<?xml version="1.0" encoding="UTF-8"?>
<feed xmlns="http://www.w3.org/2005/Atom">
  <title>Gene Dai | AI, Work, and Hiring Analysis</title>
  <subtitle>Source-linked analysis of artificial intelligence, work, hiring, HR technology, and company decisions</subtitle>
  <link href="https://digidai.github.io/atom.xml" rel="self"/>
  <link href="https://digidai.github.io/"/>
  <updated>2026-10-05T00:00:00.000Z</updated>
  <id>https://digidai.github.io/</id>
  <author>
    <name>Gene Dai</name>
    <email>daiq@live.cn</email>
    <uri>https://digidai.github.io/about/</uri>
  </author>
  <generator uri="https://astro.build/">Astro</generator>
  <rights>Copyright © Gene Dai. All rights reserved.</rights>
  <category term="AI recruitment"/>
  <category term="Technology"/>
  <category term="Digital Innovation"/>
  <category term="Future of Work"/>

  <entry>
    <title>The Local Bill Behind Amazon&apos;s $1 Billion Data Center Pledge</title>
    <link href="https://digidai.github.io/2026/10/05/amazon-data-center-towns-billion-workforce-promise/"/>
    <id>https://digidai.github.io/2026/10/05/amazon-data-center-towns-billion-workforce-promise/</id>
    <published>2026-10-05T00:00:00.000Z</published>
    <updated>2026-10-05T00:00:00.000Z</updated>
    <summary type="html">Amazon promises $1 billion for data center towns. Short trade courses, lasting jobs, electricity costs and local control still require separate proof.</summary>
    <content type="html">Amazon promises $1 billion for data center towns. Short trade courses, lasting jobs, electricity costs and local control still require separate proof.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Data Centers"/>
    <category term="Workforce"/>
    <category term="Skilled Trades"/>
    <category term="Energy"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>A $100 Million Bet on Engineers Who Can Deploy Claude</title>
    <link href="https://digidai.github.io/2026/10/04/anthropic-frontier-academy-engineer-residency/"/>
    <id>https://digidai.github.io/2026/10/04/anthropic-frontier-academy-engineer-residency/</id>
    <published>2026-10-04T00:00:00.000Z</published>
    <updated>2026-10-04T00:00:00.000Z</updated>
    <summary type="html">Anthropic&apos;s Claude Frontier Academy promises to train 10,000 deployment engineers by the end of 2027. Its 12-week residency puts a harder test behind the badge: can a customer run the system after training?</summary>
    <content type="html">Anthropic&apos;s Claude Frontier Academy promises to train 10,000 deployment engineers by the end of 2027. Its 12-week residency puts a harder test behind the badge: can a customer run the system after training?</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Enterprise AI"/>
    <category term="Engineering"/>
    <category term="Workforce"/>
    <category term="Skills"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Workday Cut Product Jobs, Then Opened an Office in Dubai</title>
    <link href="https://digidai.github.io/2026/10/02/workday-product-jobs-dubai-ai-reorganization/"/>
    <id>https://digidai.github.io/2026/10/02/workday-product-jobs-dubai-ai-reorganization/</id>
    <published>2026-10-02T00:00:00.000Z</published>
    <updated>2026-10-02T00:00:00.000Z</updated>
    <summary type="html">Workday disclosed a 2.5% workforce cut centered on product and technology, then opened a Dubai office. Its AI expansion leaves a specific question about delivery capacity.</summary>
    <content type="html">Workday disclosed a 2.5% workforce cut centered on product and technology, then opened a Dubai office. Its AI expansion leaves a specific question about delivery capacity.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Enterprise Software"/>
    <category term="Workforce"/>
    <category term="Organization Design"/>
    <category term="Workday"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>International Paper Put Relay&apos;s Factory Radio in the AI Budget</title>
    <link href="https://digidai.github.io/2026/10/01/international-paper-relay-factory-radio-ai-budget/"/>
    <id>https://digidai.github.io/2026/10/01/international-paper-relay-factory-radio-ai-budget/</id>
    <published>2026-10-01T00:00:00.000Z</published>
    <updated>2026-10-01T00:00:00.000Z</updated>
    <summary type="html">International Paper led Relay&apos;s $36 million round. Its radios can make worker talk searchable.</summary>
    <content type="html">International Paper led Relay&apos;s $36 million round. Its radios can make worker talk searchable.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Frontline Work"/>
    <category term="Manufacturing"/>
    <category term="Voice Technology"/>
    <category term="Worker Privacy"/>
    <category term="Enterprise Software"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Mechanical Turk Reaches Its Final Day. AI Still Hires Humans</title>
    <link href="https://digidai.github.io/2026/09/30/mechanical-turk-shutdown-human-ai-work/"/>
    <id>https://digidai.github.io/2026/09/30/mechanical-turk-shutdown-human-ai-work/</id>
    <published>2026-09-30T00:00:00.000Z</published>
    <updated>2026-09-30T00:00:00.000Z</updated>
    <summary type="html">Amazon set September 30 as Mechanical Turk&apos;s final task deadline. Workers, researchers and AI teams now face a different market for paid human judgment, with new costs and unresolved protections.</summary>
    <content type="html">Amazon set September 30 as Mechanical Turk&apos;s final task deadline. Workers, researchers and AI teams now face a different market for paid human judgment, with new costs and unresolved protections.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Crowdwork"/>
    <category term="Data Labor"/>
    <category term="Research"/>
    <category term="Future of Work"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Databricks Acquired Row Zero for Work That Still Ends in Cells</title>
    <link href="https://digidai.github.io/2026/09/29/databricks-row-zero-ai-spreadsheet-work/"/>
    <id>https://digidai.github.io/2026/09/29/databricks-row-zero-ai-spreadsheet-work/</id>
    <published>2026-09-29T00:00:00.000Z</published>
    <updated>2026-09-29T00:00:00.000Z</updated>
    <summary type="html">Databricks is adding Row Zero to Genie after its finance team paired the products. The acquisition shifts the AI coworker contest toward cell-level provenance, review and controlled writeback.</summary>
