I recently watched a senior backend engineer realize his role was becoming less valuable. Not because he lacked technical skills—his architecture was solid, his code was clean, and his database knowledge ran deep. The issue was that he couldn’t connect his technical decisions to business outcomes.
Across the table sat a junior developer who had spent six months learning to use AI tools effectively, understanding frontend constraints, and sitting in on product strategy meetings. When stakeholders asked why certain technical choices mattered, the junior engineer could explain the customer impact. When requirements changed, they understood the business reasoning and could adapt quickly.
Both engineers could solve the technical problems. But only one could solve the right problems for the business.
The senior engineer built elegant systems in isolation. The junior engineer shipped features that customers used and stakeholders valued.
This isn’t a future scenario. It’s happening now across engineering organizations, though the pace varies by industry and context. The direction is consistent even where the timeline differs. AI isn’t replacing engineers—it’s making narrow specialists much easier to replace while supercharging those who can work across multiple dimensions.
The collaboration advantage is measurable
At Split Software, I led an organization where engineers who learned to work across traditional boundaries outshipped entire teams organized around narrow specializations. This was part of our broader transformation to fluid mission-driven teams that could rapidly reconfigure around customer problems.
Before our transformation to mission-driven teams, features took an average of 12 weeks from concept to customer value. Backend engineers wrote services according to specs. Frontend engineers built interfaces from designs. Product managers translated between teams. The handoff overhead was brutal.
After we moved to cross-functional crews where engineers worked across multiple dimensions, the same features shipped in 3-4 weeks. Backend engineers who learned frontend constraints made better API decisions. Frontend engineers who understood data flow caught integration issues during development instead of QA.
The difference wasn’t better tools or processes—it was engineers who could think like product managers, understand customer problems, and make technical decisions with business context. The narrow specialists who stayed in their lanes became bottlenecks.
AI-augmented development exposes systemic thinking gaps
When I work with teams implementing AI-augmented development, the engineers who adapt fastest aren’t necessarily the most technically skilled. They’re the ones who already understand how their work connects to the broader system.
A frontend engineer who understands APIs can prompt AI to generate backend endpoints that integrate seamlessly. A backend engineer who grasps user experience can create services that respond appropriately to interface needs.
Engineers who only know their domain generate technically correct code that creates integration problems. AI-augmented development amplifies whatever systemic understanding you already have—or exposes its absence. This follows the same pattern I documented in vibe coding: AI multiplies whatever rigor and understanding you bring to the process.
The blurring line between engineering management and technical leadership
The boundary between engineering management and senior engineering is dissolving, and AI makes this transition mandatory rather than optional.
Staff engineers who can’t facilitate decisions or guide AI-human workflows become expensive individual contributors. Engineering managers who can’t evaluate technical trade-offs get bypassed by senior engineers who can both lead and implement.
At Tinybird, we’ve eliminated traditional engineering manager roles entirely. Technical leads code and mentor. Product engineers manage and implement. Senior engineers shift fluidly between leading and following based on expertise rather than hierarchy.
This pattern is spreading because AI-augmented teams need what I call “contextual leadership”—technical depth combined with collaborative intelligence. The artificial separation between management and technical tracks becomes a liability when work requires both skills simultaneously. This aligns with the dual systems approach where leaders must operate effectively in both stable and dynamic contexts.
The transformation manifests through gradual erosion of influence. Backend engineers who refuse to learn frontend concepts get assigned maintenance work while multi-dimensional engineers ship features. Frontend engineers who don’t understand data architecture create interfaces requiring expensive modifications. Single-dimensional engineers find themselves on less strategic projects while multi-dimensional engineers drive business outcomes.
Domain exceptions matter
This doesn’t apply universally. Highly regulated industries like healthcare or finance still need deep specialists who understand complex compliance requirements. Infrastructure engineering in large-scale systems requires expertise that can’t easily be generalized.
But even in these domains, the most valuable specialists understand how their work connects to broader business outcomes. A security engineer who can communicate risk trade-offs to executives is more valuable than one who only speaks in technical terms.
The shift isn’t about eliminating expertise—depth still matters, but it’s insufficient on its own. Success requires combining deep knowledge with broader context and collaborative skills.
Market pricing hasn’t caught up yet
While most organizations still hire for narrow specializations, some companies are identifying engineers who can work effectively across multiple dimensions.
“Senior Backend Engineer” and “Senior Full-Stack Engineer” command similar salaries, but their value creation differs significantly in AI-augmented workflows. One of my consulting clients restructured their hiring around adaptability rather than narrow expertise, looking for engineers who have worked across domains and contributed to product decisions. Their development velocity increased 40% within six months.
Another client saw a different benefit: their multi-dimensional engineers caught a major security vulnerability during feature development that their specialized security team had missed in code review. The engineer understood both the business logic and the technical implementation well enough to spot the edge case.
This pricing gap won’t last. As more companies recognize the shift, compensation will adjust accordingly. The early movers are building advantages while the market is still catching up.
Your strategic response
The engineers reading this who work primarily in single domains will resist the premise. “I’m really good at what I do. Why should I have to learn other people’s jobs?”
Because AI can now handle much of what you do independently, but it can’t replace someone who combines your expertise with multi-dimensional thinking and collaborative skills.
Start by identifying the adjacent domains that most impact your work. Backend engineers: learn enough frontend to understand user experience constraints. Frontend engineers: understand data flow and API design. Engineering managers: develop enough technical depth to evaluate AI-generated solutions.
Find opportunities to work outside your traditional boundaries. Volunteer for cross-functional projects. Participate in product discovery sessions. Present technical trade-offs to business stakeholders.
Most importantly, develop rapid learning as a meta-skill. The specific technical knowledge will continue evolving, but the ability to quickly acquire new context and apply it effectively is becoming the primary source of engineering value.
The competitive reality
Organizations that will thrive in AI-augmented software development are already restructuring around multi-dimensional capabilities. They’re not waiting for perfect AI tools or complete certainty about the future.
Companies falling behind are still hiring “Senior React Developer” and “Backend Team Lead” while competitors hire “Product Engineer” and “Technical Leader.”
The talent market is starting to reflect this shift. Multi-dimensional engineers command higher compensation, get better opportunities, and have more career security than narrow specialists.
The engineers who adapt quickly will shape the industry’s evolution. Those who wait for the transformation to stabilize will find themselves adapting to changes designed by their more flexible colleagues.
The tools are available now. The competitive advantages are being built today. The narrow model of engineering expertise is rapidly becoming much easier to replace—the only question is how long it will take the market to fully recognize it.