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  <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>2026-11-04T00:00:00.000Z</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>OpenAI&apos;s One-Week Device Puts AI Engineering Through a Factory Test</title>
    <link href="https://digidai.github.io/2026/11/04/openai-cad-factory-review-gap/"/>
    <id>https://digidai.github.io/2026/11/04/openai-cad-factory-review-gap/</id>
    <published>2026-11-04T00:00:00.000Z</published>
    <updated>2026-11-04T00:00:00.000Z</updated>
    <summary type="html">OpenAI&apos;s rapid device prototype still needed manufacturing corrections. Autodesk and Siemens show other routes into CAD and simulation, while engineering teams face a slower test: which AI output is safe to build?</summary>
    <content type="html">OpenAI&apos;s rapid device prototype still needed manufacturing corrections. Autodesk and Siemens show other routes into CAD and simulation, while engineering teams face a slower test: which AI output is safe to build?</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Engineering"/>
    <category term="Manufacturing"/>
    <category term="Future of Work"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Arjun Karanam Puts Expert Corrections Into the Training Loop</title>
    <link href="https://digidai.github.io/2026/11/03/arjun-karanam-trajectory-expert-feedback/"/>
    <id>https://digidai.github.io/2026/11/03/arjun-karanam-trajectory-expert-feedback/</id>
    <published>2026-11-03T00:00:00.000Z</published>
    <updated>2026-11-03T00:00:00.000Z</updated>
    <summary type="html">Trajectory&apos;s co-founder wants deployed AI agents to learn from expert edits. Its new graders and one-rollout research expose a harder question: who decides which corrections should change a model?</summary>
    <content type="html">Trajectory&apos;s co-founder wants deployed AI agents to learn from expert edits. Its new graders and one-rollout research expose a harder question: who decides which corrections should change a model?</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Arjun Karanam"/>
    <category term="Trajectory"/>
    <category term="Continual Learning"/>
    <category term="Expert Feedback"/>
    <category term="AI Agents"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Workday Sees Prompting Fall While LinkedIn Sees AI Literacy Rise</title>
    <link href="https://digidai.github.io/2026/11/03/workday-linkedin-ai-skills-hiring-divergence/"/>
    <id>https://digidai.github.io/2026/11/03/workday-linkedin-ai-skills-hiring-divergence/</id>
    <published>2026-11-03T00:00:00.000Z</published>
    <updated>2026-11-03T00:00:00.000Z</updated>
    <summary type="html">Workday&apos;s recruiting data show fewer requests for basic prompting, while LinkedIn sees broader AI literacy spreading through job ads. The gap changes how employers should define, test and fund AI skills.</summary>
    <content type="html">Workday&apos;s recruiting data show fewer requests for basic prompting, while LinkedIn sees broader AI literacy spreading through job ads. The gap changes how employers should define, test and fund AI skills.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="AI Skills"/>
    <category term="Hiring"/>
    <category term="Career Development"/>
    <category term="Workforce"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Amritanshu Jain Sells Inference Without Owning the GPUs</title>
    <link href="https://digidai.github.io/2026/11/02/amritanshu-jain-simplismart-inference-software/"/>
    <id>https://digidai.github.io/2026/11/02/amritanshu-jain-simplismart-inference-software/</id>
    <published>2026-11-02T00:00:00.000Z</published>
    <updated>2026-11-02T00:00:00.000Z</updated>
    <summary type="html">Simplismart&apos;s founder is selling software to application teams and GPU clouds while Nvidia backs the company. The hard test is whether claimed savings survive a customer&apos;s own latency, reliability and data-residency requirements.</summary>
    <content type="html">Simplismart&apos;s founder is selling software to application teams and GPU clouds while Nvidia backs the company. The hard test is whether claimed savings survive a customer&apos;s own latency, reliability and data-residency requirements.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Amritanshu Jain"/>
    <category term="Simplismart"/>
    <category term="AI Inference"/>
    <category term="GPU Economics"/>
    <category term="AI Infrastructure"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>At Dropbox, a New Copilot Link and 200 Fewer Jobs</title>
    <link href="https://digidai.github.io/2026/11/02/dropbox-copilot-integration-200-role-reduction/"/>
