Last week in this newsletter, I summarized some interesting results from a study that analyzed the behavior of 164,000 knowledge workers. It found that introducing ... Read more
Why Hasn’t AI Made Work Easier? - Cal Newport
I’ve been studying the intersection of digital technology and office work for quite some time. (I find it hard to believe that my book, Deep ... Read more
Has AI Changed Work Forever? Not Really... | Cal Newport
More! More! More! Tech Workers Max Out Their A.I. Use.
At a number of companies, employees compete on leaderboards to show how much A.I. they’re using. They’re racking up big bills along the way.
See what you think
Allegra A. Beal Cohen's blog about knowledge curation, new interfaces, and large-scale qualitative data.
Prompting Science Report 4: Playing Pretend: Expert Personas Don't Improve Factual Accuracy
<span> <p><span>This is the fourth in a series of short reports that help business, education, and policy leaders understand the technical details of working w
Prompting Science Report 4: Playing Pretend: Expert Personas Don't Improve Factual Accuracy
<span> <p><span>This is the fourth in a series of short reports that help business, education, and policy leaders understand the technical details of working w
2026 State of the Workplace
Get the latest insights from state of the workplace report spanning more than 443 million hours of work activity across 1,111 organizations and 163,638 employees over three years.
Notion’s Knowledge Board
Evaluating how models perform across a range of knowledge work tasks, using live anonymized traffic and judged by models from Anthropic, OpenAI, and Google. An ongoing experiment.
Please stop multitasking. I’m begging you. Please.
The greatest trick we ever pulled on ourselves as knowledge workers was convincing ourselves we could juggle multiple projects with no consequences.
"AI" and Productivity
[I keep bringing these up on Bluesky, so I think it’s time to gather them up and make a post out of them.] This is a collection of articles...
How tech workers are feeling in 2026: a workforce splitting in two
Results from our second annual tech worker sentiment survey
Microsoft reports are exposing AI's real cost problem: Using the tech is more expensive than paying human employees | Fortune
Companies are racing to incentivize employees to use AI. But as some companies are finding, the more employees that use the technology, the heavier the bill.
How NASA is Using Graph Technology and LLMs to Build a People Knowledge Graph
Missed NASA’s People Graph webinar? Catch the recap and see how graph technology and AI are shaping the future of workforce intelligence.
Why Is Everyone In Tech So Sad?
A lot of people seem to be realizing that knowledge work is mostly pointless. AI might give us the pleasure of finding out what happens if an entire class of workers loses faith in their careers.
The shape of a knowledge worker
In a recent post, I threw around the term 'cognitive exponent' a bunch. Today I'd like to talk about a thing that might help us frame our investigation of what puts someone on the right side of that exponential graph.
The unintended consequences of large language models as a labor-augmenting technology in science
As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.
