AI Skill Now

Morgan Stanley Layoffs Hit 2,500 Workers — What We Know About Severance and Benefits  International Business Times

After 20,000-plus layoffs, how Oracle's 'promise' to Sam Altman is hurting banks across America, includin  The Times of India

Bengaluru techies: Layoffs, AI fears and rising costs hit security.  Deccan Herald

Apple store closure sparks major union backlash amid layoffs  MSN

Meta layoffs confirmed: 8,000 jobs slashed, 6,000 roles frozen – reason behind the move, timeline and severance details explained  MSN

Tech giants laid off 92,000 people in four months to invest in artificial intelligence, and now robots do the work that humans took years to learn.  ubirataonline

Oracle layoffs 2026: How much severance US employees are getting?  MSN

Layoffs, closures tracker: Baptist Health cuts services, clinics  Modern Healthcare

Whitbread Stock May 02: 3,800 Job Cuts Hit UK Hospitality  Meyka

Meta layoffs: More cuts possible after 10% workforce reduction on May 20  The Economic Times

Disney layoffs 2026: Walt Disney Company plans 1,000 job cuts under Josh D’Amaro  MSN

FEMA Reverses Layoffs Before Hurricane Season Hits Florida  The Space Coast Rocket

'H-1B system works only when...': Vance flags visa fraud concerns amid rules overhaul, layoffs  MSN

AI vs human jobs: Chinese court sets red line on automation layoffs, backs fired worker  The Tribune

Press freedom review: Arrests, raids, and layoffs escalate  Journalism Pakistan

List of 26 Companies Conducting Massive Layoffs in 2026, According to Official Statements  mibolsillo.co

The 9 Companies Planning Layoffs in May 2026: Is Yours on the List?  mibolsillo.co

List of 26 companies conducting massive layoffs in 2026, according to official statements  MSN

Microsoft and Meta Layoffs Show Need for a Working-Class Movement to Protect Jobs  TechStory

UPS planned up to 30,000 job cuts before Teamsters capped driver buyouts in new deal  MSN

AI reshapes urban planning with new tools and research focus  MSN

… a national Workers First AI Summit to ensure workers help shape the policies governing AI in the workplace. The summit arrives at a moment when debates about AI and jobs often center on a single question: Will AI destroy jobs, change jobs, create new jobs, or leave work largely unchanged? There are doomsayers and champions … The post A people-fir

Founded by Jake Donoghue PhD ’19 and former MIT researcher Jarrett Revels, the company is creating an AI-driven platform to help diagnose and treat disease.

Bad news for Tilly Norwood.

When the world feels this uncertain, optimism can look like denial. But that depends on how broadly you frame the past and future.

The Batch AI News and Insights: The ways we prompt AI are very different in 2026 than 2022 when ChatGPT came out.

Stop letting ChatGPT and other AI chatbots train on your data. Here’s why—and how  Fast Company

President Sally Kornbluth spoke in front of a packed crowd about growing challenges to the U.S. research ecosystem as funding for America’s top research universities becomes increasingly strained.

AI-powered dictation apps are useful for replying to emails, taking notes, and even coding through your voice

Does generative artificial intelligence (AI) reinforce or reduce productivity differences across workers? Existing evidence largely studies AI within firms and occupations, where organizational selection compresses educational heterogeneity, leaving unclear whether AI narrows productivity gaps across individuals with substantially different levels of formal education. We address this question using a randomized online experiment conducted outside firms, in which 1,174 adults aged 25–45 with heterogeneous educational backgrounds complete an incentivized, workplace-style business problem-solving task. The task is a general (not domain-specific) exercise, and participants perform it either with or without access to a generative-AI assistant. Unlike prior work that studies heterogeneity within relatively homogeneous worker samples, our design targets the between–education-group productivity gap as the primary estimand. We find that AI increases productivity for all participants, with substantially larger gains for lower-education individuals. In the absence of AI access, higher-education participants outperform lower-education participants by 0.548 standard deviations; with AI access, this gap falls to 0.139 standard deviations, implying that generative AI closes three-quarters of the initial productivity gap. We interpret this pattern as evidence that generative AI narrows effective productivity differences in task execution by relaxing constraints that are more binding for lower-education individuals, even though underlying skill differences remain, as reflected in persistent education gaps in task performance and in a follow-up exercise without AI assistance.

