The CEO Who Codes: Why the Future of Business Leadership Means Building with AI
The era of the executive who simply delegates technology decisions is ending. In 2026, the leaders who will define their industries are the ones who build alongside AI, not the ones who wait for a briefing deck about it. Matt Britton, CEO of the AI-powered consumer intelligence platform Suzy and bestselling author of Generation AI, is among a rare breed of Fortune 500-serving executives who don't just talk about artificial intelligence on conference stages. He builds production software with it, every day, that is already being used by some of the world's largest brands.
In a recent conversation with entrepreneur Bryan Silverman Suzy’s COO, Britton laid bare his methodology for using AI as a development partner. The discussion revealed a philosophy that challenges everything enterprise leaders thought they knew about who gets to build technology, and how. It also surfaced a growing divide in the business world: the leaders who are genuinely AI-native versus the vast majority who are still, as Britton puts it, "sprinkling AI on old workflows."
The implications extend well beyond software development. They point to a fundamental shift in what it means to lead an enterprise in the age of artificial intelligence.
Why Most Executives Are Getting AI Wrong
According to Gartner, more than 80 percent of enterprises will have deployed generative AI-enabled applications in production environments by the end of 2026, up from less than 5 percent in 2023. But there is a critical distinction between deploying AI and understanding it at a visceral level. Most C-suite leaders fall into the former category. They approve AI budgets, sit through vendor demos, and nod along at board meetings. Very few of them have ever opened a terminal, pasted an error log into an AI model, and pushed through until something worked.
This is the gap Britton has been closing for years, and it is what makes his perspective as an AI keynote speaker fundamentally different from the majority of voices on the conference circuit. In his keynotes to enterprise audiences across five continents, Britton frequently references a stark observation: 99 percent of people who talk about AI on stage have never built anything with it. That claim is not hyperbole. It is the defining line between theoretical AI leadership and applied AI fluency.
The conversation with Silverman underscored this point repeatedly. Britton described a working style where the CEO is not reviewing architecture documents or approving sprint plans. Instead, the CEO is the one defining outcomes in plain language, directing AI models to generate the code, diagnosing errors from logs, and shipping working software that Fortune 500 clients are already using. It is a radically different model of executive leadership, and one that the data suggests is becoming essential.
The Rise of the AI-Native Executive
Andrej Karpathy, a founding member of OpenAI, coined the term "vibe coding" in early 2025 to describe an approach where builders describe software in natural language and let AI write, refine, and debug the code. The concept has since exploded. According to Stack Overflow's 2025 Developer Survey, 65 percent of developers now use AI coding tools at least weekly. Capgemini's UK CTO has predicted that 2026 will be the year "AI-native engineering goes mainstream." Gartner forecasts that by 2030, 80 percent of organizations will have transformed their large developer teams into smaller, AI-enhanced units.
But what happens when the person doing the vibe coding is not an engineer at all? What happens when it is the CEO of a company serving Fortune 500 brands?
That is the model Britton described in his conversation with Silverman. The approach centers on a few core principles that upend traditional assumptions about who can build enterprise-grade technology.
The first principle is outcome over technique. Britton starts every project by defining the business outcome in plain language. What problem needs solving? What should the end user be able to do? The AI handles the "how." This is not a shortcut or a workaround. It is a fundamentally different paradigm for how technology gets built, one where domain expertise and customer intimacy matter more than syntax knowledge.
The second principle is relentless forward progress. Things break constantly. Blank screens, authentication failures, server errors. The methodology demands pushing through each barrier with the AI model rather than stopping to manually diagnose every issue. Copy the error log. Paste it into the model. Get the fix. Move to the next problem. This cycle happens dozens of times in a single session.
The third principle is parallelization. Britton works with multiple AI tools simultaneously, keeping several lines of progress moving at once rather than working sequentially through a single task. While one AI agent is generating code, another is debugging an infrastructure issue, and a third is helping rethink the architectural approach. As Britton told Silverman, the builders who will thrive in this era are the ones who can orchestrate multiple AI agents toward a shared outcome.
What Separates Builders from Talkers in the AI Era
In his keynote presentations to thousands of business leaders annually, Britton draws a clear line between three stages of AI adoption. The first stage, where roughly 90 percent of professionals currently sit, involves using AI as a tool: asking ChatGPT a question, generating a document, summarizing an email. The second stage, occupied by perhaps 9 percent, involves using AI for genuine automation of workflows and processes. The third stage, where fewer than 1 percent operate, involves deploying AI agents that can reason, execute multi-step tasks, and deliver outcomes with minimal human intervention.
Britton operates at that third stage. And the conversation with Silverman illustrated why this distinction matters so profoundly for business leaders.
When a CEO has personally built and shipped AI-powered software that Fortune 500 companies rely on daily, the quality of their strategic thinking changes. They understand what AI can and cannot do, not from a McKinsey report, but from direct, repeated experience with the technology's capabilities and limitations. They know what it feels like when an AI-generated solution looks elegant but contains a subtle flaw. They understand the difference between a demo and a production system. They can evaluate vendor claims with the precision of someone who has been in the trenches.
This applied knowledge creates a compounding advantage. Every product Britton builds deepens his understanding of how enterprises can leverage AI more effectively. Every error he debugs sharpens his intuition about where AI projects succeed and where they stall. That feedback loop between building and advising is what gives his keynotes and his work with major brands a credibility that pure theorists simply cannot replicate.
