The Race for AI Developer Tools: Why Now Is the Time to Lean In
This summer, while much of Europe slowed down for vacations, something very different was happening in the world of developer tooling: the pace of innovation accelerated. GitHub Copilot alone released close to 100 new features and news points in just three months. In parallel, we have seen the emergence of "agentic" AI capabilities integrated directly into Visual Studio Code and GitHub.com—capabilities that move Copilot from being a helpful autocomplete tool into the realm of intelligent, task-driven collaboration.
I had the opportunity to test this new coding agent first-hand, and my impression is clear: we are entering a new phase where AI is not just assisting with typing speed, but reshaping the way we structure workflows, delegate routine development, and measure developer velocity.
Why This Matters for Leaders
For C-level executives, VPs of Engineering, and lead architects, the message is straightforward: the race for AI developer tools is fully on, and those who embrace it early will see compounded efficiency gains. This isn't about hype—it's about tangible shifts in how work gets done.
Here's a simple example. I asked the new GitHub Copilot agent to add an IAM user via AWS CDK/CloudFormation—a routine task. The agent completed it successfully, but it took six minutes to finish. An experienced engineer could complete the same task in less time.
But here's the nuance: a developer's work isn't just writing code. It's context switching, setting up their environment, preparing the right references, double-checking security policies, and shifting their focus from one domain to another. That six minutes of “AI waiting time” doesn't carry the same cognitive cost. In fact, the true promise of agentic AI is not about outperforming engineers in raw speed, but about offloading the repetitive, attention-draining tasks so developers can preserve focus for high-value problem solving.
From Incremental Gains to Workflow Redesign
Until recently, AI tools for developers felt like autocomplete “on steroids.” Useful, yes—but not transformative. The new wave of agentic AI tooling is different. It allows leaders and teams to think less about keystrokes saved and more about workflows redesigned:
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Delegation of boilerplate: Infrastructure setup, scaffolding, and testing templates can be largely automated.
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Standardization at scale: Consistent implementation of policies (IAM, security, logging) with less variance across teams.
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Time to creativity: Engineers spend less time on mechanical work and more time on the high-value design and problem-solving that drives differentiation.
The Opportunity Ahead
The acceleration we're seeing is only the beginning. As tools like Copilot evolve into full-fledged coding agents, the potential for reducing cycle times and increasing engineering impact grows exponentially. While I reference GitHub Copilot here, it's not exclusive to that ecosystem—it's simply the tool I've been following most closely and experimenting with for now. The broader trend is clear: AI development tooling, in many forms, is rapidly maturing, and the opportunity space is wide open.
For leaders, this is an opportunity not only to make teams faster, but to reimagine the developer experience altogether.
The organizations that lean into this shift will not just deliver code faster; they will create cultures where developers spend their energy solving meaningful problems rather than burning focus on boilerplate or context switching. And in a market where velocity is often the differentiator between success and stagnation, that cultural shift is everything.