What we’re reading (2/26)
“Jack Dorsey’s Block To Lay Off 40% Of Its Workforce In AI Remake” (Wall Street Journal). “Block the payments company founded by Jack Dorsey that includes Square and Cash App, said Thursday that it plans to lay off 40% of its workforce, or more than 4,000 employees. Dorsey alluded to artificial-intelligence tools as the reason for the cuts in a letter to shareholders. ‘The core thesis is simple,’ wrote Dorsey. ‘Intelligence tools have changed what it means to build and run a company.’”
“The 2026 Global Intelligence Crisis” (Citadel). “[M]arkets often extrapolate the acceleration phase linearly but history implies pace of adoption plateaus as organizational integration is costly, regulation emerges and diminishing marginal returns exist in economic deployment. The risk of displacement declines with a slower pace of adoption.”
“I Thought I Understood A.I. Companies. I Couldn’t Have Been More Wrong.” (Jason Furman). “[T]he real story of the most consequential technology of our time is strikingly different from what it seems. Instead of consolidating, as so many other industries have done, the leading edge of A.I. has become fiercely competitive. The result has been a staggering pace of innovation, significant reductions in costs and an expanding array of choices for consumers and businesses alike.”
“Data Center Builders Thought Farmers Would Willingly Sell Land, Learn Otherwise” (ars technica). “One 82-year-old Kentucky woman, Ida Huddleston, turned away a ‘Fortune 500 company’ offering $33 million for 650 acres. NBC News reported that several of her neighbors received similar offers. Huddleston joined at least five other residents in the county who refused to move forward after learning they’d have to sign a non-disclosure agreement just to find out who they would be dealing with.”
“It Must Be Very Hard To Publish Null Results” (Ryan Briggs, Jonathan Mellon, and Vincent Arel-Bundock). “In this article, we use large language models to extract granular and validated data on about 100,000 articles published in over 150 political science journals from 2010 to 2024. We show that fewer than 2% of articles that rely on statistical methods report null-only findings in their abstracts, while over 90% of papers highlight significant results. To put these findings in perspective, we develop and calibrate a simple model of publication bias. Across a range of plausible assumptions, we find that statistically significant results are estimated to be one to two orders of magnitude more likely to enter the published record than null results.”
