AI Is Reshaping Engineering Teams. Here’s What Smart CTOs Are Doing About It.
The smartest companies aren’t scaling headcount—they’re scaling leverage.
It’s not hyperbole. AI is rewriting the playbook on how we think about engineering teams—how we staff them, where we hire, and what we even expect them to do.
For growth-stage startups, especially, the implications are massive. Not only is the economics of team size shifting, but so is the composition and structure of the team itself. If you’re still scaling engineering headcount the same way you did two years ago, you’re doing it wrong.
Let’s unpack what’s changing—and what forward-thinking leaders are doing about it.
🧠 AI Has Reduced the Need for Headcount (But Raised the Bar for Thought)
The traditional logic of “more engineers = more velocity” has always been imperfect. But with LLMs and automation tools embedded in day-to-day workflows—Copilot, Cody, Cursor, etc.—the marginal productivity of individual engineers has exploded.
Use of tools like Cursor and GitHub Copilot in our engineering team has made a clear increase in our velocity, automating the creation of boilerplate (but time-consuming) code. This has allowed us to shift our focus to the tasks that require domain expertise and creative problem-solving.
Eric Whitten, Head of Engineering at Twentyeight Health
A single product engineer today can ship at a velocity that would’ve required a team just 18 months ago.
“The tech is transformational, it is real. So headcount needs… I don’t know if they will be the same as they were two years ago.”
— Naga Ravi Vadrevu, CTO at Wonderschool
The implication? Don’t think in terms of how many engineers you need. Think in terms of how much product velocity or platform maturity you need, and then back into what team shape will get you there.
“Our definition of productivity and impact is being forced to evolve with the times past more primitive standards (lines of code, raw output, even features completed).
By the same token, creativity, problem-solving, resourcefulness, passion for the craft of inventing solutions—these talents were already at a premium before. From our vantage point, they are now at an even higher premium, supercharged by new tools. It’s more important than ever to find the right talent and the right talent partners.”
— Luis Castaneda, CTO at Credit Mountain
The bar is rising for thoughtful engineering, not raw output. This new era rewards experience, systems thinking, and taste.
“The hard part about software has never been building things—it’s been choosing what to build and then how it should be built. LLMs go off the rails with ambiguous requests so specificity is your friend.
— Mike Sukmanowsky, CTO at Elvex
✍️ Editing > Authoring: The New Craft of Software Development
As AI handles more of the first draft, engineers are shifting from authors to editors of code. The shift in tools is also reshaping the muscle memory of developers themselves.
“It’s magical to have an agent generate a large changeset for you, but humans are still responsible for the systems we’re building.
As we all spend more time "editing" code versus writing it, it's all the more important to be able to read code, "run it" in your head and spot areas for improvement. There's also an aspect of taste that you develop over building a lot of stuff over time. You can more easily spot an abstraction that's leaky or won't scale well. Don't be in a position where you can't really understand what an LLM has written. You should have strong opinions, informed by experience.”
Mike Sukmanowsky, CTO at Elvex
This shift requires a higher level of judgment and experience, particularly among juniors. It’s not enough to generate output—you have to understand if it’s good, scalable, and maintainable.
🔧 Engineers Are Moving “In Between the Seams”
One of the more interesting shifts happening is in the kind of work engineers are gravitating toward—or being asked to focus on.
“Engineers will spend most of their time in between the seams—not on products.”
— Naga Ravi Vadrevu, CTO at Wonderschool
Translation: there’s less work in building isolated features or flows. The AI era favors modular, composable systems—many of which are stitched together from OSS packages, APIs, or platform tools. The real value? Comes from platform thinking: designing the glue that connects the pieces.
"AI is shifting the value equation in engineering. It’s no longer just about building—it’s about designing systems that unlock leverage, speed, and impact.”
— Greg Pellegrino, VP of Engineering at Electric AI
“Engineers that are intentional in their decisions about how to architect their systems will bring the most value, with AI filling the gaps in direct competition with more execution-only focused engineers.”
