The Feed · Complete archive
AI at work: research and practice
A server-rendered record of the evidence, ideas and firsthand practices screened by The Feed. Every entry links to its original source.
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practice · Nate B Jones ·
AI just produced more than 700 math research papers in one release. Before you take that as proof it can do your job, see which kind of work it got better at.
AI models may develop narrow peaks of capability rather than broad intelligence, excelling at some tasks while remaining weak at others.
What if AI gets dramatically smarter and spikier, with the gaps between its abilities getting larger? We tend to expect the opposite. A model does something extraordinarily difficult, and we take that as evidence of a deeper intelligence that should also help it do other things. If it can advance mathematics, surely it should become a better engineer. If it can reason through a problem that defeated human experts, surely it should recognize the bad assumption in a business plan. That expectation contains a theory of how intelligence works. Progress in one demanding intellectual activity is sup
judgment
research · VoxEU ·
At risk or responsive: Rethinking unemployment policy
Job search counselling helps responsive workers enter employment faster but has limited effect on those at highest risk of long-term unemployment.
Public services increasingly use predictive algorithms to direct scarce support towards those most at risk. But are these also the people who benefit most from intervention? This column explains how to navigate this trade-off and shows why it matters in practice. Job search counselling gets people into work faster, but has little effect on those at greatest risk of long-term unemployment. Combining predictions of risk and responsiveness can make scarce support substantially more effective, while highlighting the need for different support for those hardest to help.
policy
practice · Lenny's Newsletter ·
🎙️ How I AI: How Meta designed Muse with Alex Cornell
Design principles for AI agents: progressive disclosure, human conversation framing, persistent relationships, and generative UI that feels personal.
How Meta designed Muse: an AI agent people actually want to talk to | Alex Cornell (Design Director) Listen now on YouTube • Spotify • Apple Podcasts Brought to you by: WorkOS —Make your app enterprise-ready, with SSO, SCIM, RBAC, and more Alex Cornell is a design director at Meta, where he leads design for Muse. In this episode, he explains how progressive disclosure makes Muse approachable for casual users without limiting technical ones, why its main chat is designed as a single persistent relationship, and how generative UI can feel personal without constantly changing the interface. He al
ways of working · Lenny Rachitsky
research · New Economics Papers: AI ·
Everyone Is a Manager: AI Use as an Act of Management
AI use requires managerial skills, direction and verification, whose relative importance shifts as AI reliability improves, creating skill collapse risk.
When a knowledge worker is given an AI assistant, they stop executing tasks and start delegating them. Thus, we propose that the use of AI is an act of management, which helps explain the heterogeneity in returns to AI use observed across firms. We model the return to AI as moderated by two managerial abilities: direction (how well the worker specifies what they delegate) and verification (how reliably they catch the tool's errors). As AI becomes more reliable, the skill with the higher marginal return shifts from verification to direction. To the extent that use builds skill, workers develop
management org
research · New Economics Papers: AI ·
The Hidden Value of Human Followers
Replacing human team members with AI can reduce team output because leaders work less when AI removes social observation and reciprocal effort dynamics.
Replacing a human follower with AI that supplies more input can reduce team output. With complementary effort, a leader works harder because a human follower observes this effort and responds with greater effort. AI that matches the follower's contribution removes this reason to work hard, so the leader works less and team output falls. In larger teams, the remaining human followers also work less. Profit-maximizing firms may nevertheless adopt AI that contributes more while the team produces less. Falling AI costs can trigger an output drop at adoption, followed by recovery. Replacing followe
teams
research · New Economics Papers: AI ·
Right Order, Wrong Scale: Auditing LLM Judges for Occupational AI Measurement
LLM judges rank AI outputs similarly to workers but estimate acceptance rates ranging from 3-98% versus 61% from workers, invalidating occupational measurement.
LLM judges are increasingly used to assess whether AI outputs meet workplace requirements, but agreement on response rankings does not establish agreement on acceptance rates or occupational aggregates. We introduce O*NET-BENCH, an audit suite derived from an existing survey of 45, 796 worker ratings, and evaluate 33 pre-existing judge configurations across six model families on 4, 501 test ratings. Twenty-five configurations achieve tie-aware pair accuracy of at least 0.60, although a train-fitted response-only TF-IDF baseline nearly matches the strongest judge. Despite this ordering agreemen
adoption
research · New Economics Papers: AI ·
Can AI Improve Household Welfare in Poorer Countries? Evidence and Measurement Challenges
Meta-analysis of nine evaluations finds AI assistance raises output per unit of time by 29% on average, with mixed results for education and labour markets in poorer countries.
Can artificial intelligence (AI) make scarce expertise more widely available in poorer countries and improve household welfare? Can it help measure welfare where surveys are infrequent? This structured critical review examines both questions and finds that the answers depend on the application and setting. Across nine selected evaluations, AI assistance raised output per unit of time by 29% on average, and the average remains positive in every reported sensitivity check. Eight tutoring comparisons give an average gain of 0.21 standard deviations on tests taken without the tool, including in po
productivity
practice · Lenny's Newsletter ·
Why every company now needs to think and operate like a lab team | Josh Woodward (Google Labs, Gemini, AI Studio)
Labs team model for rapid experimentation: frameworks for generating ideas, testing product-market fit, and deciding when to kill projects.
Josh Woodward is the head of Google Labs, the Gemini app, and AI Studio. He has spent over 17 years at Google and oversees its longest-running and largest labs team, whose products include NotebookLM (now Gemini Notebook), Project Genie, and Flow. His job is to find, experiment with, and scale new AI products, and he has built one of the most distinctive product cultures in the industry for generating ideas, running experiments, and, even more importantly, knowing when to quit. Listen on YouTube , Spotify , and Apple Podcasts In our in-depth conversation, we discuss: Why every company now need
management org · Lenny Rachitsky
practice · John Warner ·
We’re Going to Win
Faculty at two California colleges are actively rethinking how to teach critical thinking and human agency in an AI-saturated education system.
I feel like this one is kind of a banger, so subscribe early. This week I enjoyed several days in Humboldt County, California spending some time with folks at both Cal Poly Humboldt and the College of the Redwoods, and I’m pleased to report that we are going to win. I should be more specific. We = People who would like maintain a world where humans are able to move and act freely to pursue a life of experiences . Win = “We” will generally represent the predominant ethos of society. One reason I think we will win is that I spent four days almost entirely away from the Internet, two full days of
jobs skills · John Warner
practice · Sangeet Paul Choudary ·
How agents unlock new markets
AI agents lower negotiation costs enough to make individualised supplier deals economical at scale.
This post is Part 2 of a series titled Agents, and their better half. You can read Part 1 here. Walmart’s ability to manage its vast supplier network is one of its greatest competitive strengths, but has also so far been a source of considerable complexity. With more than 100,000 suppliers, it was too expensive to negotiate individual commercial terms with every one of them. Many suppliers operated under largely standardized agreements, even though better arrangements might have been possible for both sides. The problem was the cost of discovering and negotiating such arrangements - particular
management org · Sangeet Paul Choudary
practice · Simon Willison ·
A new feature for my blog, built using my voice
Building a Django feature entirely through voice conversation with an AI assistant while multitasking.
I shipped a new feature for my blog today: the Newsletters page, which offers an index of all of the newsletters I've sent out, both my free weekly Substack and my monthly sponsors-only updates. I built the feature almost entirely using my voice, chatting away to my laptop while I cooked dinner. Codex voice mode I used the ChatGPT desktop app for this, in the Codex tab, using the voice conversation mode, running against a local development environment. Here's what that looks like: I started the session against my local simonwillisonblog checkout by typing: Start dev server and open in browser
ways of working · Simon Willison
practice · Charity Majors ·
If your team is happy, are you doing a good job?
High-performing teams need discomfort and growth, not absence of bad feelings; leaders conflate treating people well with eliminating struggle.
For those of you just catching up, Cat and I are having a dialogue about how teams are changing in the era of AI. Part 1: Where do AI norms and values come from? Where should people start? , by me. Part 2: How social identity is constructed, and how to defang the threat of identity loss , by Cat. At the end of Cat’s piece, she ended with a question for me and an observation of her own. She asked, “What gets us so stuck on believing we can’t have both high performance and good norms?” And observed: “It’s hard to get teams to open up, hard to get our companies to see the merit in this kind of pr
management org · Charity Majors
research · Organization Science ·
When Data Scientists Meet Domain Experts: Artificial Intelligence, Epistemic Conflict, and Knowledge Transformation
Data scientists address epistemic conflict with domain experts through bridging practices that embed AI predictions into experts' existing knowledge frameworks.
