Tag Archives: AI

Why an AI Soft Landing Might Look Like Victorian England

LAST UPDATED: April 18, 2026 at 3:29 PM

Why an AI Soft Landing Might Look Like Victorian England

by Braden Kelley and Art Inteligencia

The Mirage of the Post-Scarcity Utopia

For decades, the prevailing narrative surrounding artificial intelligence has been one of a post-scarcity “Star Trek” future. The logic was simple: as machines took over the labor, the dividends of automation would be harvested by the state and redistributed via Universal Basic Income (UBI), freeing humanity to pursue art, philosophy, and leisure.

The AI Promise vs. The Fiscal Reality

However, this utopian vision ignores the gravity of The Great American Contraction. As we approach 2026 and beyond, the friction between exponential technological growth and a $37 trillion+ national debt (with a $2 trillion annual budget deficit) creates a structural barrier to redistribution. When the tax base of human labor erodes, the math for a livable UBI simply fails to compute.

The Victorian Hypothesis

If UBI is a mathematical and political impossibility fueled by corporate and human greed, we must look toward an alternative “soft landing.” This hypothesis suggests a vertical restructuring of society. As AI drives the cost of production and the demand for goods into a deflationary spiral, the purchasing power of the remaining “employed elite” will skyrocket.

The result isn’t a horizontal distribution of wealth, but a return to a Neo-Victorian social hierarchy. In this reality, the new digital gentry will use their outsized wealth to employ a massive “servant class” to maintain stately homes and personal lives, creating a world where status is defined by the human labor one can afford to command.

Neo-Victorian Hypothesis Infographic

The Great American Contraction: Why UBI is a Non-Starter

The conversation around the transition to an AI-driven economy often treats Universal Basic Income as an inevitability — a safety net that will naturally catch those displaced by the silicon wave. However, this assumes a level of fiscal elasticity that no longer exists. We are entering The Great American Contraction, a period where the traditional levers of government spending are restricted by the sheer weight of historical obligation and systemic greed.

The Debt Ceiling of Compassion

With a national debt exceeding $37 trillion, a $2 trillion budget deficit and rising interest rates, the federal government’s “room to maneuver” has effectively vanished. A livable UBI requires a massive, consistent tax base. As AI begins to hollow out the middle class, the very tax revenue needed to fund such a program disappears. To fund UBI under these conditions would require a level of sovereign borrowing that the global markets simply will not support, leading to a reality where the government cannot afford to be the savior of the displaced.

The Greed Variable

Even if the math were more favorable, the human element remains a constant. Corporate interests, focused on margin preservation and shareholder value, are unlikely to support the aggressive taxation required to fund a social floor. In the race to the bottom of production costs, the primary goal of the “winners” in the AI revolution will be wealth concentration, not social equity. The political willpower to force a massive transfer of wealth from AI-profiting corporations to the idle masses is a historical outlier that we should not count on repeating.

The Velocity of Displacement

Finally, the speed of the AI transition is its most disruptive feature. Legislative bodies move in years, while AI cycles move in weeks. By the time a political consensus for UBI could be formed, the economic floor will have already fallen out. This lag time creates a vacuum that will be filled not by government checks, but by a desperate search for subsistence, setting the stage for the return of the domestic labor economy.

The Deflationary Paradox: Collapse of Demand and Cost

In a traditional economy, unemployment leads to recession, which usually leads to stagflation or managed recovery. However, the AI-driven “soft landing” introduces a unique mechanical failure: the Deflationary Paradox. As AI and advanced robotics permeate every sector, the labor cost of producing goods and services begins to approach zero, but the pool of consumers capable of buying those goods simultaneously evaporates.

The Production Floor Drops

We are witnessing the end of the labor theory of value. When an AI can design, a robot can manufacture, and an automated fleet can deliver a product without a single human touchpoint, the marginal cost of production hits the floor. In a desperate bid to capture the dwindling “active” capital in the market, companies will engage in a race to the bottom, causing the prices of physical and digital goods to deflate at a rate unseen in modern history.

