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5 ways to use AI to sharpen your thinking

5 ways to use AI to sharpen your thinking

This article is republished with permission from Wonder Tools, a newsletter that helps you discover the most useful sites and apps. My brain’s nearly melting in this ongoing heat wave. To avoid the temptation to outsource my thinking to AI, I’m pushing in the opposite direction. How can I use AI to challenge me to think more, not less? Read on for five tactics I’m leaning on to avoid cognitive surrender when my brain’s overheating. 1. Challenge Me, Don’t Praise Me The Tactic: Use AI as an intellectual sparring partner. Explore alternative perspectives. Ask an AI assistant: How might someone from X or Y professional background think about this? What tough questions would they ask? The Benefit: Identify potential blind spots and weak assumptions before finalizing a presentation or making an important decision. Try This: Present a plan, idea, draft, or decision to an AI assistant with instructions to challenge your thinking in multiple ways. Identify risks you haven’t considered and add nuance to your analysis. Prompt Template I’m planning to [decision/plan] because [reasoning] and with a goal of [objective]. Play devil’s advocate. Give me multiple perspectives on this. Be bold and surprising in your reply, and address these questions: What are the strongest arguments against this approach? What alternatives should I consider? What risks might I be overlooking? What questions should I be asking myself? What challenges should I expect to face? What could I do to learn more before I move forward? Who else could I talk to about this? What could I do to increase the chances of success? Pro Tip: Ask your AI assistant to role-play. It can respond as a coach, a teacher, a family member, a competitor, or an audience member. Varied viewpoints can be useful. Or ask it to act like a person you admire, living or dead, real or fictional. You can even ask it to simulate a council of advisors, each with a distinct perspective. Ethan Mollick has written about using that tactic for feedback on material in his book. Microsoft’s Copilot AI now has a new free “Team of Advisors” experimental feature to give you multiple perspectives from an animated expert panel. Here’s an example of what a panel suggested in reply to my open-ended query (though you won’t hear the animated voices). And here’s the transcript. Limitation: Unless you provide detailed context, you may get bland responses. To Keep it Private: Use a free offline AI tool like Jan, which I wrote about recently. Example: During a dialogue with ChatGPT about a new morning routine I was adopting, I realized I had left several things out of my planning. In addition to posing questions that helped me think more carefully about my goals and my timing, it produced a PDF plan with tables. 2. Think Out Loud The Tactic: I like talking out loud to clarify my ideas. It’s my way of free writing. I use voice AI tools to turn my stream of consciousness into organized text. I call it bionic dictation. The AI assistant records and transcribes what I say. It then goes a step beyond dictation by transforming my words into an outline or summary. That helps me add structure to my meandering thoughts. Step 1: Pick an AI dictation app. Apple Notes and Google Keep work well for free mobile dictation. They won’t summarize your text or transform your words into an outline, as Letterly can. Letterly is the reliable mobile app I prefer. It’s a standalone app, or you can use it within your iPhone keyboard to dictate texts, email, or notes. I make lots of typos when thumb typing, so I often use my voice instead in any app. It’s more accurate than the iPhone’s built-in dictation. It now also works on my laptop. Wispr Flow is what I often use on my computer. It lets me hold a function key to dictate into any app. It’s several times faster than typing and works in 100+ languages. It’s free for 2,000 weekly words, or 12/month for unlimited use. Lispr is a good new free alternative to Wispr. It can instantly translate your spoken words into any of 99 languages. I tested it by dictating various messages and translating an email reply into Portuguese, which worked well. MacWhisper is another excellent free option. It can record your system audio for online meetings, or even transcribe uploaded audio files. I paid 73 for a one-time upgrade to use the best AI models, but the free version has almost all of the same features. Step 2. Create a new note. Hit record. Talk. The app won’t judge you or care whether you speak quickly or slowly. It doesn’t care whether you’re saying something brilliant or sputtering nonsense. When you finish it will summarize your rambling. It doesn’t care how long you pause, or whether you contradict yourself or digress. It just patiently transcribes and then sums up your words. Pro Tip: Record with AirPods while walking, screen off. The lack of a visual editing screen frees me up to just talk. The editing part of my brain interferes less when I’m talking than when I’m typing. Limitation: Voice AI won’t sharpen weak thinking. Read and edit your own words and refine them before sending anything important. Example: I sometimes start my day at work by talking through my priorities for a few minutes with a voice AI tool. 3. Do Deep Research Before a Big Decision The Tactic: Before making an important decision or beginning a new phase of a project, I run multiple deep research queries. Within 5 to 25 minutes I have a citation-rich report that would in the past have taken days to prepare. After reading I can follow-up on the most promising citations and links. The Tools: ChatGPT, Gemini, Perplexity, Copilot, and Claude all have similar deep research modes. How it Works: Dictate or type in an extensive research query. The AI assistant will then analyze your query and search through dozens of sites, or sometimes hundreds. It will synthesize its findings in a report with citations, so you can go to the original sources. I usually export the analysis into a Google Doc for easier reading or printing. Try This: Toggle on the deep research setting in your AI of choice. Then write a query that includes: What you’re researching and why you care. Any relevant supplemental documents. You can attach relevant PDFs or other materials you’ve already gathered. What format you want. Maybe you’d like tables, or a timeline, or a beginner’s step-by-step guide, adapted for your specific context. What level of understanding you’re aiming for, or what you already know. Are you a scientist looking for updates on a field related to yours, or a novice trying to make sense of something brand new? Which sources to prioritize, if you have preferences or favorites. Maybe you want only peer-reviewed research, or info from particular publications or specific countries or government agencies. What questions the report needs to address, and what it should avoid. If you’re not interested in historical background or scientific explanations, say so. If you don’t want it to assume prior knowledge, say that. Vague queries produce generic reports. Detailed direction is what makes deep research worth the wait. Pro Tip: Tell the AI assistant to ask you questions to clarify your objectives before it begins the research. That gives you an extra chance to refine your query. Edit the Research Plan: ChatGPT, Gemini, and Copilot let you review and edit their research plans before beginning. When editing, narrow or broaden the scope based on what you care about most. Limitation: A report is only as good as its sources. For sparse topics, AI may lean on publishers with flimsy fact-checking. Specifying preferred sources can help. Afterwards, check the source list. Spot-check the citations, which don’t guarantee accuracy. Each Model’s Strengths: Perplexity races to return results in three minutes. It’s concise and clear. Gemini is also fast, and exports neatly into a Google Doc. ChatGPT produces exhaustive reports and helpful tables. It can sometimes take 20 to 30 minutes. Claude is similarly thorough. It researched 574 sites in 17 minutes for one report. Examples: Copilot took 10 minutes to prepare this brief summary report about why some nations excel at the World Cup. Gemini’s analysis on the same query was much more detailed. 4. Learn Something New Design Your Own Curriculum: Ask your AI assistant to guide you through whatever you’re curious about. Unlike textbooks or generic explanatory sites (e.g. Wikipedia) designed for anyone and everyone, an AI assistant can tutor you in ways you prefer. ChatGPT has Study and Learn mode. Gemini has Guided Learning. These prioritize helping you learn, not giving you answers. Ask for diagrams, analogies, examples, or case studies that relate to topics or places or people that interest you. Get a detailed overview of key concepts in archeology, or a beginner’s guide to soccer’s offside rule. Specify your prior understanding. Ask follow-up questions. Space your learning over days or weeks. AI assistants excel at creating structured learning plans tailored to your schedule and interests. And they’re better now than they used to be at keeping track of what you’ve already learned. Summarize Your Learning Preferences Include context about what you want to learn, why, and how. Specify sources you prefer. Explain what you already know. Note whether you learn best by reading, listening, watching, or trying something yourself. Mention if you like quizzes, drills, or exercises you can do while commuting or during breaks at work. Ask for learning games. Ask for specific book or article recommendations using web search or deep research. Hallucinations are less common now, but ask for verification links and check them yourself. If you need a human learning partner, ask for guidance on finding one. Or ask for sample language you can adapt when reaching out to someone. Connect your calendar to ChatGPT or Claude to have the AI schedule learning time for you. Making the plan concrete increases the likelihood you’ll follow through. Pro Tip: Request a learning plan you can print out. Get suggestions for useful experts to follow and strategies for avoiding learning pitfalls. Ask for help setting learning targets, measuring progress, choosing resources, and motivating yourself. Short Prompt Template: Make a [timeframe] learning plan for me so I can learn more about [skill/topic] because [reasons for learning]. I’d like to spend [hours/week]. As a [beginner/professional/other skill level], I prefer to learn in [learning style]. I have the following [learning goals]. Include milestones, resources, and practice ideas. 5. Make a Learning Dashboard You can ask an AI assistant to help you make an app to track reading or eating goals, fitness metrics, your Wordle streaks, or your book writing progress. Try This: Describe an idea you have for a learning tracker. Experiment with Claude Artifacts or Gemini’s Canvas. Or try Lovable or Bolt, specialized AI tools that make it easy for anyone to create a site or app. Glaze is another option for making a mini app that lives on your computer. Bonus: I like these examples of how journalist David Bauer uses AI. Resources: Here’s my Glaze guide, and tips on making artifacts. This article is republished with permission from Wonder Tools, a newsletter that helps you discover the most useful sites and apps.