    <id>https://digidai.github.io/2026/11/02/dropbox-copilot-integration-200-role-reduction/</id>
    <published>2026-11-02T00:00:00.000Z</published>
    <updated>2026-11-02T00:00:00.000Z</updated>
    <summary type="html">Dropbox added a Microsoft Copilot integration on October 27, then disclosed a roughly 9% workforce cut. Its filing and product releases show what is known about the cost, strategy and work left to the smaller team.</summary>
    <content type="html">Dropbox added a Microsoft Copilot integration on October 27, then disclosed a roughly 9% workforce cut. Its filing and product releases show what is known about the cost, strategy and work left to the smaller team.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Dropbox"/>
    <category term="Workforce"/>
    <category term="Enterprise Software"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Carta Finds First Hires Taking 1.75% of Startup Equity</title>
    <link href="https://digidai.github.io/2026/11/01/carta-first-hire-equity-small-teams/"/>
    <id>https://digidai.github.io/2026/11/01/carta-first-hire-equity-small-teams/</id>
    <published>2026-11-01T00:00:00.000Z</published>
    <updated>2026-11-01T00:00:00.000Z</updated>
    <summary type="html">Carta reports a 1.75% median initial grant for first startup hires. The offer also sets a cash salary, leaves room for later hires and gives an employee a risky path to ownership.</summary>
    <content type="html">Carta reports a 1.75% median initial grant for first startup hires. The offer also sets a cash salary, leaves room for later hires and gives an employee a risky path to ownership.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Startups"/>
    <category term="Compensation"/>
    <category term="Equity"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>John Ternus Bets Apple&apos;s AI on the Device</title>
    <link href="https://digidai.github.io/2026/11/01/john-ternus-apple-ai-device-strategy/"/>
    <id>https://digidai.github.io/2026/11/01/john-ternus-apple-ai-device-strategy/</id>
    <published>2026-11-01T00:00:00.000Z</published>
    <updated>2026-11-01T00:00:00.000Z</updated>
    <summary type="html">Apple&apos;s new CEO is putting Siri AI beside touch on a new Mac. John Ternus&apos;s product argument faces a harder test: useful app actions, visible consent and results beyond a launch demonstration.</summary>
    <content type="html">Apple&apos;s new CEO is putting Siri AI beside touch on a new Mac. John Ternus&apos;s product argument faces a harder test: useful app actions, visible consent and results beyond a launch demonstration.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="John Ternus"/>
    <category term="Apple"/>
    <category term="Siri AI"/>
    <category term="App Intents"/>
    <category term="AI Products"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Edward Johns Tests the One-Demonstration Robot</title>
    <link href="https://digidai.github.io/2026/10/31/edward-johns-one-demonstration-robot-learning/"/>
    <id>https://digidai.github.io/2026/10/31/edward-johns-one-demonstration-robot-learning/</id>
    <published>2026-10-31T00:00:00.000Z</published>
    <updated>2026-10-31T00:00:00.000Z</updated>
    <summary type="html">Machenta says one demonstration can teach a robot a new task without further training. Edward Johns&apos;s older papers, a new three-robot demo and rival results show what is established and what a factory still needs to measure.</summary>
    <content type="html">Machenta says one demonstration can teach a robot a new task without further training. Edward Johns&apos;s older papers, a new three-robot demo and rival results show what is established and what a factory still needs to measure.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Edward Johns"/>
    <category term="Machenta"/>
    <category term="Robot Learning"/>
    <category term="Physical AI"/>
    <category term="Manufacturing"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>OpenAI and Google Plan One AI Training Opt-Out While Search Stays Open</title>
    <link href="https://digidai.github.io/2026/10/31/shared-ai-training-opt-out-publishers-search/"/>
    <id>https://digidai.github.io/2026/10/31/shared-ai-training-opt-out-publishers-search/</id>
    <published>2026-10-31T00:00:00.000Z</published>
    <updated>2026-10-31T00:00:00.000Z</updated>
    <summary type="html">OpenAI and Google backed a shared robots.txt signal for AI training on October 30. Publishers still face separate decisions about search visibility, AI answers, existing bot rules and paid reuse.</summary>
    <content type="html">OpenAI and Google backed a shared robots.txt signal for AI training on October 30. Publishers still face separate decisions about search visibility, AI answers, existing bot rules and paid reuse.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Publishing"/>