As Britton has framed it in numerous speaking engagements, it is the difference between visiting the gift shop at the entrance of a national park and hiking deep into the woods. Most people in the AI conversation are still in the gift shop. They have bought the hat and the postcard. They have not actually been on the trail.
The Mindset Shift Enterprise Leaders Need to Make
The conversation with Silverman also revealed what Britton expects from the teams around him, and by extension, what he believes every enterprise leader should demand from their organizations.
The expectation is not that every employee becomes a software developer. Rather, it is that knowledge workers across the enterprise become comfortable living inside AI tools. They should be able to describe what they want to build, interpret error messages, feed logs and screenshots back to AI models, and keep multiple workstreams moving in parallel. They should approach problems with the relentless bias toward action that AI-native workflows demand.
What Britton described as unacceptable is what he calls "sprinkling AI on development." This means using AI autocomplete features while still operating as though it were 2019. Still writing lengthy specification documents by hand. Still coding everything manually. Still getting blocked by infrastructure challenges that an AI model could solve in minutes. Still waiting for someone else to provide direction.
This perspective aligns with broader workforce trends. Research from the Federal Reserve found that workers using generative AI saved an average of 5.4 percent of their work hours weekly, with frequent users saving over nine hours per week. But these productivity gains only materialize when workers fundamentally change how they approach their jobs, not just bolt an AI tool onto existing processes.
The shift Britton advocates is from knowledge workers to problem solvers. In the economy that is emerging, the value of knowing how to do something is declining rapidly. AI can handle execution. The value that remains, and grows, is the ability to identify the right problems, define the desired outcomes clearly, and orchestrate AI systems to deliver solutions. That is the leadership skill of the next decade.
What This Means for the Future of Enterprise Technology
The implications of Britton's approach extend well beyond his own companies. If a CEO with no formal engineering background can build production software that Fortune 500 brands depend on, what does that mean for how enterprises structure their technology teams? How they evaluate leadership candidates? How they think about competitive advantage?
Gartner predicts that 40 percent of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5 percent today. The organizations that capture this opportunity first will not be the ones with the largest engineering departments. They will be the ones whose leaders understand, from personal experience, what AI-native development looks like and how to deploy it at scale.
Britton's conversation with Silverman was not a theoretical exercise. It was a window into how one of the most AI-fluent CEOs in the Fortune 500 ecosystem actually operates, day in and day out. The tools he builds are in production. The clients using them are among the world's most recognized brands. The methodology is proven and repeatable.
For enterprise leaders watching from the sidelines, the message is clear. The gap between those who build with AI and those who merely talk about it is widening every quarter. And the market rewards builders.
Key Takeaways for Business Leaders
Adopt an outcome-first mindset: Define what you want AI to accomplish in plain language before worrying about technical implementation. Domain expertise matters more than coding syntax.
Embrace relentless iteration: Expect failures, error messages, and blank screens. The methodology requires pushing through dozens of obstacles per session, not abandoning the effort after the first error.
Parallelize your AI workflows: Work with multiple AI tools simultaneously rather than sequentially. The speed advantage compounds when you orchestrate several agents toward a shared outcome.
Reject "AI sprinkling": Using AI as autocomplete for existing workflows misses the transformation entirely. Rethink processes from the ground up with AI as the primary builder.
Demand applied AI fluency from leadership: The executives who will drive competitive advantage are those who have personally built with AI, not those who have merely approved AI budgets.
Frequently Asked Questions
What is vibe coding and why does it matter for business leaders?
Vibe coding is an approach to software development where builders describe what they want in natural language and let AI models write, debug, and refine the code. For business leaders, it represents a fundamental shift in who can create technology. Domain experts with deep customer knowledge can now build production software without traditional coding skills, collapsing the distance between business strategy and technical execution.
Can a CEO realistically build enterprise software with AI?
The evidence says yes. Matt Britton, a CEO with no formal engineering background, builds production software using AI tools that Fortune 500 brands use daily. The key is treating AI as the engineering team while the leader serves as product owner and operator, defining outcomes, interpreting errors, and driving relentless forward progress. This model is becoming increasingly viable as AI coding tools mature.
How is AI changing what enterprise leaders need to know about technology?
AI is shifting the required competency from technical knowledge to outcome definition and AI orchestration. Leaders no longer need to understand every line of code. They need to clearly articulate business problems, direct AI systems toward solutions, and evaluate results. Gartner predicts that by 2030, 80 percent of organizations will transform large developer teams into smaller, AI-enhanced units, making this orchestration skill essential for competitive leadership.
What does "sprinkling AI on development" mean and why is it a problem?
The phrase describes organizations that adopt surface-level AI features, like autocomplete or text generation, without fundamentally rethinking their workflows. The problem is that this approach captures minimal value from AI. True transformation requires rebuilding processes with AI as the primary builder, not an add-on. Leaders who only sprinkle AI on existing workflows risk falling behind competitors who have reimagined their entire approach.
The Builders Will Win
The conversation between Matt Britton and Bryan Silverman offered more than a playbook for AI-native development. It offered a preview of how the most effective business leaders will operate in the coming decade. The executives who thrive will not be the ones who can recite AI statistics at a board meeting. They will be the ones who have personally shipped AI-built software, who understand the technology's strengths and limitations from direct experience, and who can lead their organizations through the transformation with credibility that only comes from doing the work.
To bring these insights to your next leadership event, explore Matt Britton's keynote platform or contact his team directly. For a deeper exploration of how AI is reshaping consumer behavior and business strategy, Britton's national bestseller Generation AI provides a comprehensive roadmap for leaders navigating this era.
