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What happens when you spend decades seeding salacious stories about evil lurking in the halls of power, demanding evidence to prove basic truths, and questioning the veracity of that evidence once its presented? Donald Trump is finding out. Over the last week, the president has been trying to fight his way out of a web of his own creation, as some of his truest followers in MAGA world call for the full release of the governments investigative files concerning convicted child sex offender Jeffrey Epstein. The outcry from Trump acolytes comes after the Department of Justice published a two-page memo earlier this month, stating that Epsteins supposed client list, which Attorney General Pam Bondi previously said was on her desk, didnt actually exist. Following a weeklong uproar from both the left and right, Trump finally called on a federal court judge to unseal the grand jury testimony related to Epsteins case. The Justice Department has also subpoenaed Epsteins associate, Ghislaine Maxwell, who is serving her own 20-year sentence for sex trafficking. But the moves have done little to quell the outrage from the right, particularly after House Speaker Mike Johnson sent the chamber into summer recess early this week to head off a vote on releasing the files. The move prompted fury from the partys MAGA wing. Crimes have been committed, Rep. Marjorie Taylor Greene of Georgia told reporters. If theres no justice and no accountability, people are going to get sick of it. As all this has played out, Trump has cast about for someone to blame, pointing the finger at Democrats and his PAST supporters for stoking the scandal. In truth, its Trump who is uniquely responsible for cultivating the culture of conspiracy in which hes now floundering. Credit where its due: Trumps long and well-documented history of conspiracy-mongering has been perhaps one of his greatest skills and has almost always worked out in his favor. His constant questioning of President Obamas birthplace was so successful that it transformed Trump, then a reality star and real estate mogul, into a cable news fixture. Later on, his success at convincing nearly three-quarters of Republicans that the 2020 election was stolen played no small role in securing his 2024 election victory. Even the speculation about which other A-listers were in Epsteins orbit were often fair game for Trump. In 2019, Trump fed rumors that the Clintons were somehow involved in Epsteins death by suicide in prison. Did Bill Clinton go to the island? That’s the question, Trump said at the time. Nevermind that Trump and Epstein were close friends or that he once told New York magazine that Epstein likes beautiful women as much as I do, and many of them are on the younger side. Trump is a devoted student of the Im rubber, youre glue school of politicsand for the most part, its worked. But now its Trump whos found himself stuck to Epstein, and he has no one to blame but himself. After all, it was Trump who taught his followers not to trust the abridged version of a story (see: Trumps campaign to secure Obamas long-form birth certificate in 2011). Now, it stands to reason those same people want more than a two-page summary of the DOJs Epstein investigation. And it was Trump who convinced a certain subset of the American electorate to scour video evidence for alleged election night aberrations in 2020. Is it any wonder theyre now spiraling over the missing minute (or minutes, according to Wired) in the video footage the government released of the night Epstein died? Meanwhile, the stories linking Trump to Epstein just keep growing. On Monday, The New York Times reported that one of Epsteins accusers encouraged the FBI to look into Trump as early as 1996. And The Wall Street Journal reported last week that Trump once sent Epstein a lewd birthday card, featuring a hand-drawn outline of a naked woman and allusions to their shared secrets. The Journal reported that the card is among the documents DOJ officials reviewed as part of the Epstein investigation. Trump has denied the story, calling the article fake news and has since sued the Journal for defamation. That controversy prompted some conservatives whod been critical of the Trump administrations approach to Epstein to leap to the presidents defense. But that reprieve may be short-lived. As one Trump ally, Mike Benz, said on Steve Bannons podcast over the weekend, You trained us to go after this issue.
