On March 5, 2026 — just days ago — Anthropic published a peer-reviewed labor market study that every professional in Eastern North Carolina should read. Not because it's alarming. Because it's specific, it's current, and it tells you exactly where the risk is concentrated — right now, not in some hypothetical future.
Anthropic is the company that builds Claude, one of the most widely used AI systems in the world. When they study how AI is actually affecting jobs, they're not working from forecasts or surveys. They're looking at millions of real workplace interactions and asking: which tasks are actually being automated, in which roles, at what pace?
The answer should focus your attention — especially if you work in customer service, administration, data-heavy roles, or any knowledge work that involves producing documents, reports, or structured information.
What They Measured (And Why It Matters)
Most AI-and-jobs research asks: what could AI theoretically do? Anthropic asked a harder question: what is AI actually doing, right now, in real professional settings?
They introduced a new metric called Observed Exposure — a score that combines theoretical AI capability with real-world usage data, weighted toward automated (not just assistive) and work-related uses. The result is the most grounded measure of job-level AI risk available anywhere.
One of their most important findings: AI is far from reaching its theoretical capability. The gap between what AI could automate and what it is automating is still large. That gap is your preparation window. But the same research makes clear it is narrowing.
"We hope future findings will more reliably identify economic disruption than post-hoc analyses. This framework is most useful when the effects are ambiguous — and could help identify the most vulnerable jobs before displacement is visible."
— Massenkoff & McCrory, Anthropic, March 2026The Numbers That Should Stop You Cold
Here are the top ten most AI-exposed occupations by observed coverage, according to Anthropic's data:
| Occupation | Leading Automated Task | Exposure |
|---|---|---|
| Computer Programmers | Write, update, and maintain software programs | 74.5% |
| Customer Service Representatives | Confer with customers to take orders, handle complaints | 70.1% |
| Data Entry Keyers | Read source documents and enter data into systems | 67.1% |
| Medical Record Specialists | Compile, abstract, and code patient data | 66.7% |
| Market Research Analysts | Prepare reports, illustrate data graphically | 64.8% |
| Sales Representatives | Contact customers to demonstrate products, solicit orders | 62.8% |
| Financial & Investment Analysts | Analyze financial information to forecast conditions | 57.2% |
Let that sink in for a moment. Customer service representatives — the largest occupational category in many Eastern NC communities — rank second in observed AI exposure at 70.1%. Not theoretical exposure. Observed. Already happening in API-based deployments, right now.
The Signal You Can't Ignore: Young Workers
Here's the finding that deserves the most attention — and gets the least press. Anthropic's research found suggestive evidence that hiring of workers aged 22–25 has slowed by approximately 14% in AI-exposed occupations since the release of ChatGPT. Not layoffs. Not pink slips. Slowed hiring.
This matters for two reasons. First, it's the earliest detectable signal of AI's labor market effect — and it's already showing up. Second, it doesn't show up in unemployment statistics, which is why most people don't feel it yet. Young people who can't get entry-level jobs in their field don't always show as unemployed. They take different jobs, return to school, or drop out of the labor force entirely.
If you are in an AI-exposed field, or if you have a son, daughter, or employee trying to break in: the door is narrowing. The time to build the skills that change your category is now.
The research found no systematic increase in unemployment for highly exposed workers — yet. But the hiring slowdown for young workers is already statistically detectable. This is what disruption looks like before it becomes a crisis. The question is whether you use that window.
Who Is Most Exposed? (This Will Surprise You)
One of the most counterintuitive findings in the entire study: workers in the most AI-exposed occupations are more likely to be female, more educated, and higher-paid than those with zero exposure.
The highest-exposure group is 16 percentage points more likely to be female, earns 47% more on average, and is nearly four times as likely to hold a graduate degree compared to unexposed workers. People with less than a high school diploma make up 13.2% of the unexposed group — but only 2.3% of the most exposed group.
This shatters the assumption that AI is coming for factory workers and delivery drivers. The most immediate, observed, right-now exposure is concentrated in white-collar, knowledge-based, credentialed work. The professionals who were supposed to be safe are not.
The Future Is Already Showing Up in the Projections
Anthropic matched their exposure data against Bureau of Labor Statistics employment projections through 2034. The correlation is clear: for every 10-percentage-point increase in AI coverage, projected employment growth drops by 0.6 percentage points.
Customer service representatives, which carry a 70.1% exposure score, are projected by the BLS to see negative employment growth. Not stagnation. Decline. The forecasters already see it. The data already shows it. The only question is whether you're prepared when it arrives locally.
What This Means for Eastern North Carolina
The Anthropic study tracks national trends. Eastern NC sits 5–7 years behind major metro markets in AI adoption. That lag is your strategic advantage — but only if you use it.
Customer service is one of the largest employment sectors across Rocky Mount, Greenville, Jacksonville, and Elizabeth City. The 70.1% observed exposure figure is a national average. By the time it reaches your local market at full force, the preparation window will be closed for most people. It doesn't have to be closed for you.
The professionals who thrive in the next wave won't be the ones who avoided disruption. They'll be the ones who saw it coming, measured their specific exposure, and moved into higher-ground roles before the tide changed.
Why IPERA™ Was Built for Exactly This Moment
The Anthropic research team built their Observed Exposure metric on the same conceptual architecture as IPERA™: task-level analysis, actual usage data, weighted toward automated (not just assistive) applications, aggregated to the occupation level. Their economists and our assessment tool are asking the same question — just at different scales.
What IPERA™ does that no national study can do: it measures your specific task exposure, in your specific role, calibrated to the Eastern NC adoption timeline. A national average means nothing if you don't know where you personally sit on the curve.
If your IPERA™ results show high AI Substitution Risk, this research tells you why that score matters. If your results show strong Adaptive Capacity and AI Fluency, this research tells you why those dimensions are your moat. Either way, the data is telling you to act — and act now, while the window is still open.
Anthropic's own economists — the team that builds the AI — now confirm in peer-reviewed research that the task-level, occupation-specific approach is exactly the right way to measure career resilience risk. That is the IPERA™ methodology. Your results are not a quiz score. They are grounded in the same framework that Anthropic uses to understand labor market disruption.
What You Should Do Today
Reading research is not a plan. Here is what action looks like:
Step 1: Know your number. If you haven't taken IPERA™, you don't know your actual exposure. Job titles are not exposure measures. Task mix is. The assessment takes about 20 minutes and gives you a specific score across five dimensions.
Step 2: Act on the dimension that matters most for you. If your AI Substitution Risk is high, the play is not to panic — it's to shift your task mix toward oversight, judgment, and relationship work before your employer does it for you. If your AI Fluency is low, that's the first gap to close.
Step 3: Use the window. Eastern NC's 5–7 year lag is not a reason to relax. It's runway. Runway is only valuable if you use it to build something. The national data is already showing the early signals. Local impact is coming. Be ready.
The preparation window is real. But it is not permanent. And it is not waiting for you to be ready.