You Don’t Need More Money—Just A Better AI Strategy

TL;DR: Today we’re releasing a new episode of our podcast AI & I. I go in depth with Mike Maples, the cofounder and partner at Floodgate. We discuss how AI is fundamentally changing the way startups are funded and run. Watch on X or YouTube, or listen on Spotify or Apple Podcasts. Here’s a link to the episode transcript.

Was this newsletter forwarded to you? Sign up to get it in your inbox.

Mike—who wrote early checks to Twitter, Twitch, Okta, and Lyft, and now invests through Floodgate, the fund he cofounded—told me it's not about the smartest model, or raising the most money. 

Startups can win in AI with better strategy.

AI is changing the economics of startups—both how they’re started and how they’re funded. A new breed of companies is emerging, and I invited Mike on the show to talk about how they can best strategize. Last year, Mike co-authored a book called Pattern Breakers, which is essentially a guidebook to why there’s no guidebook to building companies. I really liked it, and my colleague Evan Armstrong reviewed it for Every, so I was glad to have him on. We talk about how shifts in technology create space for smaller players to compete—even with AI giants like OpenAI—and how to capitalize on them. Here’s a link to the episode transcript.

You can check out our conversation here:

If you want a quick summary, here are some of the themes we touched on:

New technology has the potential to create asymmetric advantages (00:04:31)

Mike calls fundamental shifts in technology “sea changes.” When there’s a sea change, such as mass connectivity to the internet in the 1990s, business models shift. SaaS businesses, for example, grew out of this digital revolution. According to Mike, these shifts create space for startups to outpace incumbents. He says you should ask yourself, “How does AI make some business models relatively more attractive…and how can I as a startup exploit those new opportunities…[in] some type of insight in my business model [or] go-to-market strategy that disorients incumbents…where they have a disincentive to retaliate or to copy your strategy.”

Innovate the business model, not just the product (00:05:44)

An interesting trend I’m seeing in AI startup business models is paying per outcome instead of per month or year, which is the traditional with SaaS companies. My guess is that it would be unappealing for incumbents—who are used to guaranteed per user revenue—to shift to a performance-based model. Mike agrees, saying that counter-positioning—taking advantage of an incumbent’s reluctance to adopt a new model that undermines their existing revenue streams—is a powerful way for startups to compete.

It’s also a tale as old as time. Back in the 1990s, when Microsoft decided to compete with a startup, it was effectively a death sentence; it just had to bundle the competing product into Windows. But Google countered this by monetizing through ads instead of per-seat licensing. Microsoft couldn’t undermine Google’s business model by bundling anything into its operating system. (In case you were wondering, the sea change in this case was the web.) 

You can compete with OpenAI (00:15:32)

If startups of the nineties were afraid of Microsoft, startups today are afraid of OpenAI. OpenAI is broadening its horizons from a developer-tool company to a consumer-facing one, and I’m curious about Mike’s take on how startups can compete with the company. He believes AI wrappers—tools built on top of foundational models to make them easier to use—can make for successful businesses if “the thing that you're wrapping on top of involves a process that you really know about that most people don't.”

This reminds Mike of an example from his book Pattern Breakers: In 1903, the New York Times ran an article called “flying machines that won't fly,” and 69 days later, two bicycle mechanics did just that. The Wright brothers’ bent toward tinkering—or “permissionless innovation,” as Mike puts it—allowed them to succeed where theorists didn’t.

Find niches that incumbents can’t or don’t want to enter (00:28:07)

As we go deeper on strategy, I ask Mike how AI companies working on foundational models can think about positioning themselves, because they’re likely to be integrated into applications that have a consumer layer. I know first-hand how difficult this can be because my last company, Firefly, was in a similar position—we were serving a customer who in turn served a different end user. When you’re in that wedge, it’s hard to create a strategic advantage.

According to Mike, you can make this kind of company work if you provide something existentially critical to a much larger customer—especially when that customer either can’t build the capability themselves “because they can't conceive of how they would” or won’t build it “because they just don’t want to.” In both cases, you’re offering something they’ve actively chosen not to develop in-house.

Live with one foot in the future (00:44:53)

One of the themes of Pattern Breakers that resonated with me was the idea of living in the future. The best way to know what's coming is to use new tools obsessively, and over time, you’ll start seeing trends and possibilities emerge, ones that most people miss because they’re living in a different reality. In this context, Mike talks about knowledge workers who are growing up in the age of AI, arguing that they’re well-positioned to innovate because they aren’t burdened by old mental models.

Young graduates working as engineers at AI companies use agentic tools like Devin and AI code editor Cursor to code as if it were second nature. “You’ll sit with them and say, ‘Well, what motivated you to do that and to think about solving the problem that way?’” Mike says. “And they look at you funny, like, ‘Well, how else would you do it?’” They aren’t deliberately setting out to disrupt industries—but they might because they’re operating in a paradigm that incumbents can’t easily relate to.

Knowledge work is changing—step out of the details and see the bigger picture (00:56:05)

My theory around our transition from a knowledge economy to an allocation economy is that in traditional knowledge work, you’re like a sculptor—your hands touch every piece, crafting each detail manually. But working with AI feels more like being a gardener. You set the conditions for something to grow, then watch it develop organically. When ChatGPT responds to a prompt, no one at OpenAI specifically pre-decided that exact output—they created the environment for the system to generate it autonomously.

Mike extends this philosophy to product building. “It’s one thing to think of yourself as building components or building tools or building the end thing,” he says. “It’s another thing to say I’m building an ecosystem and the elements of the ecosystem operate under certain first principles.”

This episode is a must watch for anyone who cares about strategy and AI. Here’s a link to the episode transcript.

You can check out the episode on X, Spotify, Apple Podcasts, or YouTube. Links are below:

• Watch on X
• Watch on YouTube
• Listen on Spotify (make sure to follow to help us rank!)
• Listen on Apple Podcasts

What do you use AI for? Have you found any interesting or surprising use cases? We want to hear from you—and we might even interview you. Reply here to talk to me!

Miss an episode? Catch up on my recent conversations with star podcaster Dwarkesh Patel, LinkedIn cofounder Reid Hoffman, a16z Podcast host Steph Smith, economist Tyler Cowen, writer and entrepreneur David Perell, founder and newsletter operator Ben Tossell, and others, and learn how they use AI to think, create, and relate.

If you’re enjoying my work, here are a few things I recommend:

• Subscribe to Every
• Follow me on X
• Subscribe to Every’s YouTube channel

Dan Shipper is the cofounder and CEO of Every, where he writes the Chain of Thought column and hosts the podcast AI & I. You can follow him on X at @danshipper and on LinkedIn, and Every on X at @every and on LinkedIn.

We build AI tools for readers like you. Automate repeat writing with Spiral. Organize files automatically with Sparkle. Write something great with Lex. Deliver yourself from email with Cora.

We also do AI training, adoption, and innovation for companies. Work with us to bring AI into your organization.

Get paid for sharing Every with your friends. Join our referral program.