    <category term="Search"/>
    <category term="AI Training"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Banks Pay a 13% Premium as AI Talent Spreads Beyond Tech</title>
    <link href="https://digidai.github.io/2026/10/30/ai-talent-bank-premium-flexible-hiring/"/>
    <id>https://digidai.github.io/2026/10/30/ai-talent-bank-premium-flexible-hiring/</id>
    <published>2026-10-30T00:00:00.000Z</published>
    <updated>2026-10-30T00:00:00.000Z</updated>
    <summary type="html">Randstad reports a bank software-engineer pay premium and rising AI skill requirements across industries. Hiring plans, job ads and actual hires tell different stories about who can afford the talent.</summary>
    <content type="html">Randstad reports a bank software-engineer pay premium and rising AI skill requirements across industries. Hiring plans, job ads and actual hires tell different stories about who can afford the talent.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Talent Market"/>
    <category term="Compensation"/>
    <category term="Hiring"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Alexander Embiricos and the Three-Minute Warning From Dots</title>
    <link href="https://digidai.github.io/2026/10/30/alexander-embiricos-dots-three-minute-warning/"/>
    <id>https://digidai.github.io/2026/10/30/alexander-embiricos-dots-three-minute-warning/</id>
    <published>2026-10-30T00:00:00.000Z</published>
    <updated>2026-10-30T00:00:00.000Z</updated>
    <summary type="html">OpenAI&apos;s product lead says Dots should anticipate work before a user asks. A late calendar alert, shared app permissions and limited safety tests show what that promise still has to prove.</summary>
    <content type="html">OpenAI&apos;s product lead says Dots should anticipate work before a user asks. A late calendar alert, shared app permissions and limited safety tests show what that promise still has to prove.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Alexander Embiricos"/>
    <category term="OpenAI Dots"/>
    <category term="Personal AI"/>
    <category term="AI Agents"/>
    <category term="Product Design"/>
    <category term="Privacy"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Finn Puklowski Puts Rival AI Chips on the Balance Sheet</title>
    <link href="https://digidai.github.io/2026/10/29/finn-puklowski-alternative-chips-balance-sheet/"/>
    <id>https://digidai.github.io/2026/10/29/finn-puklowski-alternative-chips-balance-sheet/</id>
    <published>2026-10-29T00:00:00.000Z</published>
    <updated>2026-10-29T00:00:00.000Z</updated>
    <summary type="html">General Compute has raised equity, arranged chip-backed debt and signed hardware partnerships for an inference cloud. Its CEO&apos;s test is to turn future racks and fast-token claims into dependable customer economics.</summary>
    <content type="html">General Compute has raised equity, arranged chip-backed debt and signed hardware partnerships for an inference cloud. Its CEO&apos;s test is to turn future racks and fast-token claims into dependable customer economics.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Finn Puklowski"/>
    <category term="General Compute"/>
    <category term="AI Inference"/>
    <category term="AI Infrastructure"/>
    <category term="Neoclouds"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Uber Opens ChatGPT Work to 30,000 After a 10% Cut</title>
    <link href="https://digidai.github.io/2026/10/29/uber-chatgpt-work-rollout-workforce-cut/"/>
    <id>https://digidai.github.io/2026/10/29/uber-chatgpt-work-rollout-workforce-cut/</id>
    <published>2026-10-29T00:00:00.000Z</published>
    <updated>2026-10-29T00:00:00.000Z</updated>
    <summary type="html">Uber is expanding ChatGPT Work from a 1,000-person pilot to more than 30,000 employees weeks after a workforce cut. The rollout tests whether agent output can be measured without mistaking access for productivity or timing for causation.</summary>
    <content type="html">Uber is expanding ChatGPT Work from a 1,000-person pilot to more than 30,000 employees weeks after a workforce cut. The rollout tests whether agent output can be measured without mistaking access for productivity or timing for causation.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Uber"/>
    <category term="OpenAI"/>
    <category term="Workforce"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>LinkedIn Finds Founder Roles Rising in Five Economies</title>
    <link href="https://digidai.github.io/2026/10/28/linkedin-founder-roles-five-economies-small-teams/"/>
    <id>https://digidai.github.io/2026/10/28/linkedin-founder-roles-five-economies-small-teams/</id>
