Buy, Build, Deploy: Using M&A to Drive AI Transformation — Prathik Naidu, Long Lake

AI Engineer · 18 min · 206 sentences · from YouTube's caption track

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00:01[music]
00:13Hey everyone.
00:13My name is Pratik Naidu.
00:15I'm a founding partner at Long Lake and today I'm excited to talk a little bit more about why acquiring businesses is a unique strategy to deploy AI into the real world.
00:26So to start out very familiar experience to many of you.
00:30Let's say you're building an AI product and now you're trying to sell it into the enterprise.
00:35Well, where do you start?
00:36You found a pain.
00:38You built an agent for it and it works, but for one very narrow use case.
00:42It's a great starting point.
00:44But now you have to actually get it into production.
00:47And that might involve dealing with security review, internal coordination, integrations with 20-year-old systems.
00:54It's a lot of hard work and this is typically the first wall in order to deploy AI into an enterprise.
01:00But once you get past this, now you have to get the team to use it.
01:05So you typically will iterate on the product interface, go through trainings, get feedback from users.
01:11And there's a typical question that comes up, which is whose job is it to actually drive up that adoption?
01:17Is it yours as the AI vendor or is it the client that you're working with?
01:22And then let's say you actually go through all this and now you're trying to figure out how do I actually get revenue for the agent that I just deployed.
01:30You'll typically work through a few different questions.
01:33Do you sell tokens or seats, maybe a bit of both?
01:36How do you deal with price sensitivity from your client?
01:39And how do you actually then measure ROI?
01:42And the bigger picture question here is as an AI startup deploying into the enterprise, how do you capture the value that you create from your deployment?
01:51This all might sound very familiar to many of you.
01:53It certainly is to me and these walls of deployment reflect a pretty common challenge, which is you're building on the outside and that makes deploying AI really hard.
02:04And importantly, it's not the model.
02:06The models, as you know, are getting incredibly good.
02:08Intelligence is exponentially increasing.
02:11So, what makes this really hard?
02:14And I want to talk about a few different observations of why deploying AI into businesses is challenging.
02:22The first is that organizations are incredibly complex.
02:26On paper, your org chart might look something on the left.
02:29You know, you have your owner, you have teams, managers, and this is a standard team structure.
02:34But, the reality is organizations are far more nuanced.
02:38There are decisions that are entrenched in a web of team members, often times folks with decades of experience.
02:45They're interacting with the sales team, the operations team.
02:48The owner often times leans on them.
02:50And so, building an agent as a third-party vendor is difficult because you actually need to embed into the organization.
02:59The second reason I want to touch on is you can't deliver the full value of your deployment if you don't understand the nuances of how a business actually runs.
03:09So, a vendor might integrate with your systems of record like a CRM or a ticketing system, or even, you know, document repositories where your SOPs, contracts, or, you know, old email threads live.
03:19And this is really valuable source of information, very useful.
03:23But, the reality is businesses, especially in complex domains, run on human judgment.
03:29It's the exceptions, the edge cases, the tacit knowledge, the, you know, quick questions that get asked to the expert that really matters.
03:37And that's really what governs a lot of how a company runs.
03:40And that's the operating reality.
03:42You need to work with the people doing the work to understand what great work actually looks like.
03:50The third reason that I want to highlight is that even if you are able to deliver end-to-end value, it's pretty hard to capture value as an AI vendor.
03:59So, let's say you land a seven-figure million-dollar contract, which is pretty impressive.
04:04But, how do you think about the enduring business value of that contract?
04:08A lot of big questions to answer.
04:10Will customers keep renewing?
04:11Can you maintain pricing as models improve and as alternatives get cheaper?
04:16But, let's say as part of that million-dollar-a-year revenue engagement, your client actually gets a lot of value from it.
04:22In fact, maybe $5 million a year in EBITDA impact.
04:26And so, what's interesting about this is that with an established business, you have years of operating history, enduring client relationships, and a proven business model with real economics.
