anthropic, one of the labs, and try to build out, take that model capability and complement what they have and superpower it. Yeah. Yeah.

Well, do you want to say why sort of some of the incumbents are holding back? Yeah. Okay. So I would say they're holding back, but they're held back. Okay. Yeah, I'm sure they want to be full force. But there's probably two pieces. One, they have this advantage of distribution, right?

They have the customer trust, which enables them to then sell more products with the customer. So take Salesforce. When Agent Force launched, it was very easy for customers to say, yeah, I'm going to sign up for the Salesforce agent, especially if it was offered at almost no extra cost. And so they have this trust, they have the distribution, it's often pretty seamless to turn on the product. The flip side is there's all these internal incentive issues, right, which is if you start getting into more complicated agents, it's an internal conflict with an existing

product that's offering workflow versus you're resolving the work. And those are two products that are, if you're solving a customer support issue end to end versus providing a workflow for a support agent, a human agent, those are different buyers, how do you, you know, those two teams are in conflict. And then on top of that, I think from a sales perspective, like what are you selling to the customer? And then oftentimes, in a classic, like, incumbent issue, right, is like there's two, two VPs, right? And they're different orgs. And they're selling different products. And like, they will never be able to figure out what the, the right set of incentives and the right person to sell to. But anyway, so there's all these

sort of classic incumbent issues, I think that the incumbent also runs into. Makes sense. Vlad, where across this kind of spectrum of retrieval agent, process agent, policy agent, principal agent, where does Leo sit? Yeah. So like we span across like, I would say like all of those categories and it really depends on the complexity and the risk our agents take. Right.

So yeah, sometimes we can like we already have use cases where we run fully autonomously. Sometimes you have the human in the loop. It really depends on the budget approval, how complexness, how risky this is. But maybe like coming back to what Sima said about trust for the incumbents, like you talked about like internal trust. I think there's like also an external trust thing, right? So like how do you convince someone to go through from, hey, just like an agent that retrieves some information and maybe runs processes to do like something completely autonomously?

It's like obviously there's like a product component to it. but mainly as a people component. So they need to trust you. And like we as a startup, scale up, we have to earn the trust. Incomments already have to trust, but this also means they, like, they can destroy the trust if they ship something like too early and the product maybe doesn't work or it's bad or it works decisions in a bad way. So that's, that's what we can do.

And then interestingly, what was like really surprising for us, like those process agents actually became kind of like like a side quest for us because it like when we talk about when we look at the invoice process like invoice agents is a process so you we retrieve some information you match this across like other documents and then you push this back to SAP Oracle like a very clear clear process and then we figure out okay there's like 100% of the software market right so that's that's invoice software that's how you build it today but it's actually only like 20% of the work of the the job to be done of the problem because 80% of the problem is like,

um, what's if like what does the invoice is fraudulent? What if there's like a mismatch? Like what is like if we don't take the happy path. Um, and so we like, um, very fast shifted to,

to the next step of agents like doing those, doing those exception, um, exception handlings. Um, and we convinced the customers by, um, I think we got like lucky being like always slightly ahead of the curve.

So we were able to pitch the next generation of agents. So for example, like when we started out three years ago, it was like just retrieving a document. Like not impressive at all today, but like three years ago, this was like crazy impressive. So we pitched this to customers. We find out, okay, that's a real problem. It's a real use case they would pay for.

And then we were able to ship this some weeks later. And in the same way, we are doing this now for the next step for process agents and for like fully autonomous agents. and for long-running agents, we are pitching this to them, fighting out as a problem, and then we're able to ship this very fast.

I think an interesting point on trust is this internal versus external trust. The other lens on that is, yeah, so you need your customer to buy the procurement software and trust to use it for their internal processes. But one of the really interesting things when we first met Vlad was that they're doing,

they're also doing the negotiation. So you have to trust that the Leo agent is going to then interface with a third party and there is that piece of trust. And of course, I think a lot of people feel burned by the incumbents and, like, they're pretty limited and haven't been able to do what they've marketed they've had in the past. But putting that aside, like, I don't know,

maybe, Vlad, I'd love to hear a little bit how you convinced the customers to trust an AI agent to now take on negotiations. Yeah, and when you do that, can you also just, like, paint the picture of, like, what's involved in procurement and who are your customers? and what kind of legacy systems are they used to? Yeah. So like when you think about procurement, you maybe just think about purchasing

or like you when you look at like B2C well, like just you buy something. But actually it's like a very intense process which runs the economy, right? And it includes multiple stakeholders and a lot of stakeholders and a lot of departments, legal, cost engineering, obviously procurement, finance,

all of them have to work on this decisions. And essentially when we, I think the reason is how we convinced them is like what I already said earlier on. So we were like always a little bit ahead of the curve. So we knew the technology is coming. So we like even before Chachbc came out, Like we just started a few weeks before like this chativity breakthrough.