The Demand Vacuum

While cheap goods sound like a boon, they are a symptom of a deeper rot: the Demand Vacuum. As the middle class is hollowed out, the velocity of money slows to a crawl. The economy shifts from a mass-consumption model to a precision-consumption model. Most businesses will fail not because they can’t produce, but because there are no longer enough customers with a paycheck to buy, even at rock-bottom prices.

The Purchasing Power of the “Remaining”

This is where the Victorian shift begins. For the small percentage of Americans who retain their income — the innovators, the orchestrators, and the entrepreneurs — this deflationary environment is a golden age. Their dollars, fixed in value while the cost of everything else drops, suddenly possess exponential purchasing power. When a gallon of milk or a digital service costs mere pennies in relative terms, the “wealthy” find themselves with a massive surplus of capital that cannot be spent on “things” alone. This surplus will naturally be redirected toward the one thing that remains scarce and high-status: the dedicated service of another human being.

The New “Stately Home” Economy

As the Deflationary Paradox takes hold, we will see a fundamental shift in the definition of luxury. In the pre-AI era, luxury was defined by the acquisition of high-tech gadgets or rare goods. In the Neo-Victorian era, where machines produce goods for nearly nothing, “luxury” will pivot back toward the human-centered experience. Status will no longer be measured by what you own, but by whose time you command.

From Software to Service

For the “In-Group” — those entrepreneurs and specialized leaders still generating significant revenue — capital will lose its utility in the digital marketplace. When software is free and manufactured goods are commoditized, wealth seeks the only remaining friction: human presence. We will see a massive migration of capital away from Silicon Valley “platforms” and toward the local domestic economy. The wealthy will stop buying more “things” and start buying “lives” — the total dedicated attention of house managers, chefs, valets, and tutors.

The Modern Manor

This economic shift will be physically manifested in the return of the Stately Home. These won’t just be houses; they will be complex ecosystems of employment. Large estates will once again become the primary employer for local communities. As traditional corporate offices vanish, the residence becomes the center of both social and economic power. These modern manors will require extensive human staffs to cook, clean, maintain grounds, and provide security — services that, while technically possible via robotics, will be performed by humans as a deliberate signal of the owner’s immense “effectively wealthy” status.

The Return of the Domestic Professional

Perhaps the most jarring aspect of this transition will be the class of worker entering domestic service. We are not talking about a traditional blue-collar service shift, but the “Victorianization” of the former middle class. Displaced white-collar professionals — accountants, teachers, and middle managers — will find that their highest-paying opportunity is no longer in a cubicle, but in managing the complex domestic affairs, private education, and logistics of the new digital aristocracy. It is a “soft landing” in name only; while they may live in proximity to grandeur, their survival is entirely tethered to the whims of their employer.

Socio-Economic Stratification: The Two-Tiered Reality

The inevitable result of the “Victorian Soft Landing” is the formalization of a rigid, two-tiered social structure. Unlike the 20th century, which was defined by a fluid and expanding middle class, the post-contraction era will be characterized by extreme polarization. The economic “missing middle” creates a vacuum that forces every citizen into one of two distinct realities: the Digital Gentry or the Dependent Class.

The Corporate and Government Gentry

A small percentage of Americans — likely less than 10% — will remain tethered to the engines of primary wealth creation. This “In-Group” consists of high-level AI orchestrators, strategic entrepreneurs, and essential government officials who maintain the infrastructure of the state. Because their income is derived from high-margin automated systems while their cost of living has plummeted due to deflation, they possess a level of functional wealth that rivals the landed gentry of the 19th century. To this group, the “Great Contraction” is not a crisis, but a refinement of their dominance.

