Faster, But Not Freer: How American Workers Experience AI and Productivity

Cover Memo

This report draws on the AI@Work survey (CHIP50 Wave 38.6), a survey of 23,462 U.S. adults (weighted n = 23,485) fielded June 21–July 13, 2026 to boost statistical power on artificial-intelligence questions. All percentages are weighted to U.S. Census population targets and are population-representative; unweighted counts are reported as a reliability guide only.

These are interim estimates. The AI@Work survey launched June 21, 2026 and data collection is ongoing; the figures in this report reflect responses received through July 13, 2026 and will be revised in a later release.

One caveat governs the entire report: every productivity measure here is a worker's self-reported perception, not measured output. The data show what workers believe AI does to their productivity, time, and workload — not an objective change in output. Read every figure with that in mind.

The productivity items form a conditional cascade, and each figure states its own base. The own-experience battery (ai_gains) was asked of all employed respondents who use AI for work at all (ai_work ≠ Never; unweighted n ≈ 6,928). The "free time" follow-ups narrow from there: whether AI frees time was asked only of workers who first said AI raises their productivity (n ≈ 2,639); whether that time becomes more work, only of those who said it frees time (n ≈ 2,003). Occupation-productivity, job-loss, and outlook items were asked of a broader employed base (n ≈ 12,400); the workplace-pressure battery of employed full- and part-timers (n ≈ 10,400). The coworker-productivity item (peer_impact; n ≈ 7,070) and the two work-penetration items (pew_extent, pew_extent2; n ≈ 12,000–12,450) round out the picture. Do not read the narrow-base figures as shares of all workers.

Three ordinal items in this supplement are stored in reverse of their display order, verified against the questionnaire and the data: ai_importance, ai_shadow_norm, and the ai_pressure battery (5 = strongly agree). See Methods.

Key Takeaways

Introduction

Whether artificial intelligence actually makes workers more productive is one of the central economic questions of the decade, and the evidence is still forming. Controlled studies of specific tasks — customer support, coding, writing — have found sizable gains, while economy-wide measures remain modest. Recent Federal Reserve research based on a national survey by Alexander Bick, Adam Blandin, and David Deming estimated that workers using generative AI saved about 5.4% of their weekly hours — roughly 2.2 hours on a 40-hour week — translating into a productivity boost of around 1.1% across the whole workforce (St. Louis Fed). Industry analyses report that a minority of frequent users save four or more hours weekly (ITIF).

Those studies measure adoption and hours. What they say less about is how workers experience the change — whether the time AI frees becomes rest or more work, whether employers now expect more, and who workers think ultimately captures the gains. The AI@Work survey asks exactly these questions of a large, population-representative sample of American workers.

The picture is neither the utopia of effortless productivity nor the dystopia of mass displacement. Most AI-using workers report genuine gains — but those gains flow into more output rather than more free time, arrive alongside rising employer expectations, and are seen by workers as accruing more to employers than to themselves. AI is making many workers feel faster. It is not, by their own account, making them freer.

Most users say AI makes them faster

Workers who use AI report clear net-positive effects on their work — but a meaningful minority report the opposite.

Asked how using AI has affected their work (workers could select any that applied), 45% of AI-using workers said it makes their job easier or less stressful, 38% said it increases their productivity, and 38% said it improves the quality of their work. Against those positives, 13% said AI makes their job harder or more stressful, 13% said it reduces the quality of their work, and 9% said it decreases their productivity; 28% reported no significant benefit or harm either way. Positive assessments outweigh negative ones by roughly three to one, but the negatives are real and should not be rounded away.

Workers are more circumspect about their peers than the buzz would suggest. Asked whether AI is currently changing the productivity of "people who do work like yours," 29% said more productive, 20% said less, and a 51% majority saw no change. Looking ahead, opinion both brightens and polarizes: 32% expect AI to eventually make people in their line of work more productive, but 23% expect it to make them less so.