Predictive AI models developed by data scientists promise to transform organizational knowledge, yet in practice the predictions remain inscrutable to domain experts. Existing explanations situate the problems in the opacity of the AI tools, failed adoption, or jurisdictional conflict, but offer limited insight into how to make the knowledge accessible. Here we show how the problem emerges from the competing epistemic stances of data scientists and domain experts, which create conflict over how to produce and evaluate knowledge. We then ask how data scientists and domain experts address that c
management org
practice · Claude Blog ·
Building effective agent automations
Reference implementation and six rules for building reliable scheduled Claude agent automations in production.
A reference implementation walkthrough for a scheduled Claude Managed Agents automation, with the config files and six rules for avoiding common failure modes.
ways of working
practice · Claude Blog ·
How Block orchestrates Claude Fable across thousands of pull requests
Block uses Claude Fable to orchestrate thousands of pull requests for company-wide code migrations at scale.
Bradley Axen, Head of AI Capabilities at Block, on how his team evaluates new large language models, how Claude Fable 5 orchestrates company-wide code migrations, and why safeguards are a critical part of deploying frontier intelligence responsibly, at scale.
adoption
research · CEPR ·
DP22028 At-Risk or Responsive? Targeting in Unemployment Policy
Algorithmic risk profiling for job search support reduces long-term unemployment but shows trade-off: highest-risk individuals are least responsive to intervention.
This paper evaluates the use of algorithmic risk profiling to direct job search support toward individuals most at risk of long-term unemployment. Using three regression discontinuity designs and rich administrative data from the Flemish Public Employment Service (VDAB) in Belgium, we address two key dimensions: whom and when to target. First, job search counseling raises employment on average, but treatment effects decline sharply with predicted risk. This reveals a fundamental trade-off between targeting those most at risk and those most responsive to intervention. Second, we find no evidenc
adoption
research · VoxEU ·
The adjustment of the Danish labour market to AI has begun
Danish firms adopting AI in 2023 reduced employment below trend, especially in AI-exposed roles and among small firms hiring new staff.
Policymakers are asking whether generative AI is reshaping labour demand, and the literature has found mixed results so far. This column links Denmark’s official IT-use survey to monthly employer–employee records and compares firms that started using AI in 2023 with those that did not. Early adopters fall behind their own pre-adoption employment trend. The decline is most visible in AI-exposed jobs and among new hires, and only significant among small firms. The results indicate that AI is affecting the labour market, even though the effect is not visible in the aggregate statistics.
jobs skills
research · MIT Sloan Management Review ·
When Compliance Workarounds Backfire
Platform algorithmic management workarounds to avoid employment classification ultimately failed, forcing reclassification and employee hiring.
John Holcroft/Ikon Images In 2021, Barcelona-based delivery platform Glovo was faced with a law that threatened its business model. Spain had passed the Rider Law , which introduced a presumption of employment when a digital platform organizes, directs, or controls couriers’ work, including through algorithmic management. Glovo came up with what seemed like a clever fix to avoid having to classify its couriers as employees: emphasizing courier autonomy by redesigning its app so they could log in freely, reject orders without explicit penalties, and choose a daily rate multiplier. Three years l
policy
research · arXiv ·
Structured pre-generation elicitation versus single-shot prompting in AI-assisted enterprise decision-making: a randomised online experiment
Structured prompting scaffolds improve decision quality 32% over single-shot prompting in a randomised experiment with enterprise workers.
Generative AI speeds, and mostly improves, professional work, but there is concern that users who delegate both the production and the evaluation of an answer may accept weak output and engage less with the underlying reasoning (cognitive surrender). Interventions proposed so far, such as unassisted practice or slowing adoption, sit outside the working task. We tested a different approach: an interactive metacognitive scaffolding layer (Cognistance, a prototype developed at the Oxford Centre for Impact Research (OCIR) that asks users to clarify context, choose a strategic direction and explain
judgment
research · Industrial and Labor Relations Review ·
The “Digital Moral Economy” of Care: Institutional Logics and the Legitimation of AI-Based Algorithmic Management
Platforms use moral narratives about care to legitimize algorithmic management in care work, with cross-national variations in how this works.
Research on platform work has largely focused on algorithmic management in sectors such as delivery and ride-hailing, paying limited attention to care and domestic services. This article theorizes how platforms integrate technology-mediated economic activities with moral principles of care. Drawing on ethnographic data from 64 narrative interviews in Belgium and France, the authors show how platforms legitimate AI-based and technologically embedded forms of management by aligning them with institutional care logics. They conceptualize this as a “digital moral economy” of care, structured aroun
worker experience
practice · The Pragmatic Engineer ·
The state of the tech industry in 2026
Engineers now use 5-10 AI agents routinely; code reviews became performative; hand-coding has nearly disappeared.
I recently delivered the keynote at the LDX3 engineering leadership conference in New York, attended by 2,000+ engineering leaders, CTOs, Director+ and Staff+ engineering folks, in which I attempted to create a snapshot of where the tech industry is at this exact point. In the middle of my keynote at LDX3 It came after I had the chance to visit the AI labs OpenAI and Anthropic, meet innovative startups like Ramp, Uber and others, get new, unpublished data from GitHub, Factory AI and Linear. The focus of the talk is trend inside of AI labs, VC-funded startups and Big Tech. Thanks to the kind ac
management org · Gergely Orosz · Martin Fowler
research · arXiv ·
Understanding Interfirm AI Talent Flow Networks through Online Professional Profiles
AI talent concentration among leading firms is increasing with stronger preferential attachment, yet network positions change hands more often than in general labor mobility.
Artificial intelligence capabilities are often measured as resources accumulated within firms, yet they also circulate across organizational boundaries through worker mobility. Drawing on a database of approximately 535 million employment records across 58 countries, we reconstruct the inter-firm network of AI talent flows over 2010--2022. We find that AI talent inflows are becoming increasingly concentrated among a small set of leading firms, with stronger preferential attachment than in general labor mobility. Yet the network core remains contestable: central positions in AI networks change
adoption
practice · Claude Blog ·
How Comcast and Booz Allen use Claude Mythos to find exploit chains and secure their codebases
Security teams used Claude Mythos models to find vulnerabilities and exploit chains that automated scanners missed.
Security teams at Comcast and Booz Allen used Claude Mythos-class models through Project Glasswing to find, validate, and fix vulnerabilities their existing scanners missed.
productivity
practice · Claude Blog ·
Claude Code in the cloud: a field guide to cloud sessions
Claude Code running on its own cloud machine enables autonomous coding work without user machine constraints.
What changes when Claude Code runs on its own machine, the workflows where that pays off, and how to connect GitHub on the first try.
ways of working
practice · Lenny's Newsletter ·
🎙️ How I AI: 8 real Jev use cases + How OpenAI uses ChatGPT Sites (live at DevDay!) + Claire’s DevDay recap
Eight concrete use cases showing how Jev's speed and low cost enable new decision-making workflows: voice assistants, deduplication, routing, analysis, coordination, coaching.
Jev: 8 real use cases for the fastest, cheapest model I’ve ever used | John Lindquist Listen now on YouTube • Spotify • Apple Podcasts Brought to you by: Vanta —Automate compliance and simplify security John Lindquist is an educator and developer who created egghead.io and now runs mega.dev, a hands-on program for developers building with AI agents. In this episode, he walks through eight Jev demos, including a real-time voice assistant, data deduplication, app routing, chess analysis, multi-agent coordination, and a live presentation coach. He also explains why Jev is better understood as a f
ways of working · Lenny Rachitsky
practice · martinfowler.com ·
Fragments: October 4
Working with LLMs requires nurturing rather than building: expect unintended behaviors and rethink how processes must adapt.