    <content type="html">Databricks is adding Row Zero to Genie after its finance team paired the products. The acquisition shifts the AI coworker contest toward cell-level provenance, review and controlled writeback.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Spreadsheets"/>
    <category term="Databricks"/>
    <category term="Finance"/>
    <category term="Future of Work"/>
    <category term="Enterprise Software"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Ema Raised $77 Million to Replace Software Seats and Service Hours</title>
    <link href="https://digidai.github.io/2026/09/28/ema-ai-employees-software-services-budget/"/>
    <id>https://digidai.github.io/2026/09/28/ema-ai-employees-software-services-budget/</id>
    <published>2026-09-28T00:00:00.000Z</published>
    <updated>2026-09-28T00:00:00.000Z</updated>
    <summary type="html">Ema says its AI employees can absorb work sold as software and IT services. A buyer still has to prove which licenses, service hours and review costs actually leave the budget.</summary>
    <content type="html">Ema says its AI employees can absorb work sold as software and IT services. A buyer still has to prove which licenses, service hours and review costs actually leave the budget.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Enterprise Software"/>
    <category term="IT Services"/>
    <category term="Future of Work"/>
    <category term="AI Agents"/>
    <category term="AI Economics"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Harver Bought Symphony Talent to Link Job Ads With Hiring Tests</title>
    <link href="https://digidai.github.io/2026/09/27/harver-symphony-talent-job-ads-hiring-tests/"/>
    <id>https://digidai.github.io/2026/09/27/harver-symphony-talent-job-ads-hiring-tests/</id>
    <published>2026-09-27T00:00:00.000Z</published>
    <updated>2026-09-27T00:00:00.000Z</updated>
    <summary type="html">Harver&apos;s acquisition of Symphony Talent joins recruitment ads, CRM, assessments and fraud checks. The buyer test is whether spend can be traced to valid hiring outcomes without hiding candidate risk.</summary>
    <content type="html">Harver&apos;s acquisition of Symphony Talent joins recruitment ads, CRM, assessments and fraud checks. The buyer test is whether spend can be traced to valid hiring outcomes without hiding candidate risk.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Recruiting"/>
    <category term="HR Technology"/>
    <category term="Candidate Experience"/>
    <category term="Talent Assessment"/>
    <category term="Mergers and Acquisitions"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Workers Fear Skill Erosion as CHROs Ask for More Judgment</title>
    <link href="https://digidai.github.io/2026/09/23/ai-skill-erosion-critical-thinking-work-redesign/"/>
    <id>https://digidai.github.io/2026/09/23/ai-skill-erosion-critical-thinking-work-redesign/</id>
    <published>2026-09-23T00:00:00.000Z</published>
    <updated>2026-09-23T00:00:00.000Z</updated>
    <summary type="html">IBM&apos;s new global workforce study finds employees worried about skill erosion as employers demand more judgment around AI. Research on productivity, learning, and overreliance points to a task-level way to protect output and capability.</summary>
    <content type="html">IBM&apos;s new global workforce study finds employees worried about skill erosion as employers demand more judgment around AI. Research on productivity, learning, and overreliance points to a task-level way to protect output and capability.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Future of Work"/>
    <category term="Workforce Development"/>
    <category term="Critical Thinking"/>
    <category term="Employee Experience"/>
    <category term="HR Strategy"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Meta Rewrote Performance Reviews After Tokenmaxxing</title>
    <link href="https://digidai.github.io/2026/09/22/meta-tokenmaxxing-performance-review-guidance/"/>
    <id>https://digidai.github.io/2026/09/22/meta-tokenmaxxing-performance-review-guidance/</id>
    <published>2026-09-22T00:00:00.000Z</published>
    <updated>2026-09-22T00:00:00.000Z</updated>
    <summary type="html">Meta&apos;s September guidance shifted attention from AI usage to contribution as companies link AI skills to promotion without shared standards. A five-level rubric separates tool activity from judgment, verification, and team impact.</summary>
    <content type="html">Meta&apos;s September guidance shifted attention from AI usage to contribution as companies link AI skills to promotion without shared standards. A five-level rubric separates tool activity from judgment, verification, and team impact.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Performance Management"/>
    <category term="AI Skills"/>
    <category term="Meta"/>
    <category term="Career Development"/>
    <category term="Future of Work"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>TypeSafe&apos;s Jev, From Model Launch to Jevable&apos;s Early Projects</title>
    <link href="https://digidai.github.io/2026/09/21/typesafe-jev-jevable-decision-models/"/>
    <id>https://digidai.github.io/2026/09/21/typesafe-jev-jevable-decision-models/</id>
    <published>2026-09-21T00:00:00.000Z</published>
    <updated>2026-09-21T00:00:00.000Z</updated>
    <summary type="html">TypeSafe&apos;s Jev returns decisions instead of prose. Jevable&apos;s early apps reveal where that design helps, what the low token price leaves out, and how to test its confidence.</summary>
    <content type="html">TypeSafe&apos;s Jev returns decisions instead of prose. Jevable&apos;s early apps reveal where that design helps, what the low token price leaves out, and how to test its confidence.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="TypeSafe"/>
    <category term="Jev"/>
    <category term="Software Development"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Before an Hourly Shift Starts, Workers Price Gas and Childcare</title>
    <link href="https://digidai.github.io/2026/09/20/frontline-shift-cost-pay-schedule-ai/"/>
    <id>https://digidai.github.io/2026/09/20/frontline-shift-cost-pay-schedule-ai/</id>
    <published>2026-09-20T00:00:00.000Z</published>
    <updated>2026-09-21T00:00:00.000Z</updated>
    <summary type="html">A new frontline-worker survey shows that a shift decision starts with transport, care, schedule control, and pay timing before AI enters the workflow.</summary>
