Set Up Your Exit Before You Launch, Tips on Building AI Companies - Tactician: #00112

Think about it, setting up a profitable exit before you launch is the smartest move.

It's like starting a chess game and already knowing you can checkmate in four moves.

'I’m not just in the game; I’m two steps away from winning it.'"

Set Up Your Exit Before You Launch

• Why Read: 

  • Valuable insights on proactively planning for acquisition by building relationships with potential acquirers early on, which can lead to a smoother exit process and faster liquidity for founders and investors.

• Featuring:

  • Elizabeth Yin, Co-founder and General Partner at Hustle Fund

• Link: 

  • “Do you set up your exit before you start your company?”

Key Concepts and Tactics:

1. Setting up an Exit Strategy Before Starting a Company:

   • Point: Founders should think about their exit path before starting a company.

   • “This week, I had a really fascinating conversation with a portfolio founder named Joshua Lee, who’s the CEO and co-founder of a company called Ardius.”

   • "He said that when he started Ardius, he not only talked with potential customers, but also talked with potential competitors – large companies who could potentially be building a competing product on their own. He was trying to see if he could manufacture his own exit before starting Ardius. He wanted to know what the M&A appetite would be if he were successful. He wanted to know how big of an opportunity large companies saw in what he was building."

2. Building Relationships with Potential Acquirers:

   • Point: Building relationships with potential competitors/acquirers from the start can lead to a smoother acquisition process.

   • "And I asked ‘wasn’t that kind of dangerous to talk to companies that would potentially be building the same thing?’ And he said, when he talked with a lot of the major players in the HR benefits space, in fact, many of them were building a competitor to Ardius. And some of them even told him they would squash his company.’" 

   • "While that was frightening to him, his thesis was that if you’re good, startups are actually way scrappier, faster, and more specialized and can run circles around most large companies. And as it would turn out, he was able to plant seeds in their heads that in case they were not happy with their own progress, they should stay in touch."

   •  "Fast forward, Ardius was acquired by Gusto in 2021 after discussing with all the major players in the HR benefits space. These were relationships Joshua had already been building for years, which made the acquisition process quite smooth."

3. Evaluating the Tradeoffs between a Faster Liquidity and Larger Exit:

   • Point: Consider the tradeoffs between a faster liquidity event (e.g., 10x in 5 years) versus a larger exit (e.g., 100x in 15 years).

   • "Joshua's view is that VCs should think about the faster liquidity they could get with a manufactured exit. Instead of waiting 15 years to get to 100x (or more), would you rather wait 5 years and take a 10x? He thinks the idea that you have to wait a long time for liquidity in venture is outdated. From an IRR perspective, his model is also way better. In fact, in this particular example, the IRR of the longer time period is 36% vs 58% for the shorter time period."

Tips on Building AI Companies

Why Read:

• Crucial insights for AI startup founders on the unique challenges they face, the importance of demonstrating business value, and the need to strategically position themselves within the AI ecosystem.

Featuring:

• Ron Miller, Enterprise Reporter at TechCrunch

Link: 

• “Your AI-native startup ain’t the same as a typical SaaS company”

Key Concepts and Tactics:

1. Differentiating AI Startups from Typical SaaS Companies:

   • Point: AI startups face unique challenges compared to traditional SaaS companies, and it's important for founders to understand these differences.

   • "Seseri made it clear that just because you connect to some AI APIs, it doesn't make you an AI company. 'And by AI-native I don't mean you're slapping a shiny wrapper with some call to OpenAI or Anthropic with a user interface that's human-like and you're an AI company,' Seseri said. 'I mean when you truly have algorithms and data at the core and part of the value creation that you are delivering.'"

2. The Challenges of Developing and Deploying AI Products:

   • Point: AI products require significant time and effort to develop mature models that can deliver value to customers, unlike the faster iteration cycle of SaaS.

   • "'Here's the thing: With the SaaS product you code, you QA and you kind of get the beta — it's not the finished product, but you can get it out there and can get going,' she said. 'AI is a completely different animal: You can't just put something out there and hope for the best. That's because an AI product requires time for the model to get to a point where it is mature enough to work for actual customers and for them to trust it in a business context.'"

3. Securing Early Adopters for AI Startups:

   • Point: Attracting early adopters is more challenging for AI startups, as they need to focus on demonstrating business value rather than just educating customers about AI.

   • "'Always articulate the problem you are solving and what metric — how are you measuring it?' she said. Optimize on what matters to the buyer. 'So you're solving a problem that has business decision outcomes.' It's OK to articulate your vision, but always be grounding your discussion in business priorities and how those are informing your algorithms."

4. Navigating the Layers of the AI Ecosystem:

   • Point: AI startups need to strategically position themselves in the application layer or middle layer, as competing directly with large players in the foundation layer (e.g., LLMs) is extremely challenging.

   • "'If you're going to compete for a new foundation layer, or you know, LLM play, it's going to be very tough with multibillion dollar capital requirements, and at the end of the day, chances are it will end up being a commodity,' she said. 'There is also a middle layer where the plumbing gets done. She points to companies like Snowflake that have succeeded in building successful businesses in the middle layer by providing a place for the application players to put their data.'"

5. Focus on Data:

   • Point: Investors like Seseri focus on AI startups in the application layer and selectively in the middle layer, as those offer the best opportunities for building defensible businesses.

   • "'I put my dollars in the application layer and very selectively in the middle layer. Because I think there is a moat around algorithms, whether it's algorithms that are proprietary to you, or open source — and data. You don't need to own the data. But if I have to pick, I'd like to have unique data access and unique algorithms. If I am forced to pick one, I will go after data,' she said."

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