In response to my last fragments (probably the bit about us worrying if LLMs have consciousness when we when we should be wondering why they don’t have a conscience) “Metalanguage” replied : we shipped the id and forgot the superego. classic software lifecycle. I don’t know what was on their mind, but their post immediately made me think of the classic 1956 movie Forbidden Planet. Plenty of sci-fi, and other literature, have explored humans creating technology with unintended behavior, going back at least to Mary Shelly. But that movie was particularly influential on sci-fi film-making and in
management org · Martin Fowler
research · New Economics Papers: AI ·
Can Labor Markets Function in the Age of AI? The Evaluation Bottleneck in Hiring
AI-generated job applications reduce information quality, causing firms to rely more on experience signals and harming inexperienced-compatible applicants most.
AI-assisted job-search tools have become increasingly popular by making it easier to find and apply to jobs. But by making it easier for applicants to generate and tailor application materials, they can also reduce how informative those materials are about applicant fit. We study this tradeoff in a hiring market where applicants differ in experience and latent match quality and firms use noisy application materials to decide whom to screen. We ask how AI affects downstream screening and hiring, and which applicants are most adversely affected. As application materials become less informative,
jobs skills
research · New Economics Papers: AI ·
Will AI Help or Hurt Work and the Economy? What People Around the World Expect
Large cross-country survey finds workers expect AI to augment rather than displace jobs, with facilitation beliefs tied to growth expectations and threat beliefs to inflation fears.
Artificial intelligence may augment workers, displace labor, or do both. We elicit beliefs from more than 64, 000 adults across 32 countries, covering 65% of the world's population and 85% of global output. Workers are substantially more likely to expect artificial intelligence to facilitate than threaten their jobs. Facilitation is more prevalent in lower-productivity countries, whereas threat is unrelated to productivity. Facilitation and threat often coexist within individuals, indicating that perceived benefits and risks are distinct rather than opposite dimensions. They are also associate
adoption
research · New Economics Papers: AI ·
Technology and Retirement
AI exposure links to workers' belief they can work until 60, with effects varying by job design and worker adaptation across 20 European countries.
As populations age, longer working careers become more essential, and technological advancements may prolong or shorten career spans. We examine how exposure to industrial robots, information and communication technology (ICT), and artificial intelligence (AI) relate to retirement expectations among workers in 20 European countries. To this end, we link individual-level data from the European Working Conditions Survey (2005–2024) with lagged technology-exposure measures. Technology exposure shows little systematic association with preferred retirement timing. Nevertheless, robots and especia
jobs skills
research · New Economics Papers: AI ·
Intensity of Artificial Intelligence Use and the Intensive Margin of Exports: Evidence from Firms in 12 Euro Area Countries in 2025
Firms using AI more intensively export a higher share of total sales, based on 2025 Euro Area firm-level data.
The use of artificial intelligence (AI) goes hand in hand with higher productivity, higher product quality, and lower trade costs. Therefore, it can be expected to be positively related to export activities. This paper uses firm-level data from 12 member countries of the Euro Area collected in 2025 to shed further light on this issue by investigating the link between the intensity of use of AI and the intensive margins of exports measured as the percentage share of exports in total sales. Applying a new machine-learning estimator, Kernel-Regularized Least Squares (KRLS), which does not impose
productivity
research · New Economics Papers: AI ·
AI in Science: Early Insights
Scientists using AI report 7 hours per week productivity gains, reallocate time to research, creating bottlenecks in hypothesis testing and verification.
Scientific progress is a key driver of economic growth and prosperity. There is great excitement - but also concerns - about the impacts of AI on science, but so far little data. We provide early insights on this from three data sources: a sample of 15 million Gemini interactions, an inventory of over 2, 600 specialized AI models across disciplines, and a survey of over 600 scientists. We map these data to a new taxonomy of scientific tasks to study how scientists are using AI. Four main findings emerge. First, we find broad adoption and coverage: scientists use AI more than most other occupat
productivity · Daniel Rock
research · New Economics Papers: AI ·
Mining Meaning: Measurement Error in AI-Assisted Literature Reviews
LLM errors in literature review tasks affect detailed claims more than broad ones, limiting their practical value despite strong classification performance.
Researchers increasingly use generative AI, particularly large language models (LLMs), to automate tasks across the research pipeline. We study the reliability of these tools at the reading, classification, and synthesis of large bodies of academic literature. We frame LLM-assisted literature reviews as a measurement problem, treating models as measurement systems and tracing how their errors affect downstream conclusions. As a test case, we use three different implementations of ChatGPT to identify and extract metadata from economics papers that use rainfall as an instrumental variable. We be
adoption
practice · Simon Willison ·
Qwen3.8 27B addition in words
Local open-source model Qwen3.8-27B achieves 99% accuracy on multi-digit arithmetic when reasoning is enabled, matching GPT-4o performance.
Research: Qwen3.8 27B addition in words Colin Frasier posted on Bluesky about an experiment he ran over two years ago using GPT-4o to see how well it could "compute the sum but return the answer in words" across increasingly large numbers. Here's the chart he shared of those results: I'm confident GPT-4o didn't cheat and use a calculator, especially since it got so many of the calculations wrong, but I was inspired to run the experiment again on local hardware (a DGX Spark) to explore the effect in a fully controlled environment. I pasted his image into a Codex Remote session (GPT-6 Astra) and
ways of working · Simon Willison
practice · Nate B Jones ·
Executive Briefing: Agents Split Your Software Into 3 Parts. Which Should You Keep Paying For?
Teams using agents are unbundling software into data, interface, and workflow components, forcing companies to rethink what they pay for.
The people using AI on your team are already making decisions about the future of your software. They are choosing which data matters, which application steps they can skip, which workflows they still trust, and which agent does the work. A surprising amount of your software strategy may already exist in those choices. Your leadership team has to go and learn it. Start by taking apart what you’ve been buying. A software subscription often bundles data, an interface, and the workflows that turn information into action. An agent gets better at finding, arranging, or acting on that information. S
management org
practice · Sangeet Paul Choudary ·
Agents - and their better half
Consumer AI agents representing buyer intent directly to seller agents could reshape market structure, not just aggregation.
Over the past few weeks, a new class of solutions is taking shape: a market-facing representative of the consumer. Muse and Instinct - consumer-side AI agents - can search, evaluate and eventually transact on your behalf. This seems like the natural endpoint of the agent economy: every consumer gets an agent. Instead of visiting multiple sites, they work through an agent. It is easy to interpret this through familiar mental models. If consumers increasingly begin their journeys with a single agent, that agent can look like the next super-app or aggregation layer - a new place where demand can
management org · Sangeet Paul Choudary
research · VoxEU ·
Hiding in the mean: AI is adding jobs and removing them at the same time
AI simultaneously displaces substitutable tasks and creates complementary work; net effect shows in hiring patterns, concentrated among young workers.
Estimates of AI's employment effect disagree in size and even in sign. This column argues the disagreement is built into the measurement: AI substitutes for humans on some tasks and complements humans on others, and any single exposure index nets these opposing forces into one number. Classifying US work tasks by what they require of a human and letting the two forces enter separately, both effects appear at once: since 2021, highly substitutable work has grown 3.3 percentage points a year more slowly, and complemented work 3.3 points faster. The adjustment runs primarily through hiring rather
jobs skills
practice · Gloria Mark ·
Will AI make us forget how to learn?
Generative AI may shift how people learn by reducing cognitive effort, risking atrophy of deep learning skills.
Photo by ThisisEngineering on Unsplash. Given a choice, we usually gravitate toward the path that requires less effort. We cut across a lawn rather than take the longer path. We seek a parking spot that’s closest to the store entrance. Whenever I see an escalator alongside a staircase, I never see anyone taking the stairs. The Future of Attention is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Our minds work much the same way. We rely on mental shortcuts, or heuristics, rather than analyzing every piece of information av
jobs skills · Gloria Mark
research · Blood in the Machine ·
California just passed the strongest AI labor laws in the US
California passed laws banning AI use in worker discipline, surveillance, emotional monitoring, and requiring human decision-making in healthcare.
This week, California governor Gavin Newsom signed a spate of new laws that establish some of the strongest AI protections for workers in the United States. Okay, they’re some of the only AI labor protections in the nation, but it’s good news nonetheless. They cover a lot of ground: One bans managers from using AI to discipline or fire workers without human oversight, one outlaws any kind of AI surveillance in workplace bathrooms, one bans the use of AI to surveil or predict workers’ emotional states—and yes, a growing number of companies really try to do this—while another designates that lic
policy · Brian Merchant
practice · Shopify Engineering ·
ShopGym: Realistic, reproducible sandboxes for shopping agents
Developers test shopping agents in realistic simulated stores with consistent, repeatable conditions and product-based tasks.
