Illustration by Eliot Wyatt
On September 8, 2026, Meta launched Muse, a personal AI agent designed to act on a user’s behalf: filling out forms, negotiating, booking travel, and making purchases. Twelve days later, Amazon blocked Muse from shopping on its site, arguing that third-party agents should identify themselves and obtain a retailer’s permission before transacting on behalf of its customers.
Illustration by Jason Ford
Generative AI has produced two competing views about which workers will benefit most. One view is that AI will narrow performance gaps by giving lower performers templates, answers, and coaching directly in their workflow. The other is that AI will widen those gaps by eliminating routine but necessary work, which will allow top performers to spend more time on higher-value activities where they already have an advantage. At the individual task level, high and low performers may both benefit, but the more important question is which effect dominates on net in real jobs, where workers perform bundles of activities. The answer matters for how firms hire, train, organize teams, and adjust practices built for their current talent mix.
October 6, 2026
In this week’s The Insider newsletter, technology editor Tom Stackpole shares a story from MIT’s Christian Catalini, who argues that when execution is cheap due to AI, verification
The Insider is a weekly, subscriber-only newsletter, where our managing editor curates HBR stories, research, and ideas that senior leaders shouldn’t miss.
Read more on AI and machine learning or related topics Generative AI, Technology and analytics, Data management, Organizational transformation and Digital transformation
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Somewhere in your company today, an expert rejected a perfectly plausible answer. A controller caught a revenue figure that counted deferred bookings. A security engineer blocked a release that passed every test but would have handed an AI agent write access to the production database. That override, escalation, or reversal is the most valuable data your firm produces. If you record enough of those moments and use them, alongside everything else your firm knows and measures, to train an AI model that learns from each correction, you could, in theory, eliminate the need for managers to route information and coordinate action. In doing so, execution gets turbocharged by automation. You might even be able to eliminate hierarchy altogether.
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The cybersecurity outlook for small and medium-sized businesses can seem dire. Cybercriminal and nation-state cyberattacks appear to be on the rise, especially against utilities and small businesses. At the same time, there are new AI-enabled cyber risks, including AI agents from frontier labs breaching confinement and attacking other companies. Generative AI and machine-learning models have also streamlined traditional attack vectors. Phishing lures are now hyper-personalized, vulnerability discovery is exponentially faster, and ransomware operations are automated and ubiquitous.
Illustration by Ana Moreno
Stories are one way that leaders cement their legacy, inspire action, and define an era. The stories they tell about where a company is going can foreshadow opportunity or foment fear—sometimes unwittingly. Today, leaders are telling employees the story of what AI will mean for them. “No one knows,” some say, leaning into the 2010s-era virtue of transparency. “AI is coming for your job, but it’s coming for mine, too,” say others, in apprehensive solidarity. Still others position AI as a personal superpower, or the dawn of a heroic unknown. Each of these narratives is a choice. Each choice has consequences.
In this week’s Insider newsletter, managing editor Gretchen Gavett shares an early release article from the upcoming November-December 2026 issue of HBR on what psychology researcher
The Insider is a weekly, subscriber-only newsletter, where our managing editor curates HBR stories, research, and ideas that senior leaders shouldn’t miss.
Read more on Business management or related topics Collaboration and teams, Teams, AI and machine learning and Generative AI
Illustration by Minjo Jung
Research on AI in the workplace can show us how the technology is really being used and what it does to the way we work. Recent studies offer lessons for leaders on how employees are actually
There’s no shortage of assertions and predictions about AI’s impact on work. But what does the research say about what’s actually happening inside organizations—and how can you use it to make better decisions about how AI is implemented at your company?
Read more on AI and machine learning or related topics Generative AI, Decision making and problem solving, Personal productivity, Managing people, Talent management, Digital transformation, Trustworthiness and Automation
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Companies today often use AI to pursue low-value opportunities: adding a new feature that doesn’t truly matter to the customer or giving employees tools that save them time but don’t
Large companies today are abuzz with AI. Leaders have funded pilots, trained their people, upgraded data platforms, and chased the latest models. But when speaking to CFOs about financial impact, the conversation can turn awkward. Firms have invested eagerly in AI, but the returns have been difficult to quantify.