Vibe coding is Topgolf. Production is Torrey Pines.

Vibe coding is rapidly changing who can build software and how quickly an idea can become a functioning application. Using natural-language prompts and AI-assisted development tools, people can translate concepts into prototypes without mastering every element of traditional software engineering.

The experience reminds me of Topgolf.

Topgolf creates a carefully curated, technology-enabled environment where almost everyone can feel capable. The tee is automated. Performance is tracked. Wind, water, sand, and other hazards are largely virtual. You can experiment, compete, and receive immediate feedback — all from the comfort of a lounge-like setting.

Vibe coding can create a similar sense of confidence. You describe what you want, AI helps build it, and a working application begins to emerge. The demonstration succeeds, colleagues are impressed, and momentum builds.

Then comes the inevitable question: “How quickly can we put this into production?”

That is when the game moves from Topgolf to Torrey Pines.

On a championship course, the controlled environment disappears. You must account for shifting winds off the Pacific, hidden bunkers, changing pin placements, difficult terrain, spectators, and countless other variables. Success requires more than the ability to strike the ball. It requires course knowledge, preparation, situational awareness, discipline, and the ability to adjust when conditions change.

Production technology environments are no different.

An application that performs well in a controlled setting must now contend with scale, cybersecurity, data quality, privacy, integration, resilience, accessibility, regulatory requirements, technical debt, and unpredictable user behavior. The number of variables expands exponentially — and every variable can affect performance, trust, cost, and organizational reputation.

Across my career as a CIO and CTO, I have seen promising technology initiatives encounter difficulty not because the underlying idea lacked value, but because the organization underestimated what it would take to operate that capability reliably at scale. AI accelerates development, but it does not eliminate operational complexity.

That distinction matters because vibe coding is not simply another developer productivity tool. It changes who can participate in creation. A business leader who previously described a need and waited for a development queue can now sit beside an AI assistant and begin shaping the solution directly. That is a meaningful and welcome shift. It creates faster learning cycles, brings domain expertise closer to the product, and gives the business a more active role in technology delivery.

But democratizing development also democratizes responsibility. The person who can create a compelling application in an afternoon may not yet have the experience to recognize an insecure dependency, an ungoverned data source, a fragile integration, or a design that becomes prohibitively expensive at scale. A successful demonstration answers whether something can work. Production requires answering whether it should operate, how it will operate, who will own it, and what happens when it fails.

This is where experienced technology professionals remain essential.

A skilled enterprise architect is like having Tiger Woods help you read the course: someone with a mental model of the topology, dependencies, and conditions most likely to affect the outcome. The architect can see the dogleg beyond the tee box — the downstream consequences that are invisible during a prototype.

The course ranger resembles the change manager, helping teams navigate competing priorities, shared resources, release schedules, and operational conflicts. Cybersecurity professionals manage the equivalent of those pesky paparazzi trying to get inside the ropes, while also preparing for threats far more consequential than a distracting camera. Data teams act as the performance analysts, measuring behavior, outcomes, crowd reaction, and whether the solution is actually producing value. Platform and operations teams ensure the course remains playable when demand surges or something unexpected happens.

These professionals are not standing in the way of innovation. They are helping the organization play the real course successfully.

Before promoting a vibe-coded application into production, leaders should review a simple production-readiness scorecard:

This review does not need to become a six-month obstacle course. Governance should be proportionate to the application’s risk, reach, and potential impact. A low-risk internal productivity tool should not face the same controls as an application making consequential decisions about customers, employees, or vulnerable populations.

The answer is not to force every vibe-coded experiment through the heaviest enterprise process. That would squander much of the speed and creativity AI makes possible. Instead, organizations need risk-based pathways: a safe practice range for experimentation, a clearly marked route for internal tools, and a more rigorous qualification process for applications that touch sensitive data, critical operations, external users, or consequential decisions.

Technology leaders can make those pathways easier to navigate by providing approved AI tools, reusable components, secure development environments, automated testing, standard integration patterns, and transparent criteria for production readiness. In other words, build guardrails and paved roads rather than relying exclusively on gates. When the safest path is also the easiest path, governance becomes an accelerator.

The goal is disciplined acceleration.

The business also has to accept a different role. Vibe coding should not mean throwing an AI-generated application over the wall to technology once the demonstration is complete. If business teams help create these capabilities, they must remain hands-on collaborators and accountable owners. They understand the domain, intended outcomes, acceptable errors, and human consequences better than anyone. Technology brings the architecture, engineering, security, data, and operational disciplines required to make that capability sustainable. Production readiness is a shared responsibility.

This partnership represents an entirely new organizational muscle. Data and AI literacy provide the foundation, but literacy alone is not transformation. Leaders must learn how to evaluate AI-generated work, challenge confident outputs, understand where automation requires human judgment, and make informed tradeoffs among speed, risk, cost, and value. Technology teams, in turn, must learn to engage earlier and more collaboratively so they are not perceived as impediments arriving at the end of the process.

I am a cautious optimist about AI. Vibe coding can democratize development, accelerate experimentation, and bring business and technology teams closer together. It can also help reposition business professionals from passive recipients of technology to active participants in creating it.

But confidence developed in a controlled environment should not be confused with production readiness. The easier development becomes, the more important it is to strengthen organizational literacy about architecture, data, cybersecurity, scale, and responsible ownership.

The central leadership question is therefore not whether organizations should permit vibe coding. That debate will soon be overtaken by adoption. The better question is how organizations will preserve its creative energy while establishing the discipline necessary to earn trust at scale. Leaders who answer that question well will move faster because their teams will know where experimentation is encouraged, when additional expertise is needed, and what evidence is required before a capability reaches customers, employees, or mission-critical operations.

Perhaps we will eventually reach a world in which AI can immediately transform a vibe-coded idea into a secure, scalable, resilient production capability — making 18 holes at Torrey Pines feel as approachable as an evening at Topgolf.

We are not there yet.

Until we are, organizations must pair AI-enabled speed with sound system hygiene, architectural discipline, testing, governance, and risk management. The objective is not to slow innovation. It is to ensure that what we accelerate can survive — and succeeding on — the real course.

Together, we will get there responsibly.