# Chapter 5: Will AI Take My Job? Exposure, Displacement, and Career Change Part of The Superintelligence Transition by Chad Everett Harris, Founder and CEO, Savrn. Published September 24, 2026. Evidence cutoff September 23, 2026. Canonical: https://savrn.com/blog/the-superintelligence-transition/ai-jobs-displacement Full edition: https://savrn.com/blog/the-superintelligence-transition Disclosure: prepared for Savrn, which has a commercial interest in AI infrastructure and publishes the seven trackers cited here. Not an independent institutional review. **Key takeaways** - In a study of 5,172 customer support agents, AI assistance raised issues resolved per hour by about 15 percent on average, but the gains concentrated among newer and lower-skilled agents, and the study does not show that wages rose. - The ILO estimates about one in four workers globally is in an occupation with some exposure to generative AI and 3.3 percent of global employment is in the highest exposure category; exposure measures task overlap, not job loss. - A Danish study linking survey and administrative records found precise null effects on earnings and recorded hours, ruling out effects larger than roughly 2 percent over about two years after ChatGPT's launch, while documenting changes in work and occupational movement. - In the WorkAdvance randomized training evaluation, one of four providers (St. Nicks Alliance) produced a year-ten earnings gain of $8,580 (p = .005); the other three providers showed no statistically significant effect on that outcome. - Announced capital is not a local job: project announcements and permits start questions about employment, and only employers, contracts, and observed hiring answer them. It's late. You can't sleep. You open your phone and type the question: will AI take my job? You're not asking about productivity statistics. You're asking whether the paycheck that covers the mortgage, the kid's braces, and the parent you help take care of will still be there in three years. That's the real question, and most of what you'll find in the search results answers a different one. I've spent my career building and operating large infrastructure, most of it in Texas, and I've read a lot of spreadsheets. Here's something every operator learns early: output per hour and a worker's paycheck are different line items. They sit on different rows. One can go up while the other stays flat, or goes down, and nothing about the first number decides the second. What decides the second is a person, usually in a meeting you're not invited to, choosing how the gains get split. That's the part of the AI and jobs debate almost nobody talks about, and it's the part that matters most to you. This chapter looks at the best evidence I could find on AI job displacement research, working life, and career change. Four study programs anchor it: a large workplace deployment in customer support, a global index of which occupations are exposed to generative AI, a Danish study that tracked earnings and hours after ChatGPT arrived, and a randomized evaluation that followed a job training program for ten years. The uncomfortable part cuts both ways. The evidence doesn't support the prophets of mass unemployment, and it doesn't support the people promising that everyone gets a raise. Both guarantees fail, and I'll show you exactly where. Then I'll give you something more useful than a forecast: what to do if your occupation shows up on an "exposed" list, how employers can split gains in good faith, what workforce boards should demand, and why an announced facility, including the kind my industry builds, is not the same thing as a job in your town. ### 5.1 Will AI Take My Job? The Question Behind the Question The productivity question (can a person produce more in an hour with assistance) is the question the technology press asks. It's also the smallest of the questions a working adult actually lives with. Think about what you want from work. Stable earnings, for one. But also safer work, useful training, some control over your schedule, a way to move up or move over, and maybe the ability to stay employed while you care for a parent or a child with needs. Any serious account of AI at work has to hold all of those outcomes in view at once, because a tool can raise output per hour while making several of them worse. The research this chapter reviews contains exactly that combination, and I'm not going to hide it. So when you ask "will AI take my job," I'd suggest breaking it into four smaller questions that evidence can actually address: 1. **Will the tasks in my job change?** This is an exposure question. The ILO index speaks to it. 2. **Has that change shown up in people's earnings and hours yet?** This is a measured labor-market question. The Danish study speaks to it. 3. **When a workplace adopts a tool, who gains and who doesn't?** This is a deployment question. The customer support study speaks to it. 4. **If I need to change careers, does training work?** This is a transition question. WorkAdvance speaks to it, with an important caveat: it didn't test AI training at all. None of these four findings establishes an economy-wide guarantee of either prosperity or displacement. That's the right starting point. The chapter's job is to show you precisely why both guarantees fail, and what you can do while the actual outcome is still being decided. ([Customer support study](https://academic.oup.com/qje/article/140/2/889/7990658), [WorkAdvance ten-year evaluation](https://www.mdrc.org/sites/default/files/WorkAdvance_10-Year_Report_MDRC.pdf), [ILO exposure index](https://www.ilo.org/publications/generative-ai-and-jobs-refined-global-index-occupational-exposure), [Danish labor-market study](https://www.nber.org/papers/w33777)) This chapter covers the middle of the working lifespan: established employment, career change, and economic participation. [Chapter 4](#ai-college-workforce-training) covers college and the start of a career; [Chapter 2](#ai-productivity-evidence) covers the task-level productivity experiments in more detail. Where those chapters matter here, I'll point you to them. > **Operator's note: Two line items, one decision** > > I've built facilities where the question of throughput per hour was reviewed constantly. It's the number every operator watches. But I have never seen a throughput gain pay a single worker by itself. Somebody decides whether the hours saved become higher pay, a lighter workload, new responsibilities, or fewer people on the schedule. That decision is made by management, and it's made on purpose. > > So when I read a headline saying AI raised productivity by some percentage, my first question isn't "is the number real." My first question is "whose line item moved." Those are different rows on the spreadsheet. If you're a worker, that's the question to ask too. If you're an employer, it's the question you should be ready to answer in writing. ### 5.2 The AI Productivity Customer Service Study, Read Carefully The strongest workplace evidence in this research remains Brynjolfsson, Li, and Raymond's 2025 study in the *Quarterly Journal of Economics*. If you've seen a headline that AI