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E-Commerce
When Microsoft announced that it would start evaluating employees on their AI usage, it sent a clear message: AI fluency is now part of the job. Business Insider recently reported that Microsoft was asking managers to track employee engagement with internal AI tools like Copilot and consider that usage in performance reviews. This signals that AI skills are becoming as measurable as teamwork or communication. AI is no longer optional in white-collar jobs. It now ranks alongside email, video calls, and spreadsheets as a workplace essential. However, unlike those tools, AI continues to occupy uncharted territory. Theres no standard for how to use it, no shared definition of what good looks like, and no clear line between acceptable and risky behavior. The technology is also evolving faster than most companies can keep up with. Many employers are encouraging or requiring the use of AI without offering the training or oversight to match. That mismatch is creating more than confusion. Its leading to misuse, uneven evaluations, and pressure on employees to guess their way through a system that doesnt even exist yet. Microsofts decision carries weight. When one of the worlds most powerful tech companies sets a precedent, others are likely to follow. But if companies adopt similar policies without fixing the missing guardrails, it can lead to misalignment and dysfunction. The consequences of throwing employees into the AI deep end Pushing AI adoption is smart, but leaving employees to figure it out alone isnt. Companies are asking workers to operate fast-changing, complex tools with little support. The results are messy. Many Gen Z professionals are already improvising in the absence of clear guidance. In a recent Resume Genius survey, 39% of Gen Z workers said theyve automated tasks without their managers approval. Another 28% admitted to submitting AI-generated work without disclosure. Nearly a third used AI in ways that might violate company policy, and 23% reported that using AI at work negatively affected their mental health. That pattern isnt just limited to Gen Z. A 2025 KPMG study of 48,000 workers across 47 countries found that 57% are hiding their AI use from managers. Most havent received formal training. Two-thirds dont verify AI outputs for accuracy, and more than half have already made AI-related mistakes on the job. Together, these findings point to workplaces where AI use is rising fast. But employees are making up the rules as they go, often under pressure and without a clear sense of whats safe, ethical, or expected. This kind of uncertainty isnt sustainable, especially as job expectations involve the use of AI. Requiring AI without structure is like handing out calculators in math class without ever teaching equations. Sure, the tools are powerful, but if you want good outcomes out of those tools, you need to teach people how to use them properly. Productivity is improving, but at what cost? AI can boost performance. That much is clear. A 2025 Harvard Business Review study found that generative AI improves both productivity and creativity. But it also uncovered a troubling side effect: employees felt less motivated and more disengaged when they used AI to complete tasks they once took pride in doing themselves. And burnout is rising. According to a July 2025 Upwork survey, employees who report the highest productivity gains from AI are also the most burned out. 88% of top AI users say theyre experiencing burnout, and theyre twice as likely to consider quitting compared to less productive peers. Many also feel disconnected from their companys AI strategy, with 62% reporting that they dont understand how their use of AI aligns with broader goals. It seems that the more AI increases productivity, the more it drains the people who use it. What happens when the rules are missing Right now, AI use at work is both essential and undefined. This creates three major risks for companies: Compliance breakdowns: Without clear policies, employees may expose sensitive data, rely on flawed outputs, or use AI in ways that open the door to legal risks. Subjective reviews: When using AI effectively becomes part of evaluations, but no standards exist, employees may be graded on personal bias instead of actual performance. Erosion of trust: Workers are left wondering whats okay and what isnt. Managers dont always know either. This results in second-guessing on all sides, which isnt ideal for anyone. What businesses need to do when it comes to AI The companies that thrive in this new era wont be the ones that push AI the fastest. Theyll be the ones that do three things well: Set clear policies: Define what responsible AI use looks like in your workplace. Spell out what your companies encourage, what you restrict, and where to ask for help. Offer practical, task-based training: Skip the generic webinars. Teach employees how to apply AI to their actual work, whether thats drafting policy language, summarizing customer feedback, or automating reports. Build real-time feedback loops: Hold regular check-ins to ask: Whats working? Whats unclear? What needs to evolve? AI is moving fast. Policies need to move with it. If AI is now part of the job, it needs its own set of guidelines. The companies that succeed wont be the ones who adopt it fastest, but the ones who build the right foundation and teach people how to use it well. Microsoft started the conversation. Now the rest of us need to define what responsible AI adoption looks like and put it into practice.
