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The fathers of Islamic science hold lessons for students: breadth over specialisation, and never let constraints get in the way
- by Mariam Sabri
It’s 9 a.m. Monday morning. To start his week, Lukas Weber, a 28-year-old copywriter for a mid-sized SaaS company, first checks on the small army of AI agents he has built and now manages. It’s been part of his daily routine since his company’s “AI-first” mandate at the start of the year.
His agents have been busy. One agent has pulled in all the industry news from the past week and combined it with customer calls to identify potential newsletter topics. That agent has since handed off its findings to a copywriting agent that has written drafts for Weber to review and send out. Another has identified prospective clients visiting the company website and, based on their activity, sent each a custom proposal. Another agent has analyzed the data from a recent campaign and, noticing some concerning trends, reached out to Weber to take a look. Yet another has responded to unread emails, and the drafts sit ready to review in Weber’s inbox.
“The expectation from our executives is I shouldn’t write at all,” says Weber, “I manage the agents and check their work before anything goes out. Our team has downsized this year, so I’ve become responsible for a wider range of our marketing activity, and the agents help me keep up.”
Weber is not unique. From HR to Sales to Customer Success to Engineering, his colleagues across the company start their days the same way: checking in on agents, reviewing their work, following up on their insights gathered and analyzed from across the organization. “It’s how everyone works now,” says Weber. “It’s only been a year, but I don’t know if I could do my job the old way.”
Across knowledge work companies, employees increasingly rely on armies, or swarms, of agents to execute their work, and they aren’t just helping humans execute administrative tasks: they’re writing code, developing marketing strategy, designing products, advising on client escalations, executing financial analysis, providing legal advice and coaching, and defining brand identities. An increasingly large portion of the cognitive load employees were paid to execute just a year ago is now outsourced to AI.
This is what every AI-bullish tech executive describes as what great looks like when it comes to AI adoption: everyone using AI for everything; human knowledge workers sitting above teams of agents all built on the same small ecosystem of LLMs.
And it’s not just Weber’s company. It’s also their competitors, who are checking on their AI agents built to execute similar tasks. And those agents are all built on the same small ecosystem of LLMs as Weber’s.
It’s a funny predicament: all day, humans across the same industry — ostensibly hired for their unique critical thinking and experience — collectively pump every thought, business challenge, idea and solution through the same set of AI tools. Almost as if all these companies have simultaneously hired the same single employee: Mr. Claude (or one of his contemporaries).
This vision of the future, in which as much work as possible is outsourced to AI, promises the ability to move faster, iterate more quickly and require less human staff. But nothing in life comes free. For companies that indiscriminately push the “AI for everything strategy,” real-world consequences lurk. Because when every company in an industry leverages essentially the same single brain indiscriminately, competitive differentiation erodes. The value proposition of companies paid for solutions only they can provide collapses. And how and where value is generated shifts in ways that fundamentally change the landscape of human work.
And it threatens to impact everyone.
Oops, I Thought It Again
The rise of Claude and ChatGPT inside knowledge work has resulted in an unlikely victim: the em dash. Beloved by writers, LLMs use them ad nauseam, and they have quickly become markers of “AI slop.” Sad as it is for those who loved them, the em dash’s sudden prominence in all products of knowledge work also hints at a brewing phenomenon inside knowledge-work organizations: intellectual homogenization.
We’ve seen technology drive global homogenization before. Last time around, social media algorithms collapsed the physical world into a unified, generic aesthetic. In his 2016 article “Welcome to Airspace” and book “Filterworld: How Algorithms Flattened Culture,” Kyle Chayka described the aesthetic homogenization of coffee shops, bars, hotels, and Airbnbs, from Berlin to Brooklyn.
“ll day, humans across the same industry — ostensibly hired for their unique critical thinking and experience — collectively pump every thought, business challenge, idea and solution through the same set of AI tools.”
Chayka attributed this phenomenon to the rise of social platforms like Instagram and Foursquare (and their underlying algorithms), and their power to shape the physical world, calling these physical locations “Airspace”:
“It’s easy to see how social media shapes our interactions on the internet, through web browsers, feeds, and apps,” writes Chayka, “yet technology is also shaping the physical world, influencing the places we go and how we behave in areas of our lives that didn’t heretofore seem so digital …We could call this strange geography created by technology ‘AirSpace.’ It’s the realm of coffee shops, bars, startup offices, and co-live/work spaces that share the same hallmarks everywhere you go: a profusion of symbols of comfort and quality, at least to a certain connoisseurial mindset. Minimalist furniture. Craft beer and avocado toast. Reclaimed wood. Industrial lighting. Cortados. Fast internet. The homogeneity of these spaces means that traveling between them is frictionless…changing places can be as painless as reloading a website. You might not even realize you’re not where you started.”