04:37And so, you're adding earnings to a business with value that you can already reason about.
04:43And so, you know, using a, you know, roughly conservative 10x multiple, you can actually generate for your client $50 million in enterprise value, which is really interesting.
04:53And so, there's this question here, which is, you know, as a vendor, you did all the work.
04:56You've deployed your agents, you went through all the walls of integrating with the systems, working with the team.
05:03But, the largest impact actually shows up in the P&L of the business that you actually worked with.
05:10And so, these are the typical walls and challenges of deploying AI outside in.
05:14So, why not just go build that firm and build that business yourself with AI at its core from day one?
05:22This is a very valid strategy, actually.
05:23You can improve the whole workflow, not just a narrow slice.
05:27You sell the work itself, not just one specific tool.
05:31And importantly, you own the outcomes.
05:32So, the P&L and the value that you're generating is yours.
05:36And many of these firms are going to be amazing.
05:38We think this is a great strategy.
05:40But, I also want to call out some of the challenges that come up with this approach.
05:47The first is you don't start with any customers.
05:49It's hard to get distribution.
05:51You don't have any minimal you don't have any a brand reputation, so that makes it challenging to to grow.
05:57And it's hard to hire expertise especially in you know niche niche industries and important industries where clients might need multiple specialty offerings.
06:07So take an example here.
06:08If you are the leader of a health system and you're planning a new surgery building, do you hand that project to an AI architecture startup or the firm that you've been working with for 20 years who knows
06:19the standards, the nuances of how you actually design your buildings, and you can trust them to deliver the full scope of your project.
06:27It's a very different kind of execution model.
06:30And so the important insight here is that the best firms have spent decades earning their customers, building brand and trust, and building an incredible team to deliver great service to their clients.
06:45So instead of being outside in and selling an AI agent to a company or trying to build from scratch, what if you could partner far more closely with incredible businesses that already exist
06:58through acquisition?
07:00And I want to talk a little bit more about why ownership is such an interesting strategy here.
07:06Starting with on the engineering side, which is access.
07:08The ability to have your team sit down with the people who do work, understand what they do, build those relationships, understand the key people, the veterans, the folks that have the 20 years of experience is super important
07:20to really designing agents that not just solve one workflow or one use case, but really tackle the full automation and service delivery opportunity for the entire business.
07:33The second is data.
07:35Every business you can think of as really a live environment with real tasks, real edge cases, and real answers on whether or not an agent actually got something right versus not.
07:44And this is a huge opportunity to use that information, use that signal to actually improve your agents and models.
07:52But importantly, on the operational side, incentive alignment is huge.
07:58Ownership solves for this.
07:59You only win when your team and your business wins.
08:02And there's no separate revenue goals, no distractions from other clients.
08:06It creates an incredible amount of alignment that enables you to uncover and solve a lot of the blockers to ultimately get to faster deployment and faster shared goals.
08:17Outcomes really matter here.
08:21The other piece is trust, which we talked about.
08:23And, you know, importantly, rather than coming outside-in or building from scratch, ownership has that trust element baked in from the start.
08:31You know, you have the brand that took decades to build with all of the customer relationships.
08:35And and naturally plays into this idea of scale.
08:38Starting with a large base where you have those customers, you have an expert team that can deliver an incredible service, and the cash flow to continue to invest and compound in growth and new products is very powerful.
08:49And the important piece here is these five characteristics aren't just independent benefits.
08:56They actually create something far more powerful, which is a compounding advantage where a combination of your agents and your people continue to act in live operations.
09:06You can understand how work is done, create real-world data that leads to real outcomes, and that learning all compounds together.
09:14And this flywheel is really interesting, and it comes with all the elements of ownership to uh you know, power this flywheel from from day one.
09:23And so, what's interesting here is that ownership uh can compound rapidly over time.
09:28So, we talked about why ownership is is really interesting.
09:30But what markets are ripe for acquisition, especially in the AI era?
09:37I want to start with the software economy.
09:39This is a global software market.
