Why acquiring businesses is a unique strategy to deploy AI into the real world.
As models continue to improve, the price of intelligence is rapidly declining. We think this is only going to accelerate.
So if intelligence is getting cheap, what remains expensive?
At Long Lake, we focus on mission-critical businesses. Do customers trust them with real end-to-end outcomes? Does the service require human judgment? Are there regulatory reasons that govern accountability?
It's not just about drafting a newsletter to a homeowner, but actually handling the end-to-end outcome of an urgent leak. It's not just about extracting data from documents, but actually taking accountability for a complicated tax decision. And it's not just about helping you book a flight, but handling the entire travel journey including last-minute disruptions.
We think acquiring businesses like these is a unique strategy to deploy AI into the real world. Here's why.
The walls of deploying AI from the outside
Let's say you're building an AI product to sell into the enterprise. You found a pain point, built an agent, and it works—but for one very narrow use case.
Now you have to get it into production. That might involve security review, internal coordination, integrations with 20-year-old systems. This is typically the first wall in deploying AI into an enterprise.
Once you get past this, you have to get the team to use it. You'll iterate on the interface, go through trainings, get feedback. Whose job is it to drive adoption: yours as the vendor, or the client's?
Then, how do you get revenue? Do you sell tokens or seats? How do you deal with price sensitivity, measure ROI, and capture the value you create?
These walls reflect a common challenge: you're building on the outside, and that makes deploying AI really hard.
Importantly, it's not the models. Intelligence is progressing faster every month. One reason why deploying AI is hard is that organizations are incredibly complex. On paper, an org chart looks structured, but in reality, decisions are entrenched in a web of team members with decades of experience. The owner leans on them, they work with the most important clients, they interact with and influence multiple teams.
A vendor might integrate with systems of record: a CRM, a ticketing system, document repositories where SOPs, contracts, and old email threads live. That's valuable information. But businesses, especially in complex domains, run on human judgment. It's the exceptions, the edge cases, the tacit knowledge, and the quick questions asked to the expert that govern how a company runs.
You have to embed in the organization and collaborate with the people doing the work to understand what great work actually looks like.
Who captures the value?
Even if you're able to deliver end-to-end value, it's hard to capture that value as an AI vendor.
Let's say you land a million-dollar-a-year contract, which is pretty impressive. But will customers keep renewing? Can you maintain pricing as models improve and alternatives get cheaper?
Now let's say your client gets $5 million a year in EBITDA impact from your deployment. With an established business, you have years of operating history, enduring client relationships, and a proven business model with real economics. You're adding earnings to a business with value you can already reason about. Using a conservative 10x EBITDA multiple, you can generate $50 million in enterprise value for your client.
As a vendor, you did the work. You deployed agents, integrated systems, and worked with the team. But the largest impact shows up in the P&L of the business you worked with.
So why not build that business yourself, with AI at its core from day one? This is a very valid strategy. You can improve the whole workflow, not just a narrow slice. You sell the work itself, not just one specific tool. And importantly, you own the outcomes.
But you don't start with customers. It's hard to get distribution. You don't have brand reputation, and it's hard to hire expertise in mission-critical industries where clients may need multiple specialty offerings.
If you're the leader of a health system building a new surgery wing, do you hand that project to an AI architecture startup, or the firm you've worked with for 20 years, who knows the standards and nuances of your design and can deliver the full scope across architecture, structural, and MEP services?
The best firms have spent decades earning their customers, building brand and trust, and organizing incredible teams to deliver great service.
So instead of selling an AI agent from the outside or building from scratch, what if you could partner far more closely with incredible businesses that already exist, through acquisition?
What ownership unlocks
Starting on the engineering side, there's access. Your team can sit down with the experts doing the work, understand what they do, and build relationships with the veterans who have 20 years of experience. That's important for designing agents that tackle not just one use case, but the full automation and service delivery opportunity for the business.
The second is data. Every business is a live environment with real tasks, real edge cases, and real answers about whether an agent actually got something right. That's a huge opportunity to use that signal to improve your agents and models.
On the operational side, incentive alignment is huge. Ownership solves for this. You only win when your team and your business wins. There are no separate revenue goals, no distractions from other clients. That enables you to uncover and solve blockers to faster deployment and shared outcomes.
Then there's trust. Rather than coming outside-in or building from scratch, you start with the brand that took decades to build and all of their deep customer relationships.
Finally, you get scale. You start with customers, an expert team that can deliver an incredible service, and cash flow to continue investing in growth and new products.
These five characteristics aren't just independent benefits. They create a compounding advantage. Your agents and people act in live operations. You understand how work is done, create real-world data that leads to real outcomes, and that learning compounds together.
The thesis of acquisition, deep partnership with incredible companies, and leveraging AI to scale these businesses and improve service quality is exactly what we're excited about at Long Lake.
What we're building at Long Lake
Long Lake is an applied AI holding company. We've raised $7.5 billion in capital from General Catalyst, Alpha Wave, Elad Gil, D1, and many others. Our thesis is to acquire best-in-class services businesses, partner with them over the long-term to transform their operations, help them grow faster, and ultimately deliver a better client experience.
Today we've partnered with 40 businesses across residential services, architecture, HR, and other markets. We're a team of 60 across technology, finance, and operations, including former founders, ex-military, and more. We recently announced our partnership with Amex GBT, a $6.3 billion take-private bringing us into corporate travel.
We're inspired by earlier holding companies (think Berkshire Hathaway, Danaher, Constellation), and believe AI excellence is a new lever of execution that raises the ceiling for business transformation.
To find businesses worth partnering with, we see more than 5,000 deals a year. Great investment decisions come with choice. We keep a big funnel and an incredibly tight filter, focusing on businesses with strong fundamentals and an AI opportunity to improve and reimagine service delivery for clients.
From deploying agents to real P&L impact
Once you've partnered with a business, what does P&L transformation look like?
Think of a J-curve. You start with an investment in AI deployment: your platform, your technology, and capturing workflows. You work closely with the team, iterate, validate the product, potentially train custom models, and understand the unique tasks and data. This requires a focused, coordinated effort beyond just engineering.
Initial investment unlocks capacity, but realizing its full value requires putting that capacity to work.
Once you make it up this hill, people start using your product. You unlock capacity: time savings, usage going through the roof, the same output in fewer hours.
But importantly, this isn't the end of the journey.
You have to put that capacity to work. You work closely with management teams to drive measurable business outcomes, drive topline growth, and improve customer and employee NPS. That's how you drive real P&L impact: higher revenue per employee and, importantly, far better service quality for clients.
What ownership enables: as we repeat and improve our playbook across businesses, we can deploy agents faster, reduce upfront investment, and accelerate P&L impact.
This isn't just a bespoke process you run one-off for a business. If you have the platform and playbook for both AI deployment and business transformation, you can get to faster agent deployment and faster P&L impact at every new business you partner with.
Under the hood, it takes a unique team to own, operate, and transform. You have to buy these businesses, with an experienced M&A team that can partner with world-class firms. You have to build: live on the ground with your partners in Minneapolis, Portland, London, spend time with the teams, and push the frontier of how agents are integrated across entire firm operations. And then you have to deploy, with a team focused on driving adoption and making sure time savings translate into actual business impact and a better client experience.
We think acquisition, owning outcomes, and long-term partnership with incredible services businesses is the path to making AI work in the real world. If you’re excited about this thesis, come build with us.