2 hours ago
This designer is using AI to make clothing with zero fabric waste

This designer is using AI to make clothing with zero fabric waste

Walk onto the cutting floor of almost any garment factory in the world and you’ll see rolls of fabric laid out in stacks. A technician arranges a pattern on the cloth to cut out the building blocks for the garment: sleeves, pockets, bodice. The problem is that the pieces never fit together perfectly. Around the curves of a sleeve, the notch of a collar, and the taper of a side seam, there are always gaps—slivers and crescents of fabric that must be cut around and swept away. That refuse is known in the industry as cut-and-sew waste. Most consumers never think about it, because it happens long before a garment reaches a store. But it adds up to one of fashion’s biggest yet least-discussed problems. By industry estimates, somewhere between 10 and 15 of fabric is lost in the cutting process. The waste matters because fabric is the single most expensive input in a garment, accounting for up to 70 of the total cost of making it. And it matters environmentally, because every discarded scrap embodies the water, energy, and carbon emissions required to make it. [Image: SXD] Shelly Xu wants to drive that fabric waste number to zero. Her company, Shelly Xu Design (SXD), is a 15-person startup building AI software that reengineers clothing patterns so the pieces interlock like puzzle parts, leaving nothing behind. Crucially, it doesn’t ask factories to change their process; it fits seamlessly into the current system. “It doesn’t change how people actually sew cloth together,” Xu says. “You’re just cutting them into different shapes so that they take up less space on the fabric.” SXD just closed an oversubscribed 4.5 million pre-seed round led by Initialized Capital. After four years of building the technology, the company is ready to scale. SXD has partnerships with the HM Foundation and the Coop bookstores at Harvard University and the Massachusetts Institute of Technology, just signed a multiyear deal with one of the largest music record labels to convert tour merch to zero waste, and is preparing to launch with a European apparel group whose clients range from Uniqlo to Ralph Lauren. Shelly Xu [Photo: Joe Thomas/Sustain/courtesy SXD] A Childhood of Limited Resources Xu traces the whole idea of her company to the very small apartment she grew up in. She was raised in Hefei, in China’s Anhui province, not far from Nanjing. The home she shared with her parents was just a few hundred square feet, so fitting life into it became a daily puzzle. They slept on bed rolls that had to be folded and put away in the morning, and they bought only enough groceries for the next meal or two. Xu also watched green spaces near her home become dumping grounds. “I saw firsthand how detrimental waste can be,” she says. These experiences taught her to see limitation as a design challenge. She often invokes Piccaso’s line that restraint is what liberates invention. Limited resources, in Xu’s view, aren’t a hindrance to good design. “Really good designs can come from the idea that we live with limited resources on our planet,” she says. To pursue these ideas, Xu studied sustainable development at Columbia University, where she explored how to bring more eco-friendly approaches to fashion. She also taught herself to design clothing and to code, then began building a platform that would allow designers to create zero-waste clothing patterns. [Photo: Stephanie Cheong/courtesy SXD, Erica Payne (model)] To test the technology, she designed a small collection of zero-waste outfits, stitched 200 garments by hand, and created an Instagram account to showcase them. Shelly Xu Design quickly amassed 20,000 Instagram followers, buyers paid thousands of dollars for her pieces, and the collection sold out. Xu deliberately didn’t highlight the sustainability aspect of her garment-making process; she wanted to show that it’s possible to create beautiful clothes that also happen to generate zero waste. “Even without people knowing its sustainable, people should be able to tell it’s a good design,” she says. The overwhelming positive response convinced Xu that there is a business in this technology. While software companies like Lectra and Dassault Systèmes are working to reduce cutting-room waste in apparel manufacturing, none have yet achieved zero-waste designs. So Xu enrolled at Harvard Business School to determine how to create a profitable business. “People like myself who love making great products, we can be some of the worst businesspeople, because we’re not thinking about how to create a business that can sustain itself,” she says. When she graduated in 2021, the nascent SDX won Harvard’s New Venture Competition, taking both the grand prize and the crowd favorite award, in a contest whose past winners include Grab, the Uber of Southeast Asia. [Photo: Lindsay Sierakowski] A Mathematical Problem Thanks to this award, Harvard professors introduced her to experts who could help her build her company. The dean of engineering connected her to Takeo Igarashi, an AI professor at the University of Tokyo, who helped her think through the software. Xu went on to hire his student, research engineer Maria Larsson. The company built software that uses AI to translate any garment into a zero-waste design. [Image: courtesy SXD] In many ways, the cut-and-sew process of making a garment is still very old-fashioned. Most factories rely on established patterns for cutting garments from cloth, which a garment worker then sews on a machine. Technicians try to conserve as much fabric as possible, but even with digital tools, it’s common to waste a tenth or more of the fabric. Xu sees this as a mathematical challenge. “Some factories are using apparel patterns that haven’t changed in literally centuries,” she says. Her software maps exactly how much fabric it has to work with, then arranges the pieces in unconventional configurations that shrink waste to 1 or less. [Photo: Brayan Mesa/The Coop/courtesy SXD] Since then, the team has taken on even thornier problems, such as sizing. A single design might need to exist in a dozen sizes, from toddler to plus-size. Up until now, Xu says, “you had to manually redesign every single pattern for every single size. Our platform does it in seconds, with the push of a button.” At first, SXD stuck to geometric shapes that are easier to translate into zero-waste designs. Now Xu’s team is working to build tools that will enable the design of zero-waste garments with curves, like tapered sleeves or trouser legs. “The idea with zero-waste design was that it was always just rectangles,” Xu says. “We’re pushing back against that.” [Image: SXD] From Tote Bags to Car Seats One of SXD’s earliest partnerships was with the Harvard Coop, the university’s bookstore, whose branded merchandise is popular among students and tourists. She started with a tall vertical tote bag with interior pockets and reinforced straps made from scraps that would usually be discarded. “When you create a tote, the corners are usually cut out and wasted,” Xu says. “So we decided to use that corner as a reinforcement for the straps.” The bag uses 30 less material than a conventional tote, wastes nothing, and is made from deadstock fabric that would otherwise have ended up in a landfill. [Image: courtesy SXD] The totes sold out quickly, so the Coop asked Xu to make branded T-shirts and aprons. Those, too, sold out within weeks, and SXD is scrambling to restock. This September, MIT has tapped SXD to create products for its flagship conference, MIT Solve. [Photo: Brayan Mesa/The Coop/courtesy SXD] The Harvard and MIT collections were proving grounds—a way to test zero-waste design and get the company off the ground. Its ambitions are now far bigger. SXD is working with Indonesia’s Busana Apparel Group, one of the world’s largest manufacturers, which churns out nearly 2 million men’s shirts per month. Converting that volume to zero waste would spare staggering quantities of fabric. Xu is also moving into the automotive industry. Automotive fabric is so expensive that suppliers battle to save every scrap, celebrating the smallest gains. “For some customers, the goal is to save 1,” she says. “But we’ve shown we can save more than 20, which is an enormous cost savings.” For years, environmental activists have implored consumers to buy fewer clothing items and wear them longer. The message has reached some, but changing consumer behavior on a large scale has proven very hard. The beauty of this zero-waste technology is that it asks nothing of shoppers. The shirt looks the same, fits the same, and costs the same. And yet the garment lands with a far smaller environmental footprint. As Xu says, “You’re able to create something that’s just as, if not more, desirable—using half the material.”