made customer service workers 15 percent more productive, this is almost certainly where it came from. It deserves a careful read, because the headline and the study are not the same thing. ([Brynjolfsson, Li, and Raymond](https://academic.oup.com/qje/article/140/2/889/7990658)) #### 5.2.1 What the study actually did Here's what the study actually did. The researchers studied 5,172 customer support agents at a business-software company. The company rolled out an AI assistant that suggested responses and surfaced relevant information while agents handled customer issues. The agents kept control: they could accept a suggestion, edit it, or ignore it entirely. The design was quasi-experimental. The assistant was adopted in a staggered way across the workforce, and the researchers used difference-in-differences estimation, which compares how outcomes changed for agents who got access against how they changed for agents who hadn't yet. That is a respectable design. It is not a company-wide randomized trial, and the distinction matters for how much weight the estimate can carry. In a randomized trial, a coin flip decides who gets the tool, so nothing about the agents or their managers can explain the difference. In a staggered rollout, the timing of access was not a coin flip, and the analysis has to assume that the groups would have moved in parallel without the tool. **Evidence card** - Title: Generative AI at Work, Brynjolfsson, Li, and Raymond (2025) - Design: Quasi-experimental; staggered adoption of an AI assistant with difference-in-differences estimation. Not a company-wide randomized trial. - Population: 5,172 customer support agents at one business-software company. - Finding: Access increased issues resolved per hour by approximately 15 percent on average, with larger gains among less-experienced and lower-skilled workers, little productivity improvement for the most skilled, and evidence of small quality declines for some higher-skilled workers. - Limit: One company and one workflow. The study does not establish that workers received a corresponding wage increase, or that staffing could be cut by the same percentage without other consequences. - Source: [Quarterly Journal of Economics](https://academic.oup.com/qje/article/140/2/889/7990658) #### 5.2.2 The 15 percent that wasn't one number Access increased issues resolved per hour by approximately 15 percent on average. Read that again, and focus on the words "on average." The gains were larger among less-experienced and lower-skilled workers. The most skilled workers saw little productivity improvement. And the study reports evidence of small quality declines for some higher-skilled workers. ([Published study](https://academic.oup.com/qje/article/140/2/889/7990658)) *Figure 5.2 (interactive on the page).* Here's the part most people skip. The internal structure of this result matters more than the headline. The gains concentrated among newer and lower-skilled workers. That pattern is consistent with the assistant passing along pieces of the organization's accumulated know-how to the people who hadn't absorbed it yet. It's the same compression pattern Noy and Zhang observed in their [professional writing experiment](https://www.science.org/doi/10.1126/science.adh2586), which [Chapter 2](#ai-productivity-evidence) covers in detail: the tool narrows the gap between the least and most experienced. At the other end, the most skilled workers gained little and showed some quality deterioration. That's consistent with a tool whose suggestions are tuned to the typical case becoming a distraction, or an anchor, for people whose judgment already exceeds it. If you've ever had a well-meaning helper hand you the textbook answer when you already knew the exception, you know the feeling. Put those together and something odd emerges. A deployment that quotes the 15 percent average is describing a workforce that does not exist. No individual worker experienced the average. The workers nearest the tool's design center (the experienced, skilled agents whose best practices the tool arguably drew on) benefited least. #### 5.2.3 What the study doesn't tell you about wages This is evidence of a particular workflow's effect. It is not a universal labor-productivity multiplier you can apply to any job. It does not establish that the average worker received a corresponding wage increase. It does not establish that staffing could be cut by the same percentage without other consequences. ([Study scope and outcomes](https://academic.oup.com/qje/article/140/2/889/7990658)) The 15 percent was the company's throughput. Whether any of it reached the agents' paychecks is a question the study could not answer. That's not a criticism of the researchers; it's a statement about what the study measured. It is, however, a sharp warning about how the result gets used. If you hear someone say "AI raised productivity 15 percent, so workers will earn more," they've converted a throughput number into a wage claim the evidence never made. That's the AI impact on wages question in one sentence: we don't know from this study, and anyone telling you otherwise is forecasting. One bookkeeping note. This study also appears in [Chapter 4](#ai-college-workforce-training), where it anchors the discussion of onboarding new workers. Its repeated use here connects career stages. This report counts it once, not twice, as evidence. #### 5.2.4 Common misreadings of the customer support result **Misreading 1: "AI makes workers 15 percent more productive."** It raised issues resolved per hour by about 15 percent on average for one workflow at one company. That's a narrower sentence, and it's the true one. **Misreading 2: "So the company can cut 15 percent of its staff."** The study explicitly doesn't establish that. Customer demand, quality, escalation rates, and the knowledge that experienced agents carry all sit outside the throughput number. **Misreading 3: "AI helps everyone."** The gains were uneven. The most skilled agents gained little, and some showed small quality declines. **Misreading 4: "AI is bad for experts."** The study shows little productivity gain and some small quality declines among some higher-skilled workers in this workflow. It does not show experts were harmed across the board, or that the same would happen in another job. #### 5.2.5 What this means for you **If you're newer in your job:** the pattern suggests assistance may help you get up to speed faster. That's real value. But speed on assisted tasks isn't the same as knowing the job. [Chapter 4](#ai-college-workforce-training) describes a technical-learning experiment in which assisted work coexisted with weaker unaided understanding. Keep checking whether you could do the task without the tool. **If you're experienced:** watch for the anchor effect. If the tool's suggestion pulls you toward the typical answer when your judgment says the case is unusual, trust the judgment and document why. Your quality is part of your value, and the study suggests it's the thing most at risk for people like you. **If you're a manager:** don't report the average without the spread. Ask how gains and quality changed by experience