The Dependent Class

For those outside the digital fortress, the reality is stark. Without a national UBI to provide a floor, the majority of the population becomes the “Dependent Class.” Their economic utility is no longer found in the marketplace of ideas or manufacturing, but in the marketplace of personal service. In this neo-Victorian landscape, you either work for the companies that own the AI, work for the government that protects it, or you work directly for the individuals who do.

The Choice: Service or Scarcity

This stratification reintroduces a primal power dynamic into the American workforce. When the cost of basic survival (food and shelter) is low due to deflation, but the opportunity for independent income is zero, the wealthy gain total leverage. The “soft landing” is, in truth, a forced labor transition. Those who are not “useful” to the gentry — either as specialized labor or domestic support — face the grim reality of the Victorian workhouse era: they must find a patron to serve, or they will starve in a world of plenty.

Experience Design in the Neo-Victorian Era

From the perspective of experience design and futurology, the shift toward a Victorian-style social structure will fundamentally alter the aesthetic of status. In a world where AI can generate perfect, flawless goods and digital experiences at zero marginal cost, “perfection” becomes a commodity. Status, therefore, will be redesigned around human friction and intentional inefficiency.

The Aesthetic of Inequality

We will see a move away from the sleek, minimalist “Apple-esque” design of the early 21st century toward a more ornate, human-heavy luxury. Experience design for the elite will emphasize things that AI cannot authentically replicate: the slight imperfection of a hand-cooked meal, the presence of a uniformed gatekeeper, and the physical maintenance of vast, non-automated gardens. Architecture will pivot back to “human-centric” layouts—designing spaces not for efficiency, but to accommodate the movement and housing of a live-in staff.

Designing for Disconnect

The most challenging aspect of this new era will be the Experience of the Invisible. Designers will be tasked with creating systems that allow the Digital Gentry to interact with their environment without acknowledging the vast economic disparity surrounding them. This involves “Social UX” — designing layers of intermediation where the “Dependent Class” provides the comfort, but the “Gentry” only interacts with the result. It is a return to the “back-stairs” architecture of the 19th century, modernized for a digital age.

The UX of Survival

For the majority, the “User Experience” of daily life will be one of Hyper-Personal Patronage. Navigation of the economy will no longer be about interfaces or platforms, but about the “UX of Relationships.” Survival will depend on the ability to design one’s persona to be indispensable to a wealthy patron. In this reality, human-centered design takes on a darker, more literal meaning: the human becomes the product, the service, and the infrastructure all at once.

Conclusion: Preparing for the Retro-Future

The “Soft Landing” we are currently engineering is not the one we were promised. As the Great American Contraction forces a collision between astronomical debt and the deflationary power of AI, the middle-class dream of a subsidized leisure class is evaporating. In its place, we are seeing the blueprints of a Retro-Future — a world that looks forward technologically but moves backward socially.

A Call for Human-Centered Transition

If we continue to view innovation solely through the lens of efficiency and margin preservation, the Victorian outcome is not just possible — it is inevitable. We must realize that without a radical redesign of how we value human contribution beyond mere “market productivity,” we are simply building a more efficient feudalism. True Experience Design must now focus on the social fabric, or we risk creating a world where the only “innovation” left is finding new ways for the many to serve the few.

Final Thought: The Soft Landing Paradox

We must be careful what we wish for when we ask for a “seamless” transition. A landing that is “soft” for the Digital Gentry is one where the friction of poverty and the noise of the displaced have been successfully silenced by the return of the servant class. History doesn’t repeat, but it does rhyme — and right now, the future sounds remarkably like 1837. The question is no longer if AI will change our world, but whether we have the courage to design a future that doesn’t require us to retreat into our past.

Frequently Asked Questions

Why would prices deflate if the economy is struggling?

In this scenario, AI and robotics drive the marginal cost of production toward zero. Simultaneously, massive job displacement creates a “demand vacuum.” To capture what little liquid currency remains, companies must drop prices drastically, leading to a reality where goods are incredibly cheap but income is even scarcer.

How does this differ from the 20th-century middle class?