    <published>2026-10-28T00:00:00.000Z</published>
    <updated>2026-10-28T00:00:00.000Z</updated>
    <summary type="html">LinkedIn ranks founder among the fastest-growing roles in five economies and reports smaller first-year startup teams. The data show a hiring question, not proof that AI replaced employees.</summary>
    <content type="html">LinkedIn ranks founder among the fastest-growing roles in five economies and reports smaller first-year startup teams. The data show a hiring question, not proof that AI replaced employees.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="LinkedIn"/>
    <category term="Startups"/>
    <category term="Hiring"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Rohit Patel Wants the AI Document to Show Its Work</title>
    <link href="https://digidai.github.io/2026/10/28/rohit-patel-ndoc-inspectable-ai-document/"/>
    <id>https://digidai.github.io/2026/10/28/rohit-patel-ndoc-inspectable-ai-document/</id>
    <published>2026-10-28T00:00:00.000Z</published>
    <updated>2026-10-28T00:00:00.000Z</updated>
    <summary type="html">The former Meta AI director is building Ndoc around a visible path from data and code to narrative and presentation. Its early alpha leaves a harder question: can a buyer verify the path and carry it elsewhere?</summary>
    <content type="html">The former Meta AI director is building Ndoc around a visible path from data and code to narrative and presentation. Its early alpha leaves a harder question: can a buyer verify the path and carry it elsewhere?</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Rohit Patel"/>
    <category term="Ndoc"/>
    <category term="AI Documents"/>
    <category term="AI Workflows"/>
    <category term="Provenance"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Jack Clark&apos;s Public Benefit Test at Anthropic&apos;s IPO</title>
    <link href="https://digidai.github.io/2026/10/27/jack-clark-anthropic-public-benefit-ipo/"/>
    <id>https://digidai.github.io/2026/10/27/jack-clark-anthropic-public-benefit-ipo/</id>
    <published>2026-10-27T00:00:00.000Z</published>
    <updated>2026-10-27T00:00:00.000Z</updated>
    <summary type="html">The Anthropic co-founder interprets AI&apos;s economic future and is named to manage a new public-benefit fund. Its IPO filing reveals where his influence begins, and where independent oversight still has to prove itself.</summary>
    <content type="html">The Anthropic co-founder interprets AI&apos;s economic future and is named to manage a new public-benefit fund. Its IPO filing reveals where his influence begins, and where independent oversight still has to prove itself.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Jack Clark"/>
    <category term="Anthropic"/>
    <category term="AI Governance"/>
    <category term="AI Economy"/>
    <category term="Public Benefit"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>A 60,000-Worker Buyout Window Opens at Microsoft</title>
    <link href="https://digidai.github.io/2026/10/27/microsoft-60000-voluntary-exit-offer-workforce-plan/"/>
    <id>https://digidai.github.io/2026/10/27/microsoft-60000-voluntary-exit-offer-workforce-plan/</id>
    <published>2026-10-27T00:00:00.000Z</published>
    <updated>2026-10-27T00:00:00.000Z</updated>
    <summary type="html">A reported 60,000 Microsoft employees can consider a new voluntary exit offer. Actual departures, package terms and the staffing plan remain undisclosed as AI investment grows.</summary>
    <content type="html">A reported 60,000 Microsoft employees can consider a new voluntary exit offer. Actual departures, package terms and the staffing plan remain undisclosed as AI investment grows.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Microsoft"/>
    <category term="Workforce Planning"/>
    <category term="Employee Experience"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Inside Ivan Burazin&apos;s Rebuild of Daytona</title>
    <link href="https://digidai.github.io/2026/10/26/ivan-burazin-daytona-agent-sandbox-pivot/"/>
    <id>https://digidai.github.io/2026/10/26/ivan-burazin-daytona-agent-sandbox-pivot/</id>
    <published>2026-10-26T00:00:00.000Z</published>
    <updated>2026-10-30T00:00:00.000Z</updated>
    <summary type="html">Daytona&apos;s founder turned a developer-workspace company into an agent-computing platform. Its funding, product docs and security advisories show what buyers can test, and what remains a company claim.</summary>
    <content type="html">Daytona&apos;s founder turned a developer-workspace company into an agent-computing platform. Its funding, product docs and security advisories show what buyers can test, and what remains a company claim.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Ivan Burazin"/>
