AI doesn’t simply add work; it modifications work in ways in which are actually empirically plain. The HBR article “AI Doesn’t Scale back Work—It Intensifies It” validates what I referred to as the “AI Tax” almost a yr in the past: AI will increase the quantity, velocity, and ambiguity of labor except organizations deliberately design in opposition to that final result.
When the Analysis Catches Up with the Flooring
Within the AI Tax put up, I argued that AI doesn’t arrive as merely as a productiveness dividend; it arrives as six classes of latest work: juggling and power sprawl, vetting, knowledge readiness, relevance and security, the burden of failed initiatives, and perpetual studying and relearning. These classes emerged from conversations with groups already utilizing AI in follow, customers toggling amongst instruments, reconciling outputs, and cleansing knowledge quite than doing the “higher-value” work they had been promised.
The HBR piece by Aruna Ranganathan and Xingqi Maggie Ye presents a uncommon longitudinal have a look at that actuality, following roughly 200 staff at a U.S. tech firm over eight months to see how generative AI truly modified their work. Their conclusion is blunt: AI instruments didn’t cut back work; they “constantly intensified it.” Staff labored at a quicker tempo, took on a broader scope of duties, and prolonged their work into extra hours of the day, usually with none supervisor asking them to take action.
Put merely, the examine supplies the ethnography for the AI Tax’s classes of labor.
Three Methods AI Intensifies Work
The HBR analysis identifies three essential patterns of intensification that emerge as soon as AI instruments transfer from demonstration to each day use.
- Job growth
As soon as AI is on the market, individuals don’t simply do the identical work quicker; they start to do extra varieties of labor. Product managers and researchers start writing and reviewing code; staff tackle duties that may beforehand have required new headcount; and people reclaim work that had been outsourced, deferred, or just prevented. At one degree, this might be perceived as empowerment. A deeper dive exposes engineers who discover themselves mentoring colleagues on AI-assisted code, reviewing a flood of partial pull requests, and fixing low-quality “work-slop” that arrives of their queue dressed up as completed work - Blurred boundaries between work and non-work
AI makes it simple to “simply attempt one thing” within the margins of the day: a fast immediate throughout lunch, another refinement earlier than heading to a gathering, a late-night concept examined in mattress on a cellphone. These micro-sessions don’t really feel like additional work, however over time, they erode breaks and restoration, making a steady sense of cognitive engagement. Employees within the examine reported that, as prompting turned their default throughout downtime, their breaks now not felt restorative. - Elevated multitasking and cognitive load
Staff run a number of AI brokers and threads in parallel, let AI generate different variations whereas they write, and hold half an eye fixed on outputs whereas making an attempt to concentrate on one thing else. The presence of a “associate” that by no means will get drained encourages fixed context switching: checking, nudging, re-prompting, and reconciling. The result’s an ambient sense of being at all times behind, at the same time as seen throughput will increase.
Should you learn my AI Tax put up, these themes will really feel very acquainted—as a result of they’re the lived expertise behind the classes.
How the AI Tax Explains Intensification
In “The AI Tax,” I described six methods AI creates extra work than it saves when deployed with out design. The brand new HBR analysis slots cleanly into that framework.
- Juggling with AI: multi-tasking, switching, sprawl
The examine’s third sample, elevated multitasking, is the human expertise of juggling throughout AI instruments, brokers, and metaphors of interplay. In my put up, I wrote about toolchain sprawl: one AI for scheduling, one other in electronic mail, a 3rd hidden in a CRM, every with a special interface, set of capabilities, and quirks. The result’s a workday that looks like a perpetual reconciliation train, with consideration sliced into dozens of skinny duties. - Vetting: oversight and the hallucination drawback
Job growth sounds environment friendly till you keep in mind that each AI-generated draft, be it a doc, snippet of code, or advertising and marketing marketing campaign, requires vetting. The HBR examine paperwork engineers who begin spending vital time reviewing AI-assisted work produced by colleagues exterior their self-discipline, usually by casual Slack exchanges and favors. That’s the AI Tax’s “shadow labor,” actual work with no line merchandise in a venture plan, absorbed by individuals already at capability. - Knowledge science and readiness: hidden work uncovered
AI makes knowledge issues seen. When staff eagerly broaden their scope: writing analyses, experiences, or prototypes they’d not beforehand have tried, they rapidly collide with scattered, mislabeled, or outdated knowledge. That collision forces them into advert hoc knowledge wrangling: reconciling codecs, looking for authoritative sources, and studying simply sufficient concerning the group’s knowledge structure to be harmful. - Relevance and security: governance lagging adoption
As AI disseminates content material extra rapidly, questions of tone, bias, confidentiality, and regulatory danger change into each day considerations quite than edge instances. The HBR article hints at this not directly, however the connection to my AI Tax class is direct: when governance lags behind adoption, every step ahead requires a detour to confirm compliance and appropriateness. That friction doesn’t present up in vendor demos, however staff really feel it instantly. - Failed initiatives and abandonment cycles
The examine depicts enthusiastic early experimentation: individuals “simply making an attempt issues” with AI. In my put up, I warned that this sample usually evolves right into a cycle of pilots that don’t connect with actual workflows, bots that die on the sting of a promise, and technical debt that somebody has to scrub up. When each failed experiment leaves behind deserted prompts, partial automations, and skeptical customers, the AI Tax compounds over time. - Studying and relearning: AI as a shifting goal
Lastly, each the HBR article and my AI Tax put up converge on the churn of studying. Each mannequin replace, interface change, and new characteristic, not to mention the arrival of completely new instruments, forces individuals again into coaching mode. Add in social FOMO (“Have you ever tried the newest mannequin?”) and also you get a tradition through which staff are anticipated to maintain up with a continually shifting AI panorama whereas additionally sustaining their present tasks.