A decade later, Airspace — or what I call, Airbrain — is coming for knowledge work. Like the coffee shops of 2016, widespread AI adoption is generating a massive amount of content and products that share an eerie sameness. Newsletters, websites and ads across competitors and industries are converging on the same Claude-aesthetic, the same tone of voice and the same way of articulating ideas. Most notably missing are the idiosyncratic markers of a human author; LLMs have processed them out to create a generic bleh, with one company’s output increasingly indistinguishable from another.
This inevitability should not be surprising. LLMs are prediction machines at the end of the day, taking a statistical guess at the likeliest next word, or token. In their paper “Large Language Models Are Homogeneously Creative” (PNAS Nexus, 2026), Duke researcher Emily Wenger and Technion cognitive scientist Yoed Kenett tested 22 commercial LLMs against more than 100 humans on standard creativity tasks. Individual models sometimes outperformed individual people. But as a population, the LLMs converged on strikingly similar answers, while the humans didn’t. “Overreliance on these tools,” Wenger concluded, “will smooth the world’s work toward the same underlying set of words or grammar, tending to make writing all look the same.”
OK, you might say. Take AI away from the marketing team. Problem solved.
But AI isn’t only generating Workslop. It is changing how employees think through problems and arrive at solutions. Rather than relying on their own skills or working through problems with other humans, employees in every company are increasingly asking AI to execute broader, higher-level tasks: solutions to business challenges, sales coaching, marketing campaign strategy and stakeholder management. The types of tasks that form the foundation of a company’s vision, direction, how teams function and how it is perceived in its market.
Does it enable companies to move faster? Maybe. They can certainly deliver more output.
But not all efficiency is created equal, a lesson the Army Corps of Engineers, back in 1962 in central Florida, learned the hard way.
Make it More Efficient …Wait! Not Like That!
In 1954, the Army Corps of Engineers had an idea. The Kissimmee River, in central Florida, kept flooding the land around it. They identified the problem: 103 miles of pesky river bends. How inefficient! So between 1962 and 1971, they straightened the river and dredged it to 30 feet deep.
The flooding stopped, but removing the river’s curves also reduced the oxygen in the water, allowing nutrient pollution to settle and accumulate instead of flushing through. The disappearance of this natural silt-trapping function sent sediment barreling downstream into Lake Okeechobee instead. Nearly 35,000 acres of floodplain habitat were destroyed, and roughly 90% of the wintering waterfowl population was wiped out. A once-renowned bass fishery was now replaced with rough fish like bowfin and gar.
And so, starting in 1999, work began to add back 44 miles of bends to the Kissimmee. The project wrapped in 2021 and cost more than $1 billion.
Like Kissimmee, work within organizations follows a particular flow. In the pre-AI days, it worked something like this: an employee had an idea; they’d share it with critical stakeholders who would evaluate it and give feedback; it would be refined further; and then, if approved, resources would be allocated to execute it.
This process is not very efficient. It takes time. And many meetings. With humans.
“Newsletters, websites and ads across competitors and industries are converging on the same Claude-aesthetic, the same tone of voice and the same way of articulating ideas.”
The excitement around AI was partially related to its potential to eliminate this meandering approach to executing work. Armed with AI, every employee could design, code or execute financial analysis. The process of innovation and iteration could be streamlined. The bends could be straightened. And so, in organizations with high levels of AI use, individual contributors — freed from the constraints of their own skills and the need to collaborate with others — began scurrying away to build and execute work — vibe-coding apps, building dashboards, doing data analysis — in isolation. No more meetings, idea sessions and alignment required.
Projects that previously required many weeks or specialists can be executed by a single person or small groups in hours or days. Gone are the endless meetings. People increasingly work, like Weber, above AI agents engaged in a broadening set of tasks. The result is a transformation of knowledge work into something more akin to manufacturing, with humans operating as the foremen keeping an eye on the metaphorical assembly line.
But manufacturing is no place for innovation. No one wants the person on the Ford assembly line coming up with new ways of screwing in the driver’s seat or preaching the value of “failing fast” as he installs the brake pads. Like the Kissimmee River, those meandering bends in the conception and execution of work removed by AI served a critical purpose. Manually developing ideas helped refine and nurture innovation through varying perspectives. They helped ensure that the end solution, whatever it may be, was unique to that organization and the group of people who worked on it.
Ultimately, an organization’s Unique Selling Points are the aggregate unique talents of all the people they hire. Building an organization with the right combination of people is, and always has been, the ultimate moat. To then shove this all through AI is to filter out all the talent you’ve paid for and your customers expect to benefit from.