    <category term="Daytona"/>
    <category term="AI Agents"/>
    <category term="Cloud Infrastructure"/>
    <category term="Security"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>ManpowerGroup Grows While US Temp Payrolls Shrink</title>
    <link href="https://digidai.github.io/2026/10/26/manpowergroup-growth-us-temp-payrolls-ai-costs/"/>
    <id>https://digidai.github.io/2026/10/26/manpowergroup-growth-us-temp-payrolls-ai-costs/</id>
    <published>2026-10-26T00:00:00.000Z</published>
    <updated>2026-10-26T00:00:00.000Z</updated>
    <summary type="html">ManpowerGroup&apos;s $4.8 billion quarter grew as US temporary-help payrolls fell. Its AI screening expansion and $200 million cost target still need placement and margin evidence.</summary>
    <content type="html">ManpowerGroup&apos;s $4.8 billion quarter grew as US temporary-help payrolls fell. Its AI screening expansion and $200 million cost target still need placement and margin evidence.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Talent Market"/>
    <category term="Staffing"/>
    <category term="HR Technology"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>A Million AI Interviews Put Dayforce&apos;s Hiring Promise to the Test</title>
    <link href="https://digidai.github.io/2026/10/25/dayforce-ribbon-million-ai-interviews-hiring-test/"/>
    <id>https://digidai.github.io/2026/10/25/dayforce-ribbon-million-ai-interviews-hiring-test/</id>
    <published>2026-10-25T00:00:00.000Z</published>
    <updated>2026-10-28T00:00:00.000Z</updated>
    <summary type="html">Dayforce agreed to buy Ribbon after more than a million AI interviews. The missing public measure is whether those conversations improve qualified hires and candidate follow-up.</summary>
    <content type="html">Dayforce agreed to buy Ribbon after more than a million AI interviews. The missing public measure is whether those conversations improve qualified hires and candidate follow-up.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Recruiting"/>
    <category term="Candidate Experience"/>
    <category term="HR Technology"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Eugenia Kuyda&apos;s Shared Agent Has to Earn the Group Chat</title>
    <link href="https://digidai.github.io/2026/10/25/eugenia-kuyda-shared-agent-group-chat/"/>
    <id>https://digidai.github.io/2026/10/25/eugenia-kuyda-shared-agent-group-chat/</id>
    <published>2026-10-25T00:00:00.000Z</published>
    <updated>2026-10-25T00:00:00.000Z</updated>
    <summary type="html">Replika founder Eugenia Kuyda has opened Wabi as an AI assistant for groups. Its new terms and privacy notice show the hard product question: who controls an agent working across other people&apos;s data?</summary>
    <content type="html">Replika founder Eugenia Kuyda has opened Wabi as an AI assistant for groups. Its new terms and privacy notice show the hard product question: who controls an agent working across other people&apos;s data?</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Eugenia Kuyda"/>
    <category term="Wabi"/>
    <category term="AI Agents"/>
    <category term="Consumer AI"/>
    <category term="Privacy"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Barclays&apos; 5,000-Job Plan Leaves AI Savings Unpriced</title>
    <link href="https://digidai.github.io/2026/10/24/barclays-5000-jobs-ai-savings-cost-accounting/"/>
    <id>https://digidai.github.io/2026/10/24/barclays-5000-jobs-ai-savings-cost-accounting/</id>
    <published>2026-10-24T00:00:00.000Z</published>
    <updated>2026-10-24T00:00:00.000Z</updated>
    <summary type="html">Barclays is planning roughly 5,000 job cuts while promising technology-led efficiency. Its quarterly accounts price the restructuring but do not isolate AI savings or the work that remains.</summary>
    <content type="html">Barclays is planning roughly 5,000 job cuts while promising technology-led efficiency. Its quarterly accounts price the restructuring but do not isolate AI savings or the work that remains.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Banking"/>
    <category term="Workforce Planning"/>
    <category term="Productivity"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Australia&apos;s $93 Billion AI Forecast Has a Roster Problem</title>
    <link href="https://digidai.github.io/2026/10/23/australia-sme-ai-forecast-roster-economics/"/>
    <id>https://digidai.github.io/2026/10/23/australia-sme-ai-forecast-roster-economics/</id>
    <published>2026-10-23T00:00:00.000Z</published>
    <updated>2026-10-23T00:00:00.000Z</updated>
    <summary type="html">A new Australian small-business AI forecast promises $93 billion in annual value. Its adoption assumptions meet a harder test in rosters, payroll and paid work.</summary>
