How I Invest with David Weisburd - E406: Why AI Won't Transform Most Enterprises for 10 Years

Episode Date: July 22, 2026

Most companies use AI to make employees slightly more productive. Sushanth Raman believes AI should do the work instead. As the CEO of Pallet, Sushanth is building an AI workforce for the $12 trillion logistics industry, helping carriers, brokers, freight forwarders, and shippers automate mission-critical operations inside the systems they already use. He explains why reasoning-driven AI represents the next wave of enterprise software, why logistics is uniquely positioned for AI transformation, and how businesses can move from copilots to fully autonomous workflows.

Everyone in Silicon Valley talks as if AI is about to transform every company. Today's guest thinks that's wrong. Shushanth Ramon is the founder and CEO of Palate in AI company backed by General Catalyst, Bain Capital Ventures, and Bessemer that helps Fortune 500 companies deploy AI into real business operations. After working with some of the world's largest enterprises, Shushanth believes AI won't transform most companies for another five to 10 years, not because the technology isn't improving, but because most organizations aren't built to take advantage of it. Without further ado, here's my conversation with Shushan.

Silicon Valley talks as if AI is going to transform every company overnight. You think Fortune 500 companies are five to ten years away. Why? Silicon Valley has this conception that just because the technology is here, it's really easy to get distributed everywhere in the world. But if you look at these Fortune 500 companies, there's a lot that they need to kind of get right from, first of all, these companies have a lot of organizational change it's needed right right like it's not like as soon as the c suite decides that we should do this

it's going to get done you need to kind of figure out how do they reorg their company how do they change these roles how they kind of scope that out secondly their technology stack is often out ready they have like a lot of on-prem systems lots of integrations that are hard to deal with legacy tech stacks and then thirdly there's a lot of like tribal knowledge poorly documented institutional knowledge, it's like not well captured. So even if you deploy the technology properly, you're going to find that it's going to be limited in the kind of context it has to kind of go and succeed. AI needs context. It needs to be able to operate with the same context that the people doing the work today have. So when you think about all these things from tribal knowledge and coding,

process change management, getting your data infrastructure and ready, it's clear that this is not going to happen overnight. And those changes, especially for organizations, have hundreds of thousands of people. Like if you take a look at a Walmart, you take a look at a Best Buy, or you take a look at a Starbucks. These are not easy changes to go and drive overnight. And I think that that's what Silicon Valley underestimates is like how to navigate all of that enterprise change management is even if the technology was, let's say, five times better. You still have a pace of change management and adoption that's going to cause us to kind of slowly dribble out. Slowly kind of happen over time. And a good example of this is we think that most software today is already in the cloud.

Like all the cloud technology came well over a decade. It's almost been close to two decades since the stuff came over two decades. Yet we still have on-prem systems running while throughout the enterprise. Like one of the largest trucking companies we know still runs on a legacy IBM AS400 mainframe system for most of their operations. So we're talking about a change that hasn't happened. happen from going from on-prem to the cloud. And yet we hear we are talking about how AI is going to go overnight. That's not going to happen. You work with some of the top Fortune 500 companies in the world. Some of them are really able to implement AI. Some of them are not. What's the biggest change between those that are able to implement AI versus those that are not? Look, all these companies have

different circumstances where they might have legacy tech stacks. They might be all over the place, but I think the number one determinant is when leadership is really bought in top down on like, we're going to personally go and drive this change. Like the CEO, CTO, or all the CIO and like various business unit leaders are committed to driving this change and they're involved in the details. And the second thing is that they've really kind of gotten the right sort of leaders to kind of propel the change and giving them a mandate that like we have a VP whose sole job is to kind of go and navigate this and we have an operations leader who's their counterpart to go and do this. And they've equipped those leaders with the right institutional power to go and drive this change. Those are the companies that

we find are doing the best. What's not. working is when you just go have the CEOs like, I'm going to hire a head of AI. The head of AI is going to be on an island. They're just going to go and figure it out. That doesn't work. There has to be like a really strong top down change management. The subtle distinction there is some companies are giving lip service to AI and some are seeing it as this existential problem that they must solve or they're going to be disrupted. Yes, exactly. And not just that. You as the C-suite of the company are pretty committed to being the details of this change versus just down delegating it to someone else and being like, you take care of it.

It's not like you can go like, there's an AI person, you take care of AI. It's like you yourself have to make this a part of your job. And you have to culturally reward push top down, keep reinforcing the urgency. Because there's going to be a lot of resistance in your organization. And only people with power can navigate all of the various paths of resistance from procurement to legal to SOP documentation. There's like so much that has to happen. So that's why there has to be strong top down buy it. Maybe unpack that a little bit more.

So it's not that technologically you can't have a head of AI or somebody implementing systems. It's that the incentives must start at the CEO level, whether or not technologically they're actually involved in that. They have to be technologically involved because like if they're not. Why is that? Because I think firstly, people pay attention to that. Like if you yourself are involved in the details, everyone in the company thinks of it seriously. There's some cultural, you're culturally setting the example. Secondly, you need to know what's possible to automate and you need to have an opinion so you can call bullshit when someone's trying to, when there's internal issues, there's always going to be internal issues that happen.

Like someone's going to push back. Someone's going to say it's not doable. Someone's going to say we should build versus buy. And you'd have an opinion on one to tiebreak. Thirdly, I would say that even if you have a head of AI, that person has to work with so many business stakeholders to get something done, legal, your finance team, the operations leaders. And there's only very, very few people in the organization that have the ability to override a lot of those decisions and kind of drive change. And that has to be someone with sufficient power. So when you look at those factors, you can't just delegate this stuff.

You have to be involved in the details. You've mentioned this multiple times, this friction, this organizational friction against AI. Double click on that. Why is there a natural friction to fight AI within organizations? I don't know if it's that like someone's like, I don't believe in the technology. I think it's that people are like, yeah, yeah, it sounds great. But when it comes to changing your behavior, that's really hard. Imagine you were doing something 20 years.