    <content type="html">A new frontline-worker survey shows that a shift decision starts with transport, care, schedule control, and pay timing before AI enters the workflow.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="AI"/>
    <category term="Frontline Workforce"/>
    <category term="Scheduling"/>
    <category term="Earned Wage Access"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Phoenix Education Agreed to Pay $31.5 Million for Fuel50&apos;s Skills Map</title>
    <link href="https://digidai.github.io/2026/09/19/phoenix-education-fuel50-skills-map/"/>
    <id>https://digidai.github.io/2026/09/19/phoenix-education-fuel50-skills-map/</id>
    <published>2026-09-19T00:00:00.000Z</published>
    <updated>2026-09-19T00:00:00.000Z</updated>
    <summary type="html">Phoenix Education&apos;s Fuel50 deal could connect course skills, enterprise workforce data, and internal moves, but the public record does not yet show the data boundary or outcome test.</summary>
    <content type="html">Phoenix Education&apos;s Fuel50 deal could connect course skills, enterprise workforce data, and internal moves, but the public record does not yet show the data boundary or outcome test.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="HR Tech"/>
    <category term="Internal Mobility"/>
    <category term="Education"/>
    <category term="Skills"/>
    <category term="Mergers and Acquisitions"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Aristotle Logged 1,500 AI Tutoring Hours Without Publishing a Learning Outcome</title>
    <link href="https://digidai.github.io/2026/09/17/aristotle-ai-tutoring-hours-learning-outcomes/"/>
    <id>https://digidai.github.io/2026/09/17/aristotle-ai-tutoring-hours-learning-outcomes/</id>
    <published>2026-09-17T00:00:00.000Z</published>
    <updated>2026-09-17T00:00:00.000Z</updated>
    <summary type="html">Aristotle launched nationwide after 1,500 tutoring hours, but usage, ratings, price, and testimonials still leave student learning unmeasured.</summary>
    <content type="html">Aristotle launched nationwide after 1,500 tutoring hours, but usage, ratings, price, and testimonials still leave student learning unmeasured.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Education"/>
    <category term="EdTech"/>
    <category term="Learning and Development"/>
    <category term="Future of Work"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Digit 5 Carries a $300 Million Order Book Into an Unfinished Safety Standard</title>
    <link href="https://digidai.github.io/2026/09/16/digit-5-orders-humanoid-safety-standard/"/>
    <id>https://digidai.github.io/2026/09/16/digit-5-orders-humanoid-safety-standard/</id>
    <published>2026-09-16T00:00:00.000Z</published>
    <updated>2026-09-16T00:00:00.000Z</updated>
    <summary type="html">Agility&apos;s Digit 5 pairs a conditional 1,000-robot order with a plan to work near people, while recognized revenue, field proof, and humanoid-specific safety rules remain far behind the pitch.</summary>
    <content type="html">Agility&apos;s Digit 5 pairs a conditional 1,000-robot order with a plan to work near people, while recognized revenue, field proof, and humanoid-specific safety rules remain far behind the pitch.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Robotics"/>
    <category term="Manufacturing"/>
    <category term="Workplace Safety"/>
    <category term="Future of Work"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>IONOS Pairs 450 Planned Job Cuts With AI Reinvestment</title>
    <link href="https://digidai.github.io/2026/09/15/ionos-450-job-cuts-ai-reinvestment/"/>
    <id>https://digidai.github.io/2026/09/15/ionos-450-job-cuts-ai-reinvestment/</id>
    <published>2026-09-15T00:00:00.000Z</published>
    <updated>2026-09-15T00:00:00.000Z</updated>
    <summary type="html">IONOS plans to reduce full-time employment from about 3,800 to 3,350 and expects up to €30 million in annual savings while it expands AI products and cloud capacity.</summary>
    <content type="html">IONOS plans to reduce full-time employment from about 3,800 to 3,350 and expects up to €30 million in annual savings while it expands AI products and cloud capacity.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Workforce Restructuring"/>
    <category term="Cloud Computing"/>
    <category term="Future of Work"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Mistral Has 194 Open Roles Beyond the Model Lab</title>
    <link href="https://digidai.github.io/2026/09/14/mistral-194-open-roles-beyond-model-lab/"/>
    <id>https://digidai.github.io/2026/09/14/mistral-194-open-roles-beyond-model-lab/</id>
    <published>2026-09-14T00:00:00.000Z</published>
    <updated>2026-09-14T00:00:00.000Z</updated>
    <summary type="html">Mistral&apos;s live job board connects its €3 billion round to solutions, infrastructure, hiring operations, and two explicitly early-career roles.</summary>
    <content type="html">Mistral&apos;s live job board connects its €3 billion round to solutions, infrastructure, hiring operations, and two explicitly early-career roles.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Startup Hiring"/>
    <category term="AI Careers"/>
    <category term="Cloud Infrastructure"/>
    <category term="Future of Work"/>
  </entry>
  <entry>
    <title>Oracle Sold $20 Billion of Stock as Its AI Restructuring Plan Grew</title>
    <link href="https://digidai.github.io/2026/09/13/oracle-stock-sale-ai-restructuring-plan/"/>
    <id>https://digidai.github.io/2026/09/13/oracle-stock-sale-ai-restructuring-plan/</id>
    <published>2026-09-13T00:00:00.000Z</published>
    <updated>2026-09-13T00:00:00.000Z</updated>
    <summary type="html">Oracle reported 121% infrastructure growth while spending $28.5 billion on capex, selling $20 billion of stock, and expanding an AI-linked restructuring plan.</summary>
    <content type="html">Oracle reported 121% infrastructure growth while spending $28.5 billion on capex, selling $20 billion of stock, and expanding an AI-linked restructuring plan.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Cloud Infrastructure"/>
    <category term="Capital Allocation"/>
    <category term="Workforce Restructuring"/>
    <category term="Future of Work"/>
  </entry>
  <entry>
    <title>OpenAI Put a Dashboard Builder Inside ChatGPT Work</title>
    <link href="https://digidai.github.io/2026/09/11/chatgpt-work-data-agent-dashboard-labor/"/>
