AI Skill Now

AI isn’t killing jobs yet: Over 50% of businesses say AI is creating roles as massive upskilling push begins  machinelearningweek.com

As frustration over AI influencers has been growing, Instagram is limiting the reach of undisclosed AI profiles.

Versions of OpenAI's ChatGPT and SpaceXAI's Grok will join Google's Gemini on the Pentagon's central portal for AI tools.

Layoffs, closures tracker: Crothall Healthcare to cut 545 workers  Modern Healthcare

Amazon’s latest layoffs hit 121 employees; India escapes job cuts  People Matters Logo

Oracle 6 a.m. layoff notice: Will more employees get this letter on September 1 that 30,000 received on Ma  The Economic Times

Tech layoff continues; ITC Infotech-Happiest Minds to merge  The Economic Times

Studio Behind The Best-Reviewed Star Wars Game In 22 Years Furloughed Up To 80 Percent Of Its Staff  Kotaku

Happiest Minds rules out job cuts after merger with ITC Infotech  The Economic Times

Happiest Minds rules out job cuts after merger with ITC Infotech  The Economic Times

TalentNeuron Research Finds AI Is Driving Workforce Redesign, Not Solely Workforce Reduction  PR Newswire

WPP Plans Up to 1,000 More Job Cuts  marketscreener.com

MUN President Accused of Trying to ‘Diminish’ University With Recent Job Cuts  VOCM

WPP plans further job cuts as restructuring and AI pressures reshape advertising  Traders Union

Meet the CEO whose century-old company never laid off employees through the Great Depression, the recession, or Covid  Yahoo! Finance Canada

Nearly 20 employees laid off  Iceland Monitor

Will ITC Infotech Merger Cause Job Cuts? Happiest Minds CEO Clarifies Employment Plans  LatestLY

Oracle Layoffs: Employees Brace for Possible Sept 1 Job Cuts  Analytics Insight

Oracle layoffs September 1: Will employees wake up to another ‘6 a.m. email’ tomorrow after 21,000 jobs we  The Economic Times

545 employees of VCU Health contractor face layoffs  Virginia Business

Bohemia utility company Reconn Utility Services to lay off 43 workers  Newsday

T-Mobile Layoffs Expand as Carrier Closes Stores and Pushes Digital Strategy  CryptoRank

Semicon 2.0 takes shape; Zomato’s Hyderabad layoffs  The Economic Times

AI isn’t killing jobs yet: Over 50% of businesses say AI is creating roles as massive upskilling push begins  machinelearningweek.com

… 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 …

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

Apple says it has evidence that a former employee destroyed evidence of data theft after learning he was under investigation.

With 160 years of experience, Boerse Stuttgart Group is Europe's sixth-largest exchange group, helping position Europe for the future through digital, blockchain innovation, and financial infrastructure.

CertiProf Expands Enterprise AI Governance Pathways to Meet Growing International Compliance Demand  EIN News

… 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

The “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.

Versions of OpenAI's ChatGPT and SpaceXAI's Grok will join Google's Gemini on the Pentagon's central portal for AI tools.

AI has the potential to improve build processes and operations within semiconductor fabs. Early adopters could see significant cost improvement and productivity gains.

The HackerNoon Newsletter: Prompt Debt: How Old Instructions Break New AI Models (8/31/2026)  HackerNoon

… 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.

Research planning is the decisive capability of AI scientists. Yet a research plan admits no verifiable answer, so reinforcement learning lacks the environment it requires: tasks paired with a critic. Rubrics extracted from scientific papers can supply the critic. Existing pipelines, however, draw the question and the criteria from the same content

Wi-Fi Channel State Information (CSI) has emerged as a key enabler for device-free Human Activity Recognition (HAR), enabling low-cost, unobtrusive sensing using existing communication infrastructure. However, Deep Learning (DL) models trained on CSI data often struggle to generalize across users, environments, and device setups due to the context

We present CogEvol, a family of models trained specifically for Learning Environment Generation: turning a course brief into a finished learning artifact (structured-JSON slides or self-contained interactive HTML pages) in a single pass. Across 220k production requests, CogEvol completes a slide in a median of 17 seconds and an interactive page in

Closed-loop human-AI systems generate high-dimensional behavioural trajectories whose collective dynamics remain obscure. Using 297,915 learners' adaptive-tutoring histories, we define semantic order variables before model fitting and test them in user-disjoint cohorts. The state exhibits reproducible basin-like flow and operationally defined, stat

Generative music systems are increasingly presented as tools that democratize music creation, yet their practical suitability for musicians remains underexplored. Prior work includes openness-focused evaluation frameworks, such as MusGO (Music-Generative Open AI), as well as qualitative studies of musicians' experiences with generative systems. How

The rapid expansion of large-scale artificial intelligence (AI) data centers is adding substantial, concentrated, and rapidly varying loads to transmission-constrained power systems. Although such load variations are generally regarded as operational challenges, this paper presents an alternative perspective in which the upward load flexibility of

End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the numerical weather prediction pipeline, including data assimilation, at a fraction of its cost. These systems are deterministic and issue no uncertainty. Here we render the Aardvark Weather model probabilis

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.