So we already heard all of the problems. Then we hear the hype in the, let's say, like, tech bubble. And we were able to pitch this enterprises. And we quickly figured out, okay, like this could be like an interesting use case, like just chatbot applications or retrieval agents, document processing. And then we are able to find out the problem and then ship this quickly to them. And then obviously, like, this is like the people factor of like trusting. So we are telling them something about

and we're able to really ship something in production. But then there's also like this product perspective where we have a lot of evils in place, right? So like we, like no company and no enterprise starts with fully autonomous negotiation agents from day one. No one does that. Why?

Because they don't trust us and they don't trust the technology from day one. So we have like a very easy approach of like having a human in the loop. And this is like extremely helpful for us because we see like we have like let's say like the perfect negotiation agent, which is like overall the perfect procurement negotiator. But we don't know exactly how a Fortune 10 enterprise, like the specific Fortune 10 enterprise operates. And by having this human in the loop approach, we are feeding our agent with all the feedback and all the learnings.

And then they suddenly trust us for like 10K negotiations, 20K negotiations. 100K negotiations. And then you also have like other agents that are like more long running. So when we talk about like multi-million dollar negotiations where you analyze complex 3D models and technical drawings, there we on purpose have always experts in the loop, right?

So there's an agent running for multiple hours and then we ask for feedback of the cost engineer and then it does the next work and so on. Maybe just to double click on that, where do you put the human in the loop the like negotiation side. You mentioned the cost engineer, but like if I were,

you were going back and forth on a deal, is it mostly around the like, you know, data for, you know, something like cost engineering or is there anything else where you have humans in a loop there? So, again, depends on the level of negotiation, right?

So like, we have to distinguish between like, um, negotiations where you just like a negotiation where in, like we're enterprises, they never did those because they didn't have the capacity. but by deploying agents, they can just capture savings that they weren't aware of.

So before agents or before Leo, they just didn't care about everything which happened below 50K. So you can just, like this is maybe a heck for like other startups. You can just send an enterprise and invoice for 40K. They will probably not negotiate because they don't have the capacity to do so.

Except they have Leo agents, then we are going to negotiate against you. But other than that, they're just like just I'm just paying that and obviously they're the risk of like like what is the risk of like you don't you didn't negotiate at all so what is the risk now of having a bad negotiation agent nearly zero right so maybe we miss out on some negotiations but like it's better than nothing but still in like in we're talking about business relationships and business relationships are not always about the cost and the money right so maybe you're not spending a lot

on the vendor, but maybe you are like, you need this business relationship, right? So a good example might be like podcasts or marketing services. Okay, that's like probably like a friction of the spend, but you don't want to, like you have a clear business relationship with someone like setting up the studio and you don't want like random, some random people doing that because they already know how how A6NZ operates and how you want to record all this stuff. So there we are, we have, have human and loop approaches where like procurement people care about the relationship so they care about the voice of tone and how it works but mostly autonomously and then we have the other set of agents where we always have a human

in a loop approach and it's like they're like multiple steps and like negotiating it's also like it's not only the price right it's also like how is the contract design so we're talking about legal how is like um collection design. So we talk about finance, obviously like cost structure. So we're talking about really like cost engineering. Then we talk about commercials. That's procurement. And those are not back office people. Those are like highly trained people where they have very specific knowledge of a very specific process, of a very specific company and very specific industry. And they feed those long running agents of Leo with those insights.