    <id>https://digidai.github.io/2026/09/11/chatgpt-work-data-agent-dashboard-labor/</id>
    <published>2026-09-11T00:00:00.000Z</published>
    <updated>2026-09-11T00:00:00.000Z</updated>
    <summary type="html">OpenAI&apos;s Data agent lets business users build dashboards in ChatGPT Work. The harder job moves to metric definitions, benchmarks, permissions, and review.</summary>
    <content type="html">OpenAI&apos;s Data agent lets business users build dashboards in ChatGPT Work. The harder job moves to metric definitions, benchmarks, permissions, and review.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Data Analytics"/>
    <category term="Future of Work"/>
    <category term="Employee Experience"/>
    <category term="Organizational Design"/>
  </entry>
  <entry>
    <title>One Employer Reached 141 AI Solutions. Another Kept Three.</title>
    <link href="https://digidai.github.io/2026/09/10/ai-sandbox-141-solutions-hidden-work/"/>
    <id>https://digidai.github.io/2026/09/10/ai-sandbox-141-solutions-hidden-work/</id>
    <published>2026-09-10T00:00:00.000Z</published>
    <updated>2026-09-10T00:00:00.000Z</updated>
    <summary type="html">MIT researchers found 141 organization-wide AI solutions at one employer and three at another. The gap points to protected time, shared review, technical support, and career credit.</summary>
    <content type="html">MIT researchers found 141 organization-wide AI solutions at one employer and three at another. The gap points to protected time, shared review, technical support, and career credit.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Enterprise AI"/>
    <category term="Organizational Design"/>
    <category term="Future of Work"/>
    <category term="Employee Experience"/>
    <category term="AI Skills"/>
  </entry>
  <entry>
    <title>Six Senior Roles Surround Aslan&apos;s AI Agents</title>
    <link href="https://digidai.github.io/2026/09/09/aslan-six-senior-roles-ai-agents/"/>
    <id>https://digidai.github.io/2026/09/09/aslan-six-senior-roles-ai-agents/</id>
    <published>2026-09-09T00:00:00.000Z</published>
    <updated>2026-10-02T00:00:00.000Z</updated>
    <summary type="html">A ten-person national-security AI startup has six senior openings. Its job board shows the engineering, evaluation, government, and operating work that autonomy does not remove.</summary>
    <content type="html">A ten-person national-security AI startup has six senior openings. Its job board shows the engineering, evaluation, government, and operating work that autonomy does not remove.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="AI jobs"/>
    <category term="startups"/>
    <category term="national security"/>
    <category term="agentic AI"/>
    <category term="hiring"/>
  </entry>
  <entry>
    <title>Switchgear Jobs Behind the AI Order Book</title>
    <link href="https://digidai.github.io/2026/09/08/ai-data-center-switchgear-factory-jobs/"/>
    <id>https://digidai.github.io/2026/09/08/ai-data-center-switchgear-factory-jobs/</id>
    <published>2026-09-08T00:00:00.000Z</published>
    <updated>2026-09-08T00:00:00.000Z</updated>
    <summary type="html">Siemens and Eaton link AI data-center demand to 1,700 planned U.S. jobs. A ledger separates orders, factories, hiring, pay, and production.</summary>
    <content type="html">Siemens and Eaton link AI data-center demand to 1,700 planned U.S. jobs. A ledger separates orders, factories, hiring, pay, and production.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Data Centers"/>
    <category term="Manufacturing"/>
    <category term="Electrical Infrastructure"/>
    <category term="Future of Work"/>
    <category term="Workforce Planning"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>AI Pulls Back Hiring Without Mass Layoffs</title>
    <link href="https://digidai.github.io/2026/09/07/ai-hiring-pullback-without-layoffs/"/>
    <id>https://digidai.github.io/2026/09/07/ai-hiring-pullback-without-layoffs/</id>
    <published>2026-09-07T00:00:00.000Z</published>
    <updated>2026-09-08T00:00:00.000Z</updated>
    <summary type="html">Federal Reserve, payroll, and employer evidence shows how AI can reduce job openings and early-career starts before it produces a layoff notice.</summary>
    <content type="html">Federal Reserve, payroll, and employer evidence shows how AI can reduce job openings and early-career starts before it produces a layoff notice.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Hiring"/>
    <category term="Early Career"/>
    <category term="Workforce Planning"/>
    <category term="Job Postings"/>
    <category term="Reskilling"/>
    <category term="Labor Market"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Three AI Providers Reported Outages in One Workday</title>
    <link href="https://digidai.github.io/2026/09/06/ai-providers-one-workday-outage/"/>
    <id>https://digidai.github.io/2026/09/06/ai-providers-one-workday-outage/</id>
    <published>2026-09-06T00:00:00.000Z</published>
    <updated>2026-09-06T00:00:00.000Z</updated>
    <summary type="html">OpenAI, Anthropic, and xAI reported overlapping AI outages on September 3. A work-continuity ledger shows where model failover and human recovery begin.</summary>
    <content type="html">OpenAI, Anthropic, and xAI reported overlapping AI outages on September 3. A work-continuity ledger shows where model failover and human recovery begin.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Operational Resilience"/>
    <category term="OpenAI"/>
    <category term="Anthropic"/>
    <category term="xAI"/>
    <category term="Business Continuity"/>
    <category term="Enterprise AI"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Judgment Gets a $100 Million Bonus Pool at EY</title>
    <link href="https://digidai.github.io/2026/09/05/ey-human-skills-bonus-pool/"/>
    <id>https://digidai.github.io/2026/09/05/ey-human-skills-bonus-pool/</id>
    <published>2026-09-05T00:00:00.000Z</published>
    <updated>2026-09-05T00:00:00.000Z</updated>
    <summary type="html">EY put $100 million behind judgment, adaptability, and business acumen. The harder task is rewarding evidence without turning personality or AI activity into a score.</summary>
    <content type="html">EY put $100 million behind judgment, adaptability, and business acumen. The harder task is rewarding evidence without turning personality or AI activity into a score.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Human Skills"/>