09:41Most of us have spent a lot of our time and careers building in this part.
09:44And you know, it's a pretty big market.
09:46But what's interesting is that software is always sold tools to people doing the work.
09:50If you zoom out a little bit, this is a services economy.
09:53It's a 7 trillion 70 trillion global market.
09:56And the important insight here is that owning the outcomes of a service end-to-end is far more valuable.
10:03And that's what's exciting to us.
10:04It's a huge market opportunity.
10:07But there's an important observation here, which is under the hood, there's a challenge that services businesses typically face.
10:15Labor costs typically grow with revenue.
10:18So, uh for example, a community manager at an HOA management company can only run so many associations.
10:25An architect can only design so many hospitals or schools.
10:28So, if you want more clients, you have to hire more people.
10:31And this is the typical challenge of uh a business.
10:34And you know, if you compare it to a software company, if you add $100 of revenue in a typical software business, you keep about 80.
10:41So, there's really low marginal cost of growth, which is why these businesses are incredible and you can scale very rapidly.
10:49On the contrary, a service business operates very differently.
10:53For every $100 of incremental revenue, you typically have around $80 or so of delivery and overhead.
10:59You have to hire, train, manage your team.
11:01And so many founders who have built incredible services businesses have really stopped chasing growth, not because they lack ambition and not because there's no market opportunity.
11:10These are massive markets.
11:12But because the challenges of scaling, when you have to do a lot of the work for every $100, you're only keeping about 20.
11:19And so you can think of this as a pretty huge, you know, 80% marginal tax on growth, which makes it very challenging to both come outside in, start these businesses, and even scale them.
11:31What's very interesting here is that AI actually reduces the cost of service while also improving the quality of your service.
11:37So, look back at our chart here where revenue scales proportionally to labor costs, but when you deploy AI you can see here that you get actually operating leverage.
11:47So, your labor cost no longer directly scales with AI.
11:50You're still going to need to hire an incredible team of experts to deliver your service to your clients, but the service economics for these businesses start to look a lot more like software.
12:01And so, the thesis of acquisition, deep partnership with incredible companies, and leveraging AI to scale these businesses and improve the quality of service is exactly what we're excited about here at Long Lake.
12:14So, just quickly a little bit more about us.
12:16Long Lake is an applied AI holding company.
12:19We partner with incredible businesses, have raised 7 and 1/2 billion in capital from Elad Gil, General Catalyst, Alpha Wave, D1, and many others.
12:27We were started about 3 years ago.
12:29Our Our whole thesis is to acquire best-in-class services businesses, partner with them to transform their operations, help them grow faster, and ultimately deliver a better client experience.
12:42We've today partnered with 40 businesses across a number of markets, residential services, architecture, HR, and many more.
12:50We're an amazing team of 55 team members across technology, finance, and operations.
12:55We're growing.
12:56We have an eclectic mix of former founders, folks from the military, Palantir, a lot of familiar names here.
13:02And we recently just announced our partnership with Amex GBT.
13:06It's a 6.3 billion take private, bringing us into the corporate travel space, which we're incredibly excited about.
13:12You know, we're modeled after a lot of the holding companies that have come before us, but with a really interesting observation that AI raises the ceiling for business transformation.
13:21And this is the lever of excellence that we're excited to go and pursue.
13:27So, we talked about why We talked about services in particular.
13:31What services businesses though are actually attractive to own?
13:34And I want to start with an important observation, which is as models continue to get better, the price of intelligence is rapidly declining.
13:43And it's pretty incredible to see.
13:44I think this is only going to continue.
13:46So, if intelligence is getting cheap, what remains expensive?
13:51We really focus on mission-critical businesses, and it starts with a few key questions that we typically evaluate these companies on.
13:58The first is is there a customer base that trust these businesses with real end-to-end outcomes?
14:04The second is is there human judgment in the service that's actually being provided?
14:10And the third is that are there, you know, regulatory reasons that govern accountability when making these acquisition decisions?