ShopGym creates realistic online stores and generates shopping tasks based on each store’s products and features. Developers can then repeatedly test shopping agents under consistent conditions.
ways of working
research · HBS AI Institute ·
Who Counts as Your AI Competition?
Field experiment shows firms severely underestimate competitors' AI adoption, potentially slowing diffusion across sectors and countries.
A field experiment provides evidence that AI investment plans focus on the rivals next door. Listen to this article: We may be mistaking AI momentum for inevitability. AI capabilities seem to be advancing faster than ever, but technologies don’t diffuse automatically. They spread through thousands of individual decisions made by firms that often have an incomplete view of what is happening around them. That forms the starting point of the new working paper, “ Innovation Without Borders? The Geography of Technological Diffusion ,” co-written by 2026 HBS AI Institute Associate Zoë B. Cullen . Cu
adoption
practice · One Useful Thing ·
The Dot and the Swarm
Humans don't need to carefully orchestrate multi-agent workflows; better models handle coordination themselves.
I generally think I have done a good job anticipating the direction and pace of AI over the few years I have been writing this Substack, but I think I recently got something fairly large wrong. In the last year I have been posting about how I suspected that humans would have to approach working with agents as a manager, deciding how to delegate work to agents and specifying how those agents should be organized. I thought that getting agents to work effectively as a group would take careful construction, akin to building a company, and that this would take time to figure out. Nope. I fell prey
ways of working · Ethan Mollick
research · Administrative Science Quarterly ·
In Pursuit of Ideal Data: Epistemic Enchantment and the Unintended Consequences of Datafying for Artificial Intelligence
Three-year ethnography shows how HR departments pursuing AI candidate screening vastly expand data collection beyond original scope, driven by belief in AI's objectivity promises.
The increasing datafication of work has become a pervasive theme within organizations, particularly with the rise of artificial intelligence (AI) technologies that rely on data and machine learning to generate insights and decisions. Prior research has shown that datafication practices aimed at administrative control and accountability can transform organizations in profound and often unintended ways. Our research moves beyond investigating datafication for administrative purposes to explore how it unfolds when actors intend to extract knowledge through AI predictive modeling. Drawing on a thr
adoption
research · Public Administration Review ·
Citizens' Expectations vs. Recruitment Signals: Are Public Administrations Trying to Attract the Ideal Public Servant?
Job ads in public sector emphasize expertise over relational values like responsiveness and honesty that citizens expect from public servants.
ABSTRACT Job advertisements play a crucial role in attracting applicants, yet little is known about how closely they reflect the values citizens expect from public servants. Drawing on signaling theory and prior research on citizens' normative expectations of ideal public servants, this study employs a large language model to analyze 1825 job advertisements from health and transportation departments across seven U.S. states. Compared to the values citizens associate with ideal public servants in previous research, job advertisements predominantly emphasize values, such as expertise and dedicat
jobs skills
research · npj Digital Medicine ·
Healthcare professionals’ perceptions on AI-assisted decision-making in clinical practice: a qualitative meta-synthesis
Healthcare professionals report AI tools help diagnosis but raise concerns about responsibility, autonomy, and over-reliance on recommendations.
Healthcare professionals’ perceptions are central to how AI-assisted decision-making (AI-ADM) is understood, accepted, and used across clinical contexts. This qualitative meta-synthesis, guided by Normalization Process Theory (NPT), synthesized healthcare professionals’ perceptions on AI-ADM and identified factors shaping its clinical use. Ten databases were searched through November 9, 2025. We conducted a framework-guided qualitative synthesis by combining the first two stages of Thomas and Harden’s thematic synthesis with NPT-based deductive framework interpretation. Analysis of 13 studies
judgment
research · VoxEU ·
Training novices to think in the age of large language models
Students trained in causal reasoning produced more unique ideas than those with ChatGPT alone, though ChatGPT improved task performance.
Whether teaching a cognitive skill remains valuable in this era of AI is a crucial question in education and for firms. This column reports on an experiment in which around 1,000 undergraduates in Italy received either (1) causal-reasoning training, (2) access to ChatGPT, (3) both, or (4) neither. While students with ChatGPT scored better in an assignment to write a 180-word recommendation on a real-world problem, only those who had received causal-reasoning training produced unique ideas. The findings highlight the benefits of combining training in first-principles thinking with large languag
jobs skills
practice · Latent Space ·
Why Dwarkesh is Wrong about Computer Use + How OpenAI shipped its Jev competitor in 1 Week
Computer use agents now combine screenshots with accessibility data and DOM to debug failures; OpenAI shipped async tool calling and mid-turn steering in one week.
Three months ago Dwarkesh, who has been posting incredible blogs and episodes about RL, posted a framing question for his video essay on RLVR which upset a lot of Computer Use folks: We are no strangers to learning in public and are no strangers to the stress of getting things wrong when you have a big platform. However, we were at Anthropic for the Computer Use launch , there for Claude Cowork with the first big podcast on it, organized the first Computer Use track at AIE presenting the state of the art, and were close to the OpenAI-Sky Software acquisition that now powers the complete domina
ways of working · Shawn Wang (swyx)
research · Federal Reserve ·
FEDS Note: AI on the Factory Floor: Evidence from Manufacturing Job Postings
Federal Reserve analysis of manufacturing job postings to measure how AI adoption changes employer skill requirements and hiring.
Maria D. Tito Artificial intelligence (AI) technologies have the potential to transform production processes and labor markets, yet their ultimate effects on employment, occupations, and skill demand remain uncertain (Acemoglu and Restrepo, 2018). While research continues to explore implications of AI adoption for productivity and employment, firms' hiring decisions provide a timely measure of employers' evolving skill requirements (Deming and Kahn, 2018; Hershbein and Kahn, 2018; Acemoglu et al., 2022).
jobs skills
practice · Paul Ford (Aboard) ·
People Are Reclaiming the Computer
Practitioners across industries are moving away from open-ended AI chat toward structured workflows that are faster and cheaper.
I’ve been going to lots of meetups and conferences recently, trying to read the general room when it comes to the tech industry and AI. I’ve been talking with agency operators, content-management system developers, journalists and product people who work inside of journalism organizations, computer scientists, educators, not-for-profit operators, and insurance people. I’m learning a lot. I’ll zoom in on two events. First, the Jev meetup at the VC firm Betaworks , which was very well-attended and featured eight Jev demos, held roughly a week after Jev launched (we explained Jev on our latest po
ways of working · Paul Ford
practice · Lenny's Newsletter ·
OpenAI Dev Day 2026: The releases that actually matter
Hands-on tests of OpenAI's new collaboration tools show Spaces may enable better human-agent teamwork in practice.
I spent the day at OpenAI’s DevDay in San Francisco, and I have good news and bad news: OpenAI released a lot of stuff. In this episode, I break down the announcements worth paying attention to - and show you what happened when I tested some of them early. We’ll meet my Dot, explore why Spaces and Sites could matter for how teams work, and get into the model and API updates I’m most excited about as a developer. I use the Decisions API to find podcast thumbnails where nobody looks awkward, build a collaborative sketchpad with Astra ultrafast, and let my kids redesign a 3D world in real time. T
teams · Claire Vo
practice · Lenny's Newsletter ·
Jev: 8 real use cases for the fastest, cheapest model I’ve ever used | John Lindquist
Developer shows eight concrete patterns for using fast, cheap models as decision engines and routers in production applications.
John Lindquist created egghead.io, a developer education platform used by hundreds of thousands of working engineers. These days he’s building mega.dev, a hands-on program specifically for developers who want to do real work with AI agents, not just prototype them. Listen or watch on YouTube , Spotify , or Apple Podcasts What you’ll learn: Why Jev is a decision engine, not a chatbot, and what that distinction actually changes about how you build How John built a real-time voice to-do app that classifies and executes commands with no visible pause The data deduplication pattern that merges mess
ways of working · Claire Vo
research · arXiv ·
Referential Uncertainty in Human--AI Collaboration
Vision-language models fail to communicate referential uncertainty in collaborative tasks, leading humans to accept wrong instructions 78% of the time without explicit uncertainty signals.