The time it frees tends to become more work

When AI saves workers time, that time overwhelmingly turns into additional output rather than rest.

This is the heart of the story, and it must be read as a funnel. Start with the 38% of AI-using workers who say AI increases their productivity. Of that group, 76% say AI gives them more free time at work. And of those workers, 95% say they then get more work done in the freed-up time — only 5% keep it as slack. Chained back to all AI-using workers, roughly 29% report both a productivity gain and freed time, and about 28% report the full sequence of productivity gain → free time → more work. The freed time, in other words, is not experienced as leisure; it is reabsorbed as output.

How much time is at stake? Among workers who report any positive effect from AI, the median estimate is about 5 hours saved per week, with a mean near 5.7 hours. That self-reported figure runs well above the roughly 2 hours per week that Federal Reserve researchers measured using a share-of-hours method — a gap that is unsurprising, since directly asking people "how many hours does AI save you" invites round, optimistic answers. The direction is consistent across sources even if the magnitude is not; the survey's numbers should be read as perceived, and probably generous, upper bounds.

Comparative Context

The direction matches the external evidence that AI's time savings are real but concentrated among heavier users, even as economy-wide productivity effects remain modest (St. Louis Fed). What the survey adds is the disposition of the saved time — that workers experience it as more output, not more rest.

Rising expectations — and who collects the gains

As AI raises output, employers are raising expectations, and workers doubt the gains flow mainly to them.

If AI-freed time becomes more work, one would expect employer expectations to climb — and workers say they have. Since AI arrived, 42% of AI-using workers say their employer now expects them to complete more work (13% "significantly" more, 28% "somewhat" more); 55% report no change and just 4% report lower expectations. Crucially, this expectation ratchet scales with use: the more intensively a worker uses AI, the more they report rising expectations, moving steadily from the lightest users toward the heaviest.

Workers are skeptical about who ultimately benefits. Among those who see AI making people in their workplace more productive, only 24% say workers benefit most — through higher pay, fewer hours, or better conditions — while 32% say employers benefit most, through higher profits or reduced headcount; 41% say the gains are split about equally. That workers, on net, name employers over themselves is the report's sharpest equity signal, and it sits naturally alongside the finding that their own freed time is being converted into more work.

The productivity that isn't

A quarter of workers feel pushed to use AI whether or not it helps — and most workers see a mixed or muted effect on their own future.

Not all AI use is productive use. Asked to react to statements about their workplace, roughly a quarter of employed workers agreed with each of a set of "AI pressure" propositions: 30% agreed their organization feels pressure to use AI mainly because competitors are, 26% that they are encouraged to mention "AI" to impress clients, 24% that they feel pressure to find uses for AI simply to justify its cost, and 27% that they are often asked to use AI for tasks they could do better or faster without it. In each case a plurality disagreed — so performative or counterproductive AI use is a real but minority experience, not the norm. (Workers who agreed they are pushed to use AI for tasks better done without it were markedly more likely to also say AI makes their job harder, a coherence check that confirmed the item's coding.)

Workers' verdict on their own futures is similarly measured. Looking at AI's likely impact on their own job over the next five years, only 17% expect it to be only positive; 46% expect both positive and negative effects, 21% expect no impact, and 17% expect it to be only negative. Displacement worry is present but not dominant: 33% think AI is currently causing people in their line of work to lose jobs, rising to 47% who think it eventually will — a boundary this report notes but does not treat as its subject.

Who gains most

Perceived productivity gains are concentrated among heavy users, the more educated, and men — not the youngest workers.

Who actually feels AI making them more productive is highly structured. In a weighted logistic regression (n = 6,904; McFadden pseudo-R² = 0.11), the dominant predictor by far is simply how much someone uses AI: each step up the usage-frequency scale raises the odds of reporting a productivity gain by about 48%. Descriptively, the share reporting a gain climbs from 14% of workers who have only tried AI to 63% of those who use it several times a day.