14:18And I think the important piece here is you have to own the outcome, and that's the typical characteristic of a lot of these services businesses.
14:25So, to color this a bit more, it's not just about drafting a newsletter to a homeowner, but actually handling the end-to-end outcome of an urgent leak.
14:35It's not just about extracting data from documents, but actually taking accountability for a complicated tax decision.
14:41And, you know, it's not just about helping you book a flight, but handling the last-minute disruptions, which I'm sure many of you have faced before.
14:50Businesses like these are rare.
14:52You have to fit all of these questions, and many more, and also have a team that's excited about that partnership.
14:59And to do this, you know, you have to see a lot of businesses.
15:02Great investment decisions come with choice.
15:05So, you have to keep a big funnel, and then an incredibly tight filter.
15:09Our focus is on businesses that have incredible fundamentals from a business and market standpoint, but also have an awesome AI opportunity where we can actually go in and partner very closely with these businesses.
15:21We see about 5,000, you know, deals a year, but again, are very focused and diligent on what we actually focus on and who we partner with.
15:32Awesome.
15:32So, once you've found your business and you've partnered with a company, what does P&L transformation actually look like?
15:37And I think a very simple way to explain this is actually through the visual of a J-curve.
15:43So, you start with your investment in AI deployment.
15:46You have your platform, you build your tech, capture the workflows, commit a lot of team time to making this work.
15:52And it's a lot of hard work.
15:54You have to make it work within the business.
15:57Thinking back to, you know, some of the challenges of building outside-in, you have to partner very closely with their team, iterate, validate your product, potentially train new models, look at all the different unique workflows and data that exist within that business.
16:11But once you make it up this hill, you'll start to see folks actually using your product, which is incredibly exciting, and you start to actually unlock a lot of capacity.
16:19Time savings, usage is going through the roof.
16:22They're producing the same output, fewer hours.
16:25But this isn't the the the end of the journey.
16:28Importantly, we want to actually capture value and we want to generate real business impact with our partner companies.
16:34And so, you actually have to put that capacity to work.
16:36Think about incentive alignment, working very closely with our management teams and trusted partners to drive business outcomes, help with growth, improve customer and employee NPS.
16:45These are all part of the equation to actually driving impact within these businesses.
16:50And once you do all the those things, you get to real P&L impact.
16:53Higher revenue per employee, but importantly, a far better service quality for our clients.
16:59And I think what's cool about this is that this isn't just a bespoke playbook that you run one-off for every single business.
17:05If you have the platform and the playbook to do both the AI deployment and the business transformation, this can rapidly compound.
17:14You You get to see faster time to deploy agents and a faster time to P&L impact for all the new businesses that you partner with.
17:21And we see this at Long Lake across the 40 plus businesses that we've already partnered with today.
17:29Of course, it's not just about AI and business transformation.
17:31Under the hood, it takes a pretty unique team to actually own, operate, and and transform.
17:36So, you have to actually go and and buy these businesses, have an M&A team that can partner with world-class services businesses, work with them very closely to build that relationship, should build that excitement for the future.
17:47Then you actually have to go to do the work of building, and this is a lot of hard work.
17:49You have to fly out to Minneapolis, fly out to Portland, uh go to London, spend time with the team, and actually build and push the frontier of how agents get deployed.
17:58Again, not just for one narrow use case, but really for the entire operation.
18:03And then to actually do that deployment, you know, you need an amazing deployment and ops team to actually drive adoption, right?
18:08Think about that at that J-curve, right?
18:10Don't just stop at capacity and time savings.
18:13How do you actually ensure that the work you're doing is driving real business impact for the companies that you work with, and delivering a far better client experience uh for the service that you're providing?
18:25So, if you're excited about owning outcomes and partnering with uh incredible services businesses, feel free to reach out.
18:32Uh we're excited about this thesis.
18:33We think there's a lot of opportunity to go continue to build in the services economy, and uh feel free to chat more if there's any questions.
18:42Thank you.