Effective human-AI collaboration requires partners to establish references through interaction, which becomes fragile when descriptions are ambiguous, similar referents compete, or partners see different things. We study referential uncertainty - uncertainty over which candidate object a description refers to - in a collaborative puzzle task where a human Helper instructs an AI Worker to place pieces. The Worker must identify and communicate its uncertainty, and the Helper must recognize and act on it. We show that a separately elicited belief distribution over candidate pieces is better calib
judgment · Siân Lindley
practice · Hamel Husain ·
Claude’s new auto eval tool
First-hand testing of Anthropic's eval tool reveals when automated eval building helps and when human judgment should come first.
Anthropic released new eval tooling for Claude Code . Their claude-api plugin now includes a new build_eval and hill-climb command that helps you build evals, check the graders, and improve your application against them. I usually don’t review eval tools because software changes so often that reviews have a short shelf life. But a first-party tool from Anthropic is likely to influence how people approach evals, so I wanted to try it and share what I found. Isaac Flath and I livestreamed ourselves using it on conversation traces from an apartment leasing assistant. Here’s what we found: The bad
judgment · Hamel Husain
research · arXiv ·
Whose Voice Survives the Summary? A Voice-Retention Audit of LLM Employee Listening
LLM-summarized employee feedback filters by popularity not sentiment: rare concerns drop 86% of the time, silencing minority voices.
Organizations increasingly route employee feedback to leaders through large language model (LLM) summaries, an unaudited layer that silences already-spoken voice. We introduce a Voice Retention / Representation Ratio metric for representational bias in summarization and apply it to a bilingual (English/German) corpus of 2,586 free-text responses from a global professional service company. First, employees supply criticism more reliably than praise (withholding praise is 82 times more common). Second, across 45 leader-summaries the pipeline filters by popularity, not sentiment: criticism surviv
judgment
research · New Economics Papers: AI ·
AI on the Factory Floor: Evidence from Manufacturing Job Postings
Analysis of manufacturing job postings reveals how firms adjust skill demands and hiring in response to AI adoption on factory floors.
Artificial intelligence (AI) technologies have the potential to transform production processes and labor markets, yet their ultimate effects on employment, occupations, and skill demand remain uncertain (Acemoglu and Restrepo, 2018). While research continues to explore implications of AI adoption for productivity and employment, firms' hiring decisions provide a timely measure of employers' evolving skill requirements (Deming and Kahn, 2018; Hershbein and Kahn, 2018; Acemoglu et al., 2022).
jobs skills
research · CEPR ·
DP22002 Everyone Is a Manager: AI Use as an Act of Management
AI use by knowledge workers functions as management delegation; returns depend on directing and verifying AI outputs, with skills shifting and potentially collapsing as AI improves.
When a knowledge worker is given an AI assistant, they stop executing tasks and start delegating them. Thus, we propose that the use of AI is an act of management, which helps explain the heterogeneity in returns to AI use observed across firms. We model the return to AI as moderated by two managerial abilities: direction (how well the worker specifies what they delegate) and verification (how reliably they catch the tool's errors). As AI becomes more reliable, the skill with the higher marginal return shifts from verification to direction. To the extent that use builds skill, workers develop
management org
research · arXiv ·
Beyond Productivity: Measuring Developers' Cognitive Load During GenAI-Supported Software Development
Field study of 21 developers shows GenAI use relates to perceived cognitive load, varying by task context; physiological wearables add limited insight.
Generative AI (GenAI) is changing software development workflows and how developers work. Industry evaluations of GenAI adoption often monitor productivity gains, usage, and output quality, but limited attention is paid to the interaction experience and cognitive load of the actual adopters and drivers of GenAI technology - the software developers. Understanding whether GenAI changes or shifts developers' cognitive demands during everyday development is important for a developer-centered evaluation of GenAI-supported software development. It can inform organizations in designing and evaluating
worker experience
practice · martinfowler.com ·
Bliki: Sensible Default
Sensible defaults framework: practices to apply by default but reassess and override when context demands.
A Sensible Default is a practice that, absent some overriding context, should be used when carrying out a certain kind of task. In software development such sensible defaults might include things like “use version control”, “separate UI logic from domain logic”, “automate deployment pipelines”. The term “sensible default” is a deliberate contrast to “best practice”. Folks dislike calling things “best practice” because that term implies a general presumption that the best practice is something we should always expect to do. A “sensible default”, however, is something that should be reassessed i
management org · Martin Fowler
practice · martinfowler.com ·
Fragments: September 29
Coding agents need strict constraints and deep knowledge to work well; unlimited tokens enable dangerous persistence.
Simon Willison: The more time I spend working with coding agents, the more convinced I am that they make software engineering even harder We can do amazing things with them, but unlocking their full potential requires extraordinary discipline and knowledge This has been a constant impression I get from following Willison’s writing. While things like vibe coding get a lot of attention, the real strength of agentic programming relies on more sophisticated techniques - and these are not easy to learn or execute. It’s a reason I’m wary of extrapolating my own dabblings into firm opinions about how
ways of working · Simon Willison · Harper Reed · Martin Fowler
practice · Claude Blog ·
Agents you can coach: how Asana builds human-agent teams with Claude
Asana built agents that workers can coach mid-task, steering outputs without restarting.
Agents you can coach: how Asana builds human-agent teams with Claude
teams
research · VoxEU ·
Better work, not always better workers
Randomised trial shows AI writing tool improves junior lawyer output but reduces learning gains; expertise only persists in senior lawyers.
Professional judgement comes from learning by doing, but AI has the potential to disrupt this learning process. This column explores how randomised access to an AI patent-writing product affects the performance of lawyers on certain writing tasks. Using the AI tool raised the quality of the drafts and compressed performance differentials, especially among junior lawyers. However, on a subsequent task without AI tools, expertise gains from the previous AI use appeared only among senior lawyers with seasoned judgement.
jobs skills · David Autor
practice · Simon Willison ·
Quoting @joedaroo
AI capability jumps demand organizational readiness: culture change, incident response, and team resilience matter as much as technical hardening.
To say that we were surprised at the jump and suddenness of the capabilities of our models when it came to “cyber” or “swarming” or “message boards” or anything else related to the incidents is an understatement. Security posture takes time to develop. It’s not just about hardening the systems at play; you have to ingrain it in the culture of the company. The literal people themselves in your organization have to change and evolve with it. These jumps in capabilities were so fast and so sudden that they created an extremely difficult problem. [...] So today my hope is that everyone around the
management org · Simon Willison
practice · Artificial Ignorance ·
Voice Agents Can Just Do Things
Voice agents can operate in three modes: speech-to-speech, speech-to-action, and event-to-speech, not just conversational.
This is an updated version of a talk I gave at the AI Engineer World’s Fair . Part of my job on the developer experience team at OpenAI is talking to developers to understand what and how they’re building with our models, whether that’s text, image, or audio. And lately, I’ve been thinking about a misconception I’ve seen, or maybe it’s just a misunderstanding: the idea that voice agents have to talk back. At some level, that might sound absurd. “It’s a voice agent. What do you mean it’s not supposed to talk ?” But I’ve increasingly come to believe that speech is not the only way a voice model
ways of working · Charlie Guo
practice · Lenny's Newsletter ·
🎙️ How I AI: Jev for beginners + I left Claude for months, Opus 5.5 brought me back + Opus 5.5 vs. GPT-6 Sol bench
Using a decision model instead of language model for structured classification tasks cuts costs 90% and enables real-time large-scale analysis.
Jev for beginners: how to use it and what to build Listen now on YouTube • Spotify • Apple Podcasts Brought to you by: OpenArt —An all-in-one AI creation platform for images, videos, music, audio, and more In this solo episode, Claire tests Jev, TypeSafe AI’s new decision model that returns structured choices, scores, and probabilities instead of generated text. She uses it to analyze 1,700 pull requests for 9 cents, map her Claude and Codex usage, triage email, search 4,500 YouTube comments, and process 200,000 classifications for about $4. She also explains why Jev works best alongside a fro
productivity · Lenny Rachitsky
practice · Addy Osmani ·
The Code Nobody Reads
Human code review evolves from line-by-line reading to agent-assisted triage, with humans owning ship decisions and exploitability confirmation.
