The EU’s tech sovereignty package targets cloud, chips, AI infrastructure, and open source as Europe tries to reduce foreign tech dependence.
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“As a senior executive, you get paid to make a small number of high-quality decisions,” the Amazon founder said.
OpenAI is expanding ChatGPT Lockdown Mode to more users, limiting web-connected tools to reduce the risks of prompt injection and data leakage.
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For some professions, “AI is coming for our jobs” is no longer a vague threat about future events. Timothy McKeon, who spent years translating to and from Irish for the European Union, knows this better than most. As machine translation has improved, the ability to produce a text that is “good enough” has taken a huge bite out of his livelihood—costing him roughly 70% of his income as his EU work dried up. “The more it learns, the more obsolete you become,” he told CNN. And McKeon is not an outlier. 43% of translators have seen their incomes drop thanks to the increasing presence of AI alternatives in the marketplace.
What is happening to translators is an early sign of an evolution that is now underway across the knowledge economy. For decades, much of the value produced by white-collar work rested on a straightforward proposition: you knew things or could find things or assemble things that most people could not, and others were willing to pay to gain the benefits of that knowledge. AI is collapsing the value of a broad swath of this market. In an increasing number of fields, a chatbot can now deliver in seconds work that is close to, or in some cases better than, that of an average professional. The bulk of the knowledge economy, the broad base of competent-but-unremarkable cognitive work, is being priced downward toward zero.
It is tempting to think that the threat stops at the door of the merely average —that deep, specialized expertise is safe in a way that ordinary competence is not. That is only half right. The useful question is no longer whether AI will reshape knowledge work; it plainly will. It is which kinds of knowing hold their value when the machine can do so much.
The way things were
For most of the modern era, your market value as a professional came from your stock of knowledge: the tax code you had memorized, the case law you could marshal, the market data you had at your fingertips, the language you had spent a decade learning to render fluently. The work was, in large part, knowing things other people did not and being paid to retrieve and apply them. AI has learned to imitate that work in an increasingly convincing way. A frontier Large Language Model has read more tax code, more case law, and more market reports than any individual ever could, and it can hand most of it back on demand, fluently and instantly.
The once-widespread idea that knowledge workers will be saved by the tendency of AI models to hallucinate is falling away. Once commonplace, hallucinations are becoming increasingly rare, and they can be mitigated in many contexts by effective prompting. Reliable LLM access isn’t quite free or frictionless, but when compared to human labor the cost is becoming negligible.
The natural move for many knowledge workers in the face of these developments is to retreat upmarket: cede the simple work to the machine and stake their future on depth. Specialized expertise, the thinking goes, is the high ground. And there is real evidence for this. Translators, for example, have found that the surviving work is migrating upward: the volume jobs have gone to the machine, but the literary translators and the high-stakes legal and diplomatic interpreters—the people whose errors carry real consequences—still find their phones ringing. The specialists look safe . . . for now. But the ground they are standing on is less solid than it appears, and the line between the work AI can take and the work it cannot is not where most people assume it to be.
Two kinds of knowing
The problem is that depth of this kind is only a temporary refuge. To a machine, rare knowledge is nothing special, and there is no reason it can’t drill down to it so long as it is made available in a recorded form. The obscure corner of tax law is, to an LLM, just another corner. To ensure that your knowledge holds a more enduring type of value, you can’t rely on depth or rarity. You need different types of knowledge altogether. Two stand out.
The first is contextual judgment. A seasoned consultant’s value was never just the industry detail in her head; it was knowing which detail mattered for this client or that board, which background fact guided how to read the problematic balance sheet, how to understand the half-articulated fear the CEO mentioned in passing. Deep expertise, however rare, involves reasoning over material that exists in the record (the obscure corner of tax law is written down somewhere), and that is something these models now do well.
Contextual judgment is different. The decisive cue—what this silence means, why this board will balk—isn’t something that’s in the record precisely because this situation has never arisen in quite this form before. This kind of judgment relies on something real but fleeting, something the individual reads from the room in that specific moment. That can’t be looked up, and current models are far less reliable at this type of inference than at the recorded-knowledge reasoning they have already mastered. It may not stay out of reach forever, but it is not the threat knowledge workers face today.
The second is procedural knowledge. Some philosophers make a useful distinction between “knowing that” and “knowing how.” You can know every proposition in every physics textbook and still be unable to keep your balance on a bicycle. You can absorb everything ever written about music theory and still not be able to play the violin.
The same holds in business. A comprehensive store of facts and opinions about leadership is not enough to make someone a great leader. Reading every book on negotiation doesn’t translate into the ability to hold your nerve, time the concession, and keep your footing when the other side pushes. This kind of knowing lives in the doing: it can be acquired only through practice and experience, and at the highest levels it is bound up with things—trust, authority, the ability to read and relate to other humans—that exist only between people. That is not a stock of facts anyone could hand you, and it is not work you can hand off without becoming the bottleneck you were trying to remove.
Neither of these types of knowledge can be downloaded. But both can be built deliberately. And that is where the serious effort of career development now belongs.
Building survivable knowledge
Here are three moves that can help put you on the right side of this historic change in what it means to be a knowledge worker.
- Own outcomes, not outputs. An AI model produces outputs: a draft, an analysis, an answer. So stop building your career around competing on this front. Audit what you’re actually paid for—your core value proposition—and strike everything that a good model can now do in minutes. What’s left are the outcomes only you can deliver: the messy problem carried from the initial diagnosis through to a result you can stand behind or the insight into what the client really needs that goes beyond what he says. Reorganize your role or your offer around these outcomes. Results—not a stock of facts—are your real moat.