level, and ask what happened to pay. ### 5.3 Exposure Is Not Displacement: ILO Generative AI Exposure and the Danish Evidence The customer support study tells you what happened in one workplace. It can't tell you what's happening across an economy. Two evidence programs measure the labor market at that larger scale, and together they tell a two-part story that resists every sweeping narrative. #### 5.3.1 The ILO exposure index: one in four, and 3.3 percent The International Labour Organization's refined 2025 index estimates that approximately one in four workers globally is in an occupation with some exposure to generative AI. It estimates that 3.3 percent of global employment falls in its highest exposure category. ([ILO working paper](https://www.ilo.org/publications/generative-ai-and-jobs-refined-global-index-occupational-exposure)) The index classifies task exposure. It is not an estimate that one in four jobs will disappear. The distance between those two sentences is where most public commentary lives. **Evidence card** - Title: Generative AI and Jobs: A Refined Global Index of Occupational Exposure, ILO (2025) - Design: Occupational exposure index that classifies which tasks generative systems could, in principle, affect. - Population: Global employment, classified by occupation. - Finding: Approximately one in four workers globally is in an occupation with some exposure to generative AI; 3.3 percent of global employment falls in the highest exposure category. - Limit: Measures task exposure, not job loss. Contains no information about adoption, demand, regulation, costs, complementary skills, or employer decisions. - Source: [International Labour Organization](https://www.ilo.org/publications/generative-ai-and-jobs-refined-global-index-occupational-exposure) What does "exposure" actually mean? It measures which tasks a system could, in principle, affect. Whether it actually affects them depends on things the index doesn't contain: adoption, demand, regulation, costs, complementary skills, and employer decisions. Exposure can legitimately motivate investigation. It's a good reason to look at how tasks are changing, what training people need, and what choices an organization is making. It cannot determine an individual worker's future. So here's a translation I'd like you to carry around. When you hear "exposed occupation," hear "occupation whose task list overlaps the tool's capabilities." Don't hear "occupation on its way out." Those are different claims, and only the first one is what the index measures. #### 5.3.2 The Danish study: still waters, rapid currents The second program is a study of Denmark's labor market. Its March 2026 revision is now titled *Still Waters, Rapid Currents*. It links survey evidence with administrative records and finds precise null effects on earnings and recorded hours over its early observation period. It rules out effects larger than approximately 2 percent for those measured outcomes over roughly two years after ChatGPT's launch, while documenting changes in work and occupational movement. ([Revised NBER working paper](https://www.nber.org/papers/w33777)) "Precise null" is a phrase worth understanding. A lot of studies fail to find an effect because they're too small or too noisy to detect one; that's an imprecise null, and it tells you very little. A precise null is different. It means the study had enough statistical power to say not just "we didn't find an effect" but "if there were an effect on these outcomes, it would have to be smaller than about 2 percent." That's a real finding. **Evidence card** - Title: Still Waters, Rapid Currents (NBER working paper, revised March 2026) - Design: Links survey evidence with administrative labor-market records. - Population: Workers in Denmark over roughly two years after ChatGPT's launch. - Finding: Precise null effects on earnings and recorded hours; effects larger than approximately 2 percent on those measured outcomes are ruled out. The study also documents changes in work and occupational movement. - Limit: A working paper about one country, one period, and a specific set of outcomes. It is not evidence that future displacement cannot occur, that no individual was affected, or that task changes have no welfare consequences. - Source: [National Bureau of Economic Research](https://www.nber.org/papers/w33777) This is a working paper about a particular country, period, and set of outcomes. It is not evidence that future displacement cannot occur. It is not evidence that no individual was affected. It is not evidence that task changes have no welfare consequences. What it does establish is economically meaningful. Two years after the most widely adopted general-purpose AI tool in history entered one of the world's most digital labor markets, the measured earnings and hours effects were statistically indistinguishable from zero, and tightly enough to exclude anything above roughly 2 percent. The water was still. The study's own title records the other half. The currents beneath the surface (task content, occupational movement) were moving. The measured outcomes simply hadn't caught them yet. That's why I'd never cite this study as proof that "AI won't affect jobs." It says the earnings and hours line hadn't moved in that window. It also says the work itself was changing. *Figure 5.1 (interactive on the page).* #### 5.3.3 Holding both findings together Held together, the two programs discipline prediction in both directions. The exposure index says the task overlap is widespread and real. The Danish study says that in the first two years, in one country, that task overlap did not convert into measured earnings or hours displacement. Both can be true at the same time, because the conversion from exposure to displacement depends on decisions (adoption, pricing, reorganization, demand) that neither study measures. Anyone who tells you which way those decisions will fall is forecasting, not reporting. That includes people selling fear and people selling reassurance. It includes me, which is why I'm not going to give you a forecast. Here's a simple table that separates what each study measured from what people often claim it says. | Study | What it measured | What it found | What it does not show | |---|---|---|---| | ILO refined index (2025) | Task overlap between occupations and generative AI | About one in four workers globally in an occupation with some exposure; 3.3% of global employment in the highest category | That any share of jobs will disappear | | Danish study (March 2026 revision) | Earnings and recorded hours, linked survey and administrative data | Precise nulls; effects above roughly 2% ruled out over about two years; changes in work and occupational movement documented | That future displacement can't happen, or that no individual was affected | | Customer support study (2025) | Issues resolved per hour at one company | About 15% average increase, concentrated among newer and lower-skilled agents | A wage increase, or that staff could be cut by 15% | #### 5.3.4 Common misreadings of exposure and displacement **"One in four jobs will be replaced by AI."