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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 solo business owner describes organizing their week by assigning specific themes to different days, such as running experiments on Wednesday mornings, handling administrative tasks on Fridays, and rotating blog writing and updates on Mondays. The approach is presented as a way to reduce the cost of switching between unrelated tasks when working alone.
A growing number of professionals who rely heavily on AI tools report a form of mental exhaustion researchers are calling 'brain fry,' distinct from traditional burnout. One CEO described it as cognitive overload from managing the volume of work AI performs on his behalf rather than from the work itself. An Entrepreneur contributor says the same productivity gains that let people take on more projects are also dividing attention and draining focus.
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 writer tested a personal workflow that divided tasks among three AI assistants—Claude, Gemini, and ChatGPT—assigning each to a specific context: professional work, household tasks, and casual use. The experiment produced uneven results, with some combinations working well and others creating friction or inefficiency.
A newly highlighted tool lets users turn loosely written plans, like trip itineraries or schedules, into structured calendar events that can be shared and accessed by others. It aims to remove the manual effort of typing dates, times and details into Google Calendar one by one.
A developer released a task-management tool on Show HN that lets users dump an unstructured list of tasks by typing or speaking, after which the system selects three priority items and explains its reasoning via email. The tool processes voice input locally in the browser and only sends typed task text externally for ranking if the user signs up.
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.
Productivity writer Ben Meer has shared five insights from his new book, 'How to Be Good at Life: Simple Systems to Achieve More and Stress Less.' The insights center on a short Sunday planning routine Meer says can make the rest of the week easier to manage.
New research finds that employees worried about losing their jobs to artificial intelligence are increasingly acting out at work, including slacking off, arriving late, and even taking coworkers' lunches. The study ties this behavior directly to anxiety over AI adoption spreading across businesses.
An Entrepreneur contributor argues that the first wave of business AI adoption focused mainly on productivity tasks like drafting emails, summarizing meetings and generating marketing content. The piece contends a second, more consequential wave is emerging where AI gets embedded directly into specialized operational workflows, such as turning product ideas into executable instructions for development and manufacturing.
A study covering more than 700 software companies found that AI coding agents raised lines of code by 30 percent, commits by 20 percent, and pull requests by 23 percent, but did not meaningfully increase the rate at which firms resolved tracked features or issues in tools like Jira. Researchers attribute the gap to code review, which took 49 percent longer on average after AI tools were adopted, with more revisions required and more reviewer comments per pull request.