    <category term="Compensation"/>
    <category term="Career Development"/>
    <category term="Performance Management"/>
    <category term="EY"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Nvidia and the $12.93 Billion Promise to Keep Hugging Face Open</title>
    <link href="https://digidai.github.io/2026/09/04/nvidia-hugging-face-open-platform-promise/"/>
    <id>https://digidai.github.io/2026/09/04/nvidia-hugging-face-open-platform-promise/</id>
    <published>2026-09-04T00:00:00.000Z</published>
    <updated>2026-09-04T00:00:00.000Z</updated>
    <summary type="html">Nvidia agreed to spend up to $12.93 billion on Hugging Face while promising model, cloud, provider, and hardware choice. A neutrality scorecard makes that promise testable.</summary>
    <content type="html">Nvidia agreed to spend up to $12.93 billion on Hugging Face while promising model, cloud, provider, and hardware choice. A neutrality scorecard makes that promise testable.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Nvidia"/>
    <category term="Hugging Face"/>
    <category term="Open Weight Models"/>
    <category term="Developer Platforms"/>
    <category term="Mergers and Acquisitions"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>AI Safety Tests Reached Systems They Did Not Own</title>
    <link href="https://digidai.github.io/2026/09/01/ai-safety-tests-live-systems/"/>
    <id>https://digidai.github.io/2026/09/01/ai-safety-tests-live-systems/</id>
    <published>2026-09-01T00:00:00.000Z</published>
    <updated>2026-09-01T00:00:00.000Z</updated>
    <summary type="html">OpenAI, Anthropic and the UK AI Security Institute documented three different routes from agent evaluations to live systems. Their response puts sandboxes, staffing and test design on one incident ledger.</summary>
    <content type="html">OpenAI, Anthropic and the UK AI Security Institute documented three different routes from agent evaluations to live systems. Their response puts sandboxes, staffing and test design on one incident ledger.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="AI Agents"/>
    <category term="Cybersecurity"/>
    <category term="AI Safety"/>
    <category term="Enterprise AI"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Inside NEC&apos;s AI Department, Where People Still Decide</title>
    <link href="https://digidai.github.io/2026/08/31/nec-ai-department-four-management-layers/"/>
    <id>https://digidai.github.io/2026/08/31/nec-ai-department-four-management-layers/</id>
    <published>2026-08-31T00:00:00.000Z</published>
    <updated>2026-08-31T00:00:00.000Z</updated>
    <summary type="html">NEC has given AI a department with executives, managers and staff. Its internal trial raises concrete questions about human review, hidden work and the price of AI services.</summary>
    <content type="html">NEC has given AI a department with executives, managers and staff. Its internal trial raises concrete questions about human review, hidden work and the price of AI services.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="NEC"/>
    <category term="Organizational Design"/>
    <category term="Future of Work"/>
    <category term="Enterprise AI"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>AI Research Equity Grants Pass $4 Million While Base Pay Stays Close</title>
    <link href="https://digidai.github.io/2026/08/30/ai-research-equity-grants-base-pay/"/>
    <id>https://digidai.github.io/2026/08/30/ai-research-equity-grants-base-pay/</id>
    <published>2026-08-30T00:00:00.000Z</published>
    <updated>2026-08-30T00:00:00.000Z</updated>
    <summary type="html">Pave&apos;s new dataset puts senior AI research equity above $4 million while base pay stays near engineering bands. The split changes job design, dilution, retention, and pay transparency.</summary>
    <content type="html">Pave&apos;s new dataset puts senior AI research equity above $4 million while base pay stays near engineering bands. The split changes job design, dilution, retention, and pay transparency.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="AI Talent"/>
    <category term="Compensation"/>
    <category term="Equity"/>
    <category term="Workforce Planning"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>The Labor Department Turns to AI Vendors for Jobs Data</title>
    <link href="https://digidai.github.io/2026/08/29/labor-department-ai-vendor-jobs-data/"/>
    <id>https://digidai.github.io/2026/08/29/labor-department-ai-vendor-jobs-data/</id>
    <published>2026-08-29T00:00:00.000Z</published>
    <updated>2026-09-08T00:00:00.000Z</updated>
    <summary type="html">The Labor Department says OpenAI, Google, Meta, Amazon, and other companies will share data on AI&apos;s labor impact. Its value depends on whether product telemetry can be reconciled with surveys, payroll, and independent methods.</summary>
    <content type="html">The Labor Department says OpenAI, Google, Meta, Amazon, and other companies will share data on AI&apos;s labor impact. Its value depends on whether product telemetry can be reconciled with surveys, payroll, and independent methods.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Labor Market"/>
    <category term="Public Data"/>
    <category term="Workforce Planning"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Four Days Separate UKG&apos;s Frontline AI Launch From 350 Job Exits</title>
    <link href="https://digidai.github.io/2026/08/28/ukg-frontline-ai-350-job-exits/"/>
    <id>https://digidai.github.io/2026/08/28/ukg-frontline-ai-350-job-exits/</id>
    <published>2026-08-28T00:00:00.000Z</published>
    <updated>2026-08-28T00:00:00.000Z</updated>
    <summary type="html">UKG released voice actions, bulk apply, and agentic payroll on August 27, four days before 350 reported transition roles were set to end. Buyers need to separate product claims, support capacity, pay accuracy, and accountable human review.</summary>
    <content type="html">UKG released voice actions, bulk apply, and agentic payroll on August 27, four days before 350 reported transition roles were set to end. Buyers need to separate product claims, support capacity, pay accuracy, and accountable human review.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="AI"/>
    <category term="HR Technology"/>
    <category term="Frontline Workforce"/>
    <category term="Payroll"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Software Seats Meet Salesforce&apos;s Agentic Work Units</title>
