The Twenty-Three Minute Myth
Vishal Sachar
Co-Founder & CEO of CLRT
Somewhere in every productivity book, corporate wellbeing deck, and focus app pitch, the same statistic appears: it takes 23 minutes to recover from an interruption. It is probably the most quoted number in the attention economy. It has no paper behind it. Trace it to its source and you arrive not at a study but at a 2006 magazine interview, in which the researcher Gloria Mark summarised her fieldwork for Gallup's Business Journal. The study she was summarising had measured something else entirely: not how long focus takes to recover, but how long passed before people returned to a task they had been pulled away from. The most influential focus statistic on earth is a soundbite about a different question.
The published paper is worth reading precisely because of how it differs from its own legend. In the 2005 study, Mark's team shadowed information workers minute by minute and found that when interrupted work was resumed the same day, which 77 per cent of it was, the average time before returning to it was 25 minutes and 26 seconds, with a standard deviation of nearly 55 minutes. During that gap, people were not staring at a wall recovering. They completed, on average, 2.26 other tasks. In the Gallup interview a year later, a slightly different cut of the same data became 23 minutes and 15 seconds, and two decades of retelling did the rest: returned to the task became recovered focus, the intervening tasks vanished, and a variance twice the size of the mean disappeared into one confident number. I would call this stat laundering: a figure passed between secondary sources until the primary is unfindable and the claim has quietly changed shape. The productivity industry runs on such numbers, and more of its famous figures dissolve the same way when you ask for the paper.
What makes the myth genuinely damaging is that it points the diagnosis in the wrong direction. Mark's own follow-up experiment, published in 2008, found something the recovery-clock story cannot accommodate: interrupted work gets finished faster. People completing an email task under interruption took just over 20 minutes against nearly 23 minutes uninterrupted, with no measurable drop in quality. People compensate, writing less and working faster. The bill arrived somewhere else. The interrupted groups reported significantly higher stress, frustration, time pressure, and effort. The cost of interruption, in other words, is not a 23-minute hole in the calendar. It is paid in the currency I have argued is the founder's true constraint, energy (the argument in Return on Energy), and in the tolerance for error that goes with it. Output holds. The human absorbs the difference. A fragmented organisation does not look slow. It looks busy, tired, and increasingly unable to catch its own mistakes.
Meanwhile the real numbers have moved somewhere the myth never imagined. Mark's screen-logging studies tracked the average time a person spends on one screen before switching: about two and a half minutes in 2004, 75 seconds by around 2012, and roughly 47 seconds in her most recent work, a figure others have replicated within a few seconds. The careful framing matters: this is time on one screen, not attention span. And the environment those screens sit inside has become an interruption machine. Microsoft's own telemetry, published in June 2025 and worth the vendor-research caveat, found the average employee interrupted every two minutes during core working hours, 275 times a day, with 117 emails daily and 153 Teams messages each weekday. Set that against the myth and it collapses in the strangest way: it is too optimistic. A 23-minute recovery clock implies you have 23 uninterrupted minutes in which to recover. At an interruption every two minutes, there is no recovery window at all. There is only the compensation mode, all day.
Follow the corrected diagnosis and the standard fix falls apart. If interruption cost a recovery clock, the answer would be to defend blocks of time, which is what the entire focus industry sells: deep-work calendars, no-meeting days, notification hygiene. But blocking time only reschedules interrupt-shaped work. The 275 arrivals still arrive, compressed into the unblocked hours, and the energy bill is unchanged. If the real cost is energy and error tolerance, the only fix that touches it is removing whole classes of interruption from the human entirely, so they never reach a person at all. Look honestly at what those arrivals contain and most are machine-shaped: a status someone could not see, a document someone could not find, a first-line question with a documented answer. That is agent work, and absorbing it is precisely what agents are for. The hard part is not the absorbing. It is knowing which classes can safely leave the human, because some interruptions are the job: the escalation, the judgment call, the client who needs a person. Draw the line wrong in one direction and you have automated away your own responsiveness. Draw it wrong in the other and your people still answer 275 pings a day.
The interruption does not cost 23 minutes. It costs energy and the margin for error, and no calendar block buys either back.
A deeper dive
It is worth being precise about what the primary literature will actually support. The 2005 observational study measured working spheres, not tasks: coherent bundles of work, with many of the switches between them self-initiated rather than imposed. The famous return figure of 25 minutes and 26 seconds carries a standard deviation of 54 minutes and 48 seconds, which is to say the distribution is so skewed that the mean is nearly useless as a planning number. Nothing in the study measured cognitive recovery, because nothing in it could: it observed when people returned, not what state they returned in. The 2008 lab experiment completes the picture. Interrupted subjects finished faster with no quality drop, and the disruption surfaced instead on NASA task-load measures: workload, stress, frustration, time pressure, and effort all significantly higher. Fragmentation, in other words, behaves like a debt instrument. Output is maintained, the human pays the interest, and the first thing sacrificed is the reserve capacity that catches errors before they ship. That is why heavily fragmented organisations feel simultaneously productive and brittle. The work gets done, and the mistakes get through.
The stat-laundering point matters beyond this one number, because an operator's defences against bad statistics are the same defences they need against bad AI advice. Numbers pass from a study to an interview to a keynote to a vendor blog, changing meaning at every hop while gaining confidence, and the prescriptions built on them inherit the error. The corrected reading, that the cost is energy and error tolerance under a load of 275 arrivals a day, points somewhere else entirely: treat interruption as a routing problem, not a willpower problem. Every interruption is a unit of work arriving on the wrong channel at the wrong altitude, and each one belongs to a class. Some classes, the status query, the document hunt, the report chase, the first-line question, can be absorbed by an agent layer completely, so the interrupt never reaches a human, which is the only intervention that reduces the 275 rather than rearranging it. Other classes are the actual job wearing the costume of an interruption, and automating them away damages the thing your customers are paying for. Sorting a real organisation's interrupt load into those classes is not a prompt and not a policy template. It is a diagnostic read on how the business actually runs.
Key terms
- Stat laundering
- A figure passed between secondary sources until the primary is unfindable and the claim has quietly changed shape, gaining confidence at every hop.
- Working sphere
- The unit Gloria Mark's fieldwork actually measured: a coherent bundle of work organised around a common goal, which workers switch between throughout the day.
Work with CLRT
CLRT builds the agent layer that absorbs machine-shaped interruption before it reaches your people, and, before anything is built, does the harder work of deciding which classes of interrupt can safely leave the human and which ones are the job itself. If you want that read on your own operation, priced in dirhams rather than in folklore, start with the Ascent diagnostic or bring us the team whose day looks like 275 pings. The myth sold you time defence. The real fix is subtraction, and knowing what to subtract is the work.

Vishal Sachar
Vishal Sachar is the Co-Founder and CEO of CLRT, where he helps UAE businesses make sense of applied agentic AI and put it to work. He writes on agentic systems, AI governance, and the economics of automation. Reach him at vishal@clrtstudio.com or on LinkedIn.