— Santi Herrero Bajo, Co-Founder & CTO at EdPuzzle
AI is challenging engineering teams to stop coding by default and start thinking in systems. That shift is opening doors to more inclusive and collaborative problem-solving.
— Shinji Kim, Founder and CEO, Select Star
This is fueling a new type of engineering org, where platform engineers become core. They’re the architects of system resilience and developer velocity.
And at a macro level, this transformation offers more than just technical leverage—it marks a paradigm shift for the industry itself:
“AI is the great equalizer of our time… it’s not just progress — it’s a new beginning!”
— Paul Abrudan, Director of Engineering at Pawlicy
In short, we’re not just moving faster. We’re changing how engineering gets done, and who gets to do it.
📍 Where You Hire Now Depends on What You Need
With this split in mind, the “where” question becomes easier to navigate.
Product Engineers: Embedded with PMs, working on user-centric flows. Proximity to customers and stakeholders matters—San Francisco, New York, or on-site.
Platform, Infra, Data Engineers: Distributed. Global. These roles are less about user research and more about deep systems thinking, performance tuning, and building scalable infrastructure.
“Where would I hire them? It really depends on what I'm trying to solve. I might have product engineers more in San Francisco sitting with my product managers, trying to do growth kind of work, trying to really think about the customer, go visit the customer, understand their problems, ship problems that are very, very relevant to the customers. But my platform engineers, infra data, they could sit anywhere. I don't, I don't think it matters as much where they are. So when I'm hiring, I use this framework to guide me on where I want to hire.”
— Naga Ravi Vadrevu, CTO at Wonderschool
This is where nearshore talent becomes incredibly powerful. In Remotely’s network, senior platform and infra engineers across LATAM are delivering at par with Silicon Valley talent—and doing so at a fraction of the cost, often in the $79K–$90K range for IC4–IC5 engineers.
The best part? They stay. Unlike traditional staff augmentation models, Remotely engineers are mission-aligned, embedded in your team, and compensated transparently.
⚠️ The AI Paradox: Everyone Has Access, Few Know What to Do With It
With powerful tools now available to everyone, the challenge isn’t access—it’s execution.
“Non-engineers assume you can ask AI to do anything. Engineers are overwhelmed trying to keep up.”
— Naga Ravi Vadrevu, CTO at Wonderschool
This has created a new kind of friction. Founders misdiagnose problems as “AI problems” and chase silver bullets. Engineers get buried under waves of toolkits and half-baked solutions.
AI is not plug-and-play. It’s not magic. It requires framing the right problem—and knowing when not to apply it.
🧩 What High-Performing Teams Are Doing Differently
Across the top startups we support, we’re seeing a new model emerge:
Smaller, Smarter Core Teams: Fewer engineers, but more experienced. They know how to wield AI tools without drowning in them.
Investing in Platform Engineering: These engineers aren’t building user features. They’re building leverage.
AI-Enhanced, Human-Centric Workflows: Smart teams embed LLMs into their pipelines, but keep humans in the loop for validation, design, and iteration.
Geo-Differentiated Hiring: Product-close roles may remain local; platform and data roles tap into global talent—especially in LATAM.
Relentless Focus on Problem Definition: They spend more time defining the right problem than building the wrong solution.
Final Thought: Don’t Just “Hire Engineers” Anymore
The real shift is this: You’re not hiring engineers. You’re hiring leverage.
That might mean fewer hires, smarter hires, or nearshore hires. It might mean investing in tooling instead of headcount. But whatever your path, start with this premise:
You don’t need more code. You need better systems, clearer thinking, and people who know how to connect the dots in a world where the building blocks are getting easier—but the architecture is getting harder.
This is the end of code monkeys.
The era of engineers as task-executing ticket machines is over. The future belongs to engineers who can reason deeply, design resilient systems, wield AI as a collaborator, not a crutch, and solve the right problems with taste, context, and conviction.
The good news? With the right team and the right model, that future’s already here.