That's another example of how procurement historically may have been more in a box. And now it's like, okay, it's touching legal, it's touching finance. And it's touching a bunch of different software systems and people and both specialists and more generalists. Yeah. Can we map this onto a specific customer, like not a specific customer, but a specific vertical. Like, I'm a drone manufacturer. I manufacture humanoid robotics or something like that. like how many parts do I have to, you know, order and procure,

how many factories am I touching? How many suppliers am I touching? Just all of all of those things. If you want to pick maybe, Vlad, a vertical that, you know, is just managing all of this complexity with Leo and just kind of take us through what their experiences. I think that would really help just illustrate exactly just everything that you touch. again like when we like when we like when we talk about procurement purchases like we

don't talk about like laptops and pencils okay like we think like that's solved also by leo agents but that's easy we solved this like three years ago um we talk about like when we like you like you want to build an aircraft or robots or drones or even like we're now like doing a podcast about um again like AI hype but even AI needs to be built right so you need data centers um and like building means procuring like someone needs to like if you build an aircraft, you need to procure thousands of suppliers. You need to build a factory to build this airplane. And like really small frictions can have like a crazy impact.

Right. So like there's a like if you're running a very large project of like building a data center, building an aircraft, if there's like one specific part which arrives two weeks later, this can have like a damage of like hundreds of millions of dollars and postpone, and postpone the project. So like all of those, that's why like all of those decisions have to be coordinated. And one part is like you need to figure out like what you need

with what suppliers you work. What are like, what is like the best supplier to like to get this part. But then once you decided all of the stuff, there's like all this operational back office stuff behind it, which minds are like unnecessary and boring. But again like operational means someone sends a confirmation of like, hey, sorry, like this part is going to arrive two weeks later. And this is like one of 500 emails in the Outlook or Gmail of a procurement manager.

And if they missed this email, hundreds of millions of damage. And this happens regularly because the only thing they store in their system of record is then just a date. So like it will like not like not this Wednesday and next Wednesday. That's what you see in the system. But you don't see like maybe that's okay. but maybe that's the $100 million damage and someone has to decide that and that's also what our agents are doing

right there, they are not only retrieving the information there and then making the decisions are like, does this have an impact? What kind of impact? And how can we resolve this? Yeah. When you are sort of so deeply embedded in the physical world, what kinds of

physical world problems can you intervene with? Like some things I would think are just like unsalvable. You know, like let's say you have a shipment coming in and a bunch of stuff like falls off the ship or the street is closed or whatever. Like they're things that like you can't do. And obviously there are things that you can do. So where can you intervene and where does that really make a difference? But actually it's about like probability.

So obviously like you can't like you can't change. if like some like if there's like a damage on the ship like every every example that you manage like you you can't change that but you can if you have like all of the context you can predict that because you can predict like how how reliable is a supplier okay so they're like there are ways on like protect the goods that you're shipping and if you have like all the context you have like one supplier where like 20% of the goods are missing and then 1% of the good is missing. And maybe like this one with 20% is like 10x cheaper.

But for this use case, it's fine for you to pay 10x the amount because you have like a higher probability that this thing actually arrives. So and this is the powerful thing because you have like context not only within one enterprise. So we like we talked about like multiple stakeholders, but there's also context on the outside world. Right. So just the agent should like have context. of all the news out there.

Maybe even having like context of like some bad sale like on polymarket of like okay those disruptions are going to happen. Then like information about like on the supplier side on the seller side on the demand side and by combining all of those contexts I wouldn't say that there is a limitation in the long run.

Obviously like today we have like different sets of like probability but we can we can help throughout the process and this is what we are building building at Leo right so it's much bigger than just procurement inter a company it's more like intra company and like how how are like businesses like how enterprises are doing business with each other so like buyer and supplier side you described Leo as a as a multi-agent system so can you describe what the different agents are doing one level is that we um that leo agents spend across like all those four categories that Zima mentioned in her article.

And it again, like, depends on the risk and the complexity. So we use like all of them. So like multiple agents. But the other thing is that like in order to do a job end to end, those agents need to share information with each other. They need to do this in like a very specific order. And when we talk about like a multi-agent system, this is essentially what we are doing.

We are solving the task end-to-end. And because the also human-level task involves eight people, eight stakeholders, and maybe like three departments and five different software tools, we need to cover all of those to do like the job end-to-end and those agents need to then communicate with each other. And only with a multi-agent system you can do in job end-to-end. When we started out, we started off with like a retrieval, like more like a co-pilot, obviously, like three years ago.

