The talent pyramid is tipping

AI coding assistants promise more code in less time. A recent telemetry analysis by Faros AI covering 22,000 developers and more than 4,000 teams shows where the time saved actually goes: under high AI usage, median pull request (PR) review time rose by 441.5 percent and the number of incidents per pull request rose by 242.7 percent (Faros AI, 2026). The bottleneck has shifted: from writing code to checking it.

For engineering leaders in medical technology, automotive, or financial services, this is especially relevant given strict regulatory requirements. When review capacity becomes the limiting factor, it hits exactly the role that is already scarcest on regulated projects: experienced engineers who take personal and professional responsibility for approvals.

From writing code to “supervisory engineering”

Developers themselves confirm this shift. In a longitudinal study of professional software developers, 82 percent reported spending less time on actual code writing by the second survey wave (Vella & Blincoe, 2026). The authors introduce a new category for this: “supervisory engineering work” – directing AI, evaluating its output, and correcting errors.

This changes what counts as valuable senior work. A principal engineer who types less code themselves can have become more productive, provided they deliver better specifications or build a verification rule that prevents hundreds of future defects. Classic metrics such as lines of code or raw PR count no longer capture this.

Career paths that reward judgment instead of output

For promotion criteria, this is a blind spot in many organizations. Gartner predicts that by 2027, 70 percent of software engineering leadership job descriptions will explicitly require GenAI oversight, up from under 40 percent previously (Gartner, 2025). At the same time, Gartner explicitly warns against pushing junior talent out of shrinking team structures: doing so weakens knowledge transfer and the very experience base from which senior judgment later emerges (Gartner, 2026).

For your leadership team, this means concretely:

• Treat review capacity as its own, plannable resource – not as a residual variable alongside more code output.
• Rework promotion criteria: evaluate verification quality and architectural decisions, not raw output volume.
• Keep junior talent on the team and deliberately expand their experience-building around verification tasks, instead of removing them from development.
• Communicate AI adoption openly instead of mandating it: according to DORA data, organizations that actively address displacement fears achieve roughly 125 percent higher team adoption; dedicated learning time correlates with 131 percent higher adoption, and clear usage guidelines with as much as 451 percent (DORA / Google Cloud, 2025).

Organizations that fail to act on these points risk deploying their scarcest and most expensive expertise not toward architecture or knowledge transfer, but toward cleaning up a growing volume of AI output. Structured AI integration consulting and targeted training, such as the sepp.med Academy’s „ISTQB® Testing with Generative AI" course, address exactly this point, long before review backlogs turn into a quality risk.

How this shift affects not only team structures but also sourcing decisions and whether AI competence is perceived as an opportunity or a threat is more than a pure process question. This broader frame is exactly what the fourth talk slot at the next sepp.med Afterwork Exchange picks up: Markus Rautert, Chief Technology Officer at adidas, will speak in Röttenbach on September 24, 2026, about a gameplan for organizations navigating AI, from shifting delivery economics to changing talent pyramids.

Sources

• Faros AI. (2026). The acceleration whiplash: AI engineering impact report 2026.
• Xu, F. A., Medappa, P. K., Tunç, M., Vroegindeweij, M., & Fransoo, J. C. (2025). AI-assisted programming may decrease the productivity of experienced developers by increasing maintenance burden.
• Vella, A., & Blincoe, K. (2026). The impact of AI coding assistants on software engineering: A longitudinal study.
• Gartner. (2025, May 8). Generative AI is redefining the role of software engineering leaders.
• Gartner. (2026, July 7). Gartner predicts 60% of organizations will adopt smaller software engineering teams by 2029.
• DORA / Google Cloud. (2025, March 19). Helping developers adopt generative AI: Four practical strategies for organizations.