** The ILO index says about one in four workers is in an occupation with some exposure. Exposure is task overlap. The index doesn't estimate replacement. **"The Danish study proves AI doesn't cost jobs."** It found precise nulls on earnings and recorded hours over roughly two years, in one country. It documented changes in work and occupational movement. It explicitly doesn't rule out future displacement or individual harm. **"If my occupation is in the top exposure category, I'm in trouble."** The top category covers 3.3 percent of global employment. Being in it tells you the task overlap is high. It tells you nothing about what your employer will decide, what customers will demand, or what new tasks will appear. **"No effect on earnings means no effect on workers."** Earnings and hours are two outcomes. Task content, stress, autonomy, and the path to the next job are others. The Danish study's own title tells you the currents were moving. ### 5.4 What to Do If Your Occupation Is "Exposed" Let's say you've seen a list, or an online calculator, or a news graphic, and your occupation is on it. Maybe it's in the highest category. Here's practical guidance built only on what the evidence in this chapter supports. It's not a forecast. It's a way to keep your options open while the actual decisions are still being made. #### 5.4.1 Translate "exposed" into your own task list The ILO index works at the level of occupations and tasks. You work at the level of your actual week. Sit down and list what you do: the tasks, roughly how much time each takes, and which ones require your judgment, your physical presence, your license, or your relationships. Then sort them into three rough piles: - **Tasks a tool could plausibly draft or retrieve.** First drafts, summaries, standard responses, looking things up. - **Tasks where the tool might help but you must verify.** Anything where a fluent wrong answer would cause harm. - **Tasks that remain human by requirement or by nature.** Safety-critical physical work, licensed judgments, relationships with customers or patients, supervising others. This is exactly the first stage of the working-life process in Section 5.6. You're doing for yourself what a responsible employer should do for the whole team. #### 5.4.2 Find out what your employer is actually deciding Exposure becomes displacement (or doesn't) through decisions. So find out what decisions are on the table. Good questions to ask your manager, your union representative, or HR: - Which tools are we adopting, for which tasks, and on what schedule? - How will we measure whether the tool helps? Will quality and rework be measured, or only speed? - If time is saved, what happens to it: pay, workload, new responsibilities, or reduced staffing? - Will workers be able to see what's measured about them and challenge incorrect records? - What training is offered, and will it be tied to actual job requirements? You may not get complete answers. But the answers you get, and the ones you don't, tell you a lot. #### 5.4.3 Protect your unaided skill The customer support study found the biggest gains among newer workers. [Chapter 4](#ai-college-workforce-training) describes an experiment in which programmers learning an unfamiliar library with an assistant scored lower on an unaided comprehension quiz afterward ([Shen and Tamkin preprint](https://arxiv.org/html/2601.20245v1)). Put those two findings side by side and a practical rule falls out: use the tool, but regularly check that you can still do the core of your job without it. Your unaided competence is portable. It goes with you if the tool changes, the vendor changes, or you change employers. #### 5.4.4 If you're thinking about a career change Career change AI tools can produce a polished resume and cover letter in minutes. A polished application should not be mistaken for readiness to perform the role. Before you invest in a pivot: - Get verified job requirements from actual employers, not from a generated summary. - Look for independent skill assessment, not just course completion. - Ask any training program for its placement, retention, and earnings records, and whether it has a comparison group. Section 5.5 explains why. #### 5.4.5 A worked example (hypothetical) Here's a hypothetical to make this concrete. No real person, and no statistics beyond what the chapter already cites. Imagine a claims processor at a regional insurer. She reads a news story saying her occupation is in the "highest exposure" category and spends a sleepless night assuming her job is gone. In the morning, she lists her week. A good part of it is reading documents and drafting standard letters, the kind of work a tool could plausibly draft. Another part is spotting inconsistencies in claims and deciding when something needs a closer look, work where a fluent wrong answer would be costly. And some of it is talking with upset customers and training the newest hire, work that stays human. She asks her supervisor the questions in Section 5.4.2. She learns that the company is piloting a drafting tool, but hasn't decided what to do with any time saved, and hasn't planned to measure rework. That tells her two things. First, the decision about her job hasn't been made yet, so exposure hasn't become anything. Second, there's a gap she can help fill: she volunteers to help track errors during the pilot, which puts her closer to the judgment work and gives her a record of quality she can point to later. She also keeps doing a share of her drafting without the tool, so her unaided skill stays sharp. Nothing in this example guarantees her job. It replaces a forecast she couldn't verify with information she could. ### 5.5 Does Job Training Work? What WorkAdvance Shows After Ten Years If the answer to "will AI take my job" is "maybe some tasks, and the decisions aren't made yet," the natural next question is whether retraining works. WorkAdvance is this chapter's non-AI benchmark, and it earns its place by testing what real workforce development looks like when evaluated rigorously. ([MDRC ten-year report](https://www.mdrc.org/sites/default/files/WorkAdvance_10-Year_Report_MDRC.pdf)) #### 5.5.1 What WorkAdvance tested The program tested employer-connected sectoral training. That means training built around the needs of specific industries and connected to employers in them, rather than generic job-readiness classes. It's the kind of intervention that AI enthusiasts sometimes assume becomes obsolete the moment a tool arrives. The randomized evaluation followed 2,564 participants across four providers. Recruitment began in 2011 to 2013. The evaluation used long-term administrative earnings records, which means outcomes came from official records rather than from what participants remembered or reported. And because it was randomized, the control group was set by lottery, which makes the comparison clean. **Evidence card** - Title: WorkAdvance Ten-Year Evaluation, MDRC - Design: Randomized evaluation of employer-connected sectoral training with long-term administrative earnings outcomes. - Population: 2,564 participants across four providers; recruitment began 2011 to 2013. - Finding: At St. Nicks Alliance, year-ten earnings averaged $35,218 for the program group and $26,638 for the control group, a difference of $8,580 (p = .005). The other three providers did not show statistically significant effects on the year-ten confirmatory earnings outcome. - Limit: Not a test of generative AI training. The result does not show that one provider's approach will transfer elsewhere; it shows that durable gains are possible and that implementation and context matter. - Source: [MDRC ten-year report](https://www.mdrc.org/sites/default/files/WorkAdvance_10-Year_Report_MDRC.pdf) #### 5.5.2 The year-ten results, provider by provider At St. Nicks Alliance, year-ten earnings averaged $35,218 for the program group and $26,638 for the control group. That's a difference of $8,580, with a reported p-value of .005. The other three providers did not show statistically significant effects on the report's year-ten confirmatory earnings outcome. ([Ten-year results](https://www.mdrc.org/sites/default/files/WorkAdvance_10-Year_Report_MDRC.pdf)) *Figure 5.3 (interactive on the page).