Net of use intensity, three gradients stand out. Education runs strongly positive: 50% of graduate-degree holders report a productivity gain versus 30% of high-school graduates (graduate degree odds ~1.3× a high-school graduate's). Gender runs negative for women: 42% of men versus 32% of women report a gain, a difference that survives controls (odds ~0.74). And — counter to the assumption that digital natives lead — the youngest workers are the least likely to report gains: perceived productivity benefit peaks among workers in their 30s and 40s (about 43%) and is lowest among those under 21 (21%) and aged 21–30 (34%). Hybrid workers report gains somewhat more than fully on-site workers; income and community type show no independent association.

Job type sharpens the picture — and resolves the partisan tilt but not the age one. Perceived gains track the kind of work a person does: 59% of workers who make budget or software-purchasing decisions report a productivity gain, along with 49% in marketing or content creation, 48% in data processing or analysis, and 46% of those who manage others — versus just 34% of manual or physical laborers. All four knowledge- and decision-work characteristics remain significant predictors in the regression; manual labor does not. Controlling for job type also renders the modest partisan tilt (Republican AI-users 44% vs. 36% of Democrats, descriptively) statistically non-significant, indicating that gap largely reflects who holds which jobs. The age pattern, by contrast, survives: even among workers doing the same kinds of tasks, those under 31 remain significantly less likely to report a gain, so the youngest workers' muted sense of benefit is not simply an artifact of junior roles.

Beyond the self: what coworkers show and how far AI runs

Workers see the productivity boost in their colleagues, not only in themselves — and yet AI still touches only a minority of the actual work, with substantial room to grow.

Because every measure so far is self-reported, a natural worry is that workers simply flatter their own AI use. One item offers a partial check by asking not about the respondent but about the people around them: what effect AI has on how much their AI-using coworkers get done. The observational read is, if anything, more positive than the personal one. 58% of workers say colleagues who use AI get more done — 28% "a lot more" and 31% "a little more" — while just 11% say it slows them and 19% see no real effect (the remaining 12% don't observe others or aren't sure). And the pattern tracks the worker's own engagement: among workers who use AI several times a day, 83% see their coworkers getting more done, versus 32% among those who have barely used it — the people closest to AI both feel and see the biggest effect. That the coworker read and the self read point the same way is a modest guard against pure self-flattery: workers describe a productivity gain they also observe in others.

Yet for all that, AI still runs only a slice of the actual work. Asked how much of their work is currently done with AI, 63% of workers say none or not much; only 37% say even "some," and just 13% say "most" or "all." The technology's productivity effects, in other words, are concentrated in a minority of workers and a minority of tasks — consistent with the modest economy-wide productivity numbers in the external research, even as individual users report large personal gains. The headroom, though, is real: when the same workers are asked how much of their work could be done with AI, the share saying at least "some" climbs from 37% to 51%, and the "most or all" share from 13% to 19%. Workers see roughly half their work as ultimately AI-amenable while only about a third of it currently runs on AI — a gap that suggests the "faster, but not freer" bargain described in this report is still early, and will intensify as the actual catches up to the possible.

Comparative Context

The penetration figures square the individual-level enthusiasm with the muted macro picture: that only about a third of workers do even "some" of their work with AI is consistent with Federal Reserve estimates of a roughly 1.1% economy-wide productivity effect to date, even as heavy users report large personal savings (St. Louis Fed). The gap between 37% "done with AI" and 51% "could be" is where the future of that macro number will be decided.

Conclusion

American workers' verdict on AI and productivity is more textured than either the boosters or the doomsayers suggest. A clear plurality of those who use AI say it makes them faster and their work easier, and only a minority report harm. But the productivity they describe does not translate into the thing workers might most value from it — time. The hours AI frees are, by workers' own accounts, largely reabsorbed as additional output; employers are expecting more as a result; and workers are more inclined to name employers than themselves as the main beneficiaries of the gains. Layered on top is a quieter current of performative pressure — a quarter of workers pushed to use AI whether or not it helps — and an outlook for their own jobs that is mixed rather than bright.