3 hours ago
Why Democrats are retrofitting their old logos for the Maine Senate race

Why Democrats are retrofitting their old logos for the Maine Senate race

On July 10, after allegations of sexual assault and mistreatment of women, former nominee Graham Platner formally withdrew from the U.S. Senate race in Maine. To pick a last-minute replacement, Maine Democrats are holding a nominating convention on July 25 in time for a state deadline to put forward a new candidate who will face off against Republican Senator Susan Collins in November. With no time to spare, would-be nominees are retrofitting old logos for the new campaign. There’s not much time for newcomers or learning on the job, and many of the candidates who have so far thrown their hat in the ring have run for office recently. That means they already have campaign logos, signs, and sticker designs ready to go. [Image: shahformaine.com] Nirav Shah, a former director of the Maine Center for Disease Control and Prevention, was the runner-up in Maine’s Democratic primary for governor in June. He was the first candidate to collect enough signatures to enter this month’s Senate race; he’s keeping green and white as campaign colors and a logo that makes the letter A in his last name from a pine tree, a Maine state symbol. Shah’s supporters recently took scissors to his old stickers, cutting off the bottom half that said “For Governor.” According to NBC News, supporters are also retrofitting campaign signs by taping over his old signs to write in “Senate” instead of “Governor.” Democrat Shenna Bellows launched an unsuccessful bid for Senator Susan Collins’s seat back in 2013. She is one of several candidates now vying for her party’s nomination. [Photo: John Patriquin/Portland Press Herald/Getty Images] Maine Secretary of State Shenna Bellows, who ran against Collins in 2014 and lost handily in a year less favorable to Democrats, dusted off her 12-year-old U.S. Senate campaign logo for another run. When Bellows ran unsuccessfully for the state’s governorship earlier this year, she used a logo that included a map of Maine and wrote out her name in a tall, sans-serif font. [Images: bellowsformaine.com] Her new Senate campaign logo is a retread of her 2014 mark: Her name is in all-caps italics in white on a blue background, with a thin, horizontal orange line underneath and “U.S. Senate” written below. It’s minimal and to the point. (Back in 2014, long before he became the mayor of New York City, Zohran Mamdani had a Bellows for Senate laptop sticker.) Other candidates are able to reuse old logos because their designs were generic to begin with. Former Maine state Senator Troy Jackson ran for governor earlier this year with a red, white, and blue logo that filled in the counter of the letter A in his last name with a Maine state map. It said “Troy Jackson for Maine,” instead of for the office he was running for, so no edits are needed. [Image: jacksonformaine.com] The same goes for Jordan Wood, a former staffer for Democratic Representative Katie Porter of California. Wood briefly ran for Senate before dropping out to run unsuccessfully for a U.S. House seat in Maine last month. Perhaps the third time’s the charm for the “Jordan for Maine” logo now that he’s running for Senate again. [Image: electjordan.com] “We were previously a candidate in the Senate race and are using our original campaign branding as a result,” a spokesperson for Wood’s campaign tells Fast Company. Democrats did something similar in 2024 when then-President Joe Biden dropped out of the race and then-Vice President Kamala Harris took his place. Supporters lopped off the top half of Biden-Harris logo yard signs or brought back old “Kamala Harris for the People” ephemera from her 2020 presidential bid until a new logo and merch could be designed. Without time for a full-scale creative and brand design process, last-minute campaigns like those for Maine Democrats are inherently resourceful and makeshift. This is no time for a rebrand. You work with what you’ve got. Voters are now deciding among a field of candidates who already have campaign experience under their belts—and the branding to prove it.

3 hours ago

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The FCC just approved a test of a giant mirror in space

The FCC just approved a test of a giant mirror in space

The Federal Communications Commission has given a California startup permission to launch and test a satellite that would use a giant mirror to reflect sunlight back to Earth after sunset, despite objections from astronomers, wildlife experts and others who say the plan could disrupt scientific research and interfere with the sleep patterns of living organisms. Reflect Orbital plans to launch the satellite, equipped with a 60-foot-wide mirror that is actually a thin-film reflector, into low Earth orbit later this year. Ultimately, the company hopes to send as many as 50,000 mirrors into orbit. They would reflect sunlight to power solar farms, illuminate city streets and assist rescue workers. “Reflect Orbital’s demonstration satellite is an example of a potentially groundbreaking technology,” the commission wrote in its order granting the license. Scientists, however, say the technology could have serious side effects. In a June letter to the FCC, the American Astronomical Society said the mirrors could compromise the work of federally funded astronomical facilities. Astronomers rely on dark skies to see deeper into space, and amateur stargazers could face disruptions as well. The group also warned that the project could temporarily cause “flash blinding” among airline pilots and nighttime drivers. Other scientists caution that the project could interfere with circadian rhythms, which humans and animals rely on to know when to sleep or migrate and plants use to know when to bloom. In total, the FCC received more than 1,800 public comments on the application, most of them negative. “It is clear that the activities that Reflect Orbital is proposing will have an impact on the Earth environment, including on human health, agriculture, and wildlife, in addition to astronomy,” the American Astronomical Society wrote. The FCC dismissed those warnings, describing the concerns as “hypothetical” and saying that activities in space are not subject to environmental laws. “Even if the commission had authority to review and condition these operations (which it does not), these harms are unlikely to occur,” the agency wrote. Although the FCC has so far approved only one satellite, Reflect Orbital is already planning larger space mirrors. The biggest currently proposed would span 180 feet and shine the equivalent of 100 full moons back toward Earth. The company hopes to launch 1,000 satellite mirrors by the end of 2028 and another 5,000 by 2030. Reflect Orbital argues that the satellites could reduce fossil-fuel use by extending the hours during which solar farms can generate power, potentially helping to slow climate change. “We’re grateful to the FCC for recognizing the importance of testing novel technologies in space,” Ben Nowack, chief executive of Reflect Orbital, said in a statement sent to Fast Company. “This license is the first step toward rigorously testing our technology’s efficacy and the safeguards we have developed.” The FCC said astronomers and others could raise their concerns again if Reflect Orbital submits a future application to launch additional satellites. Beyond the potential effects on astronomers, pilots, plants and animals, scientists warn that adding thousands of satellites would worsen the growing problem of orbital debris. At a June 4 roundtable hosted by the National Academies, Tony Tyson, a distinguished research professor at the University of California, Davis, and chief scientist of the Vera C. Rubin Observatory, said Reflect Orbital’s plans were “even crazier” than the proliferation of broadband satellites operated by companies such as SpaceX and Amazon. Tyson also raised concerns that the mirrors’ thin-film reflectors could scatter sunlight across a broad area rather than direct it precisely toward a target. “Imagine the sky full of moons,” he said.