* The provider variation is the finding. One provider's program produced a large, durable earnings gain a decade out. Three structurally similar providers produced none that reached statistical significance. That's worth sitting with. Same program model. Same evaluation. Same randomized design. Wildly different results depending on who ran it and where. The result is not a claim that St. Nicks Alliance's approach will transfer to your town. It demonstrates two things at once: that durable gains from training are possible, and that implementation and context decide whether you get them. #### 5.5.3 What WorkAdvance does and doesn't tell us about AI WorkAdvance reminds us of a distinction this report enforces everywhere: the trial did not test a generative AI training curriculum. It tested sectoral training. So whatever AI can add to training, the claim "the tool replaces the program" has no support here. The program's own results depend on details no tool configures: the provider, the employer connections, the local labor market. If three of four well-designed providers didn't produce a significant ten-year earnings gain, a chatbot that promises to "reskill" you by itself is making a claim nobody has tested. The reverse misreading is also wrong. WorkAdvance doesn't show that training is futile. One provider produced a gain of $8,580 per year, ten years out, in a randomized design. That's the kind of result any workforce leader should want to understand and try to reproduce, carefully, with measurement. #### 5.5.4 What this means for workers weighing a training program Ask any program three questions. Does it connect to documented employer demand? Does it track completion, placement, retention, and earnings? Does it have, or will it allow, a comparison group? A program that can answer all three is behaving like the evidence says training should behave. A program that answers with testimonials is asking you to trust it. For a workforce discussion involving an infrastructure company like mine, the implications are concrete: connect training to documented employer demand, and track completion, placement, retention, and earnings. That is not a basis for advertising a guaranteed return to a particular course or facility. [Chapter 4](#ai-college-workforce-training)'s permitted-claims discipline applies unchanged here. ### 5.6 The Seven-Stage Working-Life Process So far, I've described what studies found. Now here's what I propose. The following process is for employers, workforce boards, training institutions, and workers. It is evidence-informed, but it has not been evaluated as an integrated program. I'm presenting it as a proposal, not a proven intervention. | Stage | Decision | Required evidence | |---|---|---| | 1. Map work | Which tasks are changing, and which responsibilities remain human? | Observed task inventory, safety and professional requirements, worker input | | 2. Establish baseline | What constitutes successful work today? | Quality, throughput, rework, customer outcomes, workload, and compensation | | 3. Choose support | Is the need information, practice, workflow redesign, or formal qualification? | Comparison with simpler tools and human support | | 4. Test bounded use | Does assistance help without degrading judgment? | Comparative performance, error analysis, unaided skill checks | | 5. Train and qualify | Can the person perform the actual job? | Independent demonstration under relevant conditions | | 6. Distribute gains | How are time, pay, workload, and responsibility affected? | Documented organizational decisions, not an assumed wage pass-through | | 7. Follow the transition | Does the change improve employment quality over time? | Retention, earnings, hours, injury or error records, worker experience | *Figure 5.4 (interactive on the page).* #### 5.6.1 Why each stage is there **Map work.** Exposure lists work at the occupation level. Real decisions happen at the task level, in a specific workplace. Observed task inventories (what people actually do, not what the job description says) plus worker input are how you find out which tasks are changing and which responsibilities have to stay human for safety, professional, or legal reasons. **Establish baseline.** You can't tell whether something improved if you didn't measure it before. The baseline includes compensation and workload on purpose. If you measure only throughput before and after, you'll only be able to report on throughput. **Choose support.** Not every problem needs an AI tool. Sometimes the need is better information, more practice, a redesigned workflow, or a formal qualification. Comparing with simpler tools and with human support keeps you clear about whether the new tool is the right answer or just the newest one. **Test bounded use.** Try it on defined tasks, compare performance, analyze errors, and check unaided skill. That last piece matters because [Chapter 2](#ai-productivity-evidence) documents a [consulting experiment](https://mitsloan.mit.edu/sites/default/files/2023-10/SSRN-id4573321.pdf) where assistance helped on some tasks and hurt on a task outside the tool's reliable competence, and [Chapter 4](#ai-college-workforce-training) documents weaker unaided comprehension after assisted learning. **Train and qualify.** The test is whether the person can perform the actual job, demonstrated independently under relevant conditions. Course completion is not the test. **Distribute gains.** This is a decision, not an outcome. More on this in Section 5.7. **Follow the transition.