A LinkedIn study published this week puts two numbers side by side that are hard to ignore: the median salary for a typical AI role in the United States sits at $177,000, while the median for non-AI roles is $80,000. That is more than double the pay, often within the same generation, sometimes from the same campus. If you have been told the job market is brutal right now, the more precise version of that truth is that the market is split, and where you land depends on which slice you are competing in.

What the LinkedIn study actually found

LinkedIn identified the 12 fastest-growing AI jobs in a report released Tuesday, tracking new hire data across its platform to show which roles are growing, who is filling them, and what they pay. The Bureau of Labor Statistics (BLS) has documented accelerating demand in tech-adjacent occupations for several years, but the LinkedIn data gets more granular on the generational breakdown.

• Gen Z workers account for more than two-thirds of new hires in forward deployed engineer and AI engineer roles, with average annual pay of $199,000 and $166,000 respectively.
• Millennials make up 60% of new hires for head-of-AI positions, which carry an average salary of $236,000.
• AI job postings on LinkedIn grew 14% between 2023 and 2024, then surged 156% between 2024 and 2025.
• 91% of workers in AI-related roles hold a bachelor's degree or higher. Nearly half of head-of-AI hires have a master's degree; 20% have a PhD.

Kory Kantenga, who heads Economics for the Americas at LinkedIn, told CNBC that a widespread view has taken hold in the labor market that it is very hard to find a job if you belong to Gen Z. His own data complicates that picture significantly. He described it as striking to see Gen Z workers tap into the AI labor market so dynamically. The broader unemployment context, though, is real: in June, the unemployment rate for workers aged 22 to 27 reached 7.2%, and 5.7% for recent college graduates in that same age band, compared with 4.1% overall. A two-tier market is forming inside a single generation, and the sorting is already underway.

The pay gap between AI and everything else

To calibrate what $177,000 actually represents, it helps to place it against what most workers in the same age bracket are taking home. Federal Reserve Bank of St. Louis data shows the median annual wage for workers aged 25 to 34 was $60,320 in Q2 2026, and $74,672 for those aged 35 to 44. Set those figures next to the LinkedIn AI median and the gap is not marginal: it is roughly triple the pay of a same-age peer outside the AI bracket.

Kantenga explains the mechanism directly: a significant amount of capital is being deployed into AI right now, and to attract the talent they need, companies have been forced to raise compensation sharply. The dynamic is consistent with what the BLS Employment Cost Index has shown across high-demand technical roles: tight supply drives wage acceleration faster than broad inflation measures capture.

The other side of the picture is less clean. The Federal Reserve Bank of New York reported that the unemployment rate for recent computer science graduates rose to 7% in 2024. At the same time, the National Association of Colleges and Employers (NACE) projects that the class of 2026 in computer science will post the highest starting salary of any major, at $81,535. A technical degree still opens doors, but the door that leads to the $177,000 bracket is considerably narrower than the door simply labeled tech.

The gender split, and why it matters if you are a woman entering the field

Only 26% of new AI hires in 2025 were women, against 50% in non-AI professions. The imbalance runs from entry-level to the top of the org chart.

• Women make up 20% of head-of-AI hires.
• Women account for 26% of AI director hires.
• Women represent just 18% of technical member-of-staff (MOTS) hires.
• The data curator role, the lowest-paid position on LinkedIn's list at an average of $51,000, is the only one where women are hired at parity with men.

Kantenga stated plainly that women are under-represented at every level of the hierarchy. A February 2025 Russell Reynolds study cited alongside the LinkedIn research found women hold only 10% of CEO and top technology positions in AI organizations. That pattern is worth understanding concretely before you accept an offer or choose a team. The useful questions are specific ones: who occupies the senior technical roles at this company, whether the organization can point to women it promoted internally, and what the retention record actually looks like. Under Title VII of the Civil Rights Act of 1964, employers are prohibited from discriminating on the basis of sex in hiring and promotion; the Equal Employment Opportunity Commission (EEOC) is the federal body that enforces those protections. Knowing your rights is one layer of preparation. Knowing the composition of the team you are joining is another.

What this means for you, in practical terms

You do not need to pivot to an AI engineering role overnight. But knowing where the money is flowing before you accept an offer, ask for a raise, or enroll in a graduate program gives you a material advantage at each of those decision points.

1. Check the benchmark before you sign

If you have a technical background and you are evaluating an offer that touches AI, machine learning, data infrastructure, or MLOps, the right comparison is not what friends in similar jobs are earning. Compare it to the market rate for that specific role. A forward deployed engineer position averaging $199,000 and a general software role at $95,000 are technically both engineering jobs. They are not the same negotiation. WorldatWork's annual Salary Budget Survey and NACE's compensation data are two publicly available benchmarks that can anchor that conversation with numbers, not impressions.

2. If AI is not your path, adapt rather than panic

The 156% year-on-year growth in AI job postings does not mean every other career is contracting. It means the composition of roles is shifting. If you work in marketing, finance, HR, design, or operations, the productive question is not whether to switch to AI entirely, but which AI tools are becoming part of your existing job description over the next 18 months. Kantenga noted that some workers he speaks with feel genuinely positive about AI, treating it as a real career opportunity, while others worry about displacement. Both reactions are rational. The workers who adapt most effectively tend to stop treating the question as binary and start identifying one concrete skill to build.

3. Credentials matter more than the discourse suggests

91% of AI hires hold a bachelor's degree or higher. Nearly half of head-of-AI hires hold a master's. If you are early in your studies and drawn to this space, the postgraduate route is not optional decoration: it is the median profile of the person who is actually getting hired into the roles with the highest pay. That is worth factoring in before you decide whether to continue your education.

4. Read the workplace before you accept the offer

A high AI salary does not automatically signal a functional workplace. Ask about team composition, what the workload cycle looks like during model launches, on-call expectations, and whether the company can name technical women it hired in the past two years who are still there. Verified employee reviews from people already inside are one of the fastest ways to surface what the offer letter will not say. The Society for Human Resource Management (SHRM) research consistently shows that retention and culture indicators available before you join are among the strongest predictors of long-term satisfaction in technical roles.

The bigger picture

The LinkedIn data describes the US labor market specifically, but the structural pattern is consistent across fast-moving hiring cycles. Wherever AI hiring accelerates, three things tend to follow: a steep pay premium concentrated in a narrow set of roles, a widening gap between that bracket and the rest of the same generation, and a gender composition that hardens quickly unless organizations actively intervene.

If you are in your mid-twenties and technical, this is one of the stronger negotiating moments in a decade. If you are in your mid-twenties and non-technical, this is the moment to identify precisely where AI intersects with your current work and to build one specific competency around it. If you are a woman entering the field, the numbers from both LinkedIn and Russell Reynolds indicate the ladder is tilted, which means the questions you ask before joining a team carry more weight than they would in a more balanced market. The BLS and Federal Reserve Bank of New York data are publicly available and updated regularly: the market signals are there, in real numbers, for anyone willing to read them.