The 20th century was defined by a “horizontal” distribution where many people owned moderate assets. The Neo-Victorian model is “vertical.” The middle class disappears, replaced by a tiny, hyper-wealthy elite (Digital Gentry) and a large class of people who provide them with personalized human services (the Servant Class).

Isn’t UBI a more logical solution to AI displacement?

While logical in theory, the “Great American Contraction” hypothesis suggests that high national debt and corporate prioritisation of margins make a livable UBI politically and fiscally impossible. Without a state-funded floor, the market defaults to the oldest form of social safety: personal patronage and domestic service.

EDITOR’S NOTE: This is a visualization of but one possible future. I will be publishing other possible futures as they crystallize in my mind (or as you suggest them for me to explore).

Image credits: Google Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Google Gemini to clean up the article, add images and create infographics.

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Liberated to Care – How AI Can Restore Humanity in Healthcare

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Liberated to Care - How AI Can Restore Humanity in Healthcare

GUEST POST from Kellee M. Franklin, PhD.

Heapy has long been a quiet force in the evolution of healthcare design – not with grand pronouncements, but with deep, thoughtful work that reshapes how we experience care. For decades, they have approached hospitals and clinics not as static buildings, but as living ecosystems – places where healing does not happen despite the surroundings, but because the space was designed to make it possible.

Their work goes beyond sustainability in the traditional sense – energy efficiency, material choices, LEED certifications – though they lead there, too. What sets Heapy apart is their commitment to human sustainability: designing spaces that support not just the planet, but the people within them. Clinicians. Patients. Families. The entire care team.

They understand that a healing environment is not just about clean lines and natural light – though those things matter. It is about creating places that reduce stress, prevent burnout, and foster connection. Spaces that are flexible enough to adapt to a pandemic, yet intimate enough to embrace the ailing or comfort a grieving family.

And they do this not in isolation, but in partnership – with providers, communities, vendors, and innovators who recognize that the future of healthcare is not only about smart technologies, but about deep human intention. It is not just what we build, but why – and for whom.

It was in that spirit last week, I had the honor of serving as the keynote speaker at Heapy’s Symposium on Sustainability in Healthcare, hosted in the beautiful “Queen City” of Cincinnati, Ohio – a gathering of dreamers and designers from across industries, all united by a shared belief: that the future of care must be human-centered.

It was in that room, surrounded by industry pioneers, who see beyond efficiency and into empathy, that the vision for a different kind of healthcare took shape – not as a distant ideal, but as a gentle uprising already underway.

We have spent decades optimizing a system that was not built to heal. It was not built for people at all. It is a machine – and both patients and caregivers are just trying to survive it.

We have chased speed, throughput, and cost-cutting – as if care were an assembly line. But in the rush to do more, faster, we have lost something irreplaceable: the human connection that lies at the heart of healing.

Clinicians drown in documentation; their eyes fixed on screens instead of faces. Patients feel like data points, shuffled through impersonal workflows. And hospital administrators, well-meaning as they are, focus on numbers that measure activity, not meaning.

But what if we stopped trying to make the machine run faster – and started asking: How might we build something entirely different? Not a smarter system, but a human one?

Not a system that grinds, but one that breathes. Not one that manages, but cares.

That is the future we are stepping into – not as a distant dream, but as a calm, determined shift, unfolding from the electricians who wire our buildings to the executives who shape our boardrooms. Not a future where technology replaces humanity, but one where it finally sees us – amplifies us – and reminds us why we are here.

And this future – the heart of healing — rests on four pillars, championed by forward-thinking organizations like The American College of Healthcare Executives (ACHE): liberating clinicians, designing for resilience, committing to learning, and personalizing care.

Automation in Healthcare

Liberating Clinicians: Letting Humans Be Humans

Imagine a clinic where the doctor looks at you – not at a screen. Where nurses spend their shifts at the bedside, not buried in charts. Where the administrative load does not fall on the shoulders of those already stretched thin – like patients juggling