The purpose isn’t that AI can’t create worth. It’s that worth and complexity scale collectively, and complexity arrives first.
The Free Time Mirage
When AI works, when it truly hastens a job or simplifies a workflow, a special query emerges: what occurs to the time that’s freed? Within the AI Tax article, I argued that this isn’t a technical query however a management and coverage problem. With out intentional design, freed time will get reabsorbed into:
- Extra duties, usually vaguely outlined as “strategic work” or “innovation.”
- Casual expectations that people will tackle additional tasks as a result of “the instruments make it quicker now.”
- Refined strain to take care of or improve output quite than use time for restoration, studying, or collaboration.
The HBR examine makes this dynamic seen. Staff used AI to shave day off duties, then crammed the margin with new work: serving to colleagues, experimenting with extra prompts, or extending their tasks into areas beforehand out of scope. They felt extra productive, however not much less busy. Over time, the preliminary thrill gave approach to exhaustion and cognitive fatigue.
That is the core of the AI Tax argument: if organizations don’t explicitly determine the way to deal with time saved by AI, the default will at all times be intensification, not liberation, and in lots of instances, substitution quite than augmentation.
Designing Towards Intensification
The HBR authors counsel that organizations want specific “AI practices” to stop intensification from changing into the default: norms about when to make use of AI, when to not use it, and the way to handle AI-enabled work sustainably. The AI Tax framework aligns with that decision and presents concrete beginning factors.
Listed below are a number of design strikes leaders could make, knowledgeable by each the analysis and the AI Tax:
- Standardize the AI stack
Scale back toolchain sprawl by selecting a small variety of platforms and constructing round them. Consolidation lowers cognitive switching prices, simplifies governance, and makes it simpler to design coaching that sticks quite than chasing each new characteristic. - Make vetting seen and accountable
Cease treating oversight as invisible heroism. Assign vetting tasks, monitor the time it takes, and issue that point into venture plans and ROI claims. This isn’t simply honest; it generates the info wanted to determine the place AI genuinely helps and the place it merely redistributes labor. - Spend money on knowledge earlier than scale
Most of the frustrations uncovered within the examine,, reminiscent of partial outcomes, complicated outputs, and reliance on “vibe” coding, stem from poor knowledge, unclear requirements, or lacking context. Cleansing, tagging, and aligning knowledge are unglamorous, however they’re important if AI is to supply outputs that cut back work quite than create extra cleanup work. - Run time-bound pilots with actual endings
Organizations ought to deal with AI pilots as experiments with specific timelines and choice gates, quite than as everlasting, half-adopted options. On the finish of a pilot, both commit and make investments, or shut it down and doc what was realized so that you don’t repeat the identical errors later. I additionally frequently argue that AI requires data administration, however accelerated AI adoption too usually overwhelms its implementation. - Defend human time as an asset
Maybe most significantly: determine, upfront, the way to reclaim free time with objective. Some portion must be explicitly allotted to relaxation, reflection, mentoring, and exploration, quite than being harvested as a shadow productiveness acquire. If AI is to be a colleague, it ought to create situations for higher human judgment, not merely better throughput.
From AI Tax to AI Follow
The convergence between the HBR analysis and the AI Tax is encouraging as a result of it suggests we’re shifting out of the speculative section of AI and right into a extra empirical, design-oriented one. We now have a rising physique of proof that, left to its personal units, AI doesn’t cut back work; it lowers friction and invitations extra work.
The duty for leaders is to deal with these realities as design constraints quite than as inconveniences. The AI Tax identifies the place prices accumulate; the HBR article exhibits how these prices manifest in an actual group over time. Between them lies the chance to construct “AI practices” that honor human limits, defend time, and be sure that depth is a alternative quite than an accident.
His work has been featured in Harvard Enterprise Evaluate, Quick Firm, Wired, NASA Ask, and dozens of different magazines and web sites. His evaluation on the way forward for work may be discovered at SeriousInsights.web. He’s additionally the creator of Understanding Synthetic Intelligence, Cyberlife, Rethinking Sensible Objects, and Empower Enterprise with Generative AI.
Rasmus frequently speaks on the way forward for work at occasions reminiscent of Comedian-Con Worldwide, Wondercon, Influence, Enterprise AI World, Worktech, CLO Symposium, KMWorld, AAAI, Computer systems in Libraries, Microsoft’s Constructing the Future, Educause, Expo Capital Humano, Devlearn, Web Librarian, CAMEX, EduComm, and Sourcing Summit Europe.
As an Affiliate Teacher, Rasmus teaches state of affairs planning on the College of Washington and is an Affiliate Adjunct Professor at Bellevue School.