3 hours ago
These four skills can help you fulfill your purpose—and anyone can master them

These four skills can help you fulfill your purpose—and anyone can master them

In a recent opinion piece for Fast Company, I wrote about five qualities that will distinguish exceptional leaders in the age of artificial intelligence: IQ, EQ, TQ, WQ, and VQ. Intelligence Quotient helps us understand. Emotional Quotient helps us connect. Trust Quotient helps us build relationships. Work Ethic Quotient helps us execute. Vision Quotient helps us see around corners. After the piece was published, I found myself wrestling with a different question: The Five Quotients help explain capability. But what explains choice? What causes someone to use those gifts in the first place? Why do some people see possibility where others see obstacles? Why do some people lean into uncertainty while others retreat from it? Why do some people help invent the future while others simply inherit it? I have come to believe the answer lies in four disciplines that are often misunderstood: Directed curiosity. Directed optimism. Directed courage. Directed gratitude. Directed Choices Most people think of these qualities as personality traits or capabilities. I believe they are something different. They are choices. They are disciplines. They are muscles that strengthen through use and weaken through neglect. More specifically, they are directed choices. That distinction matters. If these qualities are gifts bestowed at birth, then some people are fortunate enough to possess them and others are not. But if they are disciplines, they become available to all of us. And during periods of uncertainty, they become leadership responsibilities. The Five Quotients help explain what leaders are capable of in this era. Purpose explains where those capabilities are directed. Without purpose, intelligence can manipulate. Vision can mislead. Work ethic can accelerate the wrong mission. Purpose is the compass. The disciplines are the fuel. The quotients are the tools. Lessons from Mandela I have long felt strongly about one of Nelson Mandela’s most famous observations. I met Mandela in 1990, and his life has remained for me one of the clearest examples of moral courage directed toward a larger purpose. Recently, over breakfast, my friend Jerry Inzerillo—who knew Mandela and helped manage his inauguration as president of South Africa—and I were discussing my piece on IQ, EQ, TQ, WQ, and VQ. Our conversation turned to Mandela and his leadership, and it brought me back to that enduring lesson: “Courage is not the absence of fear. It is the triumph over it.” As Jerry pointed out, Mandela was not describing fearlessness. He was describing direction. He was describing the conscious decision to move forward despite fear. The courageous person is not the one who feels no fear. The courageous person is the one who decides that fear will not determine what happens next. The same principle applies to curiosity, optimism, and gratitude. Viktor Frankl, who understood human resilience as well as anyone, wrote that “between stimulus and response there is a space. In that space is our power to choose our response.” That space is where leadership lives. That space is where direction lives. That space is where curiosity, optimism, courage, and gratitude become choices rather than reactions. Curiosity is not merely asking questions. Directed curiosity is asking questions that matter. Benjamin Franklin looked at a lightning storm and wondered whether something dangerous and mysterious might also be understood and harnessed. The Wright brothers looked at the sky and wondered whether human beings might someday fly. Every meaningful discovery begins with a question. But not every question changes the world. Directed curiosity does. Choosing to believe Optimism is also often misunderstood. Many people confuse optimism with positivity. They are not the same thing. Positivity is a mood. Directed optimism is a discipline. It is the decision to believe that progress is possible even when certainty is unavailable. When President Kennedy challenged America in 1961 to land a man on the moon and return him safely to Earth before the end of the decade, the technology required to accomplish that goal did not yet exist. The road map had not been drawn. The capability had not been built. The outcome was anything but certain. What existed was vision. And belief. The same was true of the scientists who spent decades pursuing messenger RNA technology despite skepticism, setbacks, and repeated failures. History’s greatest advances rarely begin with certainty. They begin with belief. Yet curiosity and optimism alone are insufficient. At some point, someone must act. The role of courage That is where courage enters. Every inventor, entrepreneur, scientist, explorer, reformer, and builder eventually encounters a moment when knowledge runs out and uncertainty begins. That is the moment courage becomes indispensable. Not because fear disappears. But because purpose becomes greater than fear. Curiosity asks, “What if?” Optimism replies, “Perhaps!” Courage says, “Let’s begin.” And then there is gratitude, perhaps the most overlooked leadership discipline of all. Gratitude reminds us that none of us arrive alone. We inherit knowledge we did not discover. Institutions we did not build. Freedoms we did not secure. Opportunities we did not create. Gratitude tempers ego. It creates humility. And humility often becomes the foundation upon which curiosity, optimism, and courage flourish. Jane Goodall has reminded us that “what you do makes a difference, and you have to decide what kind of difference you want to make.” That decision is ultimately what directed curiosity, optimism, courage, and gratitude are all about. They are not ends in themselves. They are forces that help us live closer to our potential and contribute to something larger than ourselves. Vision Quotient may be the rarest of the five quotients because it allows people to see possibilities others miss. But vision alone changes nothing. History is filled with people who saw the future. The people who shaped the future were the ones who combined vision with directed curiosity, directed optimism, directed courage, and directed gratitude. Who changes history The people who change history are often not those who see what no one else can see. They are the people who see what everyone else sees and notice something different. Today, these ideas feel especially important. Artificial intelligence is transforming nearly every aspect of society. Machines are becoming increasingly capable of processing information, recognizing patterns, generating content, and accelerating productivity. These developments are extraordinary. But they also raise an important question: What remains uniquely human? I believe the answer is found in our ability to direct our own development. AI can process information. AI can recognize patterns. AI can generate answers. But it cannot choose curiosity. It cannot choose optimism. It cannot choose courage. It cannot choose gratitude. Most importantly, it cannot choose purpose. Those choices remain ours. And they may become even more important in the years ahead. When organizations encounter uncertainty, disruption, technological change, crisis, competition, or transformation, leaders face a choice. They can amplify fear. Or they can create belief. Fear rarely creates action. Fear more often creates hesitation, second-guessing, paralysis, and retreat. Belief creates movement. Belief creates resilience. Belief creates progress. Perhaps the highest purpose of leadership is helping people live closer to their full potential. The Five Quotients describe what we are capable of. Directed curiosity, directed optimism, directed courage, and directed gratitude determine whether those capabilities are put to work. Purpose gives them direction. The story of human progress has always been the story of people choosing possibility over inevitability, courage over fear, curiosity over complacency, and gratitude over cynicism. The leaders who shape the future will not be those who wait for certainty. They will be those who choose courage before certainty arrives. They will be those who choose optimism while uncertainty still exists. They will be those who remain curious when others stop asking questions. They will be those who remain grateful for the opportunity to contribute. The future belongs to people willing to direct all four. And the people who create that future are rarely distinguished by what they possess. They are distinguished by what they choose to become.