The throughline is distributional, not technological. The question these data raise is less whether AI makes workers more productive — many say it does, and they see the same gains in their coworkers — than who ends up collecting the dividend. On the current evidence, workers suspect it is not mainly them. And the bargain is still early: AI runs only about a third of the actual work today against roughly half that workers think it eventually could, so whatever the arrangement, more of it is coming. Because every measure here is perceptual, these findings describe how the AI-productivity bargain feels from the worker's side of it; pairing them with objective output data is the natural next step.

Appendix A — Methods

Data source. AI@Work survey — CHIP50 Wave 38.6 (Civic Health and Institutions Project / COVID States Project). Total unweighted n = 23,462; weighted n = 23,485. Fielded June 21–July 13, 2026, with an oversample of students and adults 18–22; it is nonetheless a broad adult sample. Estimates are weighted to U.S. Census population targets. Data collection was ongoing at the time of this extract — figures reflect responses through July 13, 2026 and are preliminary.

Perception caveat. All outcomes are self-reported perceptions, not objective productivity measures. No causal or output claim is made from them.

Universe and conditional bases. ai_gains_1…7 (own-experience battery): asked if ai_work ≠ Never (n ≈ 6,928). ai_gains_time (frees time): asked only if ai_gains_1 "increases my productivity" selected (n ≈ 2,639). ai_gains_vol (more work in freed time): asked only if ai_gains_time = Yes (n ≈ 2,003). ai_time_saved (hours): asked if any of ai_gains_1/2/3 selected (n ≈ 4,470). ai_work_exp, ai_work_imp, ai_loss_*: asked if ai_work ≠ Never. ai_prod_current/ultim and job-loss items: broader employed base (n ≈ 12,400). ai_prod_benefit: asked only of workers who said workplace AI "helps people get more done" (n ≈ 4,100). ai_pressure_1…4: employed full/part-time (n ≈ 10,400). peer_impact (coworkers' productivity): asked of workers (n ≈ 7,071). pew_extent (how much of your work is done with AI) and pew_extent2 (how much could be): all workers (n ≈ 12,447 and 11,988).

Coding. Question wording is verbatim in Appendix C. Three items are stored reverse of display order, confirmed against instrument and data: ai_importance (5 = extremely … 1 = not important; 0 = do not use AI at work), ai_shadow_norm (5 = strongly approve), and ai_pressure_1…4 (5 = strongly agree — confirmed because agreement with "asked to use AI for tasks I could do better without it" rises monotonically with reporting that AI makes one's job harder). ai_prod_current/ultim are forward-coded (1 less, 2 no change, 3 more), confirmed because the weighted mean rises with AI-use intensity. These items were mapped from the authoritative codebook and direction-checked in-data: peer_impact runs 1 = a lot more done … 5 = slows a lot (6 = don't observe, 7 = not sure) — i.e. LOW = more productive — confirmed because the "more done" (1–2) share rises monotonically with the worker's own AI-use frequency, from 32% among non-users to 83% among daily users; and pew_extent/pew_extent2 run 5 = all … 1 = none — confirmed because the pew_extent weighted mean rises monotonically with AI-use frequency (1.29 among non-users to 3.37 among daily users). peer_impact's "don't observe / not sure" categories (6–7) are reported but excluded from the "more/slows" shares.

Estimation. Cross-tabulations are weighted shares; ai_time_saved central tendency read from the weighted distribution (median ≈ 5 hours; mean ≈ 5.7). One weighted logistic regression (survey-weighted GLM, logit) models reporting a productivity gain (ai_gains_1) on AI-use intensity plus party, age, gender, education, income, community type, and remote arrangement. Standard errors are model-based, not design-based. Differences are called independent only where the coefficient is significant at p <.05; raw crosstab gaps are described as descriptive. Cells with unweighted n < 10 are flagged (workers 80+, n = 15).