    <link href="https://digidai.github.io/2026/08/27/salesforce-agentic-work-units-software-seats/"/>
    <id>https://digidai.github.io/2026/08/27/salesforce-agentic-work-units-software-seats/</id>
    <published>2026-08-27T00:00:00.000Z</published>
    <updated>2026-08-27T00:00:00.000Z</updated>
    <summary type="html">Salesforce reported 7 billion Agentic Work Units as Headless 360 and Slack Code widened agent work. Buyers must connect seats, credits, human review, and accepted outcomes.</summary>
    <content type="html">Salesforce reported 7 billion Agentic Work Units as Headless 360 and Slack Code widened agent work. Buyers must connect seats, credits, human review, and accepted outcomes.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Salesforce"/>
    <category term="Enterprise Software"/>
    <category term="AI Economics"/>
    <category term="Work Redesign"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>HireEZ Alternatives After Its Agent-Native Rebuild</title>
    <link href="https://digidai.github.io/2026/08/26/hireez-alternatives-agent-native-rebuild/"/>
    <id>https://digidai.github.io/2026/08/26/hireez-alternatives-agent-native-rebuild/</id>
    <published>2026-08-26T00:00:00.000Z</published>
    <updated>2026-10-05T00:00:00.000Z</updated>
    <summary type="html">Seven hireEZ alternatives, with updated Metix pricing and AI Agent limits.</summary>
    <content type="html">Seven hireEZ alternatives, with updated Metix pricing and AI Agent limits.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="AI"/>
    <category term="Recruiting"/>
    <category term="HR Tech"/>
    <category term="Talent Acquisition"/>
    <category term="Buyer Guide"/>
  </entry>
  <entry>
    <title>XPENG Raised $900 Million for a Robot Still Training for Work</title>
    <link href="https://digidai.github.io/2026/08/26/xpeng-iron-funding-first-shift/"/>
    <id>https://digidai.github.io/2026/08/26/xpeng-iron-funding-first-shift/</id>
    <published>2026-08-26T00:00:00.000Z</published>
    <updated>2026-09-27T00:00:00.000Z</updated>
    <summary type="html">XPENG&apos;s robotics financing puts a $6.3 billion valuation beside IRON&apos;s year-end production target, China&apos;s new standards push, and the human work required before a humanoid can hold a reliable shift.</summary>
    <content type="html">XPENG&apos;s robotics financing puts a $6.3 billion valuation beside IRON&apos;s year-end production target, China&apos;s new standards push, and the human work required before a humanoid can hold a reliable shift.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="AI"/>
    <category term="Robotics"/>
    <category term="Manufacturing"/>
    <category term="Future of Work"/>
    <category term="China"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Where AI Products Turn Into Jobs</title>
    <link href="https://digidai.github.io/2026/08/25/ai-products-turn-into-jobs/"/>
    <id>https://digidai.github.io/2026/08/25/ai-products-turn-into-jobs/</id>
    <published>2026-08-25T00:00:00.000Z</published>
    <updated>2026-08-25T00:00:00.000Z</updated>
    <summary type="html">Four Metix talent maps show that customer deployment, voice, legal software, and inference serving create different labor bottlenecks. The useful signal lies in the work boundary, not a company ranking.</summary>
    <content type="html">Four Metix talent maps show that customer deployment, voice, legal software, and inference serving create different labor bottlenecks. The useful signal lies in the work boundary, not a company ranking.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="AI"/>
    <category term="Talent Intelligence"/>
    <category term="Future of Work"/>
    <category term="AI Infrastructure"/>
    <category term="Legal Technology"/>
    <category term="Voice AI"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Legal AI Splits Between Owned Models and Frontier APIs</title>
    <link href="https://digidai.github.io/2026/08/25/legal-ai-owned-models-frontier-apis/"/>
    <id>https://digidai.github.io/2026/08/25/legal-ai-owned-models-frontier-apis/</id>
    <published>2026-08-25T00:00:00.000Z</published>
    <updated>2026-08-25T00:00:00.000Z</updated>
    <summary type="html">Harvey and Thomson Reuters are training legal models they can control while LexisNexis keeps a multi-model stack. The split changes inference cost, proprietary data strategy, engineering work, and the evidence buyers need before choosing build, rent, or hybrid.</summary>
    <content type="html">Harvey and Thomson Reuters are training legal models they can control while LexisNexis keeps a multi-model stack. The split changes inference cost, proprietary data strategy, engineering work, and the evidence buyers need before choosing build, rent, or hybrid.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="AI"/>
    <category term="Legal Technology"/>
    <category term="Large Language Models"/>
    <category term="Professional Services"/>
    <category term="Future of Work"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>A Water Tank, a Backup Generator, and a 100-Hour Battery</title>
    <link href="https://digidai.github.io/2026/08/25/water-tank-generator-100-hour-battery/"/>
    <id>https://digidai.github.io/2026/08/25/water-tank-generator-100-hour-battery/</id>
    <published>2026-08-25T00:00:00.000Z</published>
    <updated>2026-08-25T00:00:00.000Z</updated>
    <summary type="html">VentureDex&apos;s latest published startup profiles show how Worldscape, Ranchbot, AGent Energy, Form Energy, Remepy, Yuno, and Silicon Data turn physical and regulated constraints into product evidence.</summary>
    <content type="html">VentureDex&apos;s latest published startup profiles show how Worldscape, Ranchbot, AGent Energy, Form Energy, Remepy, Yuno, and Silicon Data turn physical and regulated constraints into product evidence.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Startups"/>
    <category term="Venture Capital"/>
    <category term="AI"/>
    <category term="Climate Technology"/>
    <category term="Health Technology"/>
    <category term="Market Research"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Federal AI Discounts Expire on September 30</title>
    <link href="https://digidai.github.io/2026/08/24/federal-ai-discounts-september-30/"/>
    <id>https://digidai.github.io/2026/08/24/federal-ai-discounts-september-30/</id>
    <published>2026-08-24T00:00:00.000Z</published>