Like, I'll give you an example. Let's say that you're a Fortune 500, 3PL, and you're doing warehousing for Nike, just a random example here. Or you're doing the transportation side, transportation deliveries for Boeing. And you're like, hey, things are working. I'm not going to get fired if I kind of continue. you're doing things as is. Now I might have to go change my behavior, take some risk, and go and make some change. It's like I'm going to have to go learn in your technology. I have

to do this off business hours and I'm already tired and I'm already overwhelmed by what I'm doing. Your default state is like, yeah, I like AI. I'm already using chat CBT on the side, but why go change the process that I have that's working? So when a lot of people in your company are overwhelmed and under a lot of stress and you're not going to get fired for making change, there's no incentive for a lot of people to drive change. And they might know the benefits of technology, but they're like, I'm taking a risk if it fails, then I look really poorly to everyone else. So that's why I think there's a lot of inertia in bringing the stuff and deploying AI. It's not that people don't believe in it. It's that there's a bit of fear of like, why disrupt something that's working.

Is there also maybe the exact opposite, which people believe that it will work really well and they'll be unemployed? There's a bit of that. But I think what I find. is that the people who are in charge of making these decisions, like if you even look at the senior director or VP level sometimes, those folks actually arguably can show better P&L metrics, can kind of show all these benefits. But even then, I would say that sometimes there's a fear of being wrong that sometimes, like, more importantly, that just prevents people from making these decisions. So I'd say, yes, the other point you called out, if there's some potential fear of job loss. but I think the overriding factor is changing the status quo is just uncomfortable for most people.

What would have to happen for you to change your belief that it's going to take up to a decade for enterprises to adopt AI? There's a couple of things. Like, what drives change in companies very aggressively? It's like change in market cap, right? Like you start to see that your stock's getting penalized and Wall Street starts penalizing you really heavy. Competitive risk.

Your competition's moving 10 times faster. than you and there is probably like a change in leadership. So let's try to think about what would drive change to happen faster. The first is you have Wall Street that looks at more of these traditional businesses today and goes like, well, if you're not successfully deploying AI at a pretty meaningful, not just lip service, but you're actually not deploying them showing some sort of meaningful financial results, we're short your stock in a pretty significant way and you start to see aggressive stock market collapses, like similar to what's happening in the SaaS world, right? Like where these companies are getting pushed really hard. If you start seeing that type of

stuff happening to your manufacturers, your retailers at scale. That's like one driving force. The second is your competition moves away faster than you in this stuff and they start to post better results in a way that gets at a better customer experience. If that happens in these traditional industries at a faster and faster pace, that'll drive change. And the third thing I would say is you just drive a change in leadership in some of these companies where people are more , boards are more aggressive about like the modern CIO looks something different. We don't think your current CIA is going to cut it. We need a new type of CIO. to navigate us through this era, we also realize that like the CEO at the helm needs to

spear this stuff better. So maybe we need to make changes there. If those three things happen pretty quickly, I think that this would happen a lot faster. It's so interesting because traditionally the innovator's dilemma, which is that the current industry gets disrupted by the new entrant. Traditionally, this has happened over five, 10, 15 years. But now that a lot of these companies are public and people are aware of this whole phenomenon of companies getting disrupted and you have public shareholders that can now short your stock, those feedback cycles don't have to take 10, 20 years like it did with Kodak and the professional camera or between Blockbuster and Netflix, that could happen over several months. Yeah, that could totally happen over seven

months. As we saw with all the SaaS stocks where they got impacted and the market voted that traditional cloud software doesn't have the durability properties they once thought. So those changes happen in the span of under a year, right? Like, we're now all these leaders are in Salesforce, HubSpot, the list can go on and on like Clavio or so on. They're just getting shorted by the public stock markets and they have to figure out how to adapt in this new world. What parts of the market are exhibiting the adoption of AI? The fastest industries, I feel like, that you've seen to adopt AI, or obviously even engineering has been the quickest. I suspect that part of it is that developers are naturally kind of at the frontier. The mindset of a developer is always to try to

to find the easiest, laziest pathway to kind of go solve a problem. And code has a lot of properties on why just from raw training sets to why AI has kind of like had just the amount of data that's available for it to be trained on. The way code's written and software engineering is done, just made it a great candidate for like for it to be disrupted first. Then obviously after that, you add transcription, customer support and some of these easier use cases. But the challenge is a line share of knowledge work is not as quite as deterministic as writing code if you think about the job of like a billing clerk in a freight forwarding company not a lot of interdata on the open internet about that the job is pretty messy lots of context that's stored in someone's head so those

jobs inherently are a bit harder to automate from like a bit harder for like a model to just going to come and automate so that's why you've seen some of the more obvious forms of knowledge work just get automated faster. There's a faster feedback loop from the software, from use cases. You don't have to deal with unknown feedback and not knowing whether something's working or not. Yeah, you don't have to go through these unknown feedback cycles of something's working or not. Like, for example, what is the best way to go quote a air freight shipment from

Shenzhen to Memphis? Like, there's no right or wrong answer. Like, every answer is pretty nuanced. Do you underpriced to win the customer over? Do you price sufficiently high where you're maximizing margin? Do you say no because delivery expectations are like a bit unrealistic and you risk the service quality? Do you just not want to work with this customer because you don't think they're like optimal long term? There's a lot of nuances to a decision like that compared to given this front end design,

what is the optimal way to like go and write the software application? That's like a more straightforward task versus the one I just presented before. Within Palet, how are you deploying AI in a non-technical? way, in other words, outside of engineering. We deploy it in a lot of ways. I give you some examples of how we do it. So if you think about the everything, anything from like our marketing outreach, we make sure that every sort of thing from researching about the company to personalizing

the decks to personalizing the message to kind of matter to the decision makers based on the problems of there are just very tightly curated. We use it to identify what is the warmest path to meet a certain customer. We have ways to make sure the palette product is. reflects a use case. Like, for example, for sales engineers, like how do we make sure that the demos are personalized to an actual pain point?

So if we're configuring those, our agents, to kind of reflect a use case or a problem that's actually relevant to a customer based on research we've done, how do we do those personalizations? We use it to automate a lot of our customer onboarding deployments. We use it internally for meeting preparations, all-hand preparations, all of that stuff.

