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What AI's Impact on Engineering Tells Us About Where Org Design Is Headed

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Brian Elliott

Executive-in-residence, Charter

Brian Elliott is Charter's executive-in-residence and CEO of Work Forward.

Apr 21, 2026 10:30 AM CUT

Illustration by Charter · Photo by Prapass Pulsub, Getty

Brian Elliott

Executive-in-residence, Charter

Brian Elliott is Charter's executive-in-residence and CEO of Work Forward.

Apr 21, 2026 10:30 AM CUT

The consensus going into 2026 seemed fairly clear: AI was going to hollow out software engineering. Amjad Masad, CEO of Replit, said at Charter's Leading with AI Summit that "in the fullness of time, I do think that software engineering as a role sort of disappears." Geoffrey Hinton, the Nobel laureate widely credited as a founder of deep learning, predicted that within a few years "there'll be very few people needed for software engineering projects."

Then there's the actual hiring data.

Software engineering job postings, some may be surprised to learn, have risen for six consecutive months. Developer job postings in the US are rising back to levels last seen more than two years ago, and data show open engineering roles are the highest they’ve been in three years—contrary to the near-existential dread in Silicon Valley and continued streams of layoffs at firms like Block, Oracle, and Meta.

Based on interviews with product, engineering, and HR leaders, I believe the picture is more complicated than either the optimists or the pessimists suggest. Hiring is up, but the skills, team structures, and workflows required for these roles are shifting fast. What engineering and product leaders are grappling with right now is a useful early signal of the broader talent and design challenges organizations and HR teams will face as AI’s footprint expands.

The Jevons paradox still applies

First, let’s unpack what’s happening with software jobs. The Jevons paradox is an economic concept which holds that cheaper resources don't reduce consumption; they expand their potential use and increase demand. BCG's research bears this out for software: when AI tools compress development timelines, organizations can accelerate roadmaps, greenlight projects that couldn't previously justify the cost, and extend engineering capacity into parts of the business that never had it. That requires more engineers, not fewer.

One important caveat: the growth is concentrated at the senior level. Entry-level hiring at major tech firms fell 25% from 2023 to 2024. What's expanding is demand for engineers with systems-level judgment, including people who can manage complexity, make architectural decisions, and take accountability for what gets shipped.

Redefining the product development teams

This is where the conversation becomes relevant for people leaders: the changes in engineering capabilities are impacting more than hiring and layoff decisions.

The old constraint was straightforward: Developers couldn’t build fast enough. Now, AI has largely addressed that for well-specified problems, and the constraints are how well product managers understand customer needs and how fast organizations can execute on the results.

AI tools substitute for implementation skills (coding, drafting, testing) while making judgment skills more valuable. Yet what Avi Goldfarb has called "opportunity judgment" (recognizing what to build or improve) and "payoff judgment" (deciding what to do with what the model produces) rarely come in a single human, making the right team structure all the more important.

Those shifts are upending how product development teams are built—the skills developers need even before they become managers, long-standing assumptions about headcount ratios across functions, and fundamentals of organization design, including shifts in roles, size and shape of team structures.

For one, many companies are rethinking the skills developers need. Katya Laviolette, chief people officer at 1Password, says skills assessments have shifted well beyond technical credentials to focus more on communication, collaboration, and problem-solving skills. "We're also wanting to find people who can influence leaders’ perspectives," she says. Universities are not keeping up, she says, teaching AI tools and coding fundamentals while“not helping us with curiosity, leadership, communication, analytical capability—all the soft pieces."

Long-held head count ratios, meanwhile, are also changing fast. Tamar Yehoshua, Atlassian's chief product and AI officer, recently said she was surprised to learn that Gamma, the AI presentation software company, has a developer–to-product manager ratio of roughly three to one. At companies like Google, where she and I both previously led teams, the norm ran closer to eight or 10 engineers per product manager.

Some companies are going further, redesigning roles and team structures. Some are merging roles into “builder” job titles, collapsing roles and expecting engineers to absorb product thinking, while asking designers and product managers to code.

Still, expertise is critical. Engineers have to be accountable when things go wrong—"responsible for the systems that build the systems," says Mike Brevoort, the principal architect at Mytra. That accountability doesn't dissolve because AI wrote the code.

Then there’s the longer-term question many are starting to voice: If entry-level engineering hiring is contracting, where does the next generation of deep expertise come from? Judgment is built on years of making and learning from mistakes at the junior level. Compressing that pipeline to save headcount today will create a capability gap that's much harder to close in three to five years.

Three gaps most organizations haven't confronted

Even with the right team size, skills, and shape in place, most organizations have not grappled with the challenges they face. They are:

Clarity about what to build. I talked with the chief product officer of one mid-sized tech firm where engineers had enthusiastically adopted AI coding agents that sped up the release of new features, but growth hasn't followed. Speed without a clear signal about what customers want produces waste at higher velocity.

Sales and marketing readiness. One early-stage software CEO told me his team compressed a three-month development cycle into days using AI coding tools. But marketing, sales, and customer support couldn't absorb the changes, and customers couldn't either. When handoffs aren’t clear or aren’t scaling at the same pace, accelerated engineering just moves the bottleneck from engineering to customer-facing functions.

Organizational agility at scale. Decreasing the ratios of engineers to product managers and designers leads to increasing numbers of smaller teams that need coordination. How to manage that isn’t yet clear. Block CEO Jack Dorsey and Sequoia's Roelof Botha propose leveraging AI as the connective tissue that replaces what managers do, eliminating almost all hierarchy, but by their own admission it’s an unproven theory. A less radical but better evidenced path is what authors Phil LeBrun and Jana Warner call the Octopus Organization, or distributed intelligence and decision-making at the team level that’s held together by clear priorities, trust, and accountability.

Three places to start

Revisit the hiring profile for technical roles. As Laviolette noted, curiosity, systems thinking, and the ability to work across functions now matter as much as technical credentials. In engineering candidates, she’s looking for analytical judgment, the ability to influence across disciplines, and comfort operating in conditions of rapid change.

Redesign team makeup, not just the headcount. Make sure the product managers and designers working with your engineers have the capacity and authority to determine what should be built, and to stop what shouldn't. Sunita Solao, former chief people officer at Upwork, says her team is actively rethinking how many developers should work with product managers.

Name the elephant in the room. HR business partners who are redesigning the organization are simultaneously anxious about what happens to their own roles, says Solao. Her advice is to put it on the table with leadership directly. "Letting it simmer in the background is not healthy," she says. Engineers who have leaned into AI tools are often energized by it. But HR teams are rightfully more apprehensive. Don’t avoid the conversation.

The role of the software engineer is changing, but hiring is going up, and the rumors of its demise are greatly exaggerated. What is disappearing is the excuse to avoid the harder questions about how the rest of the organization needs to change to take advantage of all the speed and capacity AI offers.

What else to read:

What software engineers will do when AI writes all the code
Why your AI adoption strategy is stalling—and what to do instead
AI’s impact on jobs is a leak, not a flood

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