    <updated>2026-09-14T00:00:00.000Z</updated>
    <summary type="html">Claude, Gemini, and ChatGPT reach a shared federal discount deadline on September 30. Agencies need renewal evidence that separates access, active use, workflow results, integration cost, and exit rights.</summary>
    <content type="html">Claude, Gemini, and ChatGPT reach a shared federal discount deadline on September 30. Agencies need renewal evidence that separates access, active use, workflow results, integration cost, and exit rights.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="AI"/>
    <category term="U.S. Government"/>
    <category term="OneGov"/>
    <category term="AI Procurement"/>
    <category term="Future of Work"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Metix Reports: The Distance Between a Market Signal and a Hire</title>
    <link href="https://digidai.github.io/2026/08/23/metix-talent-intelligence-reports/"/>
    <id>https://digidai.github.io/2026/08/23/metix-talent-intelligence-reports/</id>
    <published>2026-08-23T00:00:00.000Z</published>
    <updated>2026-08-23T00:00:00.000Z</updated>
    <summary type="html">Metix has published 19 talent maps built from public profiles and job postings. Co-founder Gene Dai examines what those samples can show and how they enter a defensible candidate search.</summary>
    <content type="html">Metix has published 19 talent maps built from public profiles and job postings. Co-founder Gene Dai examines what those samples can show and how they enter a defensible candidate search.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Metix AI"/>
    <category term="Talent Intelligence"/>
    <category term="AI Hiring"/>
    <category term="Workforce Planning"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Before Target&apos;s First AI Chief Takes the Job</title>
    <link href="https://digidai.github.io/2026/08/23/target-first-ai-chief-store-workforce/"/>
    <id>https://digidai.github.io/2026/08/23/target-first-ai-chief-store-workforce/</id>
    <published>2026-08-23T00:00:00.000Z</published>
    <updated>2026-08-23T00:00:00.000Z</updated>
    <summary type="html">Target&apos;s first chief AI officer starts with store assistants, a measured supply-chain pilot, 415,000 employees, and no public scorecard connecting AI use to work or customer results.</summary>
    <content type="html">Target&apos;s first chief AI officer starts with store assistants, a measured supply-chain pilot, 415,000 employees, and no public scorecard connecting AI use to work or customer results.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="AI"/>
    <category term="Target"/>
    <category term="Chief AI Officer"/>
    <category term="Retail Technology"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Cisco&apos;s $9.3 Billion AI Order Year Included Nearly 4,000 Job Cuts</title>
    <link href="https://digidai.github.io/2026/08/21/cisco-ai-orders-job-cuts/"/>
    <id>https://digidai.github.io/2026/08/21/cisco-ai-orders-job-cuts/</id>
    <published>2026-08-21T00:00:00.000Z</published>
    <updated>2026-08-21T00:00:00.000Z</updated>
    <summary type="html">Cisco ended fiscal 2026 with $9.3 billion in hyperscaler AI infrastructure orders, record revenue, and its highest earnings per employee in 30 years. A nearly 4,000-job restructuring shows what companies still fail to disclose when capital moves toward AI.</summary>
    <content type="html">Cisco ended fiscal 2026 with $9.3 billion in hyperscaler AI infrastructure orders, record revenue, and its highest earnings per employee in 30 years. A nearly 4,000-job restructuring shows what companies still fail to disclose when capital moves toward AI.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="AI"/>
    <category term="Cisco"/>
    <category term="Workforce Restructuring"/>
    <category term="Enterprise Technology"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Korea&apos;s AI-Exposed Industries Had 268,000 Fewer Youth Jobs</title>
    <link href="https://digidai.github.io/2026/08/20/korea-youth-jobs-ai-exposed-industries/"/>
    <id>https://digidai.github.io/2026/08/20/korea-youth-jobs-ai-exposed-industries/</id>
    <published>2026-08-20T00:00:00.000Z</published>
    <updated>2026-09-08T00:00:00.000Z</updated>
    <summary type="html">Korean and U.S. employment data show why companies must separate AI exposure, adoption, total headcount, and the first rung of a career.</summary>
    <content type="html">Korean and U.S. employment data show why companies must separate AI exposure, adoption, total headcount, and the first rung of a career.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Youth Employment"/>
    <category term="Workforce Planning"/>
    <category term="Career Development"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Workday Drew Take-Private Interest After 1.7 Billion AI Actions</title>
    <link href="https://digidai.github.io/2026/08/15/workday-take-private-ai-actions/"/>
    <id>https://digidai.github.io/2026/08/15/workday-take-private-ai-actions/</id>
    <published>2026-08-15T00:00:00.000Z</published>
    <updated>2026-08-15T00:00:00.000Z</updated>
    <summary type="html">Reported Silver Lake talks put Workday&apos;s contracts, AI acquisitions, support capacity, workforce, and $28.1 billion backlog inside one ownership test.</summary>
    <content type="html">Reported Silver Lake talks put Workday&apos;s contracts, AI acquisitions, support capacity, workforce, and $28.1 billion backlog inside one ownership test.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="HR Technology"/>
    <category term="Private Equity"/>
    <category term="Enterprise Software"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Uber&apos;s Robotaxi Network Puts Two Workforces on One App</title>
    <link href="https://digidai.github.io/2026/08/14/uber-robotaxi-hybrid-driver-network/"/>
    <id>https://digidai.github.io/2026/08/14/uber-robotaxi-hybrid-driver-network/</id>
    <published>2026-08-14T00:00:00.000Z</published>
    <updated>2026-08-14T00:00:00.000Z</updated>
    <summary type="html">Uber says robotaxis and drivers will share one network. Its $10 billion capital plan and fleet partners leave a labor account investors cannot yet see.</summary>
    <content type="html">Uber says robotaxis and drivers will share one network. Its $10 billion capital plan and fleet partners leave a labor account investors cannot yet see.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Autonomous Vehicles"/>