This paper defines pro-worker technologies, including Artificial Intelligence, as technologies that make human skills and expertise more valuable by expanding worker capabilities. Our conceptual framework distinguishes among five categories of technological change: labor-augmenting, capital-augmenting, automating, expertise-leveling, and new task-creating. Only the last category is unambiguously pro-worker, generating demand for novel human expertise rather than commodifying it. We illustrate these distinctions through hypothetical and real-world examples spanning aviation maintenance, electrical services, custodial work, education, patent examination, and gig delivery. While AI’s capacity to automate work is substantial, we argue that its potential to serve as a collaborator, by extending human judgment, enabling new tasks, and accelerating skill acquisition, is equally transformative and currently underexploited. We identify market failures, including misaligned firm and developer incentives, path dependence, and a pervasive pro-automation ideology, that may lead to underinvestment in pro-worker AI. We consider nine policy directions that would change incentives, including targeted investments in health care and education, tax code reform, antitrust enforcement, and intellectual property protections for worker expertise.

Production is a sequence of steps that can be executed (1) manually, (2) augmented with AI, or (3) fully automated within contiguous AI-executed steps called “chains.” Firms optimally bundle steps into tasks and then jobs, trading off specialization gains against coordination costs. We characterize the optimal assignment of humans and AI to steps and the firm’s resulting job structure, showing that comparative advantage logic can fail with AI chaining. The model implies non-linear productivity gains from AI quality improvements and admits a CES representation at the macro level. Empirical evidence supports the model’s key predictions that (1) AI-executed steps co-occur in chains, (2) dispersion of AI-exposed steps lowers AI execution at the job level, and (3) adjacency to AI-executed steps increases the likelihood that a step is AI-executed.

Transformative artificial intelligence (TAI) - machines capable of performing virtually all economically valuable work - may gradually erode the two main tax bases that underpin modern tax systems: labor income and human consumption. We examine optimal taxation across two stages of artificial intelligence (AI)-driven transformation. First, if AI displaces human labor, we find that consumption taxation may serve as a primary revenue instrument, with differential commodity taxation gaining renewed relevance as labor distortions lose their constraining role. In the second stage, as autonomous artificial general intelligence (AGI) systems both produce most economic value and absorb a growing share of resources, taxing human consumption may become an inadequate means of raising revenue. We show that the taxation of autonomous AGI systems can be framed as an optimal harvesting problem and find that the resulting tax rate on AGI depends on the rate at which humans discount the future. Our analysis provides a theoretically grounded approach to balancing efficiency and equity in the Age of AI. We also apply our insights to evaluate specific proposals such as taxes on robots, compute, and tokens, as well as sovereign wealth funds and windfall clauses.

This paper studies how minimum wage policy affects firms’ adoption of automation technologies. Using both state-level measures of robot exposure and novel plant-level data on industrial robot imports linked to U.S. Census microdata from 1992–2021, we show that increases in minimum wages raise the likelihood of robot adoption in manufacturing. Our preferred identification exploits discontinuities at state borders, comparing otherwise similar firms exposed to different wage floors. Across specifications, a 10 percent increase in the minimum wage increases robot adoption by roughly 8 percent relative to the mean.

The standard approach to portfolio selection involves two stages: forecast the asset returns and then plug them into an optimizer. We argue that this separation is deeply problematic. The first stage treats cross-sectional prediction errors as equally important across all securities. However, given that final portfolios might differ given distinct risk preferences and investment restrictions, the standard approach fails to recognize that the investor is not just concerned with the average forecast error - but the precision of the forecasts for the specific assets that are most important for their portfolio. Hence, it is crucial to integrate the two stages. We propose a novel implementation utilizing machine learning tools that unifies the expected return generation process and the final optimized portfolio. Our empirical example provides convincing evidence that our end-to-end method outperforms the traditional two-stage approach. In our framework, each investor has their own, endogenously determined, efficient frontier that depends on risk preferences, investor-specific constraints, as well as exposure to market frictions.

We present the GABRIEL software package, which uses GPT to quantify attributes in qualitative data (e.g. how “pro innovation” a speech is). GPT is evaluated on classification and attribute rating performance against 1000+ human annotated tasks across a range of topics and data. We find that GPT as a measurement tool is accurate across domains and generally indistinguishable from human evaluators. Our evidence indicates that labeling results do not depend on the exact prompting strategy used, and that GPT is not relying on training data contamination or inferring attributes from other attributes. We showcase the possibilities of GABRIEL by quantifying novel and granular trends in Congressional remarks, social media toxicity, and county-level school curricula. We then apply GABRIEL to study the history of tech adoption, using it to assemble a novel dataset of 37,000 technologies. Our analysis documents a tenfold decline of time lags from invention to adoption over the industrial age, from ~50 years to ~5 years today. We quantify the increasing dominance of companies and the U.S. in innovation, alongside characteristics that explain whether a technology will be adopted slowly or speedily.

We study how generative AI, and in particular agentic AI, shapes human learning incentives and the long-run evolution of society’s information ecosystem. We build a dynamic model of learning and decision-making in which successful decisions require combining shared, community-level general knowledge with individual-level, context-specific knowledge; these two inputs are complements. Learning exhibits economies of scope: costly human effort jointly produces a private signal about their own context and a “thin” public signal that accumulates into the community’s stock of general knowledge, generating a learning externality. Agentic AI delivers context-specific recommendations that substitute for human effort. By contrast, a richer stock of general knowledge complements human effort by raising its marginal return. The model highlights a sharp dynamic tension: while agentic AI can improve contemporaneous decision quality, it can also erode learning incentives that sustain long-run collective knowledge. When human effort is sufficiently elastic and agentic recommendations exceed an accuracy threshold, the economy can tip into a knowledge-collapse steady state in which general knowledge vanishes ultimately, despite high-quality personalized advice. Welfare is generally non-monotone in agentic accuracy, implying an interior, welfare-maximizing level of agentic precision and motivating information-design regulations. In contrast, greater aggregation capacity for general knowledge—meaning more effective sharing and pooling of human-generated general knowledge—unambiguously raises welfare and increases resilience to knowledge collapse.

Comprehensive annual report tracking AI progress, adoption, and impact across industries and society.

ARC-AGI-3 environments only leverage Core Knowledge priors and are difficulty-calibrated via extensive testing with human test-takers. Testing shows humans can solve 100% of the environments, in contrast to frontier AI systems which, as of March 2026, score below 1% — underscoring a vast remaining gap between human general intelligence and current AI capabilities.

Sam Altman validated widespread anxieties about the future of employment, admitting the traditional balance between labor and capital is shifting drastically. He noted AI has become a scapegoat for corporate downsizing: "Almost every company that does layoffs is blaming AI, whether or not it really is about AI." While some immediate blame may be misplaced, Altman confirmed the underlying threat to traditional employment is real, warning "the next few years are going to be a painful adjustment" marked by "very intense and uncomfortable debates" over how to reshape society. Google DeepMind's Sir Demis Hassabis similarly forecasts a "kind of new renaissance" but expects a significant shakeout over the next 10 years en route to it.

$1M-Bench is a benchmark of 400 expert-curated tasks spanning Law, Finance, Industry, Healthcare, and Natural Science, built to evaluate agents across economically consequential scenarios. It provides a unified testbed for assessing agentic reliability, professional depth, and practical readiness in domain-intensive scenarios.

Introduces 'observed exposure' — a new measure combining theoretical LLM capability with real-world usage data. Finds computer programmers, customer service reps, and financial analysts most exposed; no unemployment spike yet, but suggestive evidence hiring of younger workers in exposed occupations has slowed.

Tracks macroeconomic signals of AI adoption including wages, productivity, and employment shifts.

Occupational early-warning signals of AI exposure across job categories.

Examines whether AI substitutes or complements human labor in key sectors.

Firm-level evidence from 5M workers showing measurable productivity gains from AI adoption.

By calibrating to the U.S. data, our model predicts more than threefold improvements in productivity in some-AI steady state, alongside a long-run employment loss of 23%, with half this loss occurring over the initial five-year transition.