But then the next step was like a single agent. But then we very quickly discovered, okay, like that's like you can't solve and, you can't solve in like negotiation even without having a contract agent without maybe like having an agent looking at the news and everything that I described before. So that's what we define as a multi-agency system. So if you have like a bolt, like an airline company needs to procure a bolt for, say, you know, Boeing needs to procure a bolt. What exactly is that process for procuring the bolt?

And like, where does Leo step in on that process? Yeah. So, like, this is like one of the, one of the purchases that we can, like, run fully autonomously. And we can do this because of this multi-agent system. So, like, first of all, someone has a demand, right? So they need to somehow communicate it. And even like this part is extremely complicated.

so you need to call someone maybe you like you open up your laptop because you're like in construction worker you open up your laptop only every second week and now you are required to like work with SAP or any other like

ERP system so you can't even like issue the amount so this is like how we make it very easy so you take a photo like you you upload a quote an Excel sheet and it's actually everything that you that you should know

about procurement like no one cares outside of the procurement department about like categories, GL accounts, framework contracts, no one cares. We in the procurement will care, but no one outside there cares.

And then our agents take off and they're like, they check the inventory. They find out, okay, they ask another plant. Okay, can we, like,

can we source those, those bolts internally? No. Okay, then I'm calling the, I'm talking to the sourcing agent, finding out, do we have internal suppliers? Do we have external suppliers?

Then some, like another agent has to draft the RFQ, send out the RFQ over, over email, then a bunch of emails arrive. Some of them are like completely nonsense. Some of them are just in the email. Some of them are PDF. Some of them are Excel sheets.

We retrieve those information. Then we do like the next step. Or maybe like based on our price benchmarking, there's an opportunity to negotiate. And then we have like agents that essentially decide on the next step. So negotiation could mean strategic negotiation with a human loop. This could mean autonomous negotiation. This could mean.

and e-octions, calling them the specific agent, doing the negotiation. And then doing this end-to-end, think about confirming the order, shipment tracking, invoices. And we are able to run this fully autonomously, capture all the context. And then obviously the next powerful thing is do this for more complex parts where we talk about direct procurement,

where we also operate. we also operate. What's direct procurement? Yeah. So like everything I just described is the main goal is here automation, right? So you can like run this process fully autonomously. And then throughout the process you can generate even more savings, right? So it's not like, so we look at it's like, okay, what is like this like end to add, like job to be done?

How does it look like? So like what are they doing like thousand times a day? But actually they want to do it like zero times a day. we've run fully autonomous agents but there's like also opportunities of like what are they doing zero times a day but if a business would do this thousand times a day that would have a crazy

P&L impact autonomous negotiations on spent they never negotiated before so this is and this is like in the indirect procurement part like thing about MRO parts building a factory the bold example that we did

but also laptops and pencils marketing services someone who are needs to build up this podcast studio, those are like all indirect. And then we have like direct parts. This is like when you build an airplane, those are like all these suppliers that actually, that you actually need to build the airplane or to build the drone or to build the robot. And then we don't talk about 50,000 suppliers. We talk about 100 suppliers or 2,000 suppliers maximum.

And those are like extremely strategically important. and you have maybe on one supplier like one billion of spend so you don't want to run an autonomous negotiation you want to run a negotiation with which takes three months and where you're like crazy prepared and where you have engineers on your team

analyzing okay what's the industry for aluminum what's the industry for oil how did the price change so you like really take over like all of those drawings you check the quality of this part and this is what like where it gets like

really exciting deploying agents. Yeah, and for something like that, presumably like you'd have the expert engineers and the other procurement people kind of more as the front of house and like the agent is more back of house. Is that the idea?

Or is the agent like actually it's like you sit across the table and you're shaking hands and it's like the robot and sort of the human who's like negotiating like is it so yeah, is it more back of house or is it like still front of house. It's obviously more back-off house, right? Because you like, because you like these complex

multimillion dollar negotiations. And that's, that's again like a beautiful example. 90% of the work is preparation. The end result that you see in your system of record is like, oh, instead of like one billion, I paid $900 million. There's like three months of preparation and like 10 people working full time on that. And obviously this is like happening in the back. But actually we have some use cases where it's also like helping in real time. So think about, let's assume we would now have a negotiation and I have like a perfect preparation. The same as like with like those notes that we are having here.