We use it for our sales team to flag deal risks and being able to audit the status of our business of our pipeline based on call transcripts, emails, and so on. So there's a lot of different use cases like that across the business. Basically, every team has a mandate in the company to be able to use AI successfully in a way that's productive and not just the sake of maximizing token usage. Outside of Palet, what's the most effective way that you've seen AI being deployed in the business? One of the most effective ways that I've seen AI go be deployed in a business.

there's a very large global warehouse operator. And if you take a look at warehousing, there's two common things that eat up a bulk of your time, which are you basically have two types of requests, scheduling, which is basically like, how do I know that the truck is reaching my dock to go and drop the shipment at the right point in time

so I can optimize when my warehouse records are available, when we can go and pick out all the stuff out of the truck, and then for outbound shipments, when can I go and make sure that I can pick, pack, and put everything set all the pallets up so the truck can kind of take it out of my warehouse. And the second thing that they have is they have like an overwhelming number of requests for everything from knowing is inventory available. Can this pack? Can this order be fulfilled? Can they handle a return request? So they just get a line share of customer support tickets.

So there's a very well known global third party logistics company that has basically started the journey of automating these two core areas of their warehousing business, basically between support and scheduling. and the gains on EBITA that you can actualize on that is somewhere in the magnitude of about 15 to 20 percent. So think about that as like about a 20 percent increase in market cap if done successfully in your business of overnight for just two very simple use cases. And this is something that is not that complicated to deploy very clearly valuable to the business and that it gives customers 24-7, gives customers better availability to kind of go and drop stuff off, gives customers better visibility in what's happening inside of a

warehouse reduces your cost to service them. So very clean way to create a lot of enterprise value for these companies. Good, simple use case deployment for a large first 500 business. Most investors don't lose because they lack information. They lose because they can't separate signal from noise. Every day, thousands of earnings calls, SAC filings, expert interviews, and market updates compete for your attention. The challenge is in finding more research. it's finding the few insights that actually change your investment decisions. That's exactly what I unpack in my conversation with Ryan Fenerty. Instead of talking about AI in theory, we discuss how leading investors are using it to surface

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So if you think about why this business is going to be durable, like, depends. Some of them potentially, yeah. Like, so I think that there's probably three categories of software that exist, right? You have your kind of like raw application software, something that's similar to like an amplitude, something that's similar to like a Zoom info. Then you have your course system of records, which are things like Salesforce, or toast, Viva, and then you have your, obviously, some of your security systems are going to probably benefit from these tailwinds. If you look at those three buckets of software,

I would see the ones that are most obviously at risk or these thin layer application software companies. Why? Because basically, they don't really have that much proprietary data. They're pretty easy to go in vibe code. It's really hard for those companies to kind of continue to justify high price points upon renewal because people are looking at, well, the cost of software development so cheap, so why would I continue to go away anyway? Those companies are obviously at a pretty bad point. Then you have these system of record companies

like your Salesforce, your TOS, your Viva as of the world. These companies arguably are a lot more sticky. They have like a valuable data set that's hard to kind of go and pull out. And I would say that those companies are at a good point depending on how they embrace the next wave of change. Like they can either the risk that they have is that if the agents start taking over all the end user experience and then they get good at porting the data over those businesses can get disrupted. On the other hand, if they take advantage of the fact that they have these

core system of records that are valuable and figure out how to acquire companies with the right capabilities to add on top of their distribution network, or get really good at figuring out how do they make the data layer, how do they structure the data layer to make it the default system that agents can go in access and almost go headless, there's potential for that layer to be durable. And then on the security side, your Palo Alto networks, your crowd strikes of the world, especially with how many agents are operating and how much surface area now there is for security risks. That has just grown, governed and security have gotten more important. Those companies will benefit. So I use that to kind of color that there's some more nuance and

perspective, that certain companies are going to really benefit, certain companies can benefit, certain companies are going to fall. There's certain companies that will suffer the SaaS apocalypse. There's certain that may or may not grow in value, but maybe more sticky than people realize and there's some that will actually benefit. Yes. What do you think enterprise software companies look like five years from now? Enterprise software companies don't look anything like enterprise software companies of the past. I think that every aspect of it is going to change, where I think what people want is almost

a hyper-personalized service that delivers value to them. And I think that software in the past used to be prepackaged. You get it off the shelf, you buy it, you have a, a, account. manager goes and deploy it, that's not what software companies are going to look like in the future. What I mean by that is let's say you are a enterprise manufacturer. What the next generation vendor is going to give you is they're going to give you an outcome that's personally curated to you. Let's say you're Nike.

And Nike has a specific need for how do they handle customs filings for moving all of their products from, let's say, Asia to the U.S. Now, what a past software vendor would have done is they would have just given them something sort of generic and they're like, here's the out of the box thing. And you'll figure out how to deploy this in a generic way and like you have to work around the process for this. But the next generation software vendor, what they're going to do is they're going to deliver the outcome that we're going to give you all of your customs filings automated from Vietnam to the U.S. And it's going to work with a remarkable degree of accuracy. It's going to plug in with all of your different systems. And it's just going to work right out of the gate.

And it feels like it was hyper-customized and personalized to you, all more. like a consulting or delivery engagement, like that personalization is what everyone's going to accomplish. It's almost like what used to be things that look like custom development and like the type of things that cognizant and all these firms did for you are going to become the default expectations of like the next generation of software vendors where it's hyper personalized, hyper tailored, slightly feels like its services. That's the thing where the future of the stuff is headed. And if you unpack that a little bit and you open it up, you open up into the solution.

Is this just a series of different decisions that the software makes in order to hyper-customize that for each client? Or what is the machinations that makes it so customized? If I had it kind of over-abstruct what LLMs are really good at, right? Is that they are able to be really good at dealing with a variety of unstructured data. So, like, before you would have to build your software design around this particular data schema, Like it would be like, I have to know that the data follows a certain pattern for me to get used to it. But now you could throw at it so much unstructured data variety of types. Like it doesn't matter if it's a voice, a spreadsheet, a PDF, and they could all come in variety of different formats.