3 hours ago
Waymo’s July 4 chaos in San Francisco raises new questions about how robotaxis can work at scale

Waymo’s July 4 chaos in San Francisco raises new questions about how robotaxis can work at scale

Call it AV déjà vu: Once again, Waymo’s performance during a major disruption is raising questions about whether it is ready to operate at scale. Over the July 4 weekend, Waymo vehicles clogged San Francisco streets near the city’s fireworks celebration. A string of robotaxis, apparently unaware of event-related road closures, worsened the already severe gridlock. The batteries in some of the autonomous vehicles died, requiring them to be towed. At least one Waymo drove straight through a firework. Much of the chaos was documented in videos posted online. Waymo says many of its vehicles were able to navigate away once the congestion cleared. While the cars are trained to respond to fireworks, the company says it is continuing to learn from what happened over the weekend. Waymo said some road closures had not been communicated and pointed to its successful operations during other large events, including the Super Bowl in Santa Clara and the SXSW festival in Austin. “Our priority is keeping San Francisco moving safely, especially during major city celebrations. On July 4th, extreme traffic congestion in Northern San Francisco disrupted normal operations for several Waymo vehicles,” Waymo said in a statement. “In coordination with local authorities and emergency services, our roadside assistance team worked quickly to clear our vehicles from the area. Our team is always evaluating ways to strengthen Waymo’s resilience in major traffic disruptions.” Still, one San Francisco city official tells Fast Company that local government officials remain frustrated with how Waymo vehicles perform during chaotic events. The official said Waymo should have been aware of the road closure and questioned whether a new emergency-response arrangement implemented after last December’s blackout was sufficient. Under that plan, a Waymo employee is stationed at the city’s emergency response department. (Waymo said an employee was on site July 4 and served as a key resource during the event.) Self-driving in circles Waymo vehicles are undoubtedly impressive. Hail one of the company’s self-driving cars, and you might find yourself stunned by how smoothly it navigates city streets and stops for cyclists and other vehicles, all while the steering wheel turns and the driver’s seat remains empty. But as Waymo has become a more prominent presence on city roads—and as the company has removed precautionary safety drivers and transitioned to fully driverless operations—we’re now getting a clearer look at how the cars perform in the real world. A string of recent incidents has raised concerns about how Waymo vehicles operate during large, chaotic public events and emergencies. A recent San Francisco blackout left Waymos stalled throughout the city. In March, several of the company’s vehicles blocked public safety workers responding to a shooting in Austin. Fast Company has previously reported on the strain that Waymo, and particularly its stalled vehicles, has placed on police and transportation officials. To be sure, the problems Waymo encountered on July 4 do not necessarily negate the potential safety benefits of self-driving cars. They do, however, underscore that vehicles operated by AI and managed as part of a fleet present their own coordination and safety challenges. A self-driving car might work well on an individual trip. That does not guarantee that a company like Waymo is ready to operate large numbers of them amid the disorder of a major city event. Jeff Tumlin, the former director of the San Francisco Municipal Transportation Agency, tells Fast Company that it’s “too early” for Waymo to be operating during large and chaotic events. Waymo’s driving software, he argues, is designed to come to a complete stop when it gets confused. When many vehicles stop simultaneously, they can create serious problems for city operations. “Cities have no ability to geo-fence off areas where we know AVs are going to face challenges,” he says. San Francisco cannot restrict the vehicles from those areas on its own, he adds, and cities receive no data on how AVs perform during major events. As a result, he adds, local officials have “very few tools” for identifying the conditions that pose the greatest challenges for AVs—or deciding how cities should invest to address them.

4 hours ago
Volkswagen is a case study in what’s wrong with many of our biggest industrial companies

Volkswagen is a case study in what’s wrong with many of our biggest industrial companies

If you want to understand why so many of our industrial giants are in trouble, you could do worse than study what is happening right now in Wolfsburg, Germany. Per The Wall Street Journal, Volkswagen’s leadership is reportedly weighing the most drastic restructuring in the company’s 89-year history: eliminating up to 100,000 jobs (roughly one in six global employees), winding down production at four German plants, and slashing investment by 15. The unions have promised to fight it “with all our might.” Shares of the automaker’s stock are trading at low levels not seen in 16 years. China, once VW’s profit engine, saw the company’s sales tumble 20 in a single quarter as BYD and its compatriots eat VW’s lunch. It is tempting to read this as a story about electric vehicles, or Chinese competition, or tariffs. Those are the proximate causes. The deeper story is that Volkswagen perfected a business model for an economy that is disappearing—and kept perfecting it long after the warning lights started flashing. The employment machine Volkswagen was never just a car company. It was deeply embedded in the socio-technological frameworks of its time. The concept of the “People’s Car” began in 1934 when Adolf Hitler commissioned Austrian engineer Ferdinand Porsche to design an affordable, mass-produced vehicle for the German public. The company was formally established on May 28, 1937, by the Nazi-controlled German Labor Front. It was, by design, a jobs machine, a vast, vertically integrated apparatus for converting steel, capital, and labor into mass-market vehicles and mass employment. Volkswagen makes its own components. It runs its own plants. It even makes the sausages served in the company cafeteria. It employs some 657,000 people, anchors entire regional economies, and operates under a governance structure in which the state of Lower Saxony and powerful works councils hold formal power. For decades, this was a strength: Scale bought cost advantage, integration bought quality control, and social embeddedness bought political protection and labor peace. Here is the problem. Every one of those advantages assumed a particular kind of world: a world of mass markets, where value lived in physical assets, where doing everything yourself was cheaper than coordinating with others, and where the winning move was to produce more of the same thing at ever-lower cost. That world is dematerializing before our eyes. Value has migrated from the physical to the intangible—from the engine block to the software stack, from the dealership to the data, from owning the asset to orchestrating the ecosystem. In the emerging economy, a car is less a product than a platform, and the companies winning in China (as well as from-scratch auto startup Tesla) understood this from the start. They don’t do everything themselves. They center on what matters and let ecosystems do the rest. Volkswagen, meanwhile, kept optimizing the machine. When your identity is “we make everything, everywhere, for everyone,” every proposal to stop doing something feels like betrayal. So nothing gets stopped. The result is a company that is simultaneously everywhere and centered nowhere. We are living through a turning point—literally The researcher Carlota Perez has shown that every technological revolution follows a similar arc: a frenzied installation period in which financial capital pours into the new infrastructure, then a crisis, then a turning point at which institutions reorganize around the new technologies, and finally, a potential golden age of deployment. By her framework, we are at precisely such a turning point now—the moment when the assumptions of the old production regime stop working and the logic of the new one takes over. Even the Bank for International Settlements, hardly a hotbed of revolutionary sentiment, used its most recent annual report to signal that the old macro-financial order is giving way to something structurally different. Turning points are brutal for incumbents built on the previous paradigm’s logic, because the very things that made them great—scale, integration, standardization, mass employment—flip from assets to liabilities. Volkswagen’s crisis is not a management failure in the ordinary sense. It is what it looks like when a mass-production-era institution collides with a dematerializing economy. The tragedy is that the collision was visible years in advance, and the company’s governance structure was purpose-built to prevent an effective response. Cost-cutting is not a strategy Which brings us to the restructuring plan. Cutting 100,000 jobs and closing four plants may well be necessary. It is emphatically not sufficient. A smaller version of the same machine is still the same machine. The question Volkswagen’s leaders should be asking is not “how much smaller do we need to be?” but “what is our center?” Strategic centering—the idea I’ve been developing in my forthcoming book—holds that in a dematerializing economy, sustainable advantage comes not from what you own or how big you are, but from a clear organizing center that does three things. First, it bounds your opportunity set, so you know which of the infinite possibilities in front of you are yours to pursue and which are not. Second, it resolves capital allocation, so resources flow to the center rather than being squandered across legacy commitments. And third, it enables permissionless action, so people deep in the organization can move at market speed because they know what the company is for, without waiting for a committee in Wolfsburg to bless every decision. Measured against those three functions, Volkswagen’s current predicament comes into sharp focus. Its opportunity set is unbounded—10 brands, every segment, every geography, every technology bet hedged. Its capital allocation is resolved politically rather than strategically, which is how you end up investing billions everywhere and decisively nowhere. And permissionless action is close to impossible in a structure where labor representatives, regional government, and family shareholders can each veto change. Contrast that with Bayer’s Dynamic Shared Ownership experiment, which—whatever its growing pains—at least recognizes that a 20th-century hierarchy cannot compete at 21st-century clock speed. What a centered Volkswagen might look like I don’t pretend to know what Volkswagen’s center should be—that is a choice only its leaders can make, and making it thoughtfully will be crucial if the company is to survive. But the reported plan to separate the core VW brand from the components business hints that someone in Wolfsburg is beginning to think this way. A centered Volkswagen might decide it is fundamentally a mobility software and experience company that contracts for manufacturing scale. Or a manufacturing ecosystem orchestrator that supplies the world’s carmakers, including Chinese ones. Or Europe’s champion of affordable electric mobility, full stop. Each of those is a defensible center. What is not defensible is trying to remain all of them at once, minus 100,000 people. The choice will be agonizing, because choosing a center means grieving for the things you will no longer be. That is why companies at turning points so often reach for the restructuring playbook instead: Head count is quantifiable; identity is not. But the arithmetic of cost-cutting only buys time. Boeing went from a centered engineering-excellence operation to one where cost-cutting and financial engineering dominated. The results were literally tragic. Volkswagen still has enormous assets: engineering depth, brand equity, manufacturing craft, and—not least—a crisis severe enough to make real change discussable. Turning points, for all their pain, are also the moments when institutions get to choose what they will become. The window is open in Wolfsburg. It will not stay open long.

