Sriram Krishnan, a partner at Andreessen Horowitz, described his personal coding arc in terms that many senior technical leaders recognized: he went from writing code daily, to writing almost none, to now generating substantial amounts of code using LLMs. The pattern is common. And it surfaces a question that companies evaluating fractional CTO engagement sometimes ask: if senior technical leaders were already moving away from writing code before AI, what does AI actually change about the role?
The answer is: less than you might expect. That turns out to be informative.
quadrantChart title Where fractional CTO value concentrates x-axis Automatable --> Human judgment required y-axis Low business impact --> High business impact quadrant-1 Own this quadrant-2 Augment with AI quadrant-3 Delegate or automate quadrant-4 Minimize Board technology reporting: [0.85, 0.9] Vendor contract negotiation: [0.82, 0.86] Architecture review: [0.78, 0.82] Team accountability: [0.8, 0.74] Sprint coordination: [0.52, 0.52] Code generation: [0.18, 0.28] Documentation: [0.3, 0.32]
What Changed and What Didn’t
AI tools changed the cost of generating code. They didn’t change what makes a technology organization perform well over time.
The things that make a technology organization work — coherent architecture, vendor relationships that don’t drift into misalignment, an engineering team that knows what it’s building and why, a board that understands the technology risks and can make decisions with that understanding — none of those are functions of how fast code gets written. They are functions of judgment, continuity, and accountability.
A fractional CTO who was primarily valuable because of their ability to write production code was probably in the wrong role to begin with. The role has always been about something else.
What Fractional CTO Engagement Is Actually For
The fractional CTO serves three functions that AI does not replace:
Strategic technology judgment. Someone has to decide what to build, what to buy, and what to shut down. Those decisions require context about the business strategy, the competitive landscape, the vendor market, and the technical debt profile of the existing platform. AI tools can inform those decisions. They can’t make them, because they don’t have accountability for the outcome.
Vendor and team accountability. The engineering vendors, contractors, and development teams that most mid-market companies rely on do their best work when there is a technically competent person in the room who can evaluate their output, hold them to commitments, and catch problems before they compound. A fractional CTO is that person. No AI tool currently substitutes for executive-level accountability in a vendor relationship.
Board and executive translation. Technology decisions have business consequences, and most boards and executive teams don’t have the context to evaluate those decisions without translation. The fractional CTO converts technical complexity into business language — not as a one-time presentation exercise but as an ongoing function that ensures the organization is making informed technology decisions rather than ratifying choices they don’t fully understand.
The Embedded Leadership Lesson
At Oakwood Worldwide — the largest corporate housing company in the United States at the time, with over 80 applications, more than 100 developers, and global operations — my role as enterprise architect was not primarily about technical complexity. It was about providing the consistent, accountable decision-making function that kept a large, distributed technology organization from drifting toward siloed choices that would compound over time into a remediation problem.
The CIO I worked with understood this clearly. He didn’t need someone to write more code. He needed someone who would arrive at every architecture decision with the same framework, represent that framework to the board, and translate technology choices into the business terms that the executive team could act on. A consistent and steady driving force — those were his words — with the right people on the team.
That function doesn’t change when AI writes code faster. When development velocity increases, the judgment about what code to write matters more, not less. Speed amplifies both good decisions and bad ones.
What This Means for Companies Evaluating Fractional CTO Engagement
The companies that get the most from fractional CTO engagement are not the ones primarily looking for technical implementation capacity. They are the ones that recognize they have a judgment and accountability gap — a technology function that generates output but lacks the executive leadership to ensure that output aligns with business direction.
AI tools have reduced the cost of technical implementation. They have not reduced the cost of getting the strategy wrong, committing to the wrong vendor for three years, or building a platform that doesn’t serve the business it’s supposed to enable. Those costs are higher in an environment where development velocity is faster — because speed of execution amplifies the consequences of strategic errors.
The fractional CTO’s value in an AI-first development world is the same as it has always been: judgment, accountability, and the ability to ensure that technology serves the business rather than running parallel to it. The tools change. That requirement doesn’t.
