Last month, Matt Shumer (CEO of HyperWrite) published an essay called “Something Big Is Happening.” It went viral. Millions read it.
His argument: AI capabilities hit a step-change in early 2026 that made everything before feel like a different era. He compared this moment to February 2020, when a few people saw what was coming and most didn’t.
I’ve spent the past month interviewing dozens of CEOs running everything from high-growth startups to established global platforms. They confirm what Shumer described. The conversation has shifted dramatically. We’ve moved past the hype cycle and into the era of operational dependence.
But here’s what most people miss about this moment: the speed of change itself has changed. Six months ago, I told CEOs to move AI from experiment to operations. Three months ago, I told them to pick specific areas and go. That advice is already outdated.
The CEOs I interviewed aren’t adopting AI, they’re dependent on it. And the distance between them and everyone else is growing faster than anyone predicted.
Here are the five patterns I’m seeing:
1. AI Isn’t Assisting Engineers. It’s the Engineer.
In January, I wrote about AI multiplying engineering output. That was two months ago. The shift since then is dramatic.
CEOs no longer describe AI as a coding assistant. They describe it as the primary builder. Humans set direction. AI executes.
Trip Adler, CEO of Created by Humans, told me his engineering team has fundamentally changed how they work:
“The vast majority of our code is now written by AI. It seems like the engineering team is ‘vibing’ more than anything these days. It has obviously been very important for how we’re growing the business.”
Shumer describes the same phenomenon at a deeper level – AI that designs apps, writes tens of thousands of lines of code, opens and tests the application itself, iterates on design flaws autonomously, and returns only when it judges the work meets its own standards.
This isn’t the “AI pair programmer” story you heard last year. The role of the engineer is transforming from “person who writes code” to “person who directs AI that writes code.”
If your engineering team still treats AI as a helper, you’re already a generation behind.
2. The Rebuild Is Deeper Than You Think
Every CEO I talk to agrees that AI is foundational. But agreement is easy. Execution is where most companies stall.
The leaders actually doing this work tell me the same thing: the rebuild is far more painful and far-reaching than they expected. It’s not enough to say “AI is part of our strategy.” You must tear down your existing architecture and rebuild from the ground up.
Bryan Murphy, CEO of Smartling, was direct about this:
“AI is not a feature. It’s a foundation. It’s a structural change. We’ve had to learn how to really adopt our software application and services to leverage this new structure. It required considerable change, not only in the product itself, but in the way that we thought about developing the product.”
Most CEOs say the right words and then bolt something onto their existing product. The leaders pulling ahead do something harder. They tear down what they have and rebuild it.
That process took Smartling months of rethinking not just the product, but how they develop it. If you haven’t started that rebuild, the gap between you and your competitors just widened again this month.
3. Decision Velocity Is the New Competitive Advantage
The old bottleneck was information. CEOs waited weeks for financial reports, competitive intelligence, and market analysis. AI eliminated that bottleneck. The new advantage isn’t what you know. It’s how fast you act on it.
Kim Hansen, CEO of Cake Equity, showed me how he bypasses traditional reporting delays:
“I have a monthly financial report… I took that and used Claude Code… so I can see the key top four strategic financial things we need to look at. To hook up the systems and ask the CFO to start doing that, that might take a month… but with these new tools, you can actually do this more and more on the fly.”
Hansen’s insight reveals something most CEOs miss. The value isn’t in the data itself. It’s in collapsing the time between question and action. When your competitor gets the same financial insight in minutes that takes your team a month to compile, they don’t just know more. They move faster.
Shumer made a similar observation: the people getting ahead aren’t using AI casually. They’re compressing the time between identifying a problem and solving it. Decision velocity, not data access, will separate winners from losers in 2026.
4. The Winning Model Is “AI Drafts, Human Sends”
The CEOs getting the best results right now use a specific pattern: AI creates the first draft. Humans refine and approve.
Eric Martell, CEO of Pear Commerce, described how this works in practice in customer success:
“We do not want AI responding directly… but AI is drafting the first draft in the response that is going back to our customers. And then there’s the human element where we’re putting our touches on it and making sure that what the AI drafted is valid.”
This pattern works because it solves two problems at once. It removes blank-page friction – the most expensive time in any workflow. And it keeps humans accountable for accuracy and brand voice.
CEOs who automate everything will learn a hard lesson. AI hallucinations don’t just waste time. They destroy customer trust. The “AI drafts, human sends” model scales faster and fails less catastrophically.
Apply this pattern beyond customer support. Use it for internal communications, proposals, reports, and strategy documents. The principle is the same: let AI remove the blank page, then let humans own the final product.
5. The Highest-Value Skill Is Knowing Where NOT to Use AI
Here’s the contrarian point most AI advocates won’t make: some CEOs I interview draw hard lines around where AI will not touch their business (at least for now).
Asad Zaman, CEO of Sales Talent Agency, was adamant about this:
“In recruitment, one of the things a lot of firms are using AI for is to source a list of candidates and to screen those candidates. We have made a decision that AI will play no role there. We actually have our most senior people do that job… We believe that the AI today is not good enough for that.”
Zaman’s reasoning is sharp. In high-stakes judgment calls – senior talent, scientific accuracy, bet-the-company decisions – AI errors don’t just cost money. They bankrupt trust. And trust, once lost, doesn’t come back.
The leaders winning right now hold two ideas simultaneously: push AI as hard as possible in operations, and forbid it from the decisions where a single mistake is fatal.
What This Means for You
Shumer compared this moment to February 2020. I think the analogy is right, but not for the reason most people assume. The point isn’t that something catastrophic is coming. The point is that by the time most people recognized what was happening in 2020, the window to prepare had already closed.
The same dynamic is playing out with AI. The CEOs in this article aren’t experimenting. They’re operationally dependent. They’ve rebuilt their products, compressed their decision cycles, and rewritten how their teams work. If you’re still in the experimentation phase, you’re not early. You’re late.
As Greg Schott, former CEO of MuleSoft, told me: we’ve always had tools that made us faster, but AI is unique, it’s a multiplier of intellect. The CEOs who win will be the ones who weave that multiplier into their organization while retaining the wisdom to know exactly where to unplug the machine.
Three things to do this week:
- Audit your engineering workflow. If AI isn’t writing the majority of your code, find out why.
- Identify the slowest decision in your company. Measure how long it takes from question to action. Then compress it with AI.
- Consider your “no AI” list. Decide explicitly where AI must not make decisions. Share it with your executive team.
The gap between AI-native companies and everyone else is no longer widening. It’s accelerating.
What will you do about it?