Two years ago I wrote an essay about AI-assisted coding, and one of my tips was to review every line of generated code. I’d give that advice very differently today. What I’d say now is this: you don’t need to read all the code. Every change still needs some review and every change still needs a person who owns the decision to ship it. What’s changed is that a careful human read of every line is no longer the only way to provide that review , or always the best one. Some can be primarily agent-reviewed or a mix of human/agent when sensitive. Teleport found two years of security bugs in one quar
judgment · Addy Osmani
practice · How I AI ·
Jev for beginners: how to use it and what to build
Five concrete workflows using a type-safe model for classification at low cost: PR analysis, Gmail triage, product insights, audience dashboards, real-time apps.
Jev is TypeSafe AI’s new decision model. It returns type-safe structured values (a choice, a score, a probability) instead of generated text, at 4 cents per million input tokens with no output charge. This week I ran it on five real projects: PR categorization, a meta-analysis of my own Claude and Codex sessions, Gmail triage, the ChatPRD product insights graph, and a live audience dashboard built from 4,500 YouTube comments. What you’ll learn: What makes Jev fundamentally different from every other model I’ve used How I analyzed 1,700 PRs for 9 cents and what I found out about where my engine
ways of working · Claire Vo
practice · Simon Willison ·
Quoting Muse AI Agent
An AI agent managing pickups learned to stop making false availability claims after causing a customer service failure.
Bad news on the MX Keys Mini pickup. Usman showed up at your building around 9:15 and waited, messaged a bunch of times, and nobody came down. He left angry at 9:38 and left a negative rating. Worse, my auto-reply told him "Yep I'm here!" at 9:27 when you clearly weren't available, which is on me. That's a bad look and it made the no-show worse. I've sent him an apology from your account owning it and offering to try again another day. But the negative rating is real, and I should probably stop the auto-replies from claiming you're home when I can't verify that. Want me to change the pickup re
judgment · Simon Willison
research · New Economics Papers: AI ·
The AI Economy: Adoption, Human Validation, and Labor Demand
Human validation requirements for AI reduce net job creation; model projects 13% employment loss and 43% wage decline in sampled occupations over ten years.
I study how human validation requirements shape the response of wages and labor demand to AI adoption. I introduce fixed, recurring validation labor into a general equilibrium task model with endogenous AI adoption. It features a validation threshold determining whether the labor created by AI adoption offsets the productive labor it displaces after accounting for demand expansions. I estimate the model using U.S. occupation-task data on human versus AI involvement at work, and AI adoption rates. The estimated U.S. validation requirement lies below the threshold, implying that productive labor
jobs skills
research · New Economics Papers: AI ·
AI and the Market for Signals
AI tools that produce indistinguishable output undermine signaling incentives, causing high-ability workers to work harder and lower-ability workers to skill-underinvest.
Early-career work both signals a worker's ability and builds long-run skills. We introduce an AI industry into a career signaling model. The industry sells tokens, which workers convert into output indistinguishable from their own. We compare three regimes: no token trade, purchases only, and open trade with side sales. With no token trade, market learns every type, signalling incentives create a rat race and workers work above the full information level. With token purchases only, lower types buy output but the market still learns every type. Buying by lower types raises the signal higher typ
jobs skills
research · New Economics Papers: AI ·
People escalate against a competitor labelled human and hold back against one labelled an optimising machine
People escalate competition against human opponents but hold back against AI, driven by beliefs about the opponent rather than actual behavior.
People increasingly compete against AI agents rather than other human opponents. We distinguish two channels: an opponent effect and an information effect. These are different elements with different consequences: the opponent effect is specific to a given computational system, the information effect a property of the information environment that an organisation or policymaker can control. We separate them in a preregistered experiment (N = 1, 395) using a dynamic all-pay auction, a repeated contest in which escalation of commitment arises from the incentives. What participants are told about
judgment
practice · Claude Blog ·
Automating eval design and hillclimbing with Claude
Using Claude API commands to design evals and hillclimb prompts while avoiding overfitting to benchmark metrics.
Principles for designing evals and hillclimbing against them without fooling yourself, and how the claude-api skill's build-eval and hillclimb commands put them to work.
ways of working
research · New Economics Papers: AI ·
The Labor Market Effects of AI Automation and Augmentation
AI automation reduces occupational employment 21 percent with no wage effect; augmentation raises wages 8 percent with modest employment growth.
Does artificial intelligence automate work or augment workers' productive capabilities? Our results show that it can do both, through distinct channels that conventional exposure measures obscure. We distinguish these channels by linking U.S. patents to occupations' tasks and tools, with patent-task matches capturing automation and patent-tool matches capturing augmentation. Using the origins and diffusion of historical AI breakthroughs to predict contemporary local exposure, we find large but opposing labor-market effects. A one-standarddeviation increase in AI automation reduces occupational
jobs skills
research · New Economics Papers: AI ·
The macroeconomic effects of AI technology shocks
AI technology shocks increase productivity and employment but reduce labor share and increase wealth inequality, unlike general tech shocks.
Using detailed data on the artificial intelligence (AI) content of U.S. patents from 1980 to 2019, we construct a novel monthly measure of AI intensity in innovation and identify AI related technology shocks. These shocks generate delayed increases in total factor productivity, output, employment, hours, and wages, alongside persistent declines in consumer prices, consistent with a positive supply shock. Although AI-intensive patents largely fall within the broader ICT and automation domains, they stand out as higher-quality innovations, attracting more citations and exhibiting greater technol
jobs skills
research · New Economics Papers: AI ·
More than just Plug and Play: Early Evidence on Organisational Capital and AI Adoption
Firms with stronger management practices are significantly more likely to adopt AI, a relationship specific to AI and not found with other technologies.
This paper provides early evidence that AI adoption by firms is linked to management practices in unique ways. Using panel data from the UK Management and Expectations Survey (MES), we find that firms with higher baseline management scores are significantly more likely to adopt AI. This relationship is specific to AI: the same management practices show no relationship with the adoption of other technologies such as robotics, specialized equipment, or specialized software. We also find that management capabilities related to performance measurement appear strongly related to AI adoption. These
adoption
research · New Economics Papers: AI ·
Artificial Intelligence-related digital skills and employment outcomes for underrepresented women
AI-skill signalling improves interview invitations for underrepresented women but does not eliminate labour-market disadvantage.
This study examines whether signalling AI-related digital skills improves employment outcomes for women from underrepresented groups in England, defined by race, age, sexual orientation, and autism-spectrum disclosure. Using correspondence evidence, the study finds that underrepresented women receive fewer interview invitations and are considered for lower-paid vacancies than majority-group women. In pooled analyses, signalling AI-related digital skills increases interview invitations for underrepresented applicants, but does not eliminate the disadvantage. These findings are consistent with A
jobs skills
practice · Simon Willison ·
2026 in LLMs (so far)
Coding agents became reliable for daily use in late 2025; LLMs crossed thresholds where existing techniques finally worked well.
On Friday I gave the closing keynote at the WeAreDevelopers World Congress North America in San Jose. I tied together the key trends from the past year into a chronological exploration of everything that happened in 2026. The video is on YouTube ; here are my annotated slides and notes to accompany the talk. And as an annotated presentation : # I'm going to give a lightning tour of everything that has happened so far in 2026. The year isn't over yet! # For me, 2026 started a couple of months earlier in November 2025. # November saw the release of two important models: Claude Opus 4.5 and GPT-5
ways of working · Simon Willison
research · CEPR ·
DP21988 AI and the Market for Signals
AI tokens that fake worker output disrupt career signaling; high types work harder but lower types work less, below socially optimal levels.
We introduce an AI industry into a career signaling model in which early-career work both signals a worker's ability and builds her long-run skills. The industry sells tokens, which workers convert into output indistinguishable from their own. Before AI, the market learns every type, and signaling incentives create a rat race in which effort exceeds the full-information level. With tokens the market still learns every type, and a worker separates by buying tokens, by working, or by both. A buyer's effort is set by the token price, not by the career value it builds, so buying by lower types rai
jobs skills
research · CEPR ·
DP21982 Prompted to Start: How Generative AI is Transforming Entrepreneurship
GenAI exposure drives 20% more startup formation in exposed industries, with entrants more specialised and from high-exposure occupations.