It'll still handle it. So that allows you to kind of deploy it in more environments. So that's like one part that allows it that makes it easy for you to have fast time to value while it feels like it's personalized. The second thing I would say is that the old form of software was like, go and write a bunch of logic, but the new form has shifted where there's like a bunch of context that exists. Like it's like if I'm Nike and I get an order from Sushanth versus if I get an order from you, I can store a bunch of context about me or you and that context can be retrieved. So it's like, okay, I know that Sushant Soushizes 11. Soushant has all these properties and

it knows how to retrieve and apply that context in the right point in time in a way that traditional software might go and suffer and not be able to do that as quickly or rapidly. So I'd say the context engineering side, the ability for LLMs to deal with unstructured data. And the third thing is that configuration used to be this thing that you would have to have like these big deployment teams to go and do. But now a lot of the logic for configuring and personalization can be automated using agents itself. So when you look at those three properties, you get a point where everyone's going to have hyper-specific agents tailor-made for them. in this hyper-personalized world, which type of companies benefit from this?

The type of companies that benefit from this are companies where there's variability in kind of client needs. The thing actually worry is that there's a generation of AI companies that came into existence in 2022, 2020, 2023, 2024, 24, where they kind of looked a bit still like the last generation of SaaS companies where it was a standard product, easy deployment, and it's almost like you're just going and rinsing and repeating the same product over and over and over again. Those companies might be at risk because if you're one of the customers of them, you're looking at this, you're like, hey, why am I spending a $3 million a bill for something

that's fairly standardized? As a rate of software deployment starts a trend towards zero. It's very easy for competitors to kind of come overnight and replicate the same functionality. It's really easy for you to go and build it. It's really easy for the labs to kind of go and emulate you as it kind of get deeper into the app layer. Those companies are most at risk. The companies least at risk are where there is a industry. or some sort of industry where there's a high degree of variability of client needs. And there's no way that you can deliver one standardized product experiences. And there's a lot of nuances between delivery types.

And by that, what I mean is if you take a look at some industry, like the industry we work in, like transportation and logistics, there are so much variety between the clients. Like there's freight forwarding, which is dealing with international shipments. there's trucking, there's ocean carriers, there's air carriers, there's warehousing, there's coal chain storage, there's parcel deliveries. And the challenge behind disrupting a business like that is that to build that business, you have to kind of master all of the subdomains and the nuances between all those. And you have to make sure your product can encapsulate all the underlying systems that spanned at, can adapt to those different contexts, has all the relevant

ingredients that it's really hard to kind of come and disrupt an industry that can service like these seven or eight submarkets within a vertical, those businesses are going to stand the test of time better because you can't just go and attack them in any one way. Why go after the logistics market? The market we're actually going after is a bit bigger than logistics. We're going after the broad supply chain universe. So that's everything. The way to think about that is manufacturing, logistics, retail, airlines, even the oil and gas

distribution, it's a pretty big market. Why go after the supply chain market? Well, the first thing if you think about is that supply chain's prevalent for almost every single thing that we touch with the chairs we sit on, to the watch you're wearing, it had to be manufactured, it had to be transported, it had to be processed by a retailer and delivered to you. So the misconception is that it's a vertical market. I actually think it's a horizontal market because something like 70% of global 2000 companies have a pretty good. clear chief supply chain officer and have a pretty sophisticated supply chain practice. So, first of all, it's like one of the largest market opportunities in the world period.

So over 10 trillion? If you factored in all companies that had a supply chain practice, I think you end up with something like about $70 trillion worth of market cap is kind of tied broadly into supply chain. Like logistics alone is like $11 trillion market globally, which is, but in context, 55% of the size of the American economy. And this isn't an industry where you have a lot of weekend vibe coding going into. Nope. It has to be very, very accurate, right? Imagine if you were filing a customs document and the customs filing was incorrect, there is a lot of drastic consequences for import and export goods if that happened.

or like let's say that you were processing, you were warehousing for one of the largest defense contractors in the world. Summer's here, which for me means occasionally trying to escape New York City on the weekend. When I get time off, the last thing I want to do is worry about keeping my personal finances organized and my budget and balance. With a little advanced planning and powerful software tools, I can enjoy the summer knowing my money is taken care of. Monarch is the personal finance app that tracks everything, accounts, investments, saving goals, and spending. Get your first year of Monarch Corp for half, off, just $50 with promo code invest.

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Use code invest at Monarch.com to get your first year off, Monarch Core, half off, at just $50. That's 50% off your first year at monarch.com with code invest. Summer is here, which for me means occasionally trying to escape New York City on the weekend. When I get time off, the last thing I want to do is worry about keeping my personal finances organized and my budget and balance. With a little advanced planning and powerful software tools, I can enjoy the summer knowing my money is taken care of.

Monarch is the personal finance app that tracks everything, accounts, investments, saving goals, and spending. Get your first year of Monarch Core for half off, just $50 with promo code invest. What I like about Monarch is that it takes the mental load of managing your finances off of your plate. So checking multiple accounts and spreadsheets, you could see everything in one place. The AI Weekly Recap is especially useful because it flags spending spikes and upcoming expenses before they become surprises. I also like Monarch's AI assistant. You can ask questions like how much I spent on travel last summer or how many subscriptions am I paying for and get answers instantly.

Use code invest at Monarch.com to get your first year off, Monarch Core, half off, at just $50. That's 50% off your first year at monarch.com with code invest. Summer is here, which for me means occasionally trying to escape New York City on the weekend. When I get time off, the last thing I want to do is worry about keeping my personal finances organized and my budget and balance. With a little advanced planning and powerful software tools, I can enjoy the summer knowing my money is taken care of. Monarch is the personal finance app that tracks everything.

Accounts, investments, saving goals, and spending. Get your first year of Monarch Corp for half off, just $50 with promo code invest. What I like about Monarch is, that it takes the mental load of managing your finances off of your plate. So checking multiple accounts and spreadsheets, you could see everything in one place. The AI Weekly Recap is especially useful because it flags spending spikes and upcoming expenses before they become surprises.