Imagine like while we are negotiating, I would have like real time insights on my screen popping up where you tell me the Indy C for Oil change 10%. So like it's increased by 10%. And so that's why we need to increase the prices by 10%. And I would have like an initial, like an immediate pop-up of like, that's true, like, oil increased by 10%. But the product has only 30% of oil contained. So like you shouldn't increase the price by 10%, but maybe only by 4%. So yeah, there are like exciting use cases also like in the real life part.

Yeah, we know that, you know, companies like Harvey and Decagon are really fine-tuning. models now. What kind of underlying models do you use and how do you approach things like fine tuning or harnessing? Yeah. So we believe you can so like we use

multiple models from like all providers and we really see this as a like obviously like as a commodity right so they are like have really good like general business purpose so like reading creating a PDF and like creating exercise like all of this stuff.

but we also believe that like for some use cases you you can get extremely far with like combining the foundation model with a harness and you can maybe reach like 100% of like the job to be done but there are also some use cases where you can have like the best foundation model the best harness whatever that means but like the best harness but you still can get only to 80% and like it would say like good examples for that is like for example what when we talk about negotiations what like cost engineers doing right so they're like analyzing drawings and then they like defining okay what should this um this part actually cost

like and that's why it's called like should cost modeling um and there's um there is definitely like an opportunity where we like thinking and already started um fine tuning the model to get then to to get then to 100% and this part. Another example is like price benchmarking where

think about like the you would have a quote and in the perfect world you would just drag and drop the quote somewhere and you would get the perfect price but it's like and all those like all those information they are like not publicly available

right so there's all like those are like all preparatory data based on like one one enterprise across multiple enterprises so like general purpose models can't train their models on that so what we are like what we are thinking about is like

maybe like not training just an LLM but I think like what we see now with models like Jeff are so popping up where you have like in you train it on like text data but the output is actually like an outcome or just like the perfect price and you can't do this with harness

because like if you would give me like a quote from BCG and a quote from McKinsey they could do like the exact same work but this could be like a 10x different price and I would have like no idea like what is better

but if you give this to a procurement manager he would like initially have a gut feeling or like okay this quote makes sense or like I think I think like good examples again like contact creation like always like I don't know like how much I should pay someone for creating a video. But there's like a gut feeling behind it

if I ask like another video creator of how to do that. But if you ask them to write down the rules, they can't do this. Because it's like just like gut feeling and instinct. And that's where we see a lot of opportunity actually training an agent, but not maybe like a classic LLM, but more like again like what companies like models like Jav are now doing on the outcome based.

So we have like price benchmarking, should cost more doing. Seema, we've talked a little bit about how labs are really moving into industry-specific work or working with incumbents to do this. When you think about what a durable vertical AI company looks like, what are the qualities that you look for? One is around, you know, owning the end-to-end work that we're talking about building up this data asset and being able to do something that hasn't been done. before in many cases. This is all said, I think when you talk a lot about moats,

it's really, really hard to forecast your moat going forward. If you look back at all the best businesses at the early stages, they were just thinking about, okay, I'm winning customer trust, I'm selling more to them, and there's a lot of opportunity versus, okay, I'm going to do these six steps and then get to the seventh step, and then we'll have a moat. And so I think we talk a lot about defensibility and durability, and I think part of that is you're locking in the customer. There's more dependencies. They find it valuable, and you're doing more of the work. And here it's truly like, okay, you know, old CRM company was a log for all of the deals. New sales AI agent is actually owning a lot of the sales prep process and the outbound

process, fielding inbound and doing a bunch of the work. the overall customer is dependent on that product, and that's like a really important signal of getting to the moat and everything we talk about in terms of, in terms of stickiness and network effects and all that is sort of downstream of that initial customer use in the value of the product. Vlad, have you had conversations with customers

or potential customers who ask you you know, why should I buy your product? Why can't I just, you know, plug into a model and do this myself or like use whatever existing system of record I have plus a model? Like, like what do you tell them and how do you, how do you convince them to use Leo? Yeah, 100%. And that's a very fair question, right? And like, even if you look like internally at Leo, so like the first use case three years ago, which kind of been viral in the procurement world was like just like having a quote

and then getting this information into SAP. So like very like again like technically like but like tremendous business value. And so you have like this retrieval agent getting like all of the information putting it into SAP. Like this was our like first product. And we had an engineering team like obviously small just like the three of us or maybe like four people building it.

and then selling it. But this is nowadays a case study if you're applying to work at Leo. So we give this to people to build this and they have like eight hours to do so. So what I want to say by that is like a product that we like one of our first use cases can now be somehow built by engineers within eight hours.

