ep. 88. AI Is An Accelerant. What Are You Feeding It?
6 min read
After posting on Substack almost daily for months, I got my first viral note. At the time of writing, I’m at 1.9K likes and counting:
I commented on the recent Harvard Business Review article, “AI Doesn’t Reduce Work—It Intensifies It”. The study comes from Berkeley Haas researchers Aruna Ranganathan and Xingqi Maggie Ye, who spent eight months studying work at a ~200-person American tech company.
They observed employees in person two days a week, tracked internal work channels, and conducted 40+ interviews across engineering, product, design, research, and operations. They found that generative AI use sped up work, broadened job scope, and stretched work into more hours of the day. In turn, expectations for speed rose. The reinforcing cycle looks something like this:
Why It Resonated
My hypothesis for why this note landed: it named something visceral.
People were promised tools that let us finish work faster. Instead, we got tools that stand to burn us out just when we thought we couldn’t get more burned out.
Making matters worse, workers in the study (like many of us) chose to use AI. The company had enterprise subscriptions, but adoption wasn’t mandated. Workers did more because AI made “doing more” feel possible, and in many cases, intrinsically rewarding. There’s a good reason for that: Tools that increase what you feel capable of attempting on your own tap into autonomy and competence, two of the strongest drivers of intrinsic motivation.
“While this sense of having a “partner” enabled a feeling of momentum, the reality was a continual switching of attention, frequent checking of AI outputs, and a growing number of open tasks. This created cognitive load and a sense of always juggling, even as the work felt productive.” - From AI Doesn’t Reduce Work—It Intensifies It, HBR
Many of us have made the same choice. Vibe coding a prototype or synthesizing themes across hundreds of customer calls in minutes is heady. Of course you do it again.
New Study, Old Pattern
Technology can deliver efficiency. The trouble is, gains get absorbed by changed norms rather than converted into rest.
In 1930, Keynes predicted that technological advancement and capital accumulation would increase productivity so much that 15-hour workweek would be possible by his grandchildren’s generation (that is, right around now). Once our material needs were met, remaining work would be spread thin to ensure everyone had some small tasks to satisfy our human need to be useful. The rest of the time would be devoted to leisure. This is not how things are netting out. Look no farther than the recent surge in 9-9-6 culture in The Bay Area.
In 1979, when VisiCalc, the first commercial spreadsheet software, hit the market. It’s been hailed as the first “killer app” that turned PCs from hobbyist tools into business infrastructure. Electronic spreadsheets made accounting work cheap and fast. VisiCalc didn’t reduce accounting work, but it did change what accounting work looked like. As of 2019, there were 400,000 fewer accounting clerks than in 1980, but 600,000 more regular accountants.
Next up, we have email, a technology that was heralded for reducing the need for meetings.
Foiled again.
Email made us ambiently available. The technology intensified work, as found by a 2012 research study. When participants didn’t use email for five workdays, they multitasked less, sustained focus longer, and reported lower stress.
“Our findings suggest that email speeds up the pace of work” - Mark et al. (2012), “A pace not dictated by electrons“
The throughline across these findings is that technology lets people produce more output faster, which shifts norms, which expands the total work that needs to be performed.
Three Dynamics of Intensified Work
There are a few underlying dynamics that can help us make sense of the Acceleration Trap. These terms can help you name and diagnose what you’re seeing and experiencing:
Jevons Paradox: When an activity gets cheaper and easier, total consumption of that activity tends to increase. William Stanley Jevons described this in the context of coal in 1865: greater fuel efficiency led to more coal use, because it expanded what coal could economically power. In product work, making synthesis faster doesn’t mean you’ll be done work earlier. It means more workstreams open, more experiments run, more documents drafted.
The Ratchet Principle. In incentive systems, strong current performance becomes the baseline for future targets. Teams that can ship twice as many prototypes find next quarter’s roadmap has doubled. The capability improvement raises the floor. You see this in the HBR study. As one engineer put it: “...you don’t work less. You just work the same amount or even more.”
Goodhart’s Law: When a measure becomes a target, it stops being a good measure. If you celebrate only tickets closed or campaigns shipped, generative AI will inflate those numbers. Output volume goes up. Quality and maintainability don’t necessarily follow. This is the signal to watch when AI gets introduced alongside existing throughput metrics.
