In the spring of 2023, engineers at Samsung's semiconductor business pasted confidential source code into ChatGPT while looking for help with routine tasks. Within weeks, Samsung barred staff in one of its largest divisions from using generative AI tools on company devices and networks (Gurman, 2023). The episode became a cautionary tale, yet the behavior behind it has only spread. In Microsoft and LinkedIn's 2024 Work Trend Index, 78% of people who use AI at work said they b

In a recent article, I made the case that the most useful prompts now run to several hundred lines and read more like a briefing for a new hire than a quick question. The first response I usually hear is a practical one: who has the time to write all of that? The instinct behind the question is understandable. When a busy executive sits down at the keyboard, the prompt tends to shrink to whatever can be typed before the next meeting, and the model is left to guess at everythi

Most professionals choose one AI model, set it as their default, and send every request through it, whether the task is rewording a two-line email or stress-testing a five-year growth plan. The habit feels efficient, yet it has become one of the quietest sources of waste in how organizations use AI. Sending routine work to a top-tier reasoning model is a bit like asking your CFO to reconcile petty cash, and sending strategic work to a lightweight model is like handing the boa

Only a few years ago, working with an AI model meant typing a sentence or two and hoping for something useful in return. Early adopters traded tips about magic phrases and clever openings, and the prevailing assumption was that a good prompt was a short one. That assumption has quietly collapsed. The most effective teams now routinely write prompts that run to several hundred lines, and those prompts read far less like questions and far more like operating manuals. Leaders wh

Most executives can now name the AI agent handling their expense reports, drafting their first-pass contracts, or triaging their inbox. Fewer can say what a “good quarter” looks like for that agent, or what happens when it quietly underperforms. While executive teams have gotten comfortable deploying AI colleagues, they have not yet gotten comfortable evaluating them. The scale of the shift explains the urgency. Gartner projects that by the end of 2026, 40% of enterprise appl

Almost everyone arguing about artificial intelligence and jobs is arguing about a different thing, and mistaking it for the same thing. Spend a week with the commentary and you will meet a dozen confident, incompatible verdicts. One says a white-collar bloodbath has already begun. Another says technology always creates more work than it destroys, so relax. A third says AI only changes tasks, not jobs. A fourth says the machines will make us all so rich that work becomes optio

A clock tells you the time by moving two hands across a face. What makes those hands move—the gears, the springs, the escapement ticking away behind the dial—stays hidden unless you open the case. For as long as language models have existed, they've worked the same way. You can read what a model says, but not what it does to arrive there. A model might reason its way toward an answer, notice something suspicious about a question, or quietly consider a shortcut it decides not

Nearly seven in ten C-suite executives report being seriously close to leaving their roles for reasons tied to their well-being (Silverglate & Fisher, 2022). That survey predates the current wave of AI adoption, but a more recent study gives the pattern a name and a mechanism. Researchers at Boston Consulting Group surveyed nearly 1,500 full-time workers and found that intensive AI oversight, rather than AI use itself, is driving a distinct form of mental fatigue they call "A

That which is old is new again. The thought has crossed my mind repeatedly as I have watched generative AI models emerge, improve, and regenerate over the past several years, and it has returned with particular force as I write the third edition of The AI Whisperer. Stephen Covey's counsel to begin with the end in mind, offered to business readers in 1989, turns out to be precisely what is needed in this new age of specification. The humble terminal window, brought to life mo

Last week, the United States marked two hundred and fifty years since its founding, and it is worth remembering what actually provoked the founders. The colonists who boarded ships in Boston Harbor in December 1773 were not protesting a large tax. The duty on tea was three pence a pound, and the Tea Act had in fact made tea cheaper. What they refused was the arrangement itself: a small, metered charge on every pound, levied by a distant monopoly, with no voice in the terms, a

Most executives know the pre-mortem. Very few use it. The concept, developed by psychologist Gary Klein and popularized in organizational strategy circles by Daniel Kahneman, is disarmingly simple. Before committing to a major decision, you imagine it is twelve months in the future and the initiative has failed catastrophically. You then work backward to explain what went wrong. The exercise forces a team to surface its private doubts, challenge its shared assumptions, and co

We have reached the point where access to artificial intelligence is no longer the scarce resource. Capable AI models are available to nearly everyone, competent drafts generate themselves by the paragraph, and the marginal cost of producing a passable piece of work has fallen close to zero. In that environment the advantage shifts to a quieter and more demanding capability, which is the discipline of directing intelligence toward outcomes that matter. That discipline is the

There is a revealing piece of recent research making its way through leadership circles. When AI systems are given distinct roles, organizational structure, and defined responsibilities within a team, they consistently outperform AI systems that operate without that structure. The finding sounds almost mundane until you sit with what it actually implies: the same organizational principles that make human teams effective also make human-AI teams effective. We are not, in other

Most executives and coaches who use AI tools think of their conversation history as something like a search history: transient, forgettable, and replaceable. That assumption is worth revisiting. Over the past year, many of us have done something more significant inside AI platforms than simple lookups. We have worked through complex coaching frameworks, drafted nuanced client communications, developed proprietary methodologies, refined our voice and reasoning across hundreds

By the middle of any given year, a senior executive has made hundreds of decisions, navigated dozens of difficult conversations, and cycled through enough strategic pivots to fill a casebook. They have also, in all likelihood, spent a significant portion of that time working alongside an AI assistant: drafting communications, thinking through problems, preparing for high-stakes meetings, and processing the complexity of organizational life in real time. What most executives h

Every few years, a technology arrives that leaders treat as a shortcut around the hard problems of organizational life. AI is not the first such technology, but it may be the most seductive. Unlike enterprise software or automation, AI feels cognitive. It reasons. It drafts. It synthesizes. And so executives are deploying it into their teams with the implicit assumption that better tools produce better outcomes. That assumption is worth examining closely, because in teams whe

For the past three years, organizations have applauded AI for writing emails, drafting presentations, and generating marketing copy. That phase, call it Generative Novelty, served a purpose: it forced executives to take AI seriously. But novelty is not strategy, and for CEOs intending to deploy AI as a core operational capability, the game has fundamentally changed. We have entered AI 3.0: the Orchestration and Execution Stage. The progression is worth naming precisely. In AI

This week, a frontier AI lab did something that has never happened before in commercial software: it voluntarily withheld its own flagship model from release. Not because a regulator demanded it. Not because the product was broken. Because it was, in Anthropic's own words, too capable to ship. That sentence belongs in your board deck, it belongs in your next leadership offsite, and it demands a response from every CEO operating a digitally dependent business, which, in 2026,

This past Friday evening, Anthropic's Head of Claude Code, Boris Cherny, posted an announcement on X that drew swift and vocal reaction across the AI builder community: starting Saturday, April 4, 2026, at noon Pacific, Claude Pro and Max subscribers would no longer be able to use their flat-rate subscriptions to power third-party agent frameworks like OpenClaw. Anyone wanting to continue would need to shift to pay-as-you-go billing or API keys. The backlash was mixed but voc

Here's an uncomfortable truth for leaders who think they're ahead on AI: you're probably still using Claude like it's 2023. Type a prompt. Get a response. Maybe refine it. That cycle of Instruction → Scaffolding → Output was the right mental model two years ago. Today, it's the equivalent of using a smartphone only to make calls. Technically functional, but strategically limiting. Claude in 2026 operates across seven distinct layers with 45+ capabilities. Most executives are