I also like Monarch's AI assistant. You can ask questions like how much I spent on travel last summer or how many subscriptions am I paying for and get answers instantly. Use code invest at Monarch.com to get your first year off, Monarch core, half off, at just $50. That's 50% off your first year at monarch.com with code invest. And they had a request and the request was handled incorrectly. There's drastic consequences to that.

Or let's say that you were working in a hard-refreted warehouse network and some of the temperature settings were set incorrectly. That's going to affect the state of produce in that warehouse. Look, these things have pretty dastic and dire real-world consequences if you get them wrong. So this is not something that could be a vibe code. but it's not something where even 99% accuracy is good enough. It has to be 99.9% accurate. And also you have to know when you're,

the 0.1% of times you're going to be wrong. You have to know when and flag that. Do you just wake up and decide I want to go into logistics and supply chain manager? No. I've had this view that you basically, there are two ways to kind of build great companies. One is you go for the most obvious market opportunity that's cool and trendy. like building a great AI coding solution and you go and you do it.

The second way of building a great business is that there's a particular part of the world or a particular part of the economy that's underlooked for whatever reason. And I remember thinking about this. I think supply chain falls into this world because I think there's a lot of properties where Silicon Valley doesn't really spend any time thinking about like how is something manufactured, how does logistics work because you're like isolated. It's a completely separate world. So as a result, what ended up happening for a long time was you would have

great commercial people go and try to build these supply chain tech companies but they don't know how to build great software or you would have these software people that would try and they had no idea how the industry worked so that's why you have a lot of shitty software a lot of crappy software that's been built there for this industry for the last two and a half decades

so when I looked at that I was like my entire life I've never been the smartest person I've never been the person that's won by doing the obvious thing I've always gone to the corner that no one ever wanted to go to and I remember like in my last job I was working with one of the largest freight foragers in the world and spending time with their operations this is a pretty tech forward freight forwarder I remember looking and seeing that even despite how tech forward they were they

were still tracking containers manually they had way more operations people than engineers so I was like there probably is a gap here that no one's going looking and solving so I left that job and I basically spent six months visiting the office of all these trucking companies from and we're freight forer's and warehousing companies across uba city stockton all these parts of the industrial parts of california and i went to the first customer's house the guy had like 150 bs of the paper on his desk and he was typing at the shipment documents off his desk one by one by one on a sunday and he's like i'm doing this instead of watching the 49ers play the playoffs i'm typing in shipment data every weekend evening and i'm like that's crazy that you're still doing this and that's where i

felt that here is such an important part of our economy. Software is really shitty. And I feel like after spending that time, I had a pretty, I kind of appreciated the nuances of it. So that's what made you want to go start this company. It's fascinating that you go after the space. I had a three and a half hour dinner with one of the chairman of largest investment banks in the U.S. And he's had this phenomenal track record as investor over 20% returns for 30 years, did a bunch of deals with Warren Buffett. And he said that he looks for two things when making investments. One is things that are boring and two are things that are hard and ideally the intersection of both things. When I think about things are boring and things that are hard, perhaps the new definition of that should be supply chain

because it's just so boring and such a space that a lot of people don't want to go after and it seems so difficult from an execution standpoint. I think that's right. If you go for things that are boring and hard, inherently, the set of people that are going to go solve it kind of continues to dwindle and dwindle and dwindle. But I will say that one thing that's interesting is that I think the supply chain industry at face value is far more interesting than like categories that I think people think are sexy. Because if you think about it, that things that you build at palate affect like 40, like we work with affect all a huge percentage of the food supply chain in the United States, or how do the leading defense contractors move their inventory across the

country, or how do retailers have stock every single day within their stores? How does e-commerce fulfillment happen globally? These problems actually have consequences in the physical world. So if we build our technology right, we actually do impact actual physical good movement and storage globally. And that to me is far more interesting than going and building another social app that gets teenagers. It gets to social media. We're going and building another chatbot that helps e-commerce brands, like basically personalized marketing campaigns. It's like all these sort of things that people are working on are just not that impactful. So it's like, why would you not want to go work on something like this?

It's a packaging question. Yeah. Oftentimes we have a media side to the business in the podcast. I have to think about how to package certain things that maybe on the surface look boring, how to make them very interesting. Has that been the secret to how you've been able to recruit such great people to your company?

The thing I tell people is that, look, first of all, the things that you're probably going to go work on at a lot of these companies, which are like a lot of other companies are just honestly not that impactful. Versus if you come to Pallet, if we were actually able to automate this $11 trillion logistics industry and create it like 2, 3% more efficient,

we're actually driving a lot of abundance. right from reducing the cost of manufacturing to reducing the cost of distribution these are things that can actually have effects on even the rate of inflation globally so you're working on something far more impactful secondly it's way better to be the smartest team in an area that's not to be attract great talent because it's like being that great poker player in a game where there's like not a bunch of good poker players versus the one dollar table yeah like imagine being the best player but the pie is still big so like what you have is this counterintuitive dynamic where we're you could be the best poker player in a place where the pot still big versus all your other friends

from MIT and Stanford are going and building coding agents and competing with each other. You don't have that dynamic here. And the final thing I would say also is that this is a business that's going to be fairly durable and sticky. It's not like overnight there's going to be someone else that comes and learns all the nuances of how to work with the international global freight forwarder, built a distribution network, deploys a system at scale, navigates the enterprise change management, it's really hard to take a pallet deployment out once it's there because it's so critical, so sticky, that this business is going to be a lot more.

And if you had to kind of stand the test of time and is pallet going to be around in a decade, the answers are probably yes. Can most AI companies say that? Probably not. We were just talking about my interview with Balajushrinavasan, interviewed him for three hours. He coined this term, earned secrets, which is the more you work on a problem, the more nuanced you start to learn about it.

And he also coined this term, the IDMAs, which is sometimes there's these decade-long journeys of the founder just going out and figuring out little parts of the business. And after 10 years, they're by far more qualified than any other person planned to go after a specific problem set. I can really agree with them. I have this thesis on business building and building these great businesses in that it's really hundreds of small decisions that compound on daily basis. and oftentimes they're extremely unremarkable, but as they start to compound, they start to give you a competitive advantage. Do you subscribe to this belief?

