Technology & innovation
Gartner projects that by 2028 AI will begin creating more jobs than it eliminates, but the advisory firm warns that organizations failing to rethink how employees build expertise will find themselves without the talent to fill those new roles.
40% of companies have already eliminated obsolete jobs, and nearly half have restructured their organizations to become more collaborative, according to a Gartner survey of 110 HR leaders.
Junior employees have fewer opportunities to develop the foundational judgment and skills needed to advance, and traditional performance metrics no longer reliably predict readiness for more senior roles.
Almost half of U.S. HR leaders said AI has raised productivity expectations for entry-level employees without changing actual staffing levels; nearly a third said companies are hiring fewer junior workers.
AI is ultimately going to result in more job gains than losses, but in the process it's going to break down millions of careers."
Read more via HR Dive
The most AI-fluent graduates in history are entering a workforce that is simultaneously recruiting them for their skills and cutting the entry-level jobs they were counting on.
Unemployment among college graduates aged 22 to 27 stood at 5.6% in March, one of the highest rates since 2013 outside the early pandemic.
Among employers investing in AI, three times as many expected it to boost entry-level hiring this year as decrease it, but the share cutting back on junior hires grew to 17% from 13% in 2025, according to a Strada Education Foundation survey of nearly 1,500 employers.
Twenty-two percent of 18- to 24-year-olds with degrees said they felt "very prepared" to compete in an AI-shaped job market, more than any other age group, according to a Gallup-Lumina Foundation survey.
Companies including Salesforce, IBM, and SharkNinja say they are actively ramping up hiring of AI-native graduates, with Salesforce fast-tracking 1,000 graduates and interns this year into what it describes as high-impact roles.
Read more via The Wall Street Journal
The largest independent study of AI-powered hiring tools to date found significant racial disparities in algorithms used to screen millions of job applicants.
Researchers from Stanford, Chapman, and Northeastern universities analyzed more than 4 million applications submitted by 3 million applicants across 156 large employers, all screened by algorithms built by talent platform Pymetrics.
More than 25% of all applications submitted by Black job seekers went to positions where the algorithm produced outcomes that trigger federal discrimination scrutiny.
Asian applicants were also affected, with nearly 15% of their applications going to positions with discriminatory outcomes.
The researchers argue Pymetrics had been measuring bias incorrectly, by pooling all applicants and outcomes across employers and positions rather than analyzing each position separately, which is how federal anti-discrimination law is designed to be applied.
We find clear racial disparities in applicant outcomes."
The study also documented what researchers call an "algorithmic blackball" effect: because Pymetrics scores are stored and reused for up to 330 days, applicants rejected by one company using the platform are effectively getting the same score, not an independent evaluation, at the next.
To reduce the probability of being shut out across multiple employers to below 0.1%, an applicant would need to apply to at least 25 different positions.
Read more via Fortune, Stanford Digital Economy Lab
A Michigan bill that passed unanimously out of committee would create a structured pilot program for generative AI use across state government, addressing what lawmakers describe as an unregulated and opaque landscape.
The bill would establish a three-member AI governing board and launch a pilot program by January 1, 2027, allowing state employees and agencies to experiment with generative AI in a controlled setting while prohibiting its use on restricted or confidential information.
All 50 states introduced AI-related legislation in 2025, according to the National Conference of State Legislatures; a 2024 Ernst & Young survey found 51% of federal, state, and local government employees already used an AI application daily or several times a week.
The pilot program is estimated to cost $600,000 to set up, with an additional $2.1 million annually for staffing, software, licensing, and training.
Read more via Government Technology
USGA launches AI rules assistant for golfers — the USGA has rolled out Rules AI, a pilot tool that answers golfers' rules questions in real time via the GHIN mobile app. The tool was trained on more than 25,000 rules queries handled by USGA staff and built on a "confidence-first" architecture designed to draw only from verified USGA content. Deloitte supported the backend build. A full rollout to all GHIN users is targeted for spring 2027. (USGA, Golf Channel)
AI is learning to fly planes, and the aviation industry is paying attention. Startup Merlin Labs has conducted hundreds of test flights using an AI pilot system on a Cessna Caravan that handles takeoff, navigation, and landing without manual input. The company recently secured a contract worth more than $100 million with the U.S. Air Force to bring the technology to C-130 cargo planes. Boeing estimates airlines will need more than 600,000 new pilots over the next two decades — a shortage Merlin argues AI could help address. The Air Line Pilots Association, which represents more than 79,000 pilots, says AI should support pilots, not replace them. (CNN)
China is embedding AI directly into its energy infrastructure. The country's National Energy Administration announced a state-backed pilot program covering 51 application scenarios across power grids, renewables, coal, and oil and gas. State-owned energy giants and major tech firms including Alibaba Cloud and Tencent are involved. The push is aimed at managing surging electricity demand from AI computing by using AI itself to make the grid more efficient. (South China Morning Post)
Record labels are moving too slowly on AI, Deloitte argues. A new analysis warns that music companies stuck in the pilot phase risk falling behind competitors willing to commit to enterprise-wide AI adoption. The report identifies fragmented data and siloed initiatives as the main barriers to scale, and frames artist trust — particularly around consent and revenue sharing — as a commercial prerequisite, not just an ethical one. (Deloitte)
