Job Titles Don’t Mean What They Used To (And That Affects Your Pay) — with Dr. Ben Zweig (Part 2 of 2)

What It Covers

Ben Zweig, CEO of Revelio Labs, explains how 90 million unique job titles create salary negotiation blind spots, why AI will elevate middle management rather than eliminate it, and how jobs historically transform from within rather than disappear — using bank tellers, typists, and consulting firms as data-backed case studies.

Key Questions Answered

• •Job Title Chaos: With 90 million unique job titles in circulation, two people sharing the same title may do entirely different work, while two people with different titles may do identical work. To negotiate salary effectively, map your actual task bundle — not your title — against market data. Tasks are the unit of comparison, not labels, since LLMs can now identify semantic equivalence across millions of job descriptions.
• •Task-Based Job Search: When searching for roles, look beyond title matching. A product manager at one company may function as an engineering lead; at another, as a client success manager. Searching by underlying work activities — scheduling, stakeholder management, technical architecture — surfaces relevant roles that title-based searches miss entirely, giving candidates a more accurate picture of what they qualify for and where they fit.
• •Management as the Scarce Skill: As AI handles execution tasks, orchestration becomes the high-value skill. Ben Zweig predicts middle management will grow in importance, not shrink, because reconfiguring roles to meet shifting business needs is a fundamentally human coordination task. For workers aged 25–55, deliberately developing managerial skills — even informally, on the job — is the highest-return career investment for the next two decades.
• •Job Crafting as a Retention and Advancement Tool: Workers can proactively reshape their roles through a practice called job crafting — identifying which tasks they perform well and find meaningful, then aligning those with business objectives in conversation with managers. Zweig recommends reviewing how your role has shifted every three months, then deliberately steering it toward higher-value, harder-to-automate activities before a manager or AI does it for you.
• •Small Firms Outadapt Large Ones: Large companies face structural disadvantages in AI adoption — bureaucratic approval chains, rigid privacy policies, and occupational licensing constraints slow implementation. Small firms, which already reconfigure roles continuously in response to client demands and staffing changes, are better positioned to absorb AI tools quickly. Workers at adaptive small organizations face lower displacement risk than those in rigid, process-heavy large institutions.
• •Transformation Happens Inside Jobs, Not Between Them: Historical data shows automation rarely eliminates job categories wholesale — it reshapes the task mix within them. Bank tellers multiplied after ATMs arrived but shifted from cash handling to relationship management. Typists evolved into database administrators. For workers in potentially vulnerable roles, taking inventory of transferable skills and interests now — before displacement pressure arrives — allows proactive repositioning rather than reactive retraining.