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Multiple award-winning CTO, researcher, and bestselling author Gene Kim hosts enterprise technology and business leaders.
In the first part of this two-part episode of The Idealcast, Gene Kim speaks with Dr. Ron Westrum, Emeritus Professor of Sociology at Eastern Michigan University.
In the first episode of Season 2 of The Idealcast, Gene Kim speaks with Admiral John Richardson, who served as Chief of Naval Operations for four years.
DevOps best practices, case studies, organizational change, ways of working, and the latest thinking affecting business and technology leadership.
Just as physical jerk throws our bodies off balance, technological jerk throws our mental models and established workflows into disarray when software changes too abruptly or without proper preparation.
Sure, vibe coding makes you code faster—that’s the obvious selling point. But if you think speed is the whole story, you’re missing out on the juicy stuff.
The values and philosophies that frame the processes, procedures, and practices of DevOps.
This post presents the four key metrics to measure software delivery performance.
February 23, 2026
The comfortable middle is vanishing. That $150K “decent Java developer” job? It’s not coming back. And despite what some tech leaders claim about the death of coding, NVIDIA is hiring 721 software engineers compared to just nine AI researchers. OpenAI’s engineering openings outnumber research roles 4:1. Microsoft is posting thousands of engineering positions across Azure, GitHub, and Copilot teams.
Software engineering isn’t dying. It’s undergoing what Dr. Matt Beane, Jonathan Hassell, Brendan Hopper, and Steve Yegge call in their recent paper a fundamental transformation—roles are “fusing and fissioning like nuclear reactions,” and we don’t yet have names for many of the resulting bundles of work.
The job market is polarizing into three distinct tiers, and where you land will determine not just your salary, but your entire career trajectory for the next decade.
The Apex Tier ($250K–$500K+): Strategic systems thinking, AI orchestration, and architectural judgment. These professionals design and oversee intricate AI-driven systems, aligning complex technical infrastructure with business strategy. Think apex builders who turn vibe-based prototypes into products serving millions, or platform engineers who make systems so stable that teams can deploy wild AI experiments without 3 a.m. pages.
The Hybrid Middle ($150K–$300K): The new sustainable middle ground, blending engineering with product, design, or operations. Cross-functional collaboration meets prompt engineering and domain-specific AI application. This tier offers meaningful work that funds life outside the office—roles like platform designers, fleet supervisors managing AI agent fleets, or agent experts (imagine a tax attorney who learned prompt engineering to maintain a tax-code agent and detect hallucinations).
The Automatable Tail ($80K–$130K, shrinking): Execution of commoditized, repetitive coding tasks. This segment faces dual pressure—both from increasingly capable AI systems and readily available global talent. Basic programming proficiency and task execution are no longer differentiators when AI completes tasks 55% faster.
Here’s the uncomfortable truth: AI isn’t replacing the middle by directly taking jobs. It’s accelerating economic forces that make routine work economically unviable at previous compensation levels. This mirrors job polarization in manufacturing since the 1970s, but at unprecedented speed.
Multiple studies consistently show developers using GitHub Copilot complete tasks 55% faster. But faster execution of routine tasks doesn’t create more routine jobs—it eliminates the economic justification for human labor on those tasks.
The U.S. Bureau of Labor Statistics projects 17% job growth for software developers through 2032, significantly exceeding average across all occupations. This apparent contradiction resolves when you understand that AI augments developers who adapt, rather than causing net job reduction. Demand isn’t diminishing—it’s shifting qualitatively.
AI provides tools that make existing market pressures—cost efficiency drives, global competition—more acute and rapid. While AI automates tasks, it simultaneously creates new roles and increases demand for engineers with specific higher-level skills. The overall growth alongside the “vanishing middle” implies massive re-skilling imperative rather than net job loss.
Employers will demand developers who can design, manage, and oversee complex AI systems and integrate them into intricate business logic. We’re seeing reallocation of human capital toward higher-order cognitive and strategic tasks.
New hybrid roles are already appearing on job boards, blending traditional skills with AI-driven responsibilities. They fall into three categories:
Platform Designer: One-third product management, one-third designer, one-third systems infrastructure engineer. Core function: making fuzzy AI features feel deterministic to users who still expect calculators to work the same way every time.
Fleet Supervisor: An “air traffic controller for bots,” managing 50 live feeds from AI agents, monitoring their error states. This extends traditional fleet management to virtual AI agent fleets.
Fleet Fixer: Debugs “conversations between machines”—a “family therapist for robots.” Requires deep understanding of emergent AI behaviors and complex multi-agent system interactions.
Product Prototyper: Former product manager who learned to code, now hands engineers semi-functional prototypes instead of wireframes. Teams ship 3x faster. This role exemplifies blending product vision with practical engineering skills.