Reproducibility. Figures map to Appendix B tables; values are copied verbatim from the underlying tabulations. Skip logic and wording sourced from the Qualtrics instrument via the project's AI@Work survey verified-updates crosswalk.

Appendix B — Data tables

"More done" (a lot + a little) = 58.5%; "slows" = 10.5%. Rises with the worker's own AI use: the "more done" share climbs from 32% among non-users to 83% among several-times-a-day users.

At least "some": 37.2% now vs. 51.2% could be. "Most or all": 12.6% now vs. 18.7% could be.

Job-type model (adds work_context; n = 6,915; McFadden R² = 0.12): makes budget/purchasing decisions OR 1.61*, manages others 1.28*, marketing/content 1.19*, data/analysis 1.16* (manual labor n.s.). The age effects persist (18–20 OR 0.63*, 21–30 OR 0.82*); the partisan effect falls to non-significance (Democrat OR 0.88, p =.05).

Appendix C — Question wording (verbatim)

• ai_work — "How often do you use AI for your work?" (Never; I tried once or twice; About once a month; About once a week; Multiple times a week; Once or twice a day; Several times a day)
• ai_gains (select all) — "Thinking about how using AI tools has affected your work…" (Using AI increases my productivity; …improves the quality of my work; …makes my job easier or less stressful; I do not see a significant benefit or harm from using AI; …makes my job harder or more stressful; …reduces the quality of my work; …decreases my productivity)
• ai_gains_time — "Do you get more free time due to using AI at work?" (Yes; No) [asked if "increases my productivity" selected]
• ai_gains_vol — "Do you get more work done in that free time you gained?" (Yes; No) [asked if ai_gains_time = Yes]
• ai_time_saved — "How much time, if any, do you think AI saves you per week, compared to your work prior to using AI tools? Please give us the approximate number of hours." (numeric) [asked if any of increases-productivity / improves-quality / easier selected]
• ai_work_exp — "Since AI, has your employer's expectation for how much work you complete…" (Increased significantly → Decreased significantly, 5-point)
• ai_prod_current — "Do you think that AI is currently changing the productivity of people who do work like yours?" (Less productive; No change in productivity; More productive)
• ai_prod_ultim — "Do you think that AI will eventually change the productivity of people who do work like yours?" (same scale)
• ai_prod_benefit — "When AI makes workers more productive in your workplace, who do you think benefits the most?" (Mostly workers (through higher pay, fewer hours, better conditions); Mostly employers (through higher profits or reduced headcount); About equally; Not sure) [asked if workplace AI "helps people get more done"]
• ai_pressure (agree/disagree, 5 = strongly agree) — "To what extent do you agree or disagree with the following statements about your workplace?" (My organization feels pressure to use AI mainly because our competitors are using it; We are encouraged to mention 'AI' in our work to impress clients or customers; I feel pressure to find uses for AI tools simply to justify the cost of the technology investment; I am often asked to use AI for tasks that I could do better or faster without it)
• ai_work_imp — "In your opinion, what would be the impact of AI tools like ChatGPT on your own job in the next 5 years?" (Only positive impact; Both positive and negative impact; Only negative impact; No impact)
• ai_loss_current / ai_loss_ultimate — "Do you think that AI is currently / will eventually cause people who do work like yours to lose their jobs?" (Yes; No)
• peer_impact (workers) — "Thinking about the people in your workplace who use AI, what effect does it have on how much they get done?" (A lot more; A little more; No real effect; Slows them a little; Slows them a lot; I don't observe others; Not sure) [coded 1 = a lot more … 5 = slows a lot; low = more productive]
• pew_extent (all workers) — "How much of your work is done with AI?" (All; Most; Some; Not much; None) [coded 5 = all … 1 = none]
• pew_extent2 (all workers) — "How much of your work could be done with AI?" (All; Most; Some; Not much; None) [same scale]