In his MIT Sloan Management Review article, Jay Barney, a strategic management professor at the University of Utah David Eccles School of Business, and his co-authors argue that AI can offer, at best, a short-term competitive work advantage. “How can AI be the centerpiece of a sustained competitive advantage when everyone has it?” they ask. “We argue that it simply cannot. The value that AI unlocks will be unlocked for all. … Far from being a source of differentiation, artificial intelligence will be a source of homogenization.”
There’s a reason for urgency here: A 2026 Nature report found it takes shockingly little time for skills to atrophy once AI has been introduced. This coincides with the anecdotal feeling knowledge workers increasingly report around AI use and it making them feel, well, dumber. In a 2026 survey of 2,500 employees, 39% said their reliance on AI was actively making them less intelligent. In this way, airbrain also captures how AI use is eroding the cognitive skills people were hired for in the first place. This should concern organizations: when they eventually wish to return to human-generated solutions and content, they might find they need time to rebuild the skills that most directly contributed to their differentiation in the first place.
This is why the organizations that were quickest to adopt AI without limits should begin preparing for a more targeted, thoughtful approach. In some cases, this will mean leaning harder into AI. In others, it will mean building back (some of) the sweeping bends that birthed the innovations that got them to where they were before the AI era.
The Zag: A Post-AI World
There’s no doubt that airspace continues to exist. Researchers recently tested whether people could identify the location of a coffee shop from a photo alone; not one participant correctly placed all six, and when asked to distinguish Chicago from San Francisco, only 6% succeeded. Every unique, local cafe had converged on the same exposed brick, the same bearded barista, the same chalkboard menu. It was Chayka’s “tyranny of the algorithm,” now measured and quantified.
But sameness stopped paying. As one industry analysis put it, “to stand out in an increasingly competitive market, cafés are turning their attention to offering differentiated experiences,” because “the novelty of a product is now just as important as the product itself.” Once algorithms normalized the industry and cafés became interchangeable, uniqueness became the premium again, and the market obliged.
“A 2026 Nature report found it takes shockingly little time for skills to atrophy once AI has been introduced.”
Knowledge Work organizations will come to the same conclusion. Once every player in an industry is leveraging AI, any competitive advantage it offers is neutralized; they will return to uniqueness — in content, in strategy, in solutions — as a competitive advantage. Since LLMs do not do this differentiation well, companies will return to seeking humans who can offer the type of differentiation only humans can. And it will mark a return to valuing unfiltered human judgment, perspective and taste as the fundamental drivers of differentiation in the eyes of prospects and customers.
The big geese in consulting agree. In their recent 2026 Global Human Capital Trends report, Deloitte writes, “Competitive advantage is now primarily less driven by technology differentiation and more by cultivating the human edge. Technology — especially something as increasingly ubiquitous as AI — is replicable. People aren’t. Humans create competitive differentiation through adaptivity, creativity, and judgment amid uncertainty and change. When it comes to AI, value is unlocked through a reimagination of work that brings the best of humans and machines together in concert.” EY has even put a monetary incentive around human work, committing $100 million to “reward employees who exhibit adaptability, innovation, and judgment — traits the firm considers indispensable to translating AI adoption into meaningful results.”
But undoing the homogenizing effects of AI is easier said than done. It requires redesigning work in a way that takes advantage of both the benefits of AI and the power of talented humans, which demands thoughtful contemplation of foundational questions about work philosophy.
How can we leverage AI to allow our employees to spend less time on mundane, time-consuming tasks without risking the innovation, attention to craft, unique perspective and all the other critical ingredients that make a company successful? What does our use of AI on a particular task say about how we see that task strategically? It is the process of determining where the bends should be added back in the river of work.
Twin Peaks: A Bifurcation Of Knowledge Work
In their paper “Human-Provenance Verification should be Treated as Labor Infrastructure in AI-Saturated Markets” (Cambridge, 2026), Erin McGurk and David Khachaturov describe a barbell-shaped concentration of value in AI-saturated markets. Value, they claim, will cluster at two poles: on the far left of the barbell will be AI-produced assets with near-zero marginal cost. At the other far end will be products of verifiably human origin that sell at a premium. Most importantly, the middle will hollow out. Simply put, most organizations will be forced to choose between competing on super-cheap, high-volume AI production or low-volume, high-value craftwork. In some instances, they might do both, but still give up the middle.
Those “human in the loop” roles will mostly shift to lower-cost labor markets. This is logical: What sense does it make to pay a premium for oversight when a worker with clear instructions and a Claude account can produce or evaluate that same, homogenized output for a fraction of the cost?
Meanwhile, highly educated and skilled people will deliver value by crafting innovative solutions and content that drive business forward. These will be the tastemakers and the strategists responsible for staying ahead of the LLMs, or at least coming up with processes, products and designs beyond the airbrain output of the rest of their industry. Performance won’t be evaluated by output volume, but instead on quality and novelty.