    <content type="html">A new Australian small-business AI forecast promises $93 billion in annual value. Its adoption assumptions meet a harder test in rosters, payroll and paid work.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Small Business"/>
    <category term="Workforce Planning"/>
    <category term="Productivity"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>ChatGPT Ads Reach 89 Countries, but Auction Wins Are Not Impressions</title>
    <link href="https://digidai.github.io/2026/10/22/chatgpt-ads-89-countries-impression-measurement/"/>
    <id>https://digidai.github.io/2026/10/22/chatgpt-ads-89-countries-impression-measurement/</id>
    <published>2026-10-22T00:00:00.000Z</published>
    <updated>2026-10-22T00:00:00.000Z</updated>
    <summary type="html">OpenAI took ChatGPT Ads to 89 countries while revealing a gap between auction selection and a visible ad. For marketers, budgets depend on which event is counted.</summary>
    <content type="html">OpenAI took ChatGPT Ads to 89 countries while revealing a gap between auction selection and a visible ad. For marketers, budgets depend on which event is counted.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Advertising"/>
    <category term="Marketing Operations"/>
    <category term="Future of Work"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Motive and Samsara Take Fleet AI Beyond the Alert</title>
    <link href="https://digidai.github.io/2026/10/21/motive-samsara-fleet-agents-safety-work/"/>
    <id>https://digidai.github.io/2026/10/21/motive-samsara-fleet-agents-safety-work/</id>
    <published>2026-10-21T00:00:00.000Z</published>
    <updated>2026-10-21T00:00:00.000Z</updated>
    <summary type="html">Motive and Samsara are moving fleet AI from warnings into messages, work orders and driver calls. Bennett&apos;s four-agent trial shows where the safety manager&apos;s job starts.</summary>
    <content type="html">Motive and Samsara are moving fleet AI from warnings into messages, work orders and driver calls. Bennett&apos;s four-agent trial shows where the safety manager&apos;s job starts.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Fleet Operations"/>
    <category term="Driver Safety"/>
    <category term="Future of Work"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Payscale Puts AI Upskilling Against a 3.5% Raise Budget</title>
    <link href="https://digidai.github.io/2026/10/20/payscale-ai-upskilling-raise-budget/"/>
    <id>https://digidai.github.io/2026/10/20/payscale-ai-upskilling-raise-budget/</id>
    <published>2026-10-20T00:00:00.000Z</published>
    <updated>2026-10-20T00:00:00.000Z</updated>
    <summary type="html">Payscale says 74% of employers plan AI upskilling while 56% of workers expect higher pay for it. Its separate 2027 raise-budget survey shows why training and compensation now need one decision.</summary>
    <content type="html">Payscale says 74% of employers plan AI upskilling while 56% of workers expect higher pay for it. Its separate 2027 raise-budget survey shows why training and compensation now need one decision.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Compensation"/>
    <category term="AI Skills"/>
    <category term="Future of Work"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Cognizant Pairs AI Engineers With Operators in a 15,000-Role Plan</title>
    <link href="https://digidai.github.io/2026/10/19/cognizant-frontier-engineers-business-operators/"/>
    <id>https://digidai.github.io/2026/10/19/cognizant-frontier-engineers-business-operators/</id>
    <published>2026-10-19T00:00:00.000Z</published>
    <updated>2026-10-21T00:00:00.000Z</updated>
    <summary type="html">Cognizant plans 5,000 Frontier engineers and 10,000 operators. The harder test is turning certified staff and agent pilots into paid client work with measurable results.</summary>
    <content type="html">Cognizant plans 5,000 Frontier engineers and 10,000 operators. The harder test is turning certified staff and agent pilots into paid client work with measurable results.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Cognizant"/>
    <category term="AI Jobs"/>
    <category term="Technology Services"/>
    <category term="Future of Work"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Anthropic&apos;s Junior Hiring Share Falls as Agent Hours Surpass Staff Time</title>
    <link href="https://digidai.github.io/2026/10/18/anthropic-agent-hours-junior-hiring-share/"/>
    <id>https://digidai.github.io/2026/10/18/anthropic-agent-hours-junior-hiring-share/</id>
    <published>2026-10-18T00:00:00.000Z</published>
    <updated>2026-10-18T00:00:00.000Z</updated>
