Most of us grew up hearing about two kinds of intelligence. Book smarts belong to the person who masters the material, remembers the research, and can explain a concept cleanly on demand. Street smarts belong to the person who reads a situation quickly, notices what is going unsaid, and knows which rules can bend and which will break something if touched. The distinction has endured because it captures something most of us have seen firsthand: knowing a great deal and knowing

Few leadership decisions carry as much weight as a hire, and few have changed as quickly in recent years. Across industries, executives are hiring, preparing to hire, or recovering from a hire that did not work out, and in nearly every case the question of artificial intelligence now surfaces early. Leaders want to know how much of the recruiting process they can responsibly hand to a machine, and the answer becomes clearer when the process is viewed in three distinct phases,

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

A RAND Corporation study published in 2024, based on interviews with sixty-five data scientists and engineers with at least five years of experience building AI and machine learning systems, set out to understand why so many AI projects fail. The researchers noted that by some estimates, more than 80% of AI projects fail to deliver the business value they were built for, roughly twice the failure rate of ordinary IT projects. The most important detail is what the interviewees

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

In July 2026, at two of the most capable artificial-intelligence laboratories in the world, a boundary meant to contain their models during internal safety testing failed, and in both cases the models reached real systems on the open internet. It would be easy to file the two events as a single story: two labs, one summer, the same lesson. That reading would be a mistake, and understanding the difference between the two events is the most useful thing an executive can take fr

A near-frontier AI model (Kimi K3) became free to copy the same week Washington weighed banning it. Issue 97 of the AI Daily Intelligencer asks what that means, and argues both sides honestly. On the same July weekend that Moonshot AI released the full weights of Kimi K3, the largest open-weight model yet built, Washington was reported to be weighing a ban on exactly that kind of release. A capability that a year ago would have sat behind a corporate paywall became a free goo

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

Optimizing AI Token Spend, while Minimizing Expense This past weekend I conducted an experiment on myself as much as on a machine. Anthropic's new Claude Fable model had been available to me for three days, and the announcement had already come down that access would soon move out of standard subscription allowances and onto metered usage credits today. The clock was running. Rather than ration my remaining allocation cautiously, I decided to do the opposite. I resolved to sp

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

For most executives, artificial intelligence has already proven its worth as an analyst, a drafter, and a sounding board. Fewer have discovered what happens when the same conversational fluency is pointed toward something far less utilitarian: music. The same discipline that produces a sharp board memo or a clear strategic brief also produces something unexpected when redirected toward melody, instrumentation, and lyric. Leaders who engage seriously with AI music creation ten

Most executives now accept that artificial intelligence will reshape how their organizations operate. Far fewer have a structured way to build the judgment required to use it well. In March 2026, Anthropic addressed that gap directly by launching Anthropic Academy, a free learning platform built by the company that develops Claude. The Academy is a working curriculum, hosted on Skilljar and linked from anthropic.com/learn, that spans twenty courses across three tracks: AI Flu

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

By the time you bring a decision to a conversation, you have usually already made up your mind. You may not realize it. The situation may feel genuinely open, the options unresolved. But in most cases, the moment you begin describing a problem to another person, you have already climbed a cognitive staircase that began with a selective observation and ended with a firmly held conclusion, moving through layers of interpretation, assumption, and belief so quickly that the proce