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“That’s not really my cup of tea” is not a measured cause of engineering losses. It is a useful warning sign: when an engineer treats customer questions or adjacent technical work as somebody else’s responsibility, teams can lose context at the boundaries where product decisions and handoffs happen. The answer is not to make every engineer a generalist. It is to preserve deep expertise while helping people understand how their work connects to customers and neighboring systems.
What the phrase signals—and what it does not prove
In an opinion essay published by Reg4Tech, the author describes interview responses such as “That’s not really my cup of tea,” “That’s more of a DevOps question,” and “not my concern.” These are reported examples, not results from a representative survey of engineering candidates. The essay uses them to illustrate a boundary: an engineer may be willing to solve a narrowly defined technical task but reluctant to engage with the person or system that gives the task its purpose.
The title’s “most expensive” language is rhetorical. Neither the essay nor the supporting material establishes a dollar amount, a controlled comparison, or a causal finding that this phrase itself makes an organization lose money. The practical concern is more specific: rigid ownership boundaries can leave teams without the context needed to make decisions, explain trade-offs, or resolve work that crosses those boundaries.
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Why customer and adjacent-system context matter
An engineer does not need to lead every customer meeting or operate every neighboring service. But some awareness of the people using a product and the systems around it can make technical work more informed. A feature request may conceal a different customer problem; a service change may affect reliability or scaling elsewhere. If nobody is prepared to connect those details, questions can bounce between teams or reach a decision-maker stripped of useful context.
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The essay’s argument is not that specialization is bad. Deep expertise remains valuable. The distinction is between being a specialist who can explain how their work fits into a larger system and treating everything outside a job label as irrelevant. That broader context can help an engineer ask better questions, recognize dependencies, and know when to involve another owner.
Communication is a labor-market signal, not proof of a productivity effect
LinkedIn’s 2024 report on in-demand skills ranked communication No. 1 among its overall most in-demand skills. LinkedIn said its analysis drew on data from one billion members across 200 regions and countries. That ranking describes LinkedIn’s platform data; it is not specific to software engineers, and it does not show that a particular communication practice reduces engineering costs.
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LinkedIn’s 2026 Skills on the Rise report also identifies growth in cross-functional collaboration and stakeholder communication. Its analysis covers 12 markets and compares skill acquisition and hiring-success signals from December 1, 2024 through November 30, 2025 with the same period a year earlier. Those findings offer labor-market context, not proof that any one team structure or coaching program will improve engineering outcomes.
How leaders can build broader context without erasing specialization
Test explanation and adjacent reasoning in interviews
Ask candidates to explain a system or technical choice to someone outside technology, then invite basic questions. The aim is not polished performance or extroversion; it is to see whether the candidate can make an idea understandable and respond constructively. A second prompt can probe a neighboring concern—for example, which service metrics should inform autoscaling and why. This tests whether a candidate can reason across a seam without expecting them to claim expertise in every area.
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Offer practice to engineers who want it
Leaders can make customer and operational context accessible through training, shadowing, participation in customer sessions, and structured debriefs. The essay recommends enabling willing engineers rather than assigning every engineer to customer-facing work. Introversion alone should not be treated as unwillingness: the useful distinction is whether someone is open to learning and practicing in a format that works for the role.
The author also recounts adding an architect to customer reviews as an example of bringing technical perspective closer to customer discussion. It is an anecdote, not a controlled demonstration or guarantee of better outcomes. The general leadership lesson is to create appropriate opportunities for engineers to hear the problem directly and reflect on what they learned.
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Look for recurring seams before reorganizing
Tool-based teams can be sensible when specialist ownership is clear. But if requests repeatedly cross the same boundaries and stall, leaders should examine the handoffs rather than assuming the answer is simply more communication. Useful questions include:
- How often does work cross the same team boundary, and where does it wait?
- Is ownership clear when a customer problem spans multiple systems?
- Do handoffs remove context or create repeated clarification work?
- Would organizing some work around customer problems improve coordination without weakening technical ownership?
The essay favors considering problem-based organization when recurring seams create friction. It does not provide a quantified comparison showing that problem-based teams are always better. The decision should follow the organization’s actual ownership, handoff patterns, and customer needs.
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Where AI fits—and where the evidence stops
The essay argues that AI may make it easier to obtain answers and produce code, increasing the value of judgment, communication, and understanding a customer’s underlying problem. That is the author’s interpretation, not a mechanism demonstrated by the LinkedIn skills reports. The essay also mentions specific AI adoption and code-assistance figures, but those claims are not independently established by the sources cited here, so they should not be treated as verified statistics.
Even without those figures, the leadership question remains concrete: can engineers explain their decisions, understand relevant dependencies, and collaborate when a problem crosses a team boundary? AI tools do not by themselves answer whether a team has the context or ownership it needs.
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