Prioritization of internal AI surges 77%, 63% of leaders rely only on informal feedback about productivity, and 1 in 3 managers is considering stepping down from leadership to become a developer again.
The software engineering sector is operating under intense pressure. The overwhelming advance of Artificial Intelligence has stopped being just a new feature in digital products and has taken on a central role in corporate strategy, deeply reshaping how code is written, reviewed, tested, and shipped to market.
As Michael Hill, editor at LeadDev, points out, organizations are going through severe structural flattening while demanding that their leaders deliver far more on both the technical and managerial fronts. The result is a silent crisis of human sustainability, where technological acceleration is running well ahead of governance structures and system quality.
Below are the 10 main takeaways from this report:
1. Internal AI use took the top spot in priorities
For the first time in the study’s historical series, internal AI adoption jumped from 45% to 77%. This strategic goal even surpassed the development of new commercial features for customers, which scored 71%.
2. Mass adoption with almost no real measurement
While 54% of companies report that AI tools and agents are already widely integrated into developers’ daily work, a striking 63% of them assess the impact on productivity based only on informal feedback from professionals. Just one-fifth use solid metrics, such as Pull Request revert rates (22%).
3. The junior-developer training crisis
An overwhelming 84% of leaders say AI will make the market much harder for new entrants (junior professionals). The paradox is that, even as AI is used for simple tasks, only 4% of organizations have talent strategies focused on supporting early-career developers.
4. Severe overload and longer workdays
The continuous expansion of responsibilities has led 45% of all leaders to work more weekly hours than in the same period last year. The situation is even more acute among senior engineers (Staff, Principal, and Distinguished Engineers), where 53% report longer hours.
5. The great exodus of managers back to code
Management burnout has fueled a strong career-reversal trend: a third (32%) of professionals in leadership roles are actively considering leaving management to return to the role of Individual Contributor (IC) focused on technical tasks. Among engineering managers and CTOs, that figure reaches 34%.
6. Constant restructuring and organizational flattening
Layoffs and hiring freezes have stabilized, but internal reorganizations have exploded: 67% of companies made drastic changes to their team structures. Cuts to management layers hit middle management hardest (65%), but rose to 23% at the executive leadership level (directors and VPs), showing that corporate flattening has reached a new peak.
7. Code maintainability and quality under serious threat
The massive volume of algorithm-generated code has set off alarm bells for managers: the impact on the long-term maintainability of codebases tops the list of biggest concerns (73%), closely followed by worries about the actual quality of what AI tools deliver (71%) — the bottleneck has simply moved.
8. Team demotivation and the “TikTok-ification” of engineering
Team motivation dropped to 41% among respondents. Qualitative reports indicate that the creative, exciting work of programming has been replaced by an exhausting routine of endless review and maintenance of AI-generated code.
9. Burnout at the executive level
Emotional exhaustion among CIOs and CTOs recorded one of the report’s steepest escalations: 54% say they feel emotionally drained at least once a week — a critical jump from 24% measured in the same period last year. The exhaustion stems from relentless pressure to produce detailed scopes to feed AI.
10. Market uncertainty erodes DEI goals
With rising short-term pressures and an exclusive focus on metric efficiency, Diversity, Equity, and Inclusion (DEI) initiatives suffered noticeable setbacks: 27% of leaders admit the topic has lost priority, and the global relevance index for the agenda fell from 63% to just 51% of companies.
Leadership roles in companies typically navigate between technical knowledge and management capacity. Engineering leaders will need to support a new software development model (AI SDLC), even as human capabilities are put to the test, with machines replacing stages of the process that were always carried out by engineers. This already happened during the Industrial Revolution with the steam engine, though the driving forces were different.
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