    <summary type="html">Anthropic&apos;s 2026 study pairs a six-to-one agent-runtime estimate with a drop in the junior share of technical hires from 27% to 9%. Outside studies complicate the case for cutting a company&apos;s first rung.</summary>
    <content type="html">Anthropic&apos;s 2026 study pairs a six-to-one agent-runtime estimate with a drop in the junior share of technical hires from 27% to 9%. Outside studies complicate the case for cutting a company&apos;s first rung.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Anthropic"/>
    <category term="Future of Work"/>
    <category term="Early Careers"/>
    <category term="Organization Design"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Greenhouse Turns Candidate Profiles Into a Sourcing Channel</title>
    <link href="https://digidai.github.io/2026/10/17/greenhouse-candidate-profiles-sourcing-channel/"/>
    <id>https://digidai.github.io/2026/10/17/greenhouse-candidate-profiles-sourcing-channel/</id>
    <published>2026-10-17T00:00:00.000Z</published>
    <updated>2026-10-17T00:00:00.000Z</updated>
    <summary type="html">Greenhouse is joining its candidate portal to sourcing, interviews and the ATS. The October launch makes a sharper test of who gets found, who hears back and what an employer actually buys.</summary>
    <content type="html">Greenhouse is joining its candidate portal to sourcing, interviews and the ATS. The October launch makes a sharper test of who gets found, who hears back and what an employer actually buys.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Greenhouse"/>
    <category term="Recruiting"/>
    <category term="Candidate Experience"/>
    <category term="HR Technology"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>New York&apos;s AI Office Boom Exposes a First-Job Gap</title>
    <link href="https://digidai.github.io/2026/10/16/new-york-ai-offices-first-job-gap/"/>
    <id>https://digidai.github.io/2026/10/16/new-york-ai-offices-first-job-gap/</id>
    <published>2026-10-16T00:00:00.000Z</published>
    <updated>2026-10-21T00:00:00.000Z</updated>
    <summary type="html">AI companies leased millions of square feet in New York while entry-level postings fell in exposed occupations. City internship results and graduate records show where office growth stops short of a paid career path.</summary>
    <content type="html">AI companies leased millions of square feet in New York while entry-level postings fell in exposed occupations. City internship results and graduate records show where office growth stops short of a paid career path.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="New York City"/>
    <category term="Early Careers"/>
    <category term="Labor Market"/>
    <category term="Startups"/>
    <category term="Workforce"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>SAP Plans to Buy Workday&apos;s Skills Partner as Both Map Jobs</title>
    <link href="https://digidai.github.io/2026/10/15/sap-techwolf-workday-task-map/"/>
    <id>https://digidai.github.io/2026/10/15/sap-techwolf-workday-task-map/</id>
    <published>2026-10-15T00:00:00.000Z</published>
    <updated>2026-10-15T00:00:00.000Z</updated>
    <summary type="html">SAP&apos;s planned TechWolf acquisition meets Workday&apos;s new task-level Work Intelligence product. Their overlapping maps could guide hiring and training, but a task score alone cannot prove a worker moved or a budget paid off.</summary>
    <content type="html">SAP&apos;s planned TechWolf acquisition meets Workday&apos;s new task-level Work Intelligence product. Their overlapping maps could guide hiring and training, but a task score alone cannot prove a worker moved or a budget paid off.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Workforce"/>
    <category term="Organizational Design"/>
    <category term="Skills"/>
    <category term="SAP"/>
    <category term="Workday"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Verizon&apos;s $70 Million AI Skills Plan Counts Its Layoff Fund</title>
    <link href="https://digidai.github.io/2026/10/14/verizon-70-million-ai-skills-layoff-fund/"/>
    <id>https://digidai.github.io/2026/10/14/verizon-70-million-ai-skills-layoff-fund/</id>
    <published>2026-10-14T00:00:00.000Z</published>
    <updated>2026-10-14T00:00:00.000Z</updated>
    <summary type="html">Verizon added $50 million to a $20 million fund for departing employees and opened AI training to the public. Goodwill&apos;s 18-site pilot shows where a course promise meets the harder test of paid work.</summary>
    <content type="html">Verizon added $50 million to a $20 million fund for departing employees and opened AI training to the public. Goodwill&apos;s 18-site pilot shows where a course promise meets the harder test of paid work.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Workforce"/>