That's probably true, and that there is, like, I think especially today, there's a lot of tiny decisions from, like, who you hire for every single function. Like, how do you think about your marketing best practices, your talent team, your engineering team, to what segments the market you go after, to which investors you pick,

to how you think about pricing, all these tiny decisions, compound and it's not like any one of them makes a difference on its own but every tiny tiny detail just really importance and when you're thinking about your job as a CEO you have to be a great architect of each of these tiny decisions and what you find is a lot of people have like really big picture visions and they often neglect the tiny details like what's the type of talent I want in every single function in my company and that cost them down the line and I think the best founders are remarkably good at appreciating those tiny details and getting all the tiny decisions right

alongside the big ones. And I think that's what creates like an enduring organization, especially today. Like where the rate of software turns to zero, it's really easy to kind of go and build decks. It's really easy to kind of, a lot of the execution pieces

are getting increasingly automated. So the thing that matters is like taste holistically and making the right decisions across 100 different things in a given year. Have you evolved where you spend your day to day? The way I think about it is pretty different. I think that at a given point in time in a company, there is one bottleneck.

And what you actually want to do is ignore it, spend 80% of your time on unblocking that bottleneck and go all in on it and diagnose it and move on to the next thing and move on to the next thing. And the bottleneck always switches. It could be first maybe your product, you need to work on your product a bit to get it to product market fit. Then it maybe becomes you need to iterate on your sales motion. Then it becomes you need to make a better deployment motion to handle that scale. Then it becomes, you need a lot more leads and you need to get your demand generation right. Then it might become, well, you need to iterate on your.

need to architect a talent org to go and scale that team again. Then it might go back to we need another product for Act 2 and to expand the potential of the company. And you keep going through these cycles. And what you as CEO need to do is just go super deep on solving that bottleneck and just move on to the next thing. And the times I've seen myself not do well is when I try to do a lot versus when I just really isolate focus on the bottleneck and make it clear that this is the bottleneck.

That's what moves the business like radically forward. How do you figure out what's the current bottleneck? if you look at your business and ask you, why are you not 100 times or 10 times bigger? There'll be one answer that's crystal clear. You just keep on asking this day after day. Every day. Every week, you should ask yourself,

why are you not 10 times bigger? Then you'll find an answer. It's like, well, we need a lot more leads. And it's like, we'll go figure that out. Or it might be that, oh, well, our reps are not converting fast enough. Why don't we enable them to do them better? Or it might be that, well, we don't have enough folks to go and deploy.

Well, that means like we should go automate some parts of our deployment or go and hire more folks in deployment. So it's like, whatever that, bottle neck is, you need to go ask yourself what is it and go and solve it. Is that also how you figure out when to fundraise when capital becomes a bottleneck? My hot take in the modern world is that capital right now is kind of like either super available or it's not available. And I think the right way to think about if you're running your business well, it'll always be capital. And if you're not running your business, there won't be

capital. And I think it's one of those binaries that if you focus on building a great company, the capital value. And do you think about making your business, you're going to be capital value? And do you think about making your business? business anti-fragile to capital itself? I do think about that, but I do think that like... Is that possible? It's possible, but I think if you want to win a market and you want to go super fast, there's straight-offs.

They're straight-offs. Like, you might need a blitz and take advantage of a point in time or you want to go and become the number one company really quickly. So having capital to accelerate is powerful. Maybe the core business on its own like ours could definitely be on a per-un economic basis, a creative, to run profitably, but capital still could be a competitive advantage to be able to accelerate your roadmap, accelerate your international expansion, accelerate your ability to go to different verticals.

I think about bottlenecks. I think about the Elon model where he'll go into one of his companies and for 14 straight hours, he meets with every single individual for five minutes discussing their one bottleneck. And what's underappreciated about this model outside of just having the ability to have that many meetings and one day focusing on people's hardest problems, is that the meeting itself is the competitive advantage, meaning everybody is forced to distill their bottleneck to their level. In other words, the way that Elon is always able to focus on the bottlenecks is he essentially crowdsources the bottlenecks from the employees. And those employees both have the carrot and the stick, the carrot being that they will have Elon's time for five

minutes a day, and the stick being that if they come in unprepared, not thinking about their bottleneck and not understanding to the most granular level what it is that's keeping them from succeeding, then that may get fired on the spot. Here's a lot to learn from how Elon runs his companies. I think that the focus, the extreme maniacal to performance management, he's almost unemotional about it, is something that 99.9% of even good CEOs are not able to do. And I think that's why he's exceptionally successful is because he can do a couple of things really well, which is one, be unemotional almost to a degree that kind of comes off is maybe a bit,

people might call in a lot of different terms that are dead, but in reality, just an extreme psychopathic, but that's just an extreme devotion to performance management. The second thing is that he is able to cut through all the noise because there's lots of ways people can kind of go and you have like a VE manager level that's handbags, a VP level that's sandbags, but he just kind of eliminates all the bullshit and keeps high performance culture. And the third is at Skis he manages intensities that very few startups have. Like even startups might not have the same intensities as lower organizations.

And like that's impressive to do at scale. And I think his style of management is like what HBS would call like, oh, this is like anti-management practices. But I do think there's something true to that style of management that more CEOs need to emulate. It's been about a year since founder mode has come out. What do you think still rings true in terms of founder mode and do you apply founder mode to your business?

What rings true about founder mode? is that like, you can't just be this person that sits like 200 feet above the ground or 2,000 feet above the ground and you're just like making these decisions and having your VPs report to you on progress and you're like, yeah, yeah, yeah, let's just like, well, let me just let them around the company. Well, I can do press, I can do PR, I can do all this stuff. Like that does not work because like fundamentally the main reason that a CEO's jobs exist is to drive performance of their organization and to drive intensity. And by default, like if you do not.

hold those standards and you don't keep those standards to an utmost high. No matter, even the best people need a little bit of pressure to kind of go ahead, right? You just need to like set the standards about. And I think that like is one thing that's true about Founder Mode is that you need to be fundamentally hands on. You can't be hands off. No great CEOs ever been hands off. Like there's very, very, very few CEOs.

