Engineers doing design, server engineers doing client, planners writing code. Crossing job-function boundaries is rising,
because AI made the execution of each domain cheap.
But the gate to crossing isn't execution. It's the taste to judge what's good in the next field,
and taste is something AI doesn't hand you. Neither broad wins nor deep wins on its own — here is the craft of crossing in the AI era.
Why is crossing possible now? Because AI made the execution of each domain cheap. Execution can be delegated. What you can't hand off is the breadth to judge whether the delegated work is any good. This isn't new — entrepreneurship research formalized it over twenty years ago.
Let's be honest about the line. "Judgment is the gate" is supported by Lazear's classic theory and the academic work below. But the causal claim that "AI made execution cheap, therefore crossing is newly feasible" is not established by primary sources in this research (the view of Netflix's role-collapse as an AI precursor was rejected in verification). Read it as the series' framing plus an observable phenomenon (Shopify, vibe coding). The "crossing distance" model below is also an organizing frame, not a proven law.
→ The numbers and quotes are checked facts. The causal arrows and the model are plausible frames, read as such.
Lump "crossing" together and you miss the point. Stepping into the next function differs from moving to another industry. Split it into three by distance. The farther you go, execution gets filled by AI but the taste gap widens — this is not evidence, it's this report's organizing frame.
Engineer ↔ design ↔ PM ↔ QA. The next role inside the same product. Lots of shared context, and taste is easy to borrow. The most feasible, and where it's actually happening.
front ↔ back ↔ infra ↔ data ↔ ML. The neighboring discipline. AI fills the syntax, but what counts as "good design" differs by domain. You have to import the taste.
A different industry or a career switch. AI accelerates learning and execution, but the sense of what's "good" in that industry is the hardest to acquire. Maximum distance, maximum taste gap.
Crossing is moving from "nice to do" to "assumed." Both in top-down mandates and in the macro numbers.
Per WEF 2025, the fastest-growing skill is AI and big data; the #1 core skill is analytical thinking (70% of employers call it essential). And it states that "a combination of both [technical and human] skillsets will increasingly be required" across growing jobs. Yet the same report also stresses "domain expertise that cannot easily be automated" (i.e., depth). Testimony that the answer is neither breadth alone nor depth alone.
Note: WEF measures skill instability / re-skilling, not boundary-crossing itself; the bridge to crossing is this report's interpretation. Figures are as of January 2025, horizon 2030.
Crossing is a new phenomenon, but the tools to think about it were built by others. Here is the lineage, dated precisely (correcting a common error).
The economics of crossing is well studied. The verdict is neither blanket praise nor contrarian rejection. The shape that works is asymmetric: a deep base, plus one measured crossing on top.
An analysis of 17.9 million papers. The highest-impact work is grounded in conventional combinations, with a single intrusion of an unusual combination. That profile was twice as likely to be highly cited. Breadth/novelty "alone" doesn't do it.
Note: measured via journal-pair co-citation within papers, not an individual's T-shape depth. This report uses it by analogy (correlation).
A longitudinal study of 2,070 animators. Depth helps creativity early in a career, but turns into a liability late (creativity −7% to −24%); breadth helps late. So "keep thickening the vertical bar" is not optimal. When you start crossing matters.
The claim that depth must mechanistically precede breadth was rejected in verification. This is an observed reversal of effect by stage, not a tested causal sequence.
Crossing isn't free. Reach wide without judgment and a quiet tab of quality and coordination debt piles up. Three traps.
AI produces a plausible-looking artifact in the next domain. But if you can't judge quality, you won't catch the flaws. Generation got cheaper; verification did not.
→ Until you can tell "good" in the new domain, make a specialist's review a condition of done.
Crossing and coordination work (Tanya Reilly's glue work) earns praise in reviews yet is undervalued at promotion ("no visible technical contribution"). Crossing too early can stall a career.
→ Counter-view: Sean Goedecke reframes it as a rational company trade-off. Read it as: unless the org rewards it structurally, the individual loses.
Declare "it's all breadth now" and let go of the vertical bar, and you get crossing with no base. Uzzi shows a conventional base is the premise; Lazear says breadth-to-judge is required. Breadth only works on top of a base.
→ Keep one deep bar. But per Mannucci, shift the ratio with career stage.
If your value is "I build it fast and right," that sits on exactly what AI cheapens most. Now that execution is cheap, a small team that crosses several domains — a startup, or a lean elite squad inside a big company — outruns a large org split into narrow specialists. The moment you narrow your role to execution, you become the replaceable part.
→ Narrow your taste, not your execution. Move to the side that judges good from bad in the domain.
How the individual moves, how the organization enables it. Guidance drawn from the evidence.
Lines that land — for meetings, slides, or a single sentence.
Primary/secondary sources that passed adversarial verification. Correlation is flagged as correlation, and claims that couldn't be substantiated (Netflix as an AI precursor, a mechanical depth→breadth causation, a decisive breadth→low-income reading) are not used as support. Numbers are identical across JA/EN.