So because it's very easy to build stuff nowadays. So obviously there's a question, okay, so someone can build this within eight hours. Okay, cool. but then couldn't like just procurement departments also just built everything in two months. And the answer is like, yes, you can build this in eight hours and you can build this,

but you will only reach 70% of the performance. And the problem is 70% of performance or currency or however you measured it, it depends really on the task, doesn't mean 70% automation. So this can mean that you're like, have 70% of the performance, but you still need to do 100% of the work. Because 70% is not that much. So again, like all of the people have to check the data.

So maybe you even created more work, more work than they did before. Two things to layer on to what Vlad just said. One, overall, it's good if there's more adoption of the base models or just like GPD products because it means that people are also willing to trust vertical specific products as well. So I think that increased familiarity, comfort, excitement about AI tools is generally just good for the market.

The second thing is I was chatting with the management team of a Fortune 500 company two or three weeks ago, and one of the things they mentioned was they had tried to build out their own cash collection product this is a big enterprise business. And they, like after, I don't know, three or four months of work at a minimum,

they had found that there wasn't enough context. There was poor quality context. They had a lot of recordings, a lot of screen grabs. They tried to pull it all into one system, but there was no, there wasn't good enough. And then there was this giant question around, okay, like, you know, we've got now two different ERPs and we were about to acquire another one.

who's going to update all the mappings, test out, okay, does it work? And then like, we keep talking about exception handling. You now need to map that onto a totally different system, a different way of doing things. And I think they quickly realized that the internal build didn't make sense. And so we keep hearing stories of this where people are like, okay, I'm going to do the internal build. And then like, wait a second. It's not different from what DIYs ever been in the past, which corporates have always tried. But I think enterprise companies generally realize that, like, they're,

there's their core competency, and then there's building internal tools, and they should focus on the first camp. Yeah, yeah, makes sense. Yeah, so that's exactly what I've meant with, like, obviously, like, they can get to 80%, but those last 20% really matter, and you can only get, like,

they matter to get into production. So that's why you need, like, this harness, right? So you need all those, like, integrations, memory, workflows. And sometimes you need vertical data to do that. and like the paratro principle are like those 20% can make like 80% of the effort or like they are making 80% of the effort.

So like to all like those Fortune 500, 150 companies so you can do this. But then let's say like procurement workforce or AI procurement agents should then become like one of your core competencies and you should evaluate whether this makes sense or not for you. to have this in your car skill.

Vlad, I'm curious if you're seeing suppliers start to use agents or AI at all and kind of like what happens when both the buyers and the suppliers are fully AI enabled?

Yeah, so like we 100% believe that like in the future there will be like agents on both sides and this makes so much sense. But surprisingly, what we see is like, so when we look at the supplier side, this also kind of equals the seller side, right?

So what we see is like the sales side was like always ahead of the procurement side. But what we're now seeing with those suppliers for this Fortune 500 companies, this actually is not true. So they are like maybe advanced in like, let's say, video recordings and like using tools like granola and all the stuff,

but like not like really. having agents deploy, they're automating the work. And the cool thing is, as procurement is like, it's unsexy, right? So sales are sexy, procurement is unsexy, but it's like one process and procurement is the counterpart. But now the good thing is, in those industrial companies, Fortune 500,000 companies,

procurement has the bigger power to the supplier. Because you as an like typical use case, like automotive supplier, you dictate to your suppliers what they should, use what the quality has to be, how they have to, how they have to answer to an specific RFQ. So the opportunity is now, if we serve the procurement department and then they can dictate what a supplier should use, why aren't we like not, like, why aren't we like also pushing them to like Leo agents that are also helping them automate the work, owning them both

sides of the transaction. And Elena, I know we were talking earlier today about, you know, how can you have two parties on the same platform as how does that work? I think it could even extend into like legal work, right? And these are two very adversarial parties, right? But like if you have two law firms with clients with different interests, but both benefit from knowing, okay, here's the latest draft, here's where we are with the open issues, here are things that have been agreed upon and just even tracking.