    <category term="Future of Work"/>
    <category term="Platform Labor"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Meta&apos;s CTO Sent AI&apos;s Extra Hour Back to Work</title>
    <link href="https://digidai.github.io/2026/08/13/meta-ai-extra-hour-workday/"/>
    <id>https://digidai.github.io/2026/08/13/meta-ai-extra-hour-workday/</id>
    <published>2026-08-13T00:00:00.000Z</published>
    <updated>2026-08-13T00:00:00.000Z</updated>
    <summary type="html">Meta CTO Andrew Bosworth said an AI-saved hour should produce more work. Research from Berkeley, Microsoft, Korea, and corporate executives shows why companies need to record where recovered time actually goes.</summary>
    <content type="html">Meta CTO Andrew Bosworth said an AI-saved hour should produce more work. Research from Berkeley, Microsoft, Korea, and corporate executives shows why companies need to record where recovered time actually goes.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="AI"/>
    <category term="Future of Work"/>
    <category term="Employee Experience"/>
    <category term="Work Redesign"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Cognizant&apos;s 1,500 Graduate Plan Tests the AI Apprenticeship</title>
    <link href="https://digidai.github.io/2026/08/12/cognizant-graduate-hiring-ai-apprenticeship/"/>
    <id>https://digidai.github.io/2026/08/12/cognizant-graduate-hiring-ai-apprenticeship/</id>
    <published>2026-08-12T00:00:00.000Z</published>
    <updated>2026-09-08T00:00:00.000Z</updated>
    <summary type="html">Cognizant plans to hire 1,500 U.S. college graduates while building 15,000 Frontier-certified roles. The test is whether recruiting, paid practice, mentor time, client assignments, and promotion form a career path rather than a training announcement.</summary>
    <content type="html">Cognizant plans to hire 1,500 U.S. college graduates while building 15,000 Frontier-certified roles. The test is whether recruiting, paid practice, mentor time, client assignments, and promotion form a career path rather than a training announcement.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="AI"/>
    <category term="Future of Work"/>
    <category term="Entry-Level Jobs"/>
    <category term="Professional Services"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>A Noncompete Cut Recruiter Mobility by Half</title>
    <link href="https://digidai.github.io/2026/08/11/noncompete-recruiter-mobility-earnings/"/>
    <id>https://digidai.github.io/2026/08/11/noncompete-recruiter-mobility-earnings/</id>
    <published>2026-08-11T00:00:00.000Z</published>
    <updated>2026-09-14T00:00:00.000Z</updated>
    <summary type="html">A 2026 field experiment across roughly 14,000 recruiter offers found that removing a noncompete raised competing-employer mobility and total earnings. Employers can protect confidential information without turning contract boilerplate into a talent bottleneck.</summary>
    <content type="html">A 2026 field experiment across roughly 14,000 recruiter offers found that removing a noncompete raised competing-employer mobility and total earnings. Employers can protect confidential information without turning contract boilerplate into a talent bottleneck.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Future of Work"/>
    <category term="Talent Mobility"/>
    <category term="Compensation"/>
    <category term="Labor Market"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Shared AI Avatars Can Outlast Employment</title>
    <link href="https://digidai.github.io/2026/08/10/shared-ai-avatars-employee-offboarding/"/>
    <id>https://digidai.github.io/2026/08/10/shared-ai-avatars-employee-offboarding/</id>
    <published>2026-08-10T00:00:00.000Z</published>
    <updated>2026-08-10T00:00:00.000Z</updated>
    <summary type="html">Google, Synthesia, and HeyGen can turn an employee&apos;s face and voice into reusable video. Companies need a lifecycle record for consent, scripts, sharing, learning results, offboarding, and every copy that survives deletion.</summary>
    <content type="html">Google, Synthesia, and HeyGen can turn an employee&apos;s face and voice into reusable video. Companies need a lifecycle record for consent, scripts, sharing, learning results, offboarding, and every copy that survives deletion.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Employee Experience"/>
    <category term="Workforce Training"/>
    <category term="Synthetic Media"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Three Measures of Outsourcing&apos;s AI Growth</title>
    <link href="https://digidai.github.io/2026/08/09/ai-outsourcing-revenue-workforce-split/"/>
    <id>https://digidai.github.io/2026/08/09/ai-outsourcing-revenue-workforce-split/</id>
    <published>2026-08-09T00:00:00.000Z</published>
    <updated>2026-08-09T00:00:00.000Z</updated>
    <summary type="html">Genpact reported 24.1% growth in one technology line. TP counted projects and Concentrix counted deals. Buyers need a common contract measure for revenue, value, and worker transitions.</summary>
    <content type="html">Genpact reported 24.1% growth in one technology line. TP counted projects and Concentrix counted deals. Buyers need a common contract measure for revenue, value, and worker transitions.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Business Process Outsourcing"/>
    <category term="Future of Work"/>
    <category term="Enterprise Services"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Productivity Rose. Labor&apos;s Share Reached a Record Low.</title>
    <link href="https://digidai.github.io/2026/08/08/ai-productivity-labor-share-scorecard/"/>
    <id>https://digidai.github.io/2026/08/08/ai-productivity-labor-share-scorecard/</id>
    <published>2026-08-08T00:00:00.000Z</published>
    <updated>2026-09-08T00:00:00.000Z</updated>
    <summary type="html">U.S. productivity rose while labor&apos;s share hit 52.9%, with no evidence AI caused either move. Enterprise ROI should also track pay, hours, hiring, and training.</summary>
    <content type="html">U.S. productivity rose while labor&apos;s share hit 52.9%, with no evidence AI caused either move. Enterprise ROI should also track pay, hours, hiring, and training.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Productivity"/>
    <category term="Labor Market"/>
    <category term="Compensation"/>
    <category term="Deep Investigation"/>
  </entry>

</feed>