Tools Change Fast. Systems Change Slow.
Stewart Brand described layered rates of change in complex systems in his pace layers: faster layers generate novelty, slower layers provide stability. Fast learns, slow remembers.
In a product organization: AI tools sit in the fast layer. Performance incentives, business models, and beliefs about productivity sit in the slower layers. Work intensification will only continue unless you start changing those deeper layers. Until then, AI will only accelerate those deeper structures.
An optimistic perspective is that because AI speeds up the build-test-learn loop, bad strategy will need to adapt or die, and “good strategy” will prevail. I’m skeptical.
For bad strategy to die, you need to know it failed. Faster iteration doesn’t improve signal quality if your metrics are already misaligned (see: Goodhart’s Law). And in most organizations, politics, sunk costs, and narrative momentum can insulate bad strategy from consequences regardless of how fast you ship.
What’s more likely is that organizations with already-strong fundamentals will pull further ahead, while organizations with weak ones will generate more output faster without improving decisions. AI makes the cost of bad strategy may indeed be visible sooner, but whether organizations act on that visibility is a leadership problem, not a technology problem.
The “So What?”
AI is an accelerant and an amplifier for whatever system it’s attached to. It doesn’t change the underlying systems. Tools change quickly, systems change slowly.
For ICs
AI shifts the “doing” work to “monitoring” work. The work doesn’t disappear; it creates new and potentially more taxing work, as human judgement moves upstream to complexity orchestration and exception handling. This is a first principle of human factors. You’ll recognize this in your own work: Effort didn’t disappear when AI took over drafting. It moved from generating to monitoring, verifying, and context-switching across more open threads. To stay in control of your output, budget explicitly for that review work.
For Managers
The shift from “We can go faster now” to “we must go faster now” happens implicitly, without anyone deciding it. One quarter your team ships twice as many prototypes with AI. The next quarter, that’s just the roadmap.
Name the effect before it sets. That might mean agreeing that AI outputs are drafts rather than deliverables or holding that line when a stakeholder sees a working demo and assumes it’s shippable.
For Product Teams Building AI
Choose metrics that reflect quality, not just volume, because AI will inflate the latter. It doesn’t matter if call times drop from 11 to 2 minutes if your customers are rage quitting, trapped in an infinite loop with an AI agent.
This applies to your own team’s output. As the HBR study reported, “workload creep can in turn lead to cognitive fatigue, burnout, and weakened decision-making. The productivity surge enjoyed at the beginning can give way to lower quality work, turnover, and other problems.”
If AI helps you complete a task more quickly, take a beat to ask who captures the savings, and what the system you’re working within does with them.
If you liked this episode, you’ll love my book. Designing Automated Futures is coming this year with Rosenfeld Media. 📣 Sign up to be the first to know about new book releases, sales, and events.
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📖 Good Reads
“The Artifact Effect” x Anthropic: When AI produces artifacts (think apps, code, docs), users were less likely to check facts, question reasoning, or identify missing context. Read: The latest flavor of automation bias.
How Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt x Dr. Margaret-Anne Storey: Tech debt lives in code; cognitive debt lives in the minds of the people who built it. AI can accelerate both, but cognitive debt is a bigger threat. It’s harder to see coming and harder to fix because it only shows up when you need to explain why the system works the way it does, and nobody can do so.
Scenario Thinking as Genre Fiction x Michael Dila: A fresh take on the much-circulated Citrini Research Macro Memo, contextualizing it as part of a larger trend of manifestos and scenarios. Michael argues for the importance of treating these pieces as conversational infrastructure. I agree, though these scenarios do move markets. The stories we tell about the futures matter and give us something to work towards. How about a potential future that’s human-centered?
That’s a wrap 🌯 . More on UX, HCI, and strategy from Sendfull in two weeks.
Great piece, Stef! AI robs us of slowness, and that’s a problem not a win. I actually did a whole series and deck based on this idea that life is accelerating beyond what we can handle: https://mutantfutures.substack.com/t/the-future-of-slowness
Excellent article