Field CTOs are important in AI. I see the Field CTO as an Executive Developer Relations position.

“Executive” describes the scope and accountability of the work. It doesn’t mean becoming distant from developers or leaving the technical details to someone else. The role still has to understand how people build, where a workflow breaks, which architecture decisions create risk, and why a developer doesn’t trust what they’ve been asked to adopt.

Then it has to carry that truth into rooms where product direction, engineering strategy, customer commitments, and market narratives are decided.

When Developer Relations is reduced to awareness and Field CTO work to enterprise deals, both definitions are too small for AI. The field needs a leader who can connect technical education, engineering enablement, product feedback, customer reality, and executive decision-making without flattening any of them.

That is executive DevRel.

AI adoption is unpredictable in a way that forces technical leaders to work across several layers at once.

A developer may be evaluating an agent inside a repository. An engineering leader is asking how that agent changes review, security, maintainability, and team responsibility. Product needs to know whether a failed workflow points to a missing feature, a poor integration, an evaluation gap, or an incorrect assumption about the customer. An executive sponsor is trying to decide whether the organization can scale the system without creating risk it cannot explain.

Those are connected questions.

OpenAI’s 2026 interviews with enterprise leaders found that scaled adoption depends on workflow ownership, security and compliance involvement, evaluation, trust, and human oversight. Google Cloud’s 2025 DORA research reported that AI adoption was associated with higher throughput and product performance while still showing a negative relationship with delivery stability.

The surrounding engineering system decides whether AI acceleration becomes durable.

That is why a Field CTO in AI cannot survive on executive presence alone. The role needs enough depth to challenge an agent architecture, inspect an evaluation approach, question an integration boundary, and understand how generated code moves through review and CI. It also needs enough range to connect those details to organizational change, market positioning, product decisions, community trust, and business risk.

Go deep. Go wide. Know when each is required.

I think of Developer Relations as the relationship between a company and the people who use, evaluate, extend, and challenge its technology. In AI, that relationship carries more organizational weight because the product can change how teams write code, make decisions, access data, and assign responsibility.

The Field CTO makes that relationship bidirectional at an executive level.

External influence builds technical trust. It helps the market develop a more accurate mental model of the technology. It gives developers useful workflows and gives engineering leaders a way to evaluate adoption beyond a polished demo.

Internal influence keeps the company honest. It brings back the missing context behind a feature request, the repeated concern that appears across workshops, the integration problem that keeps slowing deployments, and the risk boundary customers cannot resolve on their own.

Neither direction works well without the other.

Public education without a path back into Product becomes broadcasting. Product feedback without technical education leaves the field to interpret complex technology alone. A strong Field CTO closes both loops.

I’ve spent much of my career helping technical ideas travel.

Sometimes that means giving a talk that helps developers understand a new workflow. Sometimes, it means running a workshop with engineering leaders who need to evaluate how AI changes code quality, review, governance, team behavior, and long-term maintainability. Sometimes it means listening closely enough to recognize that a technical question is carrying an organizational concern underneath it.

That range matters to me.

I’m a developer-to-engineering-leader person. I care about the developer working inside the codebase and the leader responsible for the system around their software products. I want to understand what the developer needs to trust the workflow, what the engineering leader needs to defend the rollout, and where the product still needs to mature before either side should commit.

Bottom-up developer trust can start adoption. Top-down confidence gives it the support, controls, and organizational clarity to survive. I don’t want to choose one audience and ignore the other because the real problem lives between them.

This is also why I think about Developer Relations as engineering enablement in AI. A talk, workshop, demo, community conversation, or open source artifact should help someone make a better technical decisions. It should also reveal where people are confused, where workflows are breaking, and what the company needs to learn.

In my work, the field is where technical truth becomes visible.

Technology evangelism has a useful place in this role, but the work has to carry more than enthusiasm.

An AI Field CTO should be able to explain:

That is the difference between promoting a capability and enabling responsible adoption.

The OpenAI Deployment Company is one visible example of the broader motion. Its embedded engineers work across use-case discovery, workflow redesign, building, testing, deployment, adoption, and feedback into the product. Companies may assign these responsibilities to different titles, but the operating need is the same: someone has to connect product capability to the conditions required for real use.

For me, technology evangelism earns influence when it helps people understand a system well enough to challenge it, improve it, and decide whether it belongs in their environment.

Field CTOs should bring more than a list of customer requests to Product.

A useful field signal includes the workflow, the people involved, the current system, the decision being made, the failure mode, the evidence collected, and the consequence of leaving the problem unresolved. That context helps Product and Engineering distinguish a recurring market need from one account’s preference.

It also improves prioritization.

“Customers want more control” is vague. A repeated inability to restrict an agent’s actions, trace a decision, reproduce a failure, or insert approval at a high-risk step is a product and architecture signal. The second description gives a team something it can investigate.

Field insight becomes valuable when it can change a roadmap decision, an evaluation suite, a reference architecture, an enablement program, or the company’s understanding of its market.

That requires a laser focus on patterns.

The field gives you a view that no single dashboard can provide.

You hear the same concern from a startup moving quickly with a small team and from an enterprise trying to coordinate security, platform, legal, and engineering. The constraint may show up differently, but the underlying pattern can be the same: unclear ownership, weak evaluation, missing workflow context, poor integration, or a gap between developer interest and leadership confidence.

A Field CTO has to notice those repetitions without forcing every customer into the same story.

That takes judgment. You need to know enough about an industry to recognize a structural constraint, enough about the product to understand what can change, and enough about engineering to separate a true architectural problem from an education or implementation gap.

This is one of the reasons the role has to be both broad and technically credible. Market pattern recognition is stronger when it comes from someone who can follow the problem all the way down.

The strongest Field CTOs in AI will be able to:

Some companies will place all of that responsibility in one executive. Others will distribute it across founders, forward-deployed engineers, solutions leaders, Product, and DevRel. Distribution can work when ownership and the feedback path are explicit.

Silence between those functions cannot.

If you’re building an AI company, ask a few direct questions:

Weak answers reveal a missing field layer.

I see the Field CTO as executive Developer Relations because AI companies need more than a technical spokesperson. They need a leader who can build trust outside the company, create movement inside it, and keep both connected to engineering reality.

That is the kind of work I’ve been building toward: developer to engineering leader, field to product, technical depth to market direction.