I can even think of one that's been hands off and successful. The second part I would say is that it's really hard to go and assess the people you manage unless you're involved in the details. Like, how do you know that the marketing team is hitting their maximum efficiency if you have not seen the marketing plan and you're not paying close to have they're generating leads and you don't have an opinion on that? How do you know your sales team is enabled if you're not watching calls and understanding what's happening at the ground level?

How do you know your product teams are shipping at a maximum cadence? So, like, you can't just let your team just go and go off and like you don't have any control. You should have an opinion about is this good, is this bad, is this not? Otherwise, there will be too much drift. The third thing, to have an opinion on strategy, you need to be involved in the details. Otherwise, you're like strategies, frankly made based on like a bunch of like, she said this, he said that, right?

So you need to be on the details. So I think about if you're not involved in the details, you're probably not running your company well. And if you're not setting those intense standards, you probably aren't running your company well. But let me caveat that with something. I do think that a lot of people are using founder remote to justify micromanagement. It's a difference.

Micromanagement is I'm going to be involved in every single I'm not going to go hire good people. I'm just going to go and fix these problems of myself. Versus founder mode is go inspect, figure out what the problem is and figure out the systematic way to go fix it. Like, you should set up systems that can make this company run on itself and you should go and solve them.

But if you're still the bottleneck for solving all problems of the company, your business is fucked. It goes back to what you're saying, which you're constantly looking for the bottlenecks to solve, but it would be the wrong strategy to sit with everybody and do all their work for. them from day-to-day. That would be the most extreme, as if you're doing routine tasks, you're not focused on the routine tasks. You're focused on these bottlenecks that are

like friction to the entire. Yeah, if you're solving the same bottleneck again and again and again, that means like you're doing the solution and your team's not learning and you don't have a system to go and solve it. You need to put systems in place to go and solve it. Like, maybe the leader's wrong. Maybe the system in place doesn't make sense and you need to work with your team to re-architect the system. Like, whatever it is, figure out the structural solution versus don't figure out the bandaid. The backdrop behind Pallet and any company that relies heavily on AI is that the foundation models are changing sometimes on a daily basis.

How do you build a business that relies so heavily on foundation models? What you have to do is you have to make sure your product benefits from every sort of foundation model like evolution. So like the way we think about Pallet is we have all this context about these businesses that's stored. Like, for example, what are their SOPs for running quotes? What are their standard operating procedures for tracking or shipments? What are the ways that they handle orders from Walmart versus targets?

So there's all this institutional context. There's an execution layer built on top of the models. So every model release should make our product better because we have our context, we have our execution layer, we have our data layer, we have our data integration layer. So in many ways, we're like a next generation system of record that contains context, but also does these actions. So every model release arguably makes our product more performant, but we're not at the risk of the models because they don't have all the institutional context, ingredients, the data that we have, so they can't come and displace this overnight.

And I think that's the right way to architect a next generation AI company. And the wrong way to do it is to basically go and write all the deterministic code. And then when the model upgrades, all that code and that logic you wrote is kind of useless. You have to rely on the models to kind of go. and do these computational intensive tasks. But you have to set up the architecture and the execution engine and the context layer

that takes advantage of them. So another way, you're managing your information, not writing specific code for a specific model. You're more managing the data infrastructure so that when that new model comes out, it's not going to be obsolete in terms of what you could execute.

Yes. So it always improves what we can execute. If you go back, to the day before you started Palet, what would have surprised you the most about building an AI company? The rules of the game have changed in a lot of ways and you have to rethink everything from first principles in terms of team building.

So let me use some counterintuitive takes. Prior world, everyone's like automated, SDR outreach and marketing are automated. Let's go and have a bunch of outbound SDRs. Let's go have them call. Let's go and use all these email prospecting solutions. That's a new way of getting meetings. But in a world where this is the cost of that increase,

channel has gone down to zero, what's happened is that there's been a return back to in-person of like events and introductions and the old-school tactics of sales and marketing are kind of back-in-play. Like people love having for a while, like now more and more companies are doing like sports partnerships and lots of aggressive brand marketing and trying to get their name out there and doing more of these old-school brand-building tactics and doing a lot of in-person events in a way that wasn't in the past. Secondly, we shifted to this role of product management under the Google and Facebook paradigm where product managers became these people that would go and do user research and go and identify

functionality and go and build stuff out. But in this modern world, we don't not have any product managers at the appellate. We have agent product managers who are like a hybrid of what was traditionally customer success in PM. So they work to deploy our agents in the field. And we have engineers. And what we find in that is that basically all the technical decisions are like which sort of models result in the best voice performance. What are the nuances of deploying with a certain freight forwarder?

Like you don't need these like traditional spec style workflow style product management in a way that you did in the past. That function has kind of become obsolete. And then the third thing I would say is that you sort of don't have a good mental model anymore of how to build out your leadership bench. Like, for example, there's a lot of leaders who learned how to build a great SaaS company. That does not mean that they would be great at leading your AI deployments team, or they would be the best at leading your engineering team because they were trained in this old school paradigm. So now you have a choice of, do I bet on my hungry, the number of people that have experience with LLMs is like less than like under four years, right?

There's not that many people with lots of experience. The 21-year-old might actually have more experience today than the 37-year-old. that looks senior. So in that world, who do you promote into your leadership bench and what does the leadership bench of the future look like? So these are the kind of counterintuitive decisions on the people side you have to deal with in a way that I didn't think was true of the last generation. One of our portfolio companies, Lagora, they talk about how they prepare their teams that the entire code may need to be rewritten in a week or two weeks and making sure that they have this mental flexibility. And I think about oftentimes maybe that's why there's so many

20-year-old and 22-23-year-old engineers is there's something about the mental plasticity of somebody younger and the lack of ego that they have not yet developed that maybe a 30, a 35-year-old engineer might have been more ossified in the way that they approach software development. This is partially true, but I do think that there's also value in those 30-year-old engineers that are still mentally flexible and that have a good understanding on how to build scale systems and have good taste on system design. So I think that what you're really looking for more than age or any factor

is how much is this person problem solving from first principles? The moment you hear someone in an interview, be like, I've done this before and I have experience immediate red flag. That sounds like that signals to me that, like, I think there was a case at Palette where someone once told me that

they have 20 years of experience. And I was like, I don't give a shit. I don't give a shit that you have 20 years. of experience in this field, it's changed. Like, what I care about is if you come to the right answer, not the fact that you had 20 years of experience in the past. And like, you have to like value first principal thinking. Zaid Rahman, who's CEO Flex, which is a newly unicorn company,

he said that his biggest red flag on an interview is when someone says best practices. That's when he knows that somebody's not a first principal thinker. I mean, there are some cases of best practices. Like, how do you deal with a client meeting or something like that? There probably is some set of best practices for things, but I generally agree with them. If you could go back and give yourself one time a piece of advice before starting palette, that would have dramatically helped you accelerate the success of the company. What would that be?