** Watch retention, earnings, hours, injury or error records, and what workers say about the experience over time, not just in the launch month. #### 5.6.2 Two design commitments Two commitments deserve emphasis. First, the worker should be able to understand what is measured and challenge incorrect records. Monitoring intensity is not itself a productivity benefit, and an employer's output gain should not be silently represented as an improvement in employee welfare. The 15 percent in Section 5.2 was the company's throughput. Whether any of it reached the agents is a question the study could not answer, and a process that never asks it has taken the employer's side by default. Second, the gains-distribution stage is a decision, not an outcome. Time saved can become higher pay, lighter workload, new responsibilities, or layoffs. Which one happens is an organizational choice that evidence should document rather than assume. #### 5.6.3 A worked example (hypothetical) Here's a hypothetical walk-through. Picture a mid-sized property management company adopting an AI assistant for tenant communications. **Map work:** The team lists the tasks. Routine maintenance acknowledgments and lease reminders could be drafted. Disputes, fair housing questions, and anything involving safety stay with trained staff, by requirement. **Establish baseline:** Before launch, the company records response times, complaint rates, how often letters need rework, staff workload, and current pay bands. **Choose support:** The team asks whether better templates would solve most of the problem. Some of it, yes. The pilot proceeds only for the parts templates don't handle. **Test bounded use:** For a defined period, some staff use the tool on routine messages. The company tracks errors, especially confident wrong statements about lease terms, and has staff periodically handle messages without the tool. **Train and qualify:** New staff must show they can handle a dispute call and a lease question without assistance before working on their own. **Distribute gains:** Management writes down what happens to saved time. In this hypothetical, they choose to move staff toward in-person inspections and tenant relationships rather than cut positions, and they say so in writing. A different company might choose otherwise; the point is that the choice is recorded. **Follow the transition:** Over the following year, they track retention, error records, and what staff and tenants say. Notice what this example doesn't include: a promised productivity percentage. The process doesn't need one. It needs the records. ### 5.7 For Employers: How to Split the Gains in Good Faith If you run a business, this section is for you. I run one too, and I'll tell you what I believe: the question of how productivity gains get split is the most important decision in this whole debate, and it's yours. #### 5.7.1 The four places saved time can go When a tool saves time, that time goes somewhere. Broadly, it can become: 1. **Higher pay** for the people whose work got more productive. 2. **Lighter workload,** meaning less overtime, less burnout, more time on hard cases. 3. **New responsibilities,** meaning people take on work that used to be out of reach. 4. **Reduced staffing,** meaning layoffs or not replacing people who leave. All four are legal. All four are choices. The evidence in this chapter doesn't tell you which to choose. What it tells you is that none of them happens automatically. The customer support study measured throughput; it did not show a wage pass-through. If your plan assumes one will happen by itself, you don't have a plan. #### 5.7.2 A checklist for employers - **Write down the decision.** Before rollout, document what you intend to do with saved time. After rollout, document what you actually did. - **Report both columns.** Put business outcomes (throughput, quality, customer outcomes) beside worker outcomes (earnings, hours, workload, retention). A report with only the business column is an investor update, not an evaluation. - **Measure by experience level.** The customer support study found gains concentrated among newer workers and small quality declines among some of the most skilled. Your averages will hide the same kind of spread unless you break them out. - **Protect your experts' judgment.** Give experienced staff explicit permission to override the tool and a way to flag when suggestions pull them in the wrong direction. - **Let workers see and challenge their records.** More monitoring is not more productivity. - **Count the people who leave.** If workers who struggle with the tool quit or stop using it, your remaining-user numbers will look better than reality. Section 5.9 explains why. - **Don't advertise a wage effect you haven't measured.** If pay went up, show the records. If it didn't, don't imply it did. #### 5.7.3 Why this is in your interest I'm an operator, and I'll give you an operator's reason, not a sermon. The skilled workers in the customer support study gained little and some showed small quality declines. Those are the people who carry your institutional knowledge, the knowledge the tool arguably draws on for everyone else. If you treat the tool's average as a reason to squeeze them, you're drawing down the very asset that made the tool useful. Splitting gains in a way you can defend in writing isn't charity. It's how you keep the people who know how the place actually works. ### 5.8 Boundaries by Work Context Different working contexts require different boundaries. The research reviewed here does not justify filling the gaps between them with invented effect estimates, so I won't. The following are proposed implementation boundaries, not study findings for each named population. **Established professionals.** Use assistance for preparation, retrieval, and drafting while testing whether review remains substantive. Faster output should not remove responsibility for judgments the professional is qualified to make. [Chapter 4](#ai-college-workforce-training)'s technical-learning experiment shows that assisted execution can coexist with eroding comprehension in exactly this kind of population. **Trades and technical workers.** Use bounded practice and documentation without replacing physical demonstration, supervised experience, or required qualification. A generated explanation of a procedure is not authorization to perform hazardous work. I've spent enough time around high-voltage and heavy mechanical systems to say this plainly: the written procedure is where safety starts, and the supervised hands-on sign-off is where it's proven. **Small businesses.** Compare total operating costs and corrected outcomes, not only the subscription price. A service that saves drafting time but creates compliance or customer-service rework may not be beneficial. The correction column of the ledger decides, not the drafting column. **Workers changing careers.** Require verified job requirements and independent skill assessment. A polished application should not be mistaken for readiness to perform the role. **Workers with care responsibilities or disabilities.