that. That doesn't really exist right now, right? That's all being human, that's being created by humans. And so that coordination effort, an agent could be doing. Yeah. Yeah. Well, it's super cool to think about how like, you know, both sides are kind of maybe evolving in tandem. One side might be going a little bit faster as you're talking about Vlad. But over time, potentially people are just like on the same platform and actually it's like it's way better coordinated for for everyone 100% because like like also like we mentioned in the beginning right so like like the obvious like the obvious question is like okay but like we also talked a lot about like negotiation agents what if like both parties have negotiation agents and prices 100% the point where it's like a zero something or like they have like different

interests but we also discuss is that like price is like the outcome of like 5,000 different other tasks that happened and on those 5,000 other tasks they have the same incentive sales wants to have as less little friction as possible

buyers want to have a really fast time to market right again like coming back to building aircrafts building data centers you want this data center to be built as fast as possible you don't want to be built six months later just because it takes so much time to analyze all of the responses from suppliers

and you want to make the sale also fast. So like all of those 500,000 other tasks, the incentive is exactly the same and that's why you can deploy or Leo can deploy agents also on both sides doing like automating the work

of all those other tasks. This is the beautiful thing. I think that there used to be this logic of like you shouldn't customize your software too much to one end user. a one-end customer. But I think something about LLMs and AI in general is like it might increasingly be possible

to customize without slowing yourself down as a business too much. So I'm curious if that is something that you're seeing Vlad or SEMA. And kind of what does that mean for end buyers of software? I think the overall principle, there's a lot of forefell. deployed work happening right now. And part of that is because the state of the customer data and understanding customer N is a lot harder than understanding N plus one. And so we're sort of in the early stages of deployment overall. And that's why there's still a lot of humans as part of this product. And by the way, that is something that is harder for the incumbents to do because

they're also like not set up in a way to have even the way their product feedback cycle works, where they have a, they have implementation teams, but that's very much an afterthought versus something that feeds into the product side. The beauty of AI is, A, it learns over time. So that's when we're talking about learning loops. And you have the right e-vel process. You can do more and more complicated jobs over time. And part of that is automating the deployment itself.

And so you can, and I'd be curious to hear how flat is doing it, but a lot of our companies even are doing that at a, you know, at a rapid clip where more of the customization, A, is being handled in an automated way, and B, the customer is able to turn the knobs and levers around customization via software versus, okay, I needed to bring in A, the original, it was like, you know, you brought an Accenture to do your SAP customization,

and now it's like, okay, I've got a forward deployed team that's going to help build and spec out, and ultimately it's going to be completely software-driven. Yeah, I mean, that's the reason why, like, when you look at the aug structure of Leo, like, 85% of the people are engineers. And even if you look at, like, the people where they don't have an engineering title, they have, like, mostly like an engineering background. And reason for that is we obviously, like, we don't want to be a consulting company, right? So we make sure that we have, like, overall the best agents in indirect and direct and finance. in those parts

but then like as you mentioned like there is a lot of like forward deployed work to do if you go to enterprises because they have like different nuances in their in their processes but how we work is

we as you mentioned like building a product in a way where it's like where we're reducing this customization but also where it's a lot of like self-service so the job of our like FDEs and forward deployed engineers is on the

one hand making it like self-s but like in internally is like automating their own job right so like their kpi is like you're seeing this happening like multiple times like you like you're literally your job is like to automate yourself and then if you automate yourself you go to the next task I think it's like also like an approach at Google so is doing so like but yeah 100% agree with you and that's exactly what we are what we are building and how we're doing it there was recently a very big system of record event and And we're not talking about Dreamforce.

We're talking about the Botten Buyers Summit that Leo hosted in New York. And just wanted to kind of hear, you know, stories from the ground and what you're seeing among buyers. What are people excited about? What are people looking ahead toward? What are people, you know, asking you for? Can you just kind of tell us some stories from that day in that event? So, I mean, it was the third time that we are doing, that we are doing this event. Now we did it in New York, just a few blocks from our office here and over 100 procurement leaders arrived.