Have more confidence in yourself? Like, I think the thing that I find that especially young founders do is that you think that you think that you, think that you've hired these leaders, they have more experience than you in something, so maybe they know something you don't and your gut feels off about something, you probably might be right because you just have so much more context on your business and your market and your end state that like you should trust your own gut instinct more than you do. And I think that sometimes you feel like, is, like, do I know what I'm talking? It's like you probably have a good sense on what's right. And I think enough founders,

they don't trust your gut instincts and something feels wrong. They need to like listen to that gut instinct more and be like, yeah, this is clearly not. good enough when it pushed the matter, I'm going to push the matter, and when it pushed the boundary. It's a knowledge paradox. The more you know about something, the more you realize that you don't know anything about that one topic. And the second order effect of that is that you're less confidence. So the people going around with extremely high confidence tend to be more ignorant than some of the people that are going around with humility. There's a balance where you have to be a truth seeker and understand what's actually happening. But if you have the right piece of

information and you're not diluting yourself, trust your opinion. more. There's a reason you started this company and you got it here. You were right more often than wrong if you got it to a certain scale. Who do you go to for first principles thinking and to test whether you're on the right path? The best thing that I do for myself is rather than any one person is I play devil's advocate by myself and I'm like, what if the opposite was true? And I aggressively counter my own opinion and try to like play devil's advocate with the other argument. And if I find that the devil's advocate perspective doesn't convince me, that's how I try to run it and run it.

Because I find that when you go and you ask five people for their opinions, three people agree with you, two people disagree with you, you're going to be like, and they're all smart, you're just going to be like back to confusion and analysis paralysis. That I find that usually, yeah, like most people ask too many people for their opinions. You know the Charlie Munger inversion? Yeah. Charlie Munger, when he approached a problem,

he figured out that it was much easier to figure out how something, would fail versus how it would succeed and then do the opposite of why it would fail. So you might look at Pallet and you might say, well, what are the failure modes in Pallet? Well, maybe we don't get customers. Maybe we aren't able to recruit. Maybe we're not able to scale. And then he inverted that to figure out what he needed to do. Somehow there's something about the human mind that makes it much easier to see why something

would fail versus why it's going to succeed. Yes. Although I used to run my company a lot from a fear of failure and I found that there are downsides to thinking a lot about failure. I think that it can take you pretty far, but the thing that gets you all the way there to the end is a desire to really win. How are you able to make that shift?

At some point, I realized because I was thinking about all the reasons things were in doing wrong, I would worry about things like making tough calls, like being like, oh my God, we like, what if we get this marketing experiment wrong? Or what if I just spent $200,000 on this big call and it goes wrong? At some point, I'm like, well,

if I don't do these things, like, yeah, we're not going to fail, but if I want to win, I need to go big and I need to go make bold calls. The way I reaffirmed it is to me, failing is not being number one. So when you rethink that, you're like, everything is going to be number one. You just almost created a new mental algorithm, which is like, failing is not actually failing. Failing is being number two in something. And when you realize that failing is being number two and winning is just like being the

absolute best, you start to think of failure in a slightly different heuristic way, which is like, if I'm not the number one, company, if we're not winning every single deal, if we're not in every major enterprise account, I failed. So like when our team shows me your pipeline, I'm like, what are the big of customers that we're not talking to? Like, who is not on this list and why are we not talking to them? It's more of my default statement of thinking.

So that way I'm always thinking about how do we maximize our winning or ability. And you start to incorporate opportunity costs. In other words, not taking action is default failing, so you must try to take action so that you have a chance of not failing. My other pot take is that, like, most people think that it's, you have a, you have a to be 100% right. I think that you should try to get to the right decision and 30% of the time and be 70% right. As long as you're making 70% of your decisions directionally right and you're doing them really fast, you'll be a better CEO than most. I'm a big believer in that quality is

downstream of quantity. So one of the ways to increase your chance of success is to do more and to do a quicker, get faster feedback loops. I've still struggled not to beat myself up when I do something. doesn't work, I get the learning and I improve the system. And I'm trying to work on how do I maybe talk to myself in a way that's more productive versus criticizing myself for making a wrong attempt. One of the things I did for myself was Huberman is a big advocate of hypnosis. So I actually did a very active period of my life where I just did hypnotherapy. I tried to rewire my own subconscious patterns.

And that actually helped me like kind of get rid of a lot of that negative dialogue in head the second thing is that when you beat yourself up like the amount of period you like waste in like kind of that defeated like back and forth thought pattern is just causing you a loss of productivity that i view it as like like the longer i disrespect and i keep thinking about the past like the past is the past we're not going to fix it like we got to keep going forward and like otherwise we're still going to lose if i keep thinking about the past we're going to lose for sure so like i made the mistake let's just take the learning and let's just keep going otherwise we're going to lose so like you have to always keep re-reforming and like what's the past

of the past we have to just keep moving all rows that we'll lose i have to ask how did you do hypnopathy i looked around and i found a guided hypnotherapist and every single week it was like how do i get over my fear of failure i hate failing so we just did a bunch of prescriptive sessions on trying to get rid of my fear of failure so that i could just make calls that i felt like we're unpopular talk about first principles thinking yeah shan this has been absolute masterclass thanks so much for stopping by