4 hours ago
Why governing AI loops requires a corporate world model

Why governing AI loops requires a corporate world model

For the last few weeks, the AI conversation has started to move from prompts to loops. That is an important shift. A prompt asks for an answer. A loop creates behavior. It observes, acts, checks, retries, learns, and repeats. That is why the recent interest in “loop engineering” matters: it signals that the unit of AI value is no longer the isolated response, but the system that keeps improving through iteration. In a previous article, I argued that this makes corporate learning loops a governance issue. A loop can be wrong and disappear? No. That was the old world of prompts. A loop can be wrong and compound. It can optimize a metric, reshape a process, create incentives and slowly teach the organization to behave differently. But that argument leaves a deeper question: if loops need to be governed, what exactly governs them? The answer cannot simply be “humans.” Humans matter, but a human who approves isolated outputs cannot govern a machine-speed system that learns continuously. The answer cannot simply be “policies,” either. Policies written in documents do not automatically constrain adaptive behavior. Nor can the answer be “dashboards,” because dashboards usually show what has already happened, while loops are constantly changing what will happen next. To govern learning loops, a company needs something more fundamental: a model of itself. Loops need a map Every loop optimizes inside some understanding of the world. In a coding environment, that world may be the repository, the tests, the build system, the documentation, and the issue tracker. The loop writes code, checks whether tests pass, asks another agent to review the changes, and continues until the task is complete. That is useful. But a company is not a repository. A company is a system of customers, products, contracts, employees, suppliers, policies, permissions, incentives, processes, exceptions, risks, obligations, and outcomes. If an AI loop operates inside that environment without understanding its structure, it does not become corporate intelligence. It becomes automated local optimization. And local optimization is often the enemy of organizational intelligence. A sales loop may optimize conversion while damaging long-term trust. A support loop may optimize resolution time while increasing churn. A procurement loop may optimize price while weakening resilience. A hiring loop may optimize retention while reducing diversity of thought. A compliance loop may optimize risk reduction while paralyzing innovation. Each loop may look successful on its own metric. The company may still get worse. That is why governing loops require a map of the terrain in which those loops operate. Not a static org chart. Not a PowerPoint operating model. Not a list of applications. A living model of the company. Memory is not enough Much of today’s AI conversation uses the word “memory” too loosely. Memory matters. A system that forgets everything cannot govern anything. But memory by itself is not a model. A memory can tell you what happened. A model tells you what can happen, what should happen, what is allowed to happen, and what the consequences may be. This distinction is central. A corporate world model must represent more than past interactions. It must represent the entities the company acts upon, the states those entities can occupy, the relationships between them, the permissions that constrain action, the processes that transform state, the metrics that define success, and the dependencies that make one action affect another. A customer is not just a text fragment in a CRM note. A contract is not just a document. A refund is not just an event. A risk threshold is not just a sentence in a policy manual. A workflow is not just a sequence of tasks. These are structured objects inside the company’s reality. This is why the shift from prompt engineering to context engineering is significant. Anthropic’s engineering team has argued that the challenge is no longer just how to phrase instructions, but how to manage the context surrounding the model: tools, external data, message history, instructions, and environment. That is a useful step. But for enterprise AI, context must go even further. It must become a structured model of organizational reality. Without that, every loop has to reconstruct the company from fragments. And when every loop reconstructs the company separately, governance becomes impossible. The world model is the governance layer AI governance today is mostly written as if organizations were governing tools: a model is assessed. A use case is approved. A risk level is assigned. A compliance process is documented. The system goes live. That approach already struggles with agents. It becomes insufficient for learning loops. The NIST AI Risk Management Framework is organized around governing, mapping, measuring, and managing AI risks. The EU AI Act requires post-market monitoring for high-risk AI systems, including the collection and analysis of performance data throughout their lifetime. ISO/IEC 42001 defines requirements for establishing, maintaining, and continually improving an AI management system. The direction is clear: AI governance has to become continuous. But continuous governance cannot happen in the abstract. To govern, you have to know what the system is acting on, what state it is changing, which constraints apply, which objective it is pursuing, and how that action affects other parts of the organization. That is precisely what a corporate world model is for. It is not a digital twin in the narrow industrial sense, though it shares the same intuition: a model of a system that allows you to understand, simulate, and improve it. It is not a knowledge graph alone, though relationships matter. It is not a data lake, though data is essential. It is not a dashboard, though measurement is necessary. It is the structured representation that allows the company to ask: what does this loop think it is optimizing, where is it allowed to act, what does it know, what has it changed, and what else will be affected? In that sense, the corporate world model becomes the governance layer for adaptive systems. The problem is coherence The old enterprise software problem was integration: getting systems to exchange data. The new enterprise AI problem is coherence: getting learning systems to pursue compatible objectives. That is much, much harder. Two systems can be integrated and still work against each other. A CRM can talk to an ERP system. A support platform can synchronize with a billing platform. A marketing system can feed a data warehouse. But none of that guarantees that the organization is optimizing for the right thing. Learning loops make this problem sharper because they do not merely execute instructions. They adapt. If one loop learns to reduce support costs by shortening interactions, another loop may later discover that retention is falling among precisely the customers who were “efficiently” handled. If one loop learns to increase sales conversion through aggressive discounting, another may discover that margin quality is deteriorating. If one loop learns to hire for immediate productivity, another may discover that the company is losing adaptability. No single loop sees the whole. That is the point. A company cannot be governed as a collection of intelligent fragments. It needs a model of the whole system: not because every decision should be centralized, but because local learning must remain compatible with global intent. World models are moving beyond physics The phrase “world model” is usually associated with robotics, autonomous driving, or physical AI: systems that need an internal representation of the environment in order to anticipate consequences and act intelligently. That makes sense. A robot that moves through the physical world needs to know something about objects, space, causality, and time. But companies are worlds too. They are not physical worlds in the same sense, but they are operational worlds: partially observable, constantly changing, full of agents, constraints, dependencies, incentives, and delayed consequences. An AI system that acts inside a company without a model of that world is like a robot moving through a warehouse without spatial awareness. It may be powerful. It is not safe. This is why the success of reinforcement learning in systems such as AlphaZero and MuZero matters beyond games. The lesson is not that companies are games: they are not. The lesson is that intelligence becomes much more powerful when actions are connected to outcomes through feedback and when the system can learn which actions improve its position over time. Enterprise AI needs the same principle, but applied to organizational reality. Not just: what answer should the model generate? But: what action should the company take, through which process, under which constraints, toward which objective, and with what expected effect on the rest of the system? That requires a corporate world model. The company needs to know what it is The hardest part of this transition may be cultural, not technical. Most companies do not actually have a formal model of themselves. They have org charts, process diagrams, ERP configurations, CRM records, policy documents, data warehouses, dashboards, Slack channels, email archives, and thousands of implicit habits held together by people who know how things really work. That is not a world model. It is an archaeological site. Humans compensate for this because they carry context in their heads. A good manager knows which policy matters, which exception is safe, which customer relationship is fragile, which process is official but ignored, which metric is being gamed, which team is overloaded, and which apparent success is hiding future damage. But AI loops do not know any of that unless the organization makes it explicit. This is why so many enterprise AI deployments still require consultants, integrators, and forward-deployed engineers. Someone has to reconstruct the company for the AI system: what matters, what is connected, what is allowed, what counts as success, and where the hidden constraints are. That manual reconstruction is the sign of a missing platform layer. A corporate world model would make that reconstruction persistent, governed, and reusable. Every loop would not need to rediscover the company. Every new agent would not need to be individually briefed on organizational reality. Every workflow would not need to rebuild context from scratch. The company would finally become legible to its own AI systems. From governance documents to governed intelligence The next stage of enterprise AI will not be defined by the number of agents a company deploys. It will be defined by whether those agents and loops operate inside a coherent model of the company. That model must include memory, but not stop at memory. It must include data, but not reduce the company to data. It must include rules, but not confuse rules with governance. It must include objectives, but also understand conflicts between objectives. It must include humans, but not treat “human in the loop” as a magic phrase. The goal is not to remove human judgment. The goal is to make human judgment govern adaptive systems at the right level. Humans should define objectives, constraints, rights of appeal, escalation paths, acceptable trade-offs, and strategic priorities. Loops should operate within that perimeter. The corporate world model should make the perimeter explicit, observable, and revisable. That is how governance becomes executable. Not a PDF policy outside the system. Not a dashboard after the fact. Not a human rubber stamp at the end of a chain. But a structured model of what the company is, what it values, what it permits, what it is trying to become, and how its learning systems are allowed to move it there. The real risk is not that loops fail The obvious fear is that AI loops will fail. That will happen. Some will generate bad outputs. Some will make mistakes. Some will hallucinate, misclassify, overreach, or break. Those are real risks. But the deeper risk is that loops will succeed locally while making the company worse globally. They will optimize exactly what they were told to optimize. They will improve the metric. They will reduce the cost, increase the conversion, shorten the cycle, raise the score. And only later will the company discover that the optimization damaged something the loop could not see. That is why governance cannot stop at the loop. A loop needs an objective. A governed loop needs a context. A system of loops needs a world model. The future of enterprise AI will not be a collection of clever agents running everywhere. That is not intelligence. That is entropy with a user interface. The future will belong to companies that can make themselves legible to machines without surrendering judgment to machines: companies that can represent their processes, constraints, objectives, and institutional memory in a form that AI can act on, learn from, and remain accountable to. In other words, companies that can build a model of themselves. Because you cannot govern what you cannot represent. And you cannot optimize what you do not understand.