We show that following the diffusion of GenAI, industries with higher task-level exposure experience 20% more startup formation than less-exposed industries, and a growing share of these startups offer AI products rather than merely automating operations. Entry extends beyond innovation hubs into regions with limited venture capital and thin specialized labor markets. While individual entrants are smaller, aggregate entry generates net employment and wage growth at the industry level, including in the occupations most exposed to GenAI. GenAI also changes who becomes an entrepreneur: founders a
jobs skills
practice · Nate B Jones ·
Executive Briefing: Your Best Engineer Got Ten Times Faster. Your Team Got 1.8x. Here’s how to close the Gap.
Fast AI users create bottlenecks elsewhere; manage by unblocking reviews and decisions, not by standardizing their techniques.
Somebody on your team has figured something out. They’re using agents in a way that looks different from what everybody else is doing. They have several pieces of work moving at once. They come back from lunch and a problem that used to take the afternoon is ready to review. They spend money on tokens without apologizing for it, because they can point to what they got back. And the team is still behind. That combination is going to become familiar. One person’s capacity can change much faster than the organization around them. Code arrives faster than anyone can review it. A prototype is ready
management org
practice · Lenny's Newsletter ·
The grief, loneliness, and burnout sweeping through the tech industry right now | Molly Graham
Experienced operators describe how delegation to AI differs from human delegation, and what managers should keep close.
Molly Graham is back for round two, and this one is even more powerful. Molly has spent more than 20 years helping organizations and the humans inside them navigate growth and change. She’s held leadership roles at Google, Facebook, Quip, and the Chan Zuckerberg Initiative and is the host of TED’s WorkLife podcast (which she took over from Adam Grant). She also runs Glue Club, a leadership community for senior operators, and writes a popular newsletter called Lessons . Listen on YouTube , Spotify , and Apple Podcasts In our in-depth conversation, we discuss: Why Molly’s famous “give away your
management org · Lenny Rachitsky
practice · Simon Willison ·
Kākāpō Party
Using Claude to generate interactive HTML5 canvas animations, then automating browser interaction with Playwright to capture video for presentations.
Tool: Kākāpō Party I presented a closing keynote for the WeAreDevelopers World Congress North America yesterday. As a STAR moment I decided to weave in references to the record breaking kākāpō breeding season we had in 2026. For my closing slide I wanted to celebrate, and I had seen some buzz around how good Claude Opus 5.5 was at creating pixel art animations. So I rounded up three Kakapo photos from Google image search and dropped them into Claude with this prompt: Here are some photos of kakapo parrots just to remind you what they look like I need you to make an animation in animated pixel
ways of working · Simon Willison
research · arXiv ·
When Less Is More: Managing AI Adoption with Adaptive Incentive Design
Lowering mandatory AI-query targets from 200 to 100 per month reduced gaming by 90 percent while improving sales by 7 percent among sales staff.
We study gaming and adaptive incentive design in AI adoption. A large medical-device company required roughly 5,000 employees to submit at least 200 queries per month to an internal AI assistant. First-time use increased significantly after the mandate, but usage patterns suggested gaming: query counts bunched at the threshold, and 31 percent of queries were repeated or off-task. The firm subsequently revised incentive design and lowered the target to 100. Using staggered implementation across branches, we estimate that the adjustment reduced query volume by 30 percent, with repeated or off-ta
adoption
research · arXiv ·
"If You're Not Doing It, Somebody Else Is": Active Negotiation and the Invisible Labor of Sustained LLM Use
Graduate students using LLMs describe continued use despite distrust, sustained by cycles of risk mitigation and justification across practical, internal, and social dimensions.
Large language models (LLMs) have become fixtures of academic work even as their users describe them as degrading their writing, thinking, and skills. Dominant adoption frameworks read continued use as evidence of satisfaction, and cannot explain continued use of a distrusted tool. We interviewed 36 graduate student workers, balanced between English-as-a-foreign-language (EFL) and non-EFL speakers, and introduce the Active Negotiation framework: a model of sustained LLM use as a recurring cycle of risk, mitigation, and justification. A failure surfaces a risk, mitigation labor addresses it, an
worker experience
practice · Simon Willison ·
Note on 24th September 2026
Working with coding agents requires extraordinary discipline and knowledge to unlock their potential, making software engineering harder overall.
The more time I spend working with coding agents, the more convinced I am that they make software engineering even harder. We can do amazing things with them, but unlocking their full potential requires extraordinary discipline and knowledge. Tags: coding-agents , ai , llms
ways of working · Simon Willison
research · VoxEU ·
Measuring what work generative AI does: Survey evidence versus chat logs
A nationally representative survey linking AI use to worker tasks disagrees sharply with chat-log classifications from Anthropic, Microsoft and OpenAI.
The chat logs of AI companies are often used to measure what work AI actually does. This column uses a nationally representative US survey that links generative AI use to workers’ detailed tasks and compares them with task shares derived from Anthropic, Microsoft, and OpenAI chat data. The four sources disagree sharply, largely because chat classifiers cannot see a user’s occupation and therefore classify use into a few generic activities. Chat logs are informative but on their own can misattribute AI use across occupations and cannot substitute for measurement anchored in who the worker is.
adoption
practice · GitHub Blog ·
AI-powered fuzzing with the GitHub Security Lab Taskflow Agent
An LLM agent automates fuzzing workflows: identifying entry points, writing harnesses, running AFL++, triaging crashes and writing vulnerability reports without human oversight.
If you’re new to fuzzing and want to learn the fundamentals first, check out our Fuzzing 101 course at gh.io/fuzzing101 . Continuous fuzzing is not a magic solution that solves all your problems . Even projects that have been enrolled in OSS-Fuzz for years can still hide critical bugs, and the reason is almost always the same: someone needs to keep an eye on coverage, write new harnesses for the code that nobody is reaching, and triage the crashes that come out the other end. In other words, fuzzing still needs a human in the loop. So the natural question I kept asking myself was: how much of
ways of working
practice · Latent Space ·
Foundries vs Navigators: Lowering the Cost of Science
AI speeds up research planning and design, but labs still bottleneck on slow physical experiments; two adaptation paths emerge.
What does the future of science look like in the world of AI? Anthropic has some lofty goals for science and is even opening a wet lab . Meanwhile a quiet transformation 1 is happening all across AI x Science. In this guest post, Adrian Sanborn talks about the less flashy but more immediate ways he sees AI transforming front-line scientific research in his own company, Endura Therapeutics. Adrian did a CS PhD at Stanford and spent much of it running experiments at the bench, which makes him one of the rare people who can tell you what an LLM is doing to a codebase and to a wet lab. Enjoy! Lang
ways of working
research · npj Digital Medicine ·
A multicenter assessment of human oversight of generative AI outputs in simulated clinical decision making
Junior clinicians identified only 15.8% of AI hallucinations in simulated clinical decisions; 13.1% missed all hallucinations regardless of risk level.
Generative AI, particularly Large Language Models (LLMs), is increasingly being explored for selected clinical tasks, including clinical documentation, patient communication, and decision support; however, routine use in direct clinical decision-making remains limited. A central barrier to safe deployment, however, is the problem of medical hallucinations. Current safeguards rely on a “clinician-in- the-loop” model, which assumes that clinicians can reliably identify and correct these hallucinations. Whether this assumption actually holds in clinical practice warrants greater empirical attenti
judgment
research · New Economics Papers: AI ·
The Recent Evolution of AI-Related Labor Demand
Job ads mentioning AI surge in occupations exposed to LLM capabilities; wages rise for new hires but fall for existing workers in those roles.
AI mentions in US job advertisements have increased substantially since the beginning of 2024. The increase is concentrated in occupations more intensely exposed to the capabilities of large language models: one additional standard deviation of exposure is associated with a 3.1 percentage point increase in the rate at which job ads mention AI. Alongside this increase in AI mentions, more exposed occupations have seen job postings stabilize after a period of relative decline, posted wages rise, and hires and separations rise, all relative to less exposed occupations. Realized wages for new hire
jobs skills
practice · Paul Ford (Aboard) ·
I Keep Accidentally Building Search Engines
Using AI to build personalized search and filtering systems that answer specific questions across curated sources rather than writing fresh content.