If there’s any doubt about this vision of the future, look no further than the AI companies. Anthropic is hiring highly paid tastemakers, inoculating itself against the very airbrain it’s creating. ChatGPT is hiring an Employer Brand Manager, amongst other strategy-defining roles. They are hinting at the future shape of knowledge work and where value will be generated both by employees and their organizations. Look also at the continued trend of trying to combine developer, designer, and product manager into a single builder role. This collapses three jobs into one, expected to execute a taste-defining role, with AI filling the technical gaps.
If we start to see three roles combined into one, what happens to the other two?
The Next Big (Silly) AI Experiment
AI presents an existential challenge for employees and organizations alike. For some employees, AI may be helpful but also take away aspects of work that were once enjoyable.
“There are tasks AI handles now that I never wanted to do,” Weber tells me, “But I do miss the actual act of writing and having the product be uniquely mine.”
“For some employees, AI may be helpful but also take away aspects of work that were once enjoyable.”
Once AI saturates industries, the value of craft, individuality and ownership may return, albeit for a relatively small number of employees. Like the growing wealth gap across much of the world, these individuals will command an increasingly larger share of the salary pie.
The future of knowledge work, then, will be ruthlessly bifurcated.
AI will take care of the routine, repeatable, low-stakes work; human-in-the-loop work will be outsourced to lower-cost locations. The innovative work that actually drives businesses forward will return to being messy, inefficient and ugly at times, but a much smaller group of tastemakers will enjoy it, and visionaries will be paid a premium to deliver something uniquely human.
But the story is unlikely to end there.
It isn’t hard to imagine, as bifurcation sets in, an executive buzzing on Zyn and pounding cans of Liquid Death wondering: If our human employees are increasingly executing this lower-volume “tastemaker” work, do we still need full-time workers? Couldn’t we just ask our human staff who are reviewing agent tasks to work once or twice a week? And so on for many of the roles that comprise a knowledge work organization.
Oh, those spreadsheets with 70% reductions in salaries will be tantalizing. Too tantalizing for executives not to at least give an Uber-like approach to knowledge work employment a spin. In fact, it may already be happening. According to Upwork’s Future of Work Index 2026, “skilled freelancing rose from 28% of skilled knowledge workers in 2025 to 38% in 2026.” And it isn’t just employees: executives are also increasingly going fractional. By 2027, Gartner predicts that more than “30% of midsize enterprises will have at least one fractional executive on retainer.” Fractional work is already on the rise, and it will only get more enticing.
But even fractional tasks performed by workers rarely encompass the entirety of any role. There is also context, relationships, institutional knowledge and trust that don’t explicitly show up in the bottom line. It’s the critical difference between a task and a job, one that is illustrated beautifully by the Ogilvy vice chairman and world’s premier vape collector Rory Sutherland. In his book “Alchemy,” Sutherland describes the “doorman fallacy.” This is “what happens when your strategy becomes synonymous with cost-saving and efficiency; first you define a hotel doorman’s role as ‘opening the door’, then you replace his role with an automatic door-opening mechanism,” Sutherland writes. “The problem arises because opening the door is only the notional role of a doorman; his other, less definable sources of value lie in a multiplicity of other functions, in addition to door-opening: taxi-hailing, security, vagrant discouragement, customer recognition, as well as in signaling the status of the hotel. The doorman may actually increase what you can charge for a night’s stay in your hotel.”
In the near future, LinkedIn feeds and podcasts will increasingly be filled with executives touting how they combined AI and the gig economy to lower the overhead in their knowledge-work organizations. Long-term, it will fail, of course. In part for the same reason removing the doorman doesn’t produce savings. But also because an organization composed of overwhelmingly fractional workers isn’t an organization at all. (And perhaps neither is an organization that has outsourced too much of its thinking to LLMs). But this is a lesson many executives will likely learn only after people’s lives have been disrupted and businesses gutted.
The next AI era, then, will require far more thought than the blunt strategy of “AI for Everything.” Success will require a more deliberate approach about where and how this technology is integrated in an organization’s operations; which roles and tasks they deem strictly human; how AI can best support their people in doing the work that differentiates them from the rest of the market; how they carve out areas of their operations that are “AI-Free” to encourage ingenious, creative and original human thinking; and how to distinguish between the friction that should be eliminated and the friction that makes the best work possible.
For employees like Weber, the future is likely one with even more pressure and incentive to develop and demonstrate a distinctive taste, perspective, voice and personal brand. Success may not only bring a higher salary, but also a return to executing work that is valued because it is uniquely his — and human.
The post Now Entering: The Era Of Bleh appeared first on NOEMA.
