A new survey of engineering leaders from three major tech economies reveals a surprising fact about the rise of artificial intelligence in software development: AI isn’t eliminating engineering jobs on a large scale. Instead, it is increasing the value of strong performers while making weaker engineers far more vulnerable.
The findings come from the “AI Workforce Transformation” report, which reflects the experiences of 400 engineering executives in the U.S., India, and China. According to these leaders, the shift toward AI-assisted development is changing talent expectations, hiring priorities, productivity norms, and what defines a high-performing engineer.
What emerges is a picture of an industry in transition: increasing productivity, growing demand for AI-native skills, and a widening performance gap that could change engineering teams for years to come.
AI Is Boosting Strong Engineers and Undermining Weaker Ones
One of the clearest findings is the large gap between high-performing engineers and those seen as weaker contributors. According to the survey:
- Nearly three-quarters of leaders (73%) say a strong engineer is now worth three times their total compensation.
- A majority (59%) believe weaker engineers provide little or no net value in the AI era.
The message is straightforward: as AI increases productivity, the divide between top talent and everyone else is growing. Strong engineers who combine core skills with AI expertise are becoming key players within teams, while those who struggle are quickly being left behind.
Engineering leaders say this gap is not just about AI skills. It’s about how well individuals can combine human judgment with machine-generated output and how quickly they can adjust to new tools and workflows.
The report highlights a rising belief: engineering excellence is no longer just about coding ability. It involves knowing when to rely on AI, how to assess its output, and how to manage autonomous systems without losing control of quality or design.
AI Increases Productivity by 34% but Changes the Nature of Work
Across the surveyed organizations, leaders reported an average productivity increase of 34% when AI tools are used in daily work. However, the impact varies.
Developers who embrace code generation tools, automated testing systems, or AI assistants are experiencing significant efficiency gains. Those who resist or have trouble using these tools often fall behind.
The most common AI applications mentioned by teams include:
- Code generation, used by 83% of engineering teams
- Testing, QA, and code review, used by 61%
- Autonomous agents for tasks like debugging, documentation, and optimization
These trends show that AI is not just supporting developers; it is taking on repetitive, time-consuming work that once defined many engineering jobs.
As a result, leaders say they now value engineers with strong problem-solving skills, product instincts, architectural thinking, and design judgment more than ever. The industry is shifting away from assessing developers based on how quickly they can code and toward measuring how effectively they can design, integrate, and manage systems built with AI.
AI Is Not Causing Mass Layoffs; Most Companies Plan to Hire More Engineers
In the midst of ongoing industry concerns about job loss, the report highlights something unexpected: despite cost pressures and rising automation, most companies still intend to expand their engineering teams.
An impressive 85% of engineering leaders expect headcounts to remain the same or increase over the next three years.
This contradicts the well-publicized layoffs that have made headlines this year. While large companies have indeed reduced engineering roles — including one major firm that cut over 600 engineering jobs in one round — the survey indicates these layoffs are more about restructuring and role adjustments than widespread replacement of humans by AI.
Engineering executives stress that while AI can write code, it still cannot replace complex architectural thinking, judgment in uncertain situations, cross-functional leadership, or strategic decision-making. Instead, teams are being restructured with a new emphasis on hiring AI-native talent who can work efficiently alongside innovative automated tools.
China Leads in AI Engineering Readiness
The survey also points out geographical differences.
Leaders from China reported significantly higher readiness for AI transformation compared to their counterparts in the U.S. and India. This includes earlier use of autonomous engineering agents, deeper integration of AI into production workflows, and greater investment in AI-native training.
The findings reflect a broader competitive shift in the global tech landscape. As AI speeds up development cycles, regions that adopt these tools sooner may gain a compounding advantage, potentially altering the future distribution of engineering innovation.
The Industry Is Still Divided About AI in Hiring and Interviews
Despite the urgent need for AI-ready talent, many companies are slow or hesitant to change their hiring processes.
Nearly two-thirds of companies still ban AI use during interviews, wanting to assess pure foundational skills.
Fewer than 30% have updated assessments or trained interviewers to evaluate AI-native engineering capabilities.
This mismatch creates a bottleneck. Teams seek developers skilled in working with AI but often assess candidates using outdated methods. Consequently, companies may unintentionally filter out essential skills they now consider crucial.
Engineering leaders say they are looking for skills such as:
- Using AI to accelerate coding
- Integrating AI-based APIs
- Understanding autonomous agent behavior
- Evaluating risk and confirming AI output
- Applying prompt engineering in real development tasks
- Collaborating effectively with AI systems
These are no longer optional skills; they are essential for modern software development.
New Hiring Models Are Emerging to Find AI-Native Talent
In response to these changing expectations, new tools are emerging to assess engineering candidates in ways that reflect actual workflows. One startup recently launched a platform that combines human interviewers with integrated AI agents.
Candidates work through complex, multi-file technical challenges while using an AI assistant embedded directly into the interview process. Human interviewers observe how each candidate interacts with AI: how they direct the system, how they evaluate its output, and how they handle situations when the machine’s answers are flawed or incomplete.
The aim is to assess a candidate’s hybrid abilities — both technical reasoning and AI collaboration — which many leaders believe will define engineering excellence in the future.
One technology executive involved in the program described the shift this way: the most significant breakthroughs occur when “human judgment and AI capabilities work together.” The challenge has been finding a dependable way to measure that combination on a large scale.
AI Is Redefining What It Means to Be an Engineer
The report’s findings signal a broader change in the engineering community. For decades, developers were primarily valued for writing clean, efficient code. Now, as AI automates basic coding tasks, the profession is redefining itself.
The new foundations of engineering excellence include:
- Strong fundamental thinking
- Systems design skills
- Ability to collaborate with autonomous agents
- Deep understanding of product context
- Talent for turning AI-generated code into solid solutions
- Judgment to recognize when AI is incorrect—and how to correct it
The profession is becoming more strategic, more interdisciplinary, and more reliant on human judgment than ever. AI is taking over tasks but not replacing the engineers who know how to manage them.
Layoffs Are Real, but They’re Not Driven by AI Alone
The report appears at a time when tech layoffs have impacted thousands of workers globally. While engineering roles have been affected, the survey suggests these cuts are more about companies restructuring for a world where fewer engineers can do more work—provided those engineers are highly skilled and proficient with AI.
Executives say AI tools compel organizations to confront performance gaps they previously accepted. Strong engineers become significantly more productive with AI assistance, while weaker performers, who might have once relied on team support, now fall further behind.
AI is emphasizing the differences-not making engineers obsolete.
A Future Where Humans and AI Work Together
The transformation goes beyond productivity tools. Some tech leaders argue that developers must learn to view autonomous agents as true teammates instead of just tools.
At a recent industry keynote, a senior cloud executive suggested that “agentic teammates” should now be considered essential—just as vital as human colleagues. This vision reflects an industry moving toward mixed teams where humans set direction while AI executes, tests, documents, and optimizes.
It’s a vision that requires new skills, new evaluation methods, and new expectations.
The Engineering Workforce Is at a Turning Point
The new survey makes one truth clear: AI is not eliminating engineering roles, but it is changing what it means to excel in them.
Strong engineers are becoming more valuable than ever. Headcounts are not drastically decreasing. Companies around the world are working to re-skill their teams for an era defined by human-AI collaboration.
The gap between those who adapt and those who resist is widening, reshaping engineering teams at an unprecedented pace.