** Measure accessibility and usable flexibility directly. Do not assume that remote software access removes scheduling, assistive-technology, or transportation barriers. #### 5.8.1 A small-business worked example (hypothetical) Imagine a two-person bookkeeping practice that subscribes to a drafting assistant for client emails and monthly summaries. The subscription is cheap, and the drafts come fast. But a few summaries mislabel expense categories, and catching those errors takes the owner's evening. A client notices one before she does. On the drafting column, the tool looks like a win. On the correction column, it's closer to a wash, maybe worse once you count the client's trust. This is the small-business version of the whole chapter: you have to count the rework, not just the speed, before you know whether the tool is helping. No figure in this example is a statistic; it's a way to set up your own ledger. ### 5.9 Measuring Benefit Without Hiding Harm The proposed evaluation reports business and worker outcomes side by side: task quality, total review time, customer outcomes, worker earnings, hours, workload, retention, and independent competence where relevant. The pairing is the safeguard. A program that reports only the business column is an investor update, not an evaluation. #### 5.9.1 The attrition trap Attrition needs special treatment, because it's the subtlest failure mode in this research. If people who struggle with a system leave the workplace or stop using the tool, then measuring only the remaining users overstates success by construction. Think about what that means. Suppose a company rolls out a tool, and the workers it doesn't suit either quit or quietly stop using it. Six months later, the company measures the users who remain and reports great numbers. Those numbers can be accurate about the people measured and still be misleading about the program. The surviving-user average is the measurement equivalent of the consulting study's outside-frontier task, described in [Chapter 2](#ai-productivity-evidence): it looks like evidence, and it's an artifact of who got counted. #### 5.9.2 Offer versus completion Training evaluation should retain a comparison group where feasible and report the offer of participation separately from completion. WorkAdvance's provider variation is a reminder that successful participants and successful programs are not interchangeable analytical categories. ([MDRC evaluation](https://www.mdrc.org/sites/default/files/WorkAdvance_10-Year_Report_MDRC.pdf)) Here's why the distinction matters. "People who completed our program earn more" may just mean that the people most likely to succeed anyway were the ones who finished. "People offered our program earn more than a comparable group who weren't offered it" is a claim about the program. The first is about participants. The second is about the program. WorkAdvance's randomized design is what let it make the second kind of claim, and it's why the provider-by-provider results mean something. #### 5.9.3 Questions to ask any AI workplace or training report - Show me the denominator. How many people started, and how many are in the numbers? - Is there a comparison group? How was it formed? - Are worker outcomes (earnings, hours, workload, retention) reported next to business outcomes? - Are results broken out by experience or skill level? - Is quality reported, or only speed? - Are the results from people offered the tool or program, or only from those who stuck with it? ### 5.10 For Workforce Boards: What to Demand Before You Fund or Endorse Workforce boards sit at a pressure point. You're asked to endorse training programs, partner with employers, and respond to announcements of new facilities. Here's what the evidence in this chapter supports. #### 5.10.1 Fund implementation, not labels WorkAdvance is the clearest lesson: one of four providers produced a significant ten-year earnings gain, and three did not. The program label was the same. What differed was implementation and context. So when a provider proposes an "AI-ready" or "future of work" curriculum, ask about the things that made the difference in a real trial: connection to documented employer demand, the quality of the provider, and whether outcomes will be tracked. #### 5.10.2 Require outcome records from the start Build these into every grant or partnership agreement: - Completion, placement, retention, and earnings, tracked over time. - Offer and completion reported separately. - A comparison group where feasible. - Independent skill demonstration, not just certificates. - Disclosure of results for participants who leave, not only those who finish. #### 5.10.3 Separate commitments from outcomes A training seat is a commitment. A completed credential is an outcome. A placement in a job is a different outcome. A job still held a year later is another. Keep them in separate columns in every report you publish. [Chapter 4](#ai-college-workforce-training) lays out the claims that are and aren't permitted when training meets infrastructure investment, and it applies here unchanged. #### 5.10.4 Treat exposure data as a starting point The ILO index can help you decide which occupations in your region deserve a closer look. It can't tell you which workers will lose jobs. Use it to prioritize conversations with employers about task changes, not to announce which jobs are disappearing. #### 5.10.5 A worked example (hypothetical) Imagine a county workforce board hears that a large technology facility has been announced nearby. A local official proposes a new training program marketed as preparing residents for "hundreds of jobs." A board applying this chapter would ask: Which employer has documented demand for which roles? Are those construction roles or ongoing operating roles? (Section 5.11 explains why that matters.) How many operating roles are direct employees versus contractors? What qualifications do they require? Will the program track placement, retention, and earnings, and report offer separately from completion? If the answers come back as a press release, the board has learned that the demand isn't documented yet. It can still fund exploration and employer conversations. What it shouldn't do is market a guaranteed job pipeline that no employer has committed to. ### 5.11 Announced Capital Is Not a Local Job: The Tracker Boundary This section is about my own industry, so I'll be especially careful. Capital, permits, and project schedules can identify questions about future labor demand. They do not establish vacancies, hiring dates, qualifications, wage levels, or durable employment. Those must come from employers, executed contracts, training partners, and observed hiring outcomes, not from project announcements alone. ([Capital Atlas scope](https://savrn.com/data-center-capital-atlas), [Permits Tracker scope](https://savrn.com/data-center-permits-tracker), [Delay Watchlist scope](https://savrn.com/data-center-delay-tracker)) The forbidden inference deserves exact statement, because it's the one Savrn has the strongest commercial temptation to make: **announced capital is not a local job.