Strategy

AI Could Shrink the Gender Pay Gap — But Only for Women Who Already Earn the Most

A mechanism economists call 'greedy jobs' explains why automation in law, finance, and consulting might quietly redistribute opportunity. The catch: it won't help the women who need it most.

Written by Elena Brooks · Bureau Business · Jun 2, 2026

The mechanism most AI-and-gender coverage misses

The standard debate about AI and women at work runs in one direction: automation is eliminating the routine cognitive jobs — administrative work, data processing, customer service — that women hold disproportionately. That story is accurate and the stakes are high.

But there is a second, less-told story operating at the opposite end of the income distribution. In the highest-paying professions, AI may be quietly dismantling the structural feature that has kept women out — or underpaid — for decades.

That feature has a name: the greedy job.

What makes a job greedy

Economist Claudia Goldin, who won the 2023 Nobel Prize in economics, identified greedy jobs as the primary remaining driver of the gender pay gap in high-income countries. A greedy job doesn't just reward long hours — it rewards them disproportionately. Work 20% more and you might earn 40% more. The premium goes to whoever is permanently, individually available.

In finance, law, consulting, and senior management, that premium is structural. You cannot easily be replaced by a colleague for a week because your value is tied to knowing this client, this deal, this case. Firms pass the cost of any flexibility request directly to the employee asking for it — in the form of a wage penalty. Mothers, overwhelmingly, are the ones asking.

A 2025 systematic review of 48 empirical studies published in *De Economist* confirmed Goldin's framework as the dominant explanation for the remaining pay gap in developed economies.

The pharmacy precedent

One profession already ran this experiment. In the early 1970s, pharmacy was male-dominated with a significant gender pay gap. Digital patient records changed the structure of the job: any pharmacist could pick up where another left off. Individual irreplaceability collapsed. The pay premium for constant availability disappeared. Women entered the field in large numbers, and the gender pay gap in pharmacy largely closed.

Technology restructured the incentives. The equity outcome followed.

What AI could do to law, finance, and consulting

Legal research that once required a junior associate to log 60 billable hours can now be completed in minutes. Financial modeling that justified analyst face time is increasingly automated. Diagnostic reasoning, contract review, pattern recognition in consulting — the cognitive tasks that made certain professionals irreplaceable are being standardized and transferred to software.

When AI makes a client's history and context instantly accessible to any competent professional rather than locked inside one person's head, it increases substitutability. It makes greedy jobs less greedy. And when the premium for individual availability shrinks, so does the penalty for reduced availability.

Three reasons not to declare victory

The substitutability argument has real limits.

First, it applies specifically to high-status, high-paying roles. Women in lower-paid work face displacement, not liberation, from the same wave of automation.

Second, firms may respond to increased substitutability by intensifying demands rather than building in flexibility — expecting workers to cover more ground precisely because any one of them is now more replaceable. The same technology that makes a lawyer substitutable also makes her more easily monitored and more easily discarded.

Third, the motherhood penalty is not only a function of job design. Social norms still dictate that when care needs to happen, women adapt. Even if AI reduces the structural penalty, those norms will continue to shape outcomes unless they change in parallel.

The decision firms haven't made yet

The pharmacy case shows that when technology restructures individual irreplaceability, the effects on women's representation and earnings can be profound. The question for professional services firms deploying AI now is whether anyone is thinking about this deliberately.

Will the reduction in individual irreplaceability get channeled into more human job structures — or just into higher billable targets? That is not a technology question. It is a management decision, and the window to make it intentionally is open right now.

Key takeaways

The gender pay gap in high-paying fields is driven largely by 'greedy jobs' — roles that penalize any deviation from constant, individual availability, according to a 2025 systematic review of 48 studies published in De Economist.
AI reduces worker irreplaceability in elite professions by standardizing knowledge — the same dynamic that made pharmacy one of the most gender-equal fields after digital records made pharmacists interchangeable.
For women in lower-paid administrative, data processing, and customer service roles, AI is more likely to mean displacement than opportunity.
Firms could absorb the substitutability AI creates by intensifying workloads rather than building in flexibility — making the equity outcome a management choice, not a technological inevitability.
Job redesign needs to be on the agenda alongside productivity metrics as firms deploy AI across professional services.

FAQ

What is a 'greedy job' and why does it matter for the gender pay gap?

A greedy job is one that rewards long hours and constant individual availability disproportionately — work 20% more and you might earn 40% more. Economist Claudia Goldin identified this structure as the primary remaining driver of the gender pay gap in high-income countries, because the workers penalized for reduced availability are overwhelmingly mothers.

How did technology close the gender pay gap in pharmacy?

Digital patient records made pharmacists interchangeable — any pharmacist could pick up where another left off. That eliminated the premium for constant individual availability, collapsed the greedy structure of the job, and women entered the field in large numbers. The gender pay gap in pharmacy largely closed as a result.

Does AI help all women workers, or just those in elite professions?

The substitutability argument applies specifically to high-status, high-paying roles in law, finance, consulting, and medicine. Women in lower-paid administrative, data processing, and customer service roles are more likely to face displacement from automation than any equity benefit.

What could prevent firms from using AI to make jobs more flexible?

Firms could respond to increased worker substitutability by intensifying workloads rather than building in flexibility — expecting professionals to cover more ground because any one of them is now more replaceable. The technology creates the possibility of more flexible structures; it does not guarantee firms will choose them.

What should firms be doing differently as they deploy AI in professional services?

Job redesign should be on the agenda alongside productivity metrics. The reduction in individual irreplaceability that AI creates can be channeled into more flexible, human job structures — but only if leadership makes that a deliberate choice rather than defaulting to higher output targets.

Citations

AI has an unexpected side effect: It could make high-paying jobs less hostile to womenAI-driven standardization of knowledge work could reduce the 'greedy job' premium for constant individual availability, potentially narrowing the gender pay gap in high-paying professions.
Claudia Goldin Nobel Prize in Economics 2023 — Royal Swedish Academy of SciencesClaudia Goldin won the 2023 Nobel Prize in economics for her research on women's labor market outcomes, including the concept of greedy jobs as a driver of the gender pay gap.
Systematic review of greedy jobs and the gender pay gap — De Economist (2025)A 2025 systematic review of 48 empirical studies published in De Economist confirmed greedy job structure as the primary driver of the remaining gender pay gap in high-income countries.