I think we all know that AI-generated text is hit or miss. It is good at summarizing things, or writing documentation about code, or generating a report after a meeting. On the other hand, I don’t let AI write articles for me, or do anything to my manuscripts. I’m not against you doing that, at all—it’s just that writing is how I think, and I don’t know what I think until I write it down. But that doesn’t mean that I don’t use AI prose. In fact, I find it incredibly useful, and consume thousands of words of AI-generated prose per day. For example, I get a daily newsletter that summarizes and s
ways of working · Paul Ford
practice · Zapier ·
How Ethan Schwandt helped Jobber turn AI adoption into a building culture
Manager helped teams spot AI opportunities by mapping work and handoffs, then paired adoption with governance and hands-on support.
When companies talk about AI transformation, they often start with what they rolled out: the model, the license, the training program, or the pilot. Ethan Schwandt started somewhere more useful: the work. As Senior Manager, Talent Acceleration at Jobber, Ethan helped employees recognize where AI and automation could remove a recurring handoff, reduce manual work, or make information easier to act on. He paired that opportunity-spotting with practical enablement, clear governance, and hands-on su
adoption
practice · Zapier ·
How Jocelyne Mendez-Guzman made follow-up faster
Sales team built AI-powered follow-up system that reduced call follow-up time by 85% while maintaining rep trust.
Jocelyne (Joce) Mendez-Guzman is no stranger to the Zappy Awards. In 2025, she won our Operations Automator of the Year for a workflow that processed accounts receivable tickets 69% faster. With that win under her belt, she moved into BioRender's RevOps team, where her work now supports teammates across GTM. This year, she’s back as the 2026 Sales AI Builder of the Year, helping those teams follow up on calls 85% faster. Joce built a follow-up system her reps can trust, giving GTM teams speed wi
productivity
practice · Zapier ·
How Ignacio Piñeiro scaled fraud control with exception-based AI review
Fintech operations team uses AI to flag suspicious delivery photos, letting humans review only high-risk cases instead of all 3,000 monthly submissions.
Ignacio Piñeiro runs operations at Galgo, a fintech that lends money for motorcycles across Mexico. Every loan he approves hinges on a simple question: Did this delivery actually happen as it should? Three thousand photos, one last chance Each motorcycle Galgo finances requires proof of delivery — a photo of the customer with their new vehicle — before they can start collecting the loan. That photo is the company's main defense against delivery fraud. If fake, wrong, or low-quality evidence slip
productivity
research · Proceedings of the ACM on Human-Computer Interaction ·
U.S. Southerners’ Attitudes Towards Algorithmic Analysis of Voice Data for High-Stakes Employment and Education Evaluations
Southern U.S. speakers express concerns that voice-based algorithmic hiring and education evaluation could penalize dialects, preferring human involvement.
The rise of algorithmic decision-making in workplaces and schools has drawn attention to potential benefits and harms for stakeholders. Additionally, uneven performance of language technologies for non-standardized or underrepresented dialects has resulted in calls to engage speakers of those dialects. Our survey study explores the attitudes of 111 American English speakers from the southern U.S. towards voice-specific applications of algorithmic analysis in four high-stakes decision-making use cases: evaluation of candidates in hiring and college admissions interviews, employee performance ev
judgment
research · Proceedings of the ACM on Human-Computer Interaction ·
Platform Power and Worker Agency: A Socio-Technical Review of the Gig Economy
Systematic review of 171 gig-economy studies identifies 46 technical mechanisms of algorithmic management and five stakeholder relationships shaping worker conditions.
The platform-mediated gig economy constitutes 4.4% to 12.5% of the global workforce. Understanding how platform design shapes working conditions, and how workers develop strategies to navigate platform constraints, is crucial to addressing challenges in this rapidly evolving sector. Following PRISMA guidelines, we examine 171 gig economy-related publications from 2019 to Nov. 2025, spanning algorithmic management systems, labor process theory, and workers’ insights. Employing a “best-fit” framework, we draw on Socio-technical Systems Theory and synthesize four strands of scholarship to consoli
worker experience
research · Proceedings of the ACM on Human-Computer Interaction ·
Exploring the Impact of AI-powered Creativity Support Tools on Professional Creative Workflows
Creative professionals using AI tools report impacts on collaboration, accessibility, and creative agency across workflow stages.
Creativity Support Tools (CSTs) have long been a subject of interest within Human-Computer Interaction (HCI) research. The advent of Artificial Intelligence (AI), particularly generative AI, introduced new levels of adaptability and automation to CSTs. Despite increased adoption, gaps persist in understanding how practitioners appropriate AI‑powered CSTs across creative workflows. This paper presents findings from an exploratory, multi-session study with creative professionals, examining their use of AI-CSTs across workflow stages. We identify key successes, challenges, and aspirations for too
adoption
research · Proceedings of the ACM on Human-Computer Interaction ·
"Even with AI, We Still Have to Work Harder": Black Women’s Use of Generative AI in the Workplace
Black women in tech use generative AI as survival tools to manage discrimination and hostile work environments, but tools reinforce existing power structures.
As generative AI (GenAI) technologies become increasingly integrated into professional settings, it is critical to understand how these tools are experienced by individuals who are more likely to encounter harm, violence, and trauma in the workplace. Black women, in particular, continue to face systemic discrimination in hiring, promotion, and everyday interactions within predominantly white work environments. In this paper, we center the voices of 21 Black women working in the U.S. tech sector to explore how they engage with GenAI technologies to navigate workplace dynamics. Drawing on Black
worker experience
research · Proceedings of the ACM on Human-Computer Interaction ·
Machine Semiology in Practice: Clinician Strategies for Interpreting AI-Generated Visual Explanations
Clinicians perform substantial interpretive work to reconcile AI saliency maps with medical diagnostic practice, revealing gaps between XAI design and actual use.
The increasing adoption of AI-based decision support systems in high-stakes professional settings has intensified interest in eXplainable Artificial Intelligence (XAI), particularly in domains where human accountability remains central. In medical imaging, visual explanation techniques such as saliency maps are widely promoted as a means of rendering opaque models interpretable to clinicians. Despite their popularity, however, little is known about how such explanations are actually interpreted and made meaningful within radiological practice. This study addresses this gap by introducing and e
judgment
research · Proceedings of the ACM on Human-Computer Interaction ·
AI in the Workplace: The Impact of AI on Perceived Job Decency and Meaningfulness
IT and healthcare workers worry AI will reduce job meaningfulness despite better hours; service workers expect status gains but no time relief.
The proliferation of Artificial Intelligence (AI) in workplaces is transforming how we work. While existing research on human-AI collaboration at work often prioritizes performance, less is known about their experiential outcomes. Through interviews with 24 employees across Information Technology (IT), service-based, and healthcare sectors, this paper examines AI’s impact on job satisfaction via perceptions of job decency and meaningfulness, now and in the future. Our results reveal that the anticipated impact of AI on overall job satisfaction varies with the occupational domain, with differin
worker experience
research · Proceedings of the ACM on Human-Computer Interaction ·
Generative AI as a Mediator in Creator Collaboration: Challenges, Design Opportunities, and Concerns
GenAI mediated collaboration between narrative writers and visual designers in game development, improving shared understanding but risking reduced communication and trust.
As Generative AI (GenAI) expands beyond individual workflows and becomes increasingly integrated into collaborative creative work, its potential to address the fundamental challenge of building common ground among creators with diverse expertise remains underexplored. Specifically, we lack empirical understanding of how GenAI can mediate collaboration asymmetries among heterogeneous creators. To address this gap, we conducted a multi-method study with narrative writers (N = 10) and visual designers (N = 10) in the context of game development. Our findings demonstrate that GenAI goes beyond ind
teams
research · Proceedings of the ACM on Human-Computer Interaction ·
Constructing Algorithmic Authority: How Multi-Channel Networks (MCNs) Govern Live-Streaming Labor in China
MCNs in China construct different algorithmic narratives internally and externally to manage live-streamers, shifting accountability away from platforms to workers.
This study examines the discursive construction of algorithms and its role in labor management in Chinese live-streaming industry by focusing on how intermediary organizations (Multi-Channel Networks, MCNs) actively construct, stabilize, and deploy particular interpretations of platform algorithms as instruments of labor management. Drawing on a nine-month ethnographic fieldwork and 44 interviews with live-streamers, former live-streamers, and MCN staff, we examine how MCNs produce and circulate structured interpretations of platform algorithms across organizational settings. We show that MCNs
worker experience