AiThority Interview with Rob Whiteley, CEO of Coder

Rob Whiteley, CEO of Coder chats about the future of coding and software development in an AI-centric ecosystem in this AiThority interview:

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Hi Rob, tell us about yourself and more about Coder’s recent funding, including the experience and story behind it.

I’m CEO of Coder, a solution which helps enterprises run secure, governed development environments in the cloud. This space is exploding in popularity as enterprises need to give safe access to powerful AI agents without ceding control..

We recently raised a $90 million Series C funding led by KKR, with participation from QRT, Uncork Capital, and other existing investors. What made this round different is that KKR and QRT aren’t just investors — they’re customers. They’re running Coder in their environments today. They invested because they see firsthand that AI in software development demands strong infrastructure, governance, and control. That conviction doesn’t come from a boardroom. It comes from production.

How is the market shaping up when it comes to AI-powered development features? What are end users seeking more of?

The market is getting more serious about incorporating AI. Early on, the focus was on trying the newest coding assistant or agent. Now, 61% of engineering teams use autonomous coding agents. Nearly three-quarters of these teams want straight from experimentation to production. The market quickly evolved from AI-as-auto completion to AI-as-teammate.

Users want agents for speed and a better developer experience, but enterprise leaders still need consistency, auditability, and control. Yet only 30% of them do so on infrastructure designed for AI agents. They fail to put governance in place to approve tools, audit activity, trace tokens, and enforce clear guardrails around how models and agents interact with code. That combination — developer velocity with enterprise governance — is exactly what Coder is built for.

What do you think the future of coding and software development will look like as AI takes center stage in development workflows?

AI is going to expand who can build. Software development will no longer be limited to traditional engineers — analysts, data scientists, and other employees will increasingly create software and automation using AI tools. That’s a meaningful shift.

As an example, one of our large financial services customers entered 2026 with 800 different applications. They had a goal to simplify that to 200. Then they rolled out coding agents across the firm. Now they have 10,000 applications. Productivity is up, but so is sprawl.

This raises the stakes for the environment where that development happens. As AI becomes embedded in how code gets written, companies will need governed workspaces where approved tools, access policies, and security controls are already in place. The future is faster, more accessible development with a much stronger emphasis on reproducibility and control.

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How do you ensure AI and agentic safety when developing tools for modern tech teams?

Safety has to be structural, not optional. AI is extremely entrepreneurial and will find a way to solve a problem — even if it introduces risk in doing so. The key is to limit agents to the tools they need to accomplish a task. No more, no less.

That means giving enterprises control over where development environments run, which models and tools are approved, what data can be accessed, and what agents are allowed to do.

It also means full visibility. Companies need audit trails, access controls, policy enforcement, and clear boundaries between development systems and sensitive environments like production. Agentic safety is about ensuring autonomy within the rules the enterprise defines.

Think of it this way. You need to be able to track every agent, tool call, and prompt with proper identity. It’s hard to do at scale if it’s not built-in from the beginning.

What privacy safeguards should be put in place as AI becomes mainstream?

The most important safeguard is knowing where your data goes. Enterprises need clarity on where code, prompts, and model interactions are processed, who can access them, and what gets retained. That visibility has to be at both the human and agent level. Least-privilege access, approved models, and standardized environments make this manageable. Privacy gets harder when every developer is running their own stack. It becomes tractable when everything runs inside a governed, managed system.

If all that seems too daunting? Start simple. Shut interact access off and limit agents to read-only. Let the human author the solution from there.

Five thoughts on AI and the future of technology before we wrap up?

1. AI is expanding who gets to build. Software development is no longer limited to software engineers. Analysts, data scientists, and other employees are increasingly using AI tools to build applications, workflows, and automated systems.
2. Infrastructure is more important than many expected. When AI shifts from testing to everyday use, companies need secure, standardized environments that can handle work at a larger scale.
3. Governance is what separates real AI adoption from shadow AI. The companies that benefit most aren’t the ones moving fastest — they’re the ones giving teams access to AI with clear rules, visibility, and consistency built in.
4. Agentic systems raise the bar for security in ways most enterprises haven’t fully reckoned with yet. As AI tools take on more autonomous work, identity, permissions, and auditability become non-negotiable, not nice-to-haves.
5. This shift will determine enterprises’ competitive advantage. We saw this 20 years ago with the shift to cloud computing. Companies that embraced cloud-native established competitive advantage. AI drives the next chapter. AI-native companies will outcompete the market.

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