** A billion-dollar facility announcement may produce hundreds of construction jobs for a defined period and dozens of permanent operating roles. Or the schedule may slip. Or the phase may be cancelled. Or the work may be contracted to specialists brought in from elsewhere. The seven Savrn trackers are instruments for discovering which of those worlds a community is in. They are not evidence of employment outcomes, and the chapters of this report that quote them never use them as such. A tracker entry starts a question. It never ends one. #### 5.11.1 How to use the trackers for job questions If you're a resident, reporter, or board member, here's how I'd use them. Use the [Capital Atlas](https://savrn.com/data-center-capital-atlas) to see what's been announced and ask who the employer is and what roles are documented. Use the [Permits Tracker](https://savrn.com/data-center-permits-tracker) to see what's been permitted and ask what construction schedule the permit implies and who's doing the work. Use the [Delay Watchlist](https://savrn.com/data-center-delay-tracker) to see whether a project's timeline has moved, and ask what that does to any training pipeline built around it. In every case, the answer you're looking for comes from an employer, a contract, or a hiring record, not from the tracker. > **Operator's note: Construction jobs and operating jobs are different animals** > > When you build large sites, you learn fast that the construction workforce and the operating workforce are two different things. The construction crews arrive in waves, hit a peak, and move on to the next project. The operating team is smaller, stays, and needs a different set of skills. Treating them as one number is the most common job-count error I see. > > Virginia's Joint Legislative Audit and Review Commission put numbers on this for data centers. Its 2024 report describes a typical 250,000-square-foot facility as employing about 50 full-time workers in ongoing operations, roughly half of them contractors, while construction can involve a peak workforce of around 1,500 over a roughly 12 to 18 month build period. Those are different categories over different time periods, and neither one is the other. ([JLARC data center report](https://jlarc.virginia.gov/landing-2024-data-centers-in-virginia.asp)) [Chapter 10](#data-centers-community-impact) goes deeper on local jobs, taxes, and community effects. > > I hold Savrn to this too. Whatever we build, the right local question isn't "how big is the investment." It's "how many construction job-years, how many ongoing employees, how many contractors, at what wages and qualifications, and how many of them are residents." If we can't answer that with records, we haven't earned the claim. ### 5.12 Conclusion: What the Evidence Supports About AI and Your Job The defensible working-life goal is not to predict a single labor-market future. It's to improve a worker's practical ability to adapt, verify competence, and share in measured gains, while preserving evidence of adverse outcomes. The research supports optimism of a disciplined kind. AI assistance measurably raised throughput in one well-studied customer support workflow, with the gains concentrated among newer workers and small quality declines among some of the most skilled. Sectoral training produced a large, durable earnings gain at one of four providers, and none that reached significance at the other three. And two years of data from Denmark after ChatGPT's arrival showed earnings and hours effects near zero, precise enough to rule out anything above roughly 2 percent, with task content and occupational movement shifting underneath. So, will AI take my job? The future isn't written in any of these numbers. It will be written in the adoption, pricing, and organizational decisions that the numbers can't see. That's why the process in this chapter puts the worker's outcomes beside the company's. And it's why the burden of proof stays where this report has kept it from the first page: on the claim, not the skeptic. Capability is not benefit. Who gets the benefit is a decision, and you have every right to ask who's making it. ### 5.13 Frequently Asked Questions #### Will AI take my job? The best evidence reviewed here doesn't support a single answer. The ILO estimates about one in four workers globally is in an occupation with some exposure to generative AI, but exposure means task overlap, not job loss. A Danish study found precise null effects on earnings and hours, ruling out effects above roughly 2 percent over about two years after ChatGPT's launch, while noting changes in work. What happens next depends on employer decisions. #### What does it mean if my occupation is exposed to AI? In the ILO's refined 2025 index, exposure means an occupation's tasks overlap with what generative AI could, in principle, affect. About one in four workers globally is in an occupation with some exposure, and 3.3 percent of global employment is in the highest category. The index doesn't include adoption, demand, costs, regulation, or employer decisions, so it can't tell you whether your job will disappear. #### Did AI make customer service workers more productive? In a study of 5,172 agents at one business-software company, AI assistance raised issues resolved per hour by about 15 percent on average. The design was quasi-experimental with staggered adoption, not a randomized trial. Gains were larger for newer and lower-skilled agents; the most skilled saw little improvement and some showed small quality declines. The study does not show that wages rose. #### Does AI raise wages? The evidence reviewed here doesn't show it. The customer support study measured throughput, about 15 percent more issues resolved per hour on average, not pay, and does not establish a corresponding wage increase. The Danish study found precise null effects on earnings, ruling out effects above roughly 2 percent over about two years. Whether productivity gains become higher pay is an employer decision. #### Has AI already reduced earnings or hours? Not in the Danish evidence. The March 2026 revision of the study, titled Still Waters, Rapid Currents, links survey and administrative data and finds precise null effects on earnings and recorded hours over roughly two years after ChatGPT's launch, ruling out effects above about 2 percent. It covers one country and period and does not prove future displacement can't happen or that no individual was affected. #### Does job training work if I need to change careers? Sometimes, and it depends heavily on who runs it. In the randomized WorkAdvance evaluation of 2,564 participants across four providers, St. Nicks Alliance participants earned $35,218 in year ten versus $26,638 for controls, a gain of $8,580 (p = .005). The other three providers showed no statistically significant year-ten earnings effect. The trial tested sectoral training, not AI training. #### Will a new data center in my area create local jobs? An announcement alone can't tell you. Virginia's JLARC describes a typical 250,000-square-foot data center as employing about 50 full-time workers in ongoing operations, roughly half contractors, while construction can peak around 1,500 workers over roughly 12 to 18 months. Those are different jobs over different periods. Real answers come from employers, executed contracts, and observed hiring records, not capital announcements. #### What should I ask my employer about AI at work? Ask which tools are being adopted and for which tasks, how success will be measured (including quality and rework, not only speed), and what happens to saved time: higher pay, lighter workload, new responsibilities, or reduced staffing. Ask whether you can see and challenge what's measured about you. The customer support study showed throughput gains without establishing wage gains, so the split is a decision worth asking about.