- Build judgment in the room, not on the page. Situation-specific judgment can only be picked up firsthand by being present for consequential decisions and watching how they actually turn out. It resists mechanical replacement because what mattered in those rooms can never be fully summarized and passed into the kind of record an LLM can read. The people who advance fastest won’t be the ones who can store the most information, but the ones who find ways to improve their contextually grounded judgment.
- Delegate the routine; protect the practice. Procedural know-how lives in the doing, so the work you hand entirely to AI is work you stop getting better at. Push the genuinely rote tasks onto the model but keep doing the high-skill work yourself—the negotiation, the argument you think through—even when the model could turn out a passable version faster. Convenience now is paid for in capability later.
Conclusion
Timothy McKeon’s verdict about AI—the more it learns, the more obsolete you become—holds for certain types of knowledge, and those are the types that most professionals have built their careers around for decades. But there are other types of knowledge that are less vulnerable. Some may even be impervious to AI, at least in the forms available today. That kind of knowing can’t be downloaded. It is knowledge you embody rather than possess—earned in the doing, carried in the person, and yours in a way a stock of facts never was.
Russia’s Rassvet satellite internet network is expected to begin commercial service in 2027 as Moscow builds a Starlink alternative.
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Walmart CEO John Furner says wealthy customers are shopping at the budget grocery chain as high prices stretch family finances to a breaking point.
Even without a college degree, Costco CEO Ron Vachris rose from the warehouse floor to the corner office—guided by his father’s advice.
A couple of years ago, I started noticing a quiet anxiety beneath the surface of nearly every leadership conversation I was having. It wasn’t about talent pipelines or quarterly earnings. It was something more existential: the fear that in racing to adopt every new AI tool, organizations were inadvertently engineering the most human parts of their culture out of existence. The spreadsheets were getting smarter. The people felt less seen.
This tension sits at the heart of my new chapter, “Algorithms and Awe,” in the second edition of The Creativity Leap. What I’ve come to believe through years of working with executives, researchers, and entrepreneurs is that we are not living through a technology revolution. We are actually living through a human revolution. And the leaders who understand the difference will be the ones who define the next decade.
We are now in the imagination era
We have moved beyond the Information Age and are now firmly rooted in what I call the Imagination Era, a time when ideas and thinking differently are our primary currency. In this landscape, technology is not replacing our humanity; it is demanding that we deepen it. AI means nothing without your imagination. It is a starting point, a lever for building new possibilities, not an endpoint.
At the 2025 Adobe Summit, Adobe CEO Shantanu Narayen declared that “creativity is the new productivity.” I believe he’s right and the implications are profound! Success is no longer measured solely by speed or output. It is measured by our ability to forge emotional connections through imagination. The leaders who thrive won’t be those who automate the most. They’ll be those who imagine the best.
The ethics of ease
Every leap forward in technological convenience brings an ethical echo. I call our current moment the “Ethics of Ease”: the temptation to mistake creative convenience for creative progress. While technology’s promise is efficiency, creativity’s purpose remains meaning. And these two things are not the same.
This creates what I describe as a creative double bind: a simultaneous desire to embrace AI’s potential and a fear of being replaced by it. When we pursue ease without ethics, we erode the very wonder we seek to amplify. The antidote is what I call WonderRigor™: the alchemy that happens when technological innovation meets moral imagination. Wonder without rigor is fantasy. Rigor without wonder is bureaucracy. The sweet spot is where the most durable creative work gets done.
Trading fours with AI
The most useful metaphor I’ve found for describing the human-AI relationship is jazz: specifically, the practice of “trading fours,” in which musicians take turns improvising, four bars at a time. AI riffs, and we respond. We lead, and AI follows. Like great jazz, this collaboration requires a mastery of the rules, and the courage to bend them.
As machines handle the mechanical tasks of drafting and structuring, we reclaim the cognitive space to do what only humans can: make meaning, sense emotion, and weigh ethics. This reframing matters enormously for how we invest in our people and design our organizations. Instead of asking “How can AI make people more productive?” we must ask “How can AI help people flourish?” Flourishing means having the time to think deeply, move naturally, and rest intentionally… the three pillars of my MTR (Move. Think. Rest.) framework for human performance.
Awe is the competitive advantage you’re not tracking
AI is the new medium for knowledge work. But awe, that spark of curiosity and connection that stops you mid-sentence and makes you lean in, remains wholly human. You cannot automate wonder. You can only cultivate it or crowd it out.
Navigating the Imagination Era requires what I call the 3 I’s of creativity: inquiry, improvisation, and intuition. Together, they form our compass for deciding when to let algorithms lead and when to let human instinct steer. Inquiry keeps us asking better questions. Improvisation keeps us agile when the script runs out. Intuition gives us the pattern recognition that no training data can fully replicate.
Success in the Imagination Era will belong to those who practice wonder as their mindset and commit to rigor as their practice. These are not soft skills. They are strategic infrastructure.
You don’t adapt to the future. You compose it.
The leaders I most admire right now are not the ones who have the most sophisticated AI stack. They are the ones who have the clearest sense of what makes their organization irreducibly human, and who protect that fiercely while still embracing what technology makes possible.
When we pair the alchemy of algorithms with the discipline of awe, something remarkable happens. We don’t just adapt to the future, we compose it. We become the jazz musicians, not the backing track. We reclaim the creative agency that the industrial productivity model trained us to surrender.
The Imagination Era is not a threat to be managed. It is an invitation to lead differently, with more curiosity, more courage, and more wonder than we thought a business context could hold. The question is not whether you will respond to that invitation. The question is whether you will do so intentionally, or by default.
Adapted from The Creativity Leap (2nd edition, June 30, 2026)
Anthropic engineers are reportedly helping the NSA use Claude Mythos for cyber operations despite the Pentagon’s supply-chain risk label.
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Old roles are evolving—and new ones are emerging.