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E-Commerce
We still use industrial-age language to describe work: running like clockwork, tightening bolts, and burning the midnight oil. Those phrases made sense when productivity meant turning raw material into widgets. But in an era of climate disruption, AI acceleration, and record-high burnout, a purely mechanical model cant keep up. It drives us to measure hours instead of impact and speed instead of sustainability. Today, with artificial intelligence reshaping knowledge work and climate urgency redefining corporate responsibility, its time for a new vision of productivityone centered on human and planetary flourishing, not just output. The next evolution of work demands a shift from efficiency to emergence, from silos to systems, and from time-tracking to meaning-making. From Machines to Living Systems The metaphors we use drive the systems we design. Treat an organization like a machine and youll optimize for speed, control, and predictable outputs. Treat it like a living systemdynamic, interdependent, and regenerativeand a different design logic emerges. Heres what changes when we swap gears for genes: Sense-and-respond edges. Like cell membranes, customer-facing teams become sensors that provide real-time insights back to the core, allowing the entire organization to adapt quickly. Organizational metabolism. Energy (attention, data, and trust) is metabolized into creativity, innovation, and renewal, not just raw output. Cultural homeostasis. Healthy feedback loops foster psychological safety and inclusion, ensuring the system remains vibrant even as external conditions change. Seeing companies this way frees leaders from predicting the future; instead, they cultivate conditions in which emergent intelligence can thrive. Measuring Vitality, Not Just Time The hours worked show little correlation with the value created. What matters is employee vitality, the degree to which people feel rested, connected, and empowered to take risks and experiment. Global four-day-week pilots demonstrate the benefits: burnout dropped by 71%, stress decreased by 39%, carbon emissions are noticeably lower, and retention is markedly higher. Microsoft Japans Work-Life Choice Challenge, a four-day workweek pilot, proves this point. By closing offices every Friday (while maintaining pay), sales per employee increased by roughly 40%, energy use decreased by 23%, and printing costs dropped by 60%. The gains werent the result of heroic hustle; they were the fruit of redesigning the system itself. Letting AI Amplify Humanity AI can be the scaffolding for a more human-centric enterprise. Routine analysis that once devoured strategic attention now finishes in minutes. In my advisory work, I have seen leadership teams channel the freed-up bandwidth into scenario building, story crafting, and high-trust stakeholder dialogues. McKinsey estimates that generative AI could lift labor productivity growth by up to 3.4 percentage points per year through 2040. Those gains appear only when roles are deliberately reinvented, not merely automated. This isnt AI versus humans; its AI with humans. From Linear Growth to Regenerative GROWTH If the industrial era taught us to measure work in widgets and hours, the new era demands a more integrated lens, one that blends systems thinking, emotional intelligence, and regenerative design. To translate organism thinking into everyday practice, we developed the GROWTH frameworka six-step shift from extractive output to regenerative impact: G Generate New Metaphors: Swap factory for forest, and assembly line for ecosystem. Fresh language surfaces hidden assumptions and fresh design options. R Reimagine Human Roles: Let AI handle the rote tasks so people can focus on curiosity, synthesis, and relationship-building. O Optimize for Human Vitality: Prioritize rest, autonomy, and connection; vital people compound long-term value. W Work in Ecosystems: Replace silos with cross-functional guilds that share data, talent, and learning loops. Complexity is tamed through connection. T Track Whole Metrics: Pair profit with carbon avoided, bias reduced, and ideas shipped. What gets measured gets improved. H Harvest Regeneration: Design projects that return energy to the systememployee upskilling, circular supply chains, and community partnerships. Questions to Spark the Shift Changing how we think about productivity begins with changing our questions. Use the prompts below in leadership sessions, strategy retreats, or team huddles to surface hidden assumptions and align around a more human-centered, system-aware approach to work. Which metaphors dominate our language today, and what behaviors do they reinforce? Where could AI relieve cognitive or emotional load so humans can do higher-order work? What value are we creating that isnt yet on a scorecardculture, climate, social trust? If we trimmed hours by 10%, where would vitality and innovation likely rise? What small regenerative habitrepair, reuse, reflective pausecould we start this quarter? We stand at a crossroads. We can cling to mechanical models that view people as cogs and growth as linear output, or we can adopt a living-systems mindset where vitality, adaptability, and shared purpose define productivity. Redesigned productivity isnt about working less; its about working more wisely. The future wont be built by those who clock the longest hours. It will be built by those who design the richest conditions for creativity, connection, and contribution. The machine age is ending; the living-system era has begun. Lets grow accordingly.
Category:
E-Commerce
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