    <category term="Reskilling"/>
    <category term="Careers"/>
    <category term="Verizon"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>AI Builder Ads Grow While Early Careers Move to Contracts</title>
    <link href="https://digidai.github.io/2026/10/13/ai-builder-ads-early-career-contract-paths/"/>
    <id>https://digidai.github.io/2026/10/13/ai-builder-ads-early-career-contract-paths/</id>
    <published>2026-10-13T00:00:00.000Z</published>
    <updated>2026-10-13T00:00:00.000Z</updated>
    <summary type="html">Draup says AI Builders account for 27% of tracked technology demand while internships and contracts take a larger share of early-career ads. Workday and Federal Reserve data show what those figures can, and cannot, establish about jobs and pay.</summary>
    <content type="html">Draup says AI Builders account for 27% of tracked technology demand while internships and contracts take a larger share of early-career ads. Workday and Federal Reserve data show what those figures can, and cannot, establish about jobs and pay.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Jobs"/>
    <category term="Early Careers"/>
    <category term="Workforce"/>
    <category term="Labor Market"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>HubSpot Plans Nearly 660 Cuts and an Outcome-Led Product Org</title>
    <link href="https://digidai.github.io/2026/10/12/hubspot-660-roles-customer-outcomes-reorganization/"/>
    <id>https://digidai.github.io/2026/10/12/hubspot-660-roles-customer-outcomes-reorganization/</id>
    <published>2026-10-12T00:00:00.000Z</published>
    <updated>2026-10-12T00:00:00.000Z</updated>
    <summary type="html">HubSpot is eliminating about 7% of its workforce while moving product teams from software hubs to customer outcomes. Its filings, AI pricing and financial results show what the new structure must prove.</summary>
    <content type="html">HubSpot is eliminating about 7% of its workforce while moving product teams from software hubs to customer outcomes. Its filings, AI pricing and financial results show what the new structure must prove.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="HubSpot"/>
    <category term="Workforce"/>
    <category term="Organization Design"/>
    <category term="Enterprise Software"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>LinkedIn&apos;s Six-Minute Prescreen and the Unanswered Candidate</title>
    <link href="https://digidai.github.io/2026/10/10/linkedin-six-minute-prescreen-unanswered-candidate/"/>
    <id>https://digidai.github.io/2026/10/10/linkedin-six-minute-prescreen-unanswered-candidate/</id>
    <published>2026-10-10T00:00:00.000Z</published>
    <updated>2026-10-10T00:00:00.000Z</updated>
    <summary type="html">LinkedIn says Hiring Assistant can prescreen in a median six minutes. Its November upgrade raises a harder buying question: whether faster screening produces a timely human decision and candidate reply.</summary>
    <content type="html">LinkedIn says Hiring Assistant can prescreen in a median six minutes. Its November upgrade raises a harder buying question: whether faster screening produces a timely human decision and candidate reply.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Recruiting"/>
    <category term="LinkedIn"/>
    <category term="Candidate Experience"/>
    <category term="HR Technology"/>
    <category term="Deep Investigation"/>
  </entry>
  <entry>
    <title>Workday&apos;s Mobility Agent Meets a Hiring Pause</title>
    <link href="https://digidai.github.io/2026/10/09/workday-mobility-agent-internal-career-freeze/"/>
    <id>https://digidai.github.io/2026/10/09/workday-mobility-agent-internal-career-freeze/</id>
    <published>2026-10-09T00:00:00.000Z</published>
    <updated>2026-10-09T00:00:00.000Z</updated>
    <summary type="html">Workday&apos;s new labor data show rising demand for applied AI skills alongside fewer internal moves. Its career tools can find a match, but a company still has to approve and fill the role.</summary>
    <content type="html">Workday&apos;s new labor data show rising demand for applied AI skills alongside fewer internal moves. Its career tools can find a match, but a company still has to approve and fill the role.</content>
    <author>
      <name>Gene Dai</name>
      <email>daiq@live.cn</email>
    </author>
    <category term="Artificial Intelligence"/>
    <category term="Workday"/>
    <category term="Internal Mobility"/>
    <category term="Career Development"/>
    <category term="HR Technology"/>
    <category term="Deep Investigation"/>
  </entry>
  <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>2026-10-17T00:00:00.000Z</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>

</feed>