And what we, like, we made sure that we, like, when we do such events that we only invite like high caliber people, right? So like C level, CPR or vice president. And there are like two things very, like very different of how we do this. So like why they're so amazed. So the first thing is, when we look at like how procurement used to work in the last 26, 27 years, a lot of tools emerge. So you can, like, they are like all those technology landscapes and you can find them on LinkedIn. And you will see like they're like 500 procurement tools. But like if you like, and this is also the reason like, but when you talk to procurement people like very painful and like I challenge someone like to find someone who like loves to work with procurement.

like no one does. Like you can really state people hate working with procurement. And when talking about the requesters and I'm talking about the suppliers and evil people in procurement hate working on procurement. So like what's going on if they are like 1,000 tools? And the reason for that is like all of the tools. They just made the process more efficient. That's all.

But they never changed how those people actually work. and it's like crazy to see that they work in emails and on Microsoft Teams and in Excel sheets and PowerPoints. It's like their main channel where they work on and it's like zero like zero AI enabled

obviously in this part and the other thing is like we we give them like a very cross department perspective on procurement right so we are not talking about like look at this crazy invoice feature that we developed. But we're more looking like, like, someone needs something.

And in the end, you have it on your table. And this can be, like, across indirect, direct logistics finance. And you can see how we are doing it here. And we're also putting it into, like, more into like a physical world. Because, like, AI agents, that's very abstract. So what we are doing is we building up booths. And we even have this in our offices in, also in New York, where you can walk through the booths and experience, like,

all of those agents like really hands on. And that's what the people love. And it's like, I mean, the next event will be with around 700 people. So you can imagine how crazy this grows. Procurement people gone wild. Yeah. Yeah. It's going to be.

That one is in Munich. Yeah. So we're doing them like in Europe, Munich and in New York all the time. Cool. If you're listening and. in procurement, you know where to go. Maybe, I guess, one question, one question for me. What do you think it takes to get, to get people excited about procurement?

Is it the agents? Is it the people? Is it the time save? Like, what, or like something else? Like, you mentioned it. Like, procurement is one of these things that I remember, you know, people aren't, they don't like, it's like a universally kind of disliked low NPS area. I remember talking to a guy who was out of procurement like, I don't know, seven or eight years ago as I was looking at this category. And he was like, oh, I hate talking about this product. Like I use, you know, this legacy system of record. I'm on Kupa and I don't want to buy anything else. I don't want to talk about it.

It's fine. It was like the most disgruntled customer call I've never done out of like millions of them. But I mean, I'm curious, yeah, what it is that you think, you know, really gets people excited about this category. Yeah. So, I mean, it's like, it's like, and this is also why why I like. procurement is like it's on one hand like so like the reason like why we started in procurement like it's not essentially like what happened but like how people react to it right so if you

talk to the to the people they're like really frustrated so this means it's like highly emotional topic but it's like let's be honest like be to bizazas okay but it's like highly emotional so that's a good thing and then if you combine this with like something which is boring and niche this is also an advantage because Again, like the, it's like also easy or easy for us to amaze those people, right? Because like the really last revolution they have seen is like 20 years ago. And then maybe a nicer user interface 10 years ago, but nothing else happened. And so you have like boring, highly emotional and then plus crazy business impact.

So like it feels like it's unnecessary, but like I told you like some examples. So it has like obviously crazy P&L impact, but it has impact on like the whole economy. Right. So we're like we are talking about like how data centers are built, how aircrafts are built, how cars are built, how drones are built. So it's extremely important that you have a fixed procurement process, not only to like make it happen and build something.

But then also when you talk about like when you look at like the competitive landscape. So to get like 1% margin increase, you need to make 10% more revenue, 10% more sales. So like if you just manage to get like 1% savings, it's like equals like 10% of sales that you have to do to get the same outcome in your P&L. So it's tremendously important. And like you combine all of those three things and then you have like a trillion dollar business opportunity. That's that's my opinion. for procurement, but they're probably also like other things. They're like emotional, boring, and have a crazy business impact.

Well, Vlad, thank you so much for joining us. This was a ton of fun. Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcast and Spotify. Follow us on X and A16Z and subscribe to our substack. at A16Z.substack.com. Thanks again for listening, and I'll see you in the next episode.

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