From Hype to Habit: Rethinking Strategy in the Age of AI
The era of optional AI adoption is over.
In nearly every conversation Corey and I are having—with founders, enterprise execs, or investors—the same pattern shows up. Startups are building AI-native from the ground up. Large companies? They're scrambling to inject AI into legacy systems, often without a clear plan. That gap is widening fast, and it’s not just about tech—it’s about mindset, incentives, and cultural readiness.
What we're seeing isn't unlike the early internet era: a few forward-looking companies leaning into the shift, while many others assume the pace of change will somehow spare them. It won’t.
The AI-Native Advantage
For AI-native startups, it’s not about whether to use AI—it’s how deep they can embed it into every function. These companies are asking sharper questions:
What problem are we solving?
How can AI help us solve it differently, better, faster?
Where do we earn our edge?
This mindset leads to leaner operations, tighter feedback loops, and faster go-to-market strategies. They’re not building AI features—they’re building companies on top of AI as infrastructure.
Why Incumbents Are Stuck
Contrast that with the typical enterprise response: a reactive attempt to “add AI” somewhere. But most legacy companies don’t have an AI strategy. What they have are departmental experiments—and in many cases, a legal team writing rules before the product team writes code.
That’s the first mistake.
Too often, legal becomes the first function to take a position on AI. Not product. Not engineering. Not leadership. Legal. And that position is usually risk-avoidance. What gets lost in that posture is velocity. When “don’t use AI” becomes the cultural message, innovation slows to a crawl.
The Three Zones of AI Injection
We believe every company—regardless of size—needs to think about AI across three core zones:
Customer Outcomes – How does AI help solve your customer’s problem better? That’s the whole point.
Product Development – How is AI changing how you build what you build? From design to QA to iteration speed.
Operations – How can AI improve internal efficiency, reduce costs, or augment human capabilities?
Companies that don’t actively map their opportunities across these three will lose to those that do.
And here’s the kicker: “injecting AI” isn’t a one-time act. It’s not a roadmap with fixed steps. The tools are simply evolving too fast for that. The real play is setting up a process of constant evaluation, experimentation, and feedback. In other words, you need an operating system for adapting to AI—because the tools will change, but the mindset has to stay nimble.
The Moat Is the Mission
So what happens when every company has access to the same models, APIs, and tech? The question of defensibility comes up fast. What’s your moat?
It’s no longer about proprietary algorithms. It’s about:
How deeply you understand the problem.
How closely your product aligns to the customer journey.
How fast you can adapt when the context shifts.
Differentiation now comes from execution, customer intimacy, and speed of insight. And while switching costs are lower in theory, behavioral inertia is still real. The companies that win will build systems people don’t want to leave—because they solve the problem so well, not because they locked users in.
Less Capital, More Leverage
Startups used to need massive capital to scale. Not anymore. AI changes the unit economics of company-building. Small, focused teams can now achieve what once took dozens. This doesn’t eliminate the need for funding—but it shifts when and how it’s needed.
At the same time, legacy companies have an opportunity to invest more efficiently than ever. The cost of innovation has dropped. The barrier is no longer budget—it’s belief and inertia.
No Company Is Immune
Whether you're a financial services firm, a CPG brand, or a healthcare provider — AI will impact your industry. If not now, soon. This isn’t about replacing human connection. It’s about enhancing it, scaling it, and freeing people to focus on what matters most.
The companies that thrive will be those that ask better questions, experiment faster, and treat AI not as a side project, but as a new foundation.
Because the wave is already here. It’s just a question of whether you’re riding it or pretending it won’t reach you.
Key Takeaways
Being AI-native is a mindset, not a feature set. Startups are building from first principles, while legacy companies risk playing catch-up unless they restructure how they think, not just what they build.
Legal departments are often the first to define AI policies—and that’s a problem. True innovation comes from product, not prevention.
Every company should assess AI in three zones: customer outcomes, product development, and operations. Miss one, and you open a gap competitors will exploit.
Defensibility today comes from depth, not breadth. It’s about how well you understand the problem, how tailored your solution is, and how fast you adapt.
The old startup playbook—raise big, scale fast—is being rewritten. AI offers more leverage with less capital. But speed and clarity matter more than ever.
This isn’t about adding AI. It’s about rethinking how you build, how you serve, and how you operate—before someone else does it better.