5 hours ago
People who get promoted do these things differently

People who get promoted do these things differently

One of the most common questions I get from young professionals is about promotions. How do you get noticed? What gets you ahead? How do you position yourself for bigger roles? I’ve watched lots of careers develop over the years. Some people rocket up the ladder while others with similar talent stay stuck. There’s a story in those differences. First, be excellent in your current role. I don’t care what it is. When I was a teenager, my first job was as an intern for a guy who owned a transportation company. One day I’d be painting the walls of a warehouse, the next I was getting him groceries. My mission was clear: paint those walls perfectly and make sure to get him exactly what he wanted from Kroger. Regardless of what your job is, be excellent at it. Nothing else matters if you haven’t gotten this right. This won’t guarantee a promotion, but failing at it promises you won’t get one. You can’t focus on the next thing without being great at the current one. Next, we have an uncomfortable truth. Your employer pays you to do your job, not to prepare you for the next one. That preparation is your responsibility. Learn what skills are needed to be excellent at the role you want and develop them on your own time, not during company working hours. The market rewards skills it values, not complaints about lack of opportunity. You want to become a better writer? Read and write every morning before work. A better speaker in front of a group? Go to improv classes on the weekend. The skills that create career value don’t always develop between the hours of 8 and 5. They’re built in the margins of life. The people who get promoted choose to do extra work and develop additional skills in their own free time. Third, become a surplus-value employee. Companies keep and promote people who create more value than they extract. This doesn’t only apply to revenue-generating roles such as product development or sales; it’s the same whether you work in marketing, human resources, inventory management, or building maintenance. Mentor others, solve problems before they become crises, and make the company culture better. When you consistently deliver multiple times what you cost, your promotion becomes a rational business decision. The people who get promoted fastest are busy doing excellent work, learning constantly, and making everyone around them better. The system works because excellence creates opportunity, skills create options, and value creation increases demand for you. During one of the meetings with my Learning Leader Circle a few years ago, we were focused on how we all can better manage up. A fellow member, Stephanie Wernick Barker, said, “Good news often. Bad news early. And never any surprises.” People hate uncertainty more than they hate bad news. Your boss can handle problems. What they can’t handle is being blindsided by something they should have known about weeks ago. The ability to manage relationships with those above you isn’t some corporate game. It’s a core skill that separates people who advance from those who stagnate, regardless of their technical abilities. When done well, it reduces friction, builds trust, and creates conditions where everyone can do their best work. This is a useful framework for communication that deserves a deeper look. Good news often: Sharing wins, progress, and positive developments regularly keeps your boss informed and builds confidence in your abilities. Don’t wait for formal reviews to highlight accomplishments. A quick email about meeting a milestone or receiving positive client feedback takes minimal effort but keeps your contributions visible. I liked sending these notes to my boss every Friday morning (or a few hours before our scheduled one-on-ones). Bad news early: Problems don’t get better with age. When issues arise, addressing them immediately gives your boss time to provide guidance, reallocate resources, or adjust expectations with their stakeholders. The alternative, hiding problems until they become big issues, erodes trust and creates unnecessary pressure for everyone. Never any surprises: Your boss hates surprises. Keep them informed about potential risks, changing timelines, or shifting priorities. Even seemingly small changes can have ripple effects throughout an organization that you might not be aware of. In addition to proactive communication, here are some other important ways to manage up: Learn Their Priorities and Pressures: Your boss has their own goals, constraints, and people to report to. Understanding their pain points allows you to frame your work in terms that matter to them. I once worked with a brilliant analyst who couldn’t understand why his meticulously researched reports weren’t appreciated. He was solving problems nobody asked him to solve. Once he started asking, “What decision does my boss need to make next month?” his work became better. Make Their Job Easier: The universal currency in all organizations is making someone else’s life better. Look for opportunities to lighten your manager’s load. This might mean handling routine tasks independently, preparing background information before meetings, or filtering information so they can focus on truly important decisions. Build Trust Through Reliability: Nothing builds credibility faster than consistently delivering on commitments. When you say something will be done by Friday, make sure it happens. If circumstances change, communicate proactively about adjustments to scope or timeline. Most trust isn’t built through grand gestures but through a hundred small promises kept. Manage Expectations: Be realistic about what you can accomplish and distinguish clearly between committed deliverables and aspirational goals. Humans tend to be optimistic about timeframes but pessimistic about outcomes. Reverse this tendency: Be conservative about when things will happen, but confident in your ability to deliver quality. Navigating Difficult Situations: Even the best manager-employee relationships face challenges. Here’s how to handle common difficult scenarios: When You Need Resources: Frame requests in terms of organizational goals rather than personal preferences. “To meet the quarterly target, we need additional design support” is more compelling than “I’m feeling overwhelmed and need help.” When You Make a Mistake: The difference between a good relationship and a poor one isn’t the absence of mistakes. It’s how they are handled. Own it completely, explain briefly what happened, present your plan to fix it, and share what you’ve learned so that it doesn’t happen again. Most bosses appreciate accountability more than perfection. When You Disagree With a Decision: Choose your battles carefully. For minor issues, sometimes it’s best to “disagree and commit.” For more significant concerns, present alternative viewpoints respectfully, backed by data when possible. Focus on shared goals rather than personal opinions. The Ultimate Goal: a True Partnership: The manager-employee relationship might be the most important dynamic in any organization. Your day-to-day experience at work, your opportunities, and your growth all flow through this relationship. When you get good at managing up, you stop seeing your boss as an obstacle to work around and start seeing them as a partner in getting things done. That shift changes everything about how you show up at work. And over time, you become the kind of person others actively want to work with and follow. Excerpt from The Price of Becoming: The Compounding Practices of High Performance © 2026 by Ryan Hawk. Published by Harper Edge, an imprint of HarperCollins LLC.