AI Quality Assurance Engineer: Value lies in “what I reject”—ensuring AI-generated solutions aren’t “stupid.” Critical for validating accuracy, reliability, and performance of AI models, identifying defects that slip past automated checks.
Agent Expert: Deep domain expertise combined with AI interaction skills. Example: tax attorney who learned prompt engineering to maintain a tax-code agent, detecting when AI hallucinates about depreciation schedules.
Platform Engineer: “Pushes the chaos up the stack,” making platforms so stable that product engineers can deploy wild AI experiments without waking anyone at 3 a.m. Focuses on designing robust, scalable infrastructure specifically for AI workloads.
Apex Builder: Turns vibe-based prototypes into real products for millions of users. High-level architectural role responsible for scaling AI solutions from experimental demos to production-grade systems.
The common pattern? Deep specialization in one area combined with practical skills in adjacent domains. The “apparently unconnected domain expertise is your new superpower” principle means a tax attorney who learns prompt engineering may have more value than a purely technical AI engineer.
The authors identify two legitimate career philosophies that are often overlooked in career guidance:
Live-to-Work (L2W): The apex tier path. Requires sacrifice for mastery. Expect 60+ hour workweeks, high-pressure environments, and steep personal costs. But compensation, impact, and cutting-edge work reflect that commitment.
Work-to-Live (W2L): The hybrid middle path. Meaningful work that funds life outside the office. Requires rapid adaptation but offers sustainability. You’re blending disciplines, solving cross-functional problems, and staying highly valued without the extreme demands of apex roles.
Not everyone seeks or suits high-pressure, 60+ hour workweeks. Formalizing this dichotomy legitimizes diverse career aspirations within tech and helps people make conscious choices about their paths.
The shift from code generation to code orchestration and validation changes everything:
Core Technical Competencies:
Human-Centric Behaviors:
The human element isn’t diminishing—it’s being elevated to supervisory and strategic roles. While AI can make individual developers faster, complexity of modern systems means team cohesion and shared ownership are crucial for preventing “hallucination cascades.”
For the Twenty-Two-Year-Old:
For the Fifty-Year-Old:
For Everyone:
Where do you actually sit today? Not where you think you should be, or where you were five years ago—where are you now?
If you’re doing work that could be described as “execution of commoditized, repetitive coding tasks,” you’re in the automatable tail. The dual pressure from AI and global talent isn’t getting better. You have maybe 12–18 months to upskill into hybrid roles before compensation pressure becomes severe.
If you’re already doing cross-functional work—blending engineering with product, design, or operations—you’re in the new middle. Focus on making those hybrid skills explicit, developing your “apparently unconnected domain expertise,” and positioning yourself for roles that don’t exist yet but will in two years.
If you’re doing strategic systems thinking and architectural judgment, you’re apex tier. Your challenge is different: can you scale your impact by directing AI and humans effectively? Can you turn prototypes into production systems serving millions?
The economic value of your experience approaches zero if it’s frozen in time. Everything you know becomes context for AI, not replacement for it. This isn’t theoretical—it’s happening now.
Talk to AI like it’s your smartest, dumbest friend. Build something impossible and share it. Read more code than you write. For experienced professionals, become a force multiplier directing work rather than doing it.
The forcing function is clear: rapidly upskill into higher-value roles or face intensifying commoditization. The organizations winning this transition recognize that technology alone isn’t enough—they need comprehensive approaches combining technical training with psychological support, leadership modeling, and experimentation opportunities.
If you’re leading engineering teams, understand that the missing middle isn’t your enemy—it’s your opportunity. The new hybrid roles require intentional cultivation. You can’t just hope developers figure it out.
Create career paths for hybrid specialists. Value cross-functional skills explicitly in compensation and advancement. Recognize that the best platform engineer or fleet supervisor might not come from traditional senior engineer ranks—they might come from operations, from product, from someone who brings unconnected domain expertise.
And most critically: stop optimizing for the old middle. Those $150K Java developer roles? They’re economic relics. The faster you help your team understand the three-tier reality, the faster they can make conscious choices about their paths.
The great developer divide isn’t coming. It’s here. The comfortable middle where you could code competently and earn solid compensation without extreme specialization or cross-functional mastery—that’s gone.
You have three choices: specialize and reach for apex roles requiring architectural judgment and strategic thinking; hybridize by blending engineering with adjacent domains; or accept commoditization pressure in the automatable tail.
Choose consciously. The market won’t wait for you to decide.
This blog post is based on “Human Vibes: How Developers Can Navigate a Job Market That’s Stretching at Both Ends” by Dr. Matt Beane, Jonathan Hassell, Brendan Hopper, and Steve Yegge, published in the Enterprise Technology Leadership Journal Fall 2025.
Managing Editor at IT Revolution working on publishing books and guidance papers for the modern business leader. I also oversee the production of the IT Revolution blog, combining the best of responsible, human-centered content with the assistance of AI tools.
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