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Tech News
A workplace analytics firm, ActivTrak, examined anonymized activity data from over 9,100 white-collar employees across 450-plus companies between April and September. It found non-AI users averaged 7.6-hour active workdays, while heavy AI users logged between 8.4 and 9.1 hours, alongside more frequent work interruptions.
The Financial Times cites two new data sources suggesting AI is affecting US employment: analyst Eric Basmajian found productivity gains concentrated in roughly 15% of jobs, especially in information, professional services, and finance. A reportedly internal Goldman Sachs report also circulated showing rising unemployment correlated with AI exposure in sectors like finance, telecoms, technical services, and administration.
Amazon published updated internal engineering guidelines on October 27 warning staff against treating AI adoption rates, token consumption, code changes and code reviews as productivity targets. The company said these metrics become 'deeply flawed' when disconnected from actual outcomes, and urged teams to focus on long-term results instead. It also said engineers who find AI tools unhelpful for certain tasks are producing a valid 'finding, not a failing.'
Apollo Global Management chief economist Torsten Slok published a note showing that inflation-adjusted revenue per worker and profit margins are rising only among the Magnificent 7 tech giants—Nvidia, Tesla, Amazon, Microsoft, Alphabet, Apple and Meta. The remaining 493 S&P 500 companies show flat revenue per worker and flat margins, with no measurable productivity boost from generative AI, according to Slok's analysis.
In an October 2026 blog post, writer dynomight recounts how large language models progressed from being 'toys' in 2024 to tools many now consider essential for mathematical work by early 2026, changing dramatically within months of late 2025. The author estimates current AI tools make their own 'doing math' process about 50% more productive, even though AI can complete some tasks in minutes that once took hours.
Apollo Global chief economist Torsten Slok reports that excluding the 'Magnificent Seven' tech giants, the S&P 493 shows flat profit margins and revenue per worker, with no measurable productivity gains from AI adoption. He says mainstream corporations are increasingly treating AI token spending as a cost burden rather than a growth driver, even as VC-backed firms keep increasing their AI spend.
Apollo Global Management chief economist Torsten Slok published a note arguing that AI has produced no measurable financial benefit for large companies outside the tech sector. Analyzing what he calls the 'S&P 493' — the S&P 500 excluding seven major tech firms — Slok found flat profit margins and revenue per worker, with no evidence of productivity gains from AI adoption.
A Nielsen survey of 1,700 senior managers across seven countries, commissioned by Monday.com, found that more than half struggle to assess employee output since adopting AI tools, because polished AI-assisted work makes it unclear how much an employee personally contributed. Nearly half of managers reported spending more time reviewing and guiding their teams as a result, even though 94% said they're excited about AI's potential.
A Harvard Business Review contributor, writing with colleagues Nicole Radziwill and Todd McClees, revisits a December 2024 warning that enterprises remain poorly positioned to benefit from AI despite widespread adoption. The piece argues the shortfall stems from organizational and human readiness issues rather than the technology itself, and outlines steps companies can take to close the gap.
A tech industry leader with over two decades of experience, including roughly ten years in leadership, argues that faster work enabled by AI tools is taking a toll on team wellbeing. The piece draws on personal management experience to make the case that output speed and employee morale are closely linked.
A University of Massachusetts professor argues that generative AI is used more productively when it is asked to challenge or critique a person's work rather than simply complete tasks for them. The recommendation centers on creating deliberate 'friction' between AI and user, with each questioning the other's output.
A tech commentary piece declares that the initial 'generative AI' era is effectively over, arguing that the industry's understanding of productivity has changed. The author states that measuring success is no longer about output volume, signaling a shift in how AI tools are evaluated.