7 hours ago
Want better outcomes? Start making bigger asks

Want better outcomes? Start making bigger asks

One big consequence of our fear of embarrassment is that we’re almost all overly sensitive to rejection. We haven’t built up our acceptance-of-rejection muscles, and this is a major impediment to agency. I’ve found that the most efficient way to build these muscles is to make audacious requests. Most of us carefully assess which requests that we might make are acceptable and which go too far. We avoid any request that’s likely to generate social friction, behaving as if it would be immediately fatal to have a request denied. “I just moved to the city, want to show me your favorite lunch spot this weekend?” “No.” Zap, dead, via bolt of lightning. What a tragic and premature demise! We also really don’t like the feeling of being told “no,” of receiving what feels like a judgment that we’ve overestimated our worth. Hence, we opt out of requesting. Yet, simultaneously, we know that if we try to prevent ourselves from ever experiencing rejection, we miss out on many opportunities in life. If you are determined to avoid rejection wholesale, you will likely never: Ask someone on a date who might be out of your league.Ask someone in a field where you have no experience for help getting a job.Ask someone you admire for mentorship or feedback on your work.Ask someone to fund your startup, research, or art project.Negotiate for higher pay.Deepen a friendship or relationship by talking about risky subjects.Publish creative work on the internet. When we treat rejection as an existential hazard, we are limited to a predictable existence. A more helpful point of view is that rejection is a form of information. When you experience rejection, you’ve learned that a given request won’t work. That avenue is now shut off, freeing you to try something else. The high-agency mindset about rejection takes things even further. It says that you should seek out rejection, or at least welcome it. Congratulate yourself on hearing no. Remind yourself that if you don’t routinely encounter rejection, you are not asking for enough—you’re only making requests that are extremely likely to receive a yes. How do you get comfortable with the high-agency approach to rejection? Alas, it’s exposure all the way down. The more times you hear the word no and notice that it’s not lethal, the less aversion to rejection you’ll feel. You can speed this up by seeking it out. I suggest that you start by asking for a few unreasonable things. The Art and Science of Asking Big I believe in asking for “unreasonable” things for many reasons, beyond the emotional training. One: If you habitually avoid rejection, you are not going to be well calibrated about what’s reasonable—which might be more than you expect. When I started training myself to make bolder asks, I found it helpful to think of myself as wearing rejection-sensitivity goggles, which I had to figure out how to take off. The goggles limit agency in many ways. By trying to shield us from rejection, they steer us away from approaching people who could be of huge help. The goggles don’t just cause us to overrate the severity of rejection. They also make us overestimate how often we will be rejected. In a series of experiments, researchers Frank Flynn of Stanford and Vanessa Lake of Columbia University got students to ask strangers for help. Requests included “Can I borrow your cellphone?” and “Can you fill out this questionnaire for a study I’m doing?” They asked the students to predict how likely people would be to help, and found that they underestimated by 50—about twice as many people as expected agreed. Flynn and Lake concluded that the students displayed an “egocentric bias,” focusing too much on their own feelings. They imagined that because asking for things felt awkward, it must also be awkward for the people receiving the requests. But of course, it often feels pleasant to be asked for a favor that you can capably grant. We just forget this when we ask for favors, acting like we’re visiting a curse on an innocent person rather than giving someone an opportunity to demonstrate their capability and generosity. When we take off our goggles and start asking, we free ourselves from this bias. We’re then able to engage in a new, accurate training process, where asking for what seems unreasonable generates data about where the lines really are. Two: When you train yourself to be less afraid of rejection, you will find your requests are granted more often, because you’ll make them with greater confidence. Human beings are subject to emotional contagion. If you’re scared to ask someone for a favor, they will feel your fear and take it as a cue: They’re scared to ask this, so it must be a really big deal. I should say no! If you don’t signal fear, they’ll be more likely to consider the request reasonable. Three: Unreasonable requests can open the door to reasonable requests. This effect was documented in a classic study by Robert Cialdini. When the large request came first, the likelihood of the strangers agreeing to the smaller, fallback request tripled. According to Cialdini, it’s because of the social norm of reciprocity, which says that if someone makes a tacit concession (“You’re right, my ask was way too big”) we should reciprocate with a concession of our own. One great way to use this effect is in salary negotiations. Four: Unreasonable requests can be a filtering mechanism. The point is that most people will say no, but a few interested clients will say yes. It not only ensures you’re paid a “cheerful price” but also acts as a filter for the most motivated clients. Making a request that 99 of people will say no to is a fantastic way of finding the 1 of the population you’re most aligned with. Five: Unreasonable requests can lead to productive conversations. I think it’s fair to say that asking to be appointed to head an organization you’ve never worked for is flat-out unreasonable. Sending this, I knew the recipient might regard me as delusional. The recipient never responded directly to my email, but I still consider my message a success: A few months later, when the organization was spinning up another major project, she reached out to see if I was interested. Finally: Making requests helps build your muscle for imagination. When you start wondering what you could ask for, you build your imaginative muscles by asking the simple question, “What else is possible that might currently seem unreasonable?” Don’t Hedge or Hector A key thing about audacious asks is that you can’t do them hesitantly or halfway. Ask for what you want, straightforwardly. It’s also vital to take rejection gracefully. Be sure to say something to the effect of, “I understand, thank you for hearing me out.” If you sense a no isn’t necessarily the end of the conversation, you can ask: “Okay, I’m not trying to convince you to change your mind—but could you tell me why it’s a no for you?” This was a lesson that Jia Jiang learned in his one hundred days of rejection, and he ended up adopting it as a major principle: Don’t run, ask why. He found out what few bother to learn: that there is an enormous difference between our stories about why people turn us down and why they actually do. When making bold asks, keep in mind that agency is all about breaking through the fictitious barriers of your life and letting yourself be limited only by real barriers. A firm no is an actual barrier. Show respect for that, and tell yourself that you are now that much more aware of another limit, and free to pursue another ask. Excerpted from the book YOU CAN JUST DO THINGS by Cate Hall with Sasha Chapin. Copyright ©2026 by Caitlin Hall and Alexander Chapin. Used with permission of Harper Edge, an imprint of HarperCollins. All rights reserved.

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