AI Skill Now

Workforce Reimagined Exchange 2026: CHCO panel on future of federal skills development  Federal News Network

Talking Point: iCIMS research highlights widening gap between worker AI skills and employer training  Intelligent CIO

AI in Infection Prevention: How IPs Can Use Artificial Intelligence for Surveillance, Training, and Patient Safety  Infection Control Today

Oracle Layoffs: Will More People Be Affected by Job Cuts? Know How Many  LatestLY

Volkswagen earmarks 16 billion euros for job cuts, plant closures  Big News Network.com

Trump administration moves to scrap H-1B grace period, threatening immediate exit for laid-off visa holders  India Weekly

McClatchy guts SLO Tribune newsroom, part of nationwide layoffs  Cal Coast News

Mass layoffs at Miami Herald and El Nuevo Herald: 28 jobs lost and four positions frozen  CiberCuba

Massive Layoffs at Miami Herald and El Nuevo Herald: 28 Jobs Cut, Four Positions Frozen  CubaHeadlines

Oracle Layoffs: Know How Much Restructuring Cost Company Amid AI Expansion  LatestLY

McClatchy papers see deep job cuts  Northwest Arkansas Democrat-Gazette

British American Tobacco’s Job Cuts Draw Investor Buzz – Certain Roles To Be Outsourced To Partners In AI Adoption Push  Stocktwits

Jack Dorsey’s Block To Lay Off Nearly Half of Its 10,000 people Workforce, Stock Rockets 20%  Stocktwits

Tech layoffs 2026: As AI takes center stage, here's a list of major companies that laid off employees  livemint.com

Tech Layoffs 2026: AI Spending Drives Major Job Cuts Worldwide  Business Outreach Magazine

Texas media layoffs in Fort Worth and San Antonio  mikemcguff.com

McClatchy laid off 30% of its unionized workers; here’s how local coverage will suffer  The NewsGuild - CWA

As immigration status for Salvadorans remains in limbo, businesses begin layoffs  The Washington Post

How many people are laid off in Washington state each month?  USAFacts

US layoffs remain muted as weekly jobless claims edge down to 2,06,000  HR Katha

H-1B Workers Could Lose 60-Day Grace Period After Layoffs  IndiaWest

Washington Sun rises from the ashes of The Washington Post’s job cuts  ThePrint

Job cuts at Jaguar Land Rover show how a sensible corporate strategy is not always enough  The Conversation

Want an AI-made 1980s makeover? Think twice before uploading your photo | World News  Hindustan Times

… of work, and developing a new social contract—demonstrating that each of these developments is going to require substantial readjustment of current structures, operations, and policies. New AI jobs Jobs in an AI-focused world are beginning to change. There will be redefinitions of existing jobs and new types of positions created. Some old jobs …

The handheld catheterization device AI-GUIDE, created by Lincoln Laboratory and Massachusetts General Hospital, promises improved health outcomes for injured service members and civilians.

While OpenAI has filed confidentially for an IPO, the company will not be going public this year, according to CEO Sam Altman.

A quarter of America’s manufactured imports face critical trade dependencies. Strengthening US manufacturing will depend not only on capital but also on talent, energy, and resilient supplier networks.

Introduction to writing effective AI legal prompts  legal.thomsonreuters.com

… from male-centric design of AIEd systems and closing the gender gap. A key element to achieving this is to ensure that women have access to technical AI jobs and that they are able to retain them once they secure them, as well as mandatory bias audits of AIEd models and datasets, with reporting of … The post Feminist and Global South perspectives

A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.

Anthropic's Dario Amodei and OpenAI's Sam Altman seem to agree that it's time to "pace the frontier." What would that actually look like?

Attackers now move at machine speed, but organizations make decisions at committee speed. To close the gap, businesses need a different operating model and committed executive leadership.

AI becomes a core professional skill as employees welcome employer-led upskilling, new eDreams ODIGEO research finds  WebWire

… ran a story titled “Five ideas for how we survive the possible AI jobs apocalypse.” The headline underscored the public’s anxiety about AI’s implications for jobs and the apparent lack of a plan, at any level of government, for addressing that. This was not the typical story about how AI could destroy jobs … The post Getting to all-of-the-above:

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 different levels of education. A related question is whether the productivity gains from AI reflect productive use of the tool or mere delegation, fading once AI is unavailable, and whether the answer differs across education groups. We address both questions using a randomized online experiment outside firms, in which 1,174 adults aged 25–45 complete an incentivized, workplace style business problem-solving task with or without a generative-AI assistant, followed by a non-AI-assisted follow-up module. AI increases performance for all participants, with substantially larger gains for lower-education individuals. In the control group without AI, higher-education participants outperform lower-education participants by 0.548 standard deviations; with AI, this gap falls to 0.139 standard deviations, closing about three-quarters of the initial gap. Chat-log measures explain both why this gap narrows and why it does not disappear: lower-education participants successfully obtain substantial assistance from AI, while higher-education participants use the tool somewhat more effectively across several margins. The follow-up results show that these gains are not purely driven by delegation; treated participants do not perform worse than controls once AI is removed, and lower-education participants retain part of their gain, although a sizable education gap re-emerges. Consistent with this interpretation, intensive use of the assistant produces strong task performance regardless of how much time and effort participants devote themselves, but follow-up performance is substantially higher only when intensive AI use is combined with sustained effort on the task. We interpret these findings as evidence that generative AI narrows effective productivity differences in task execution while underlying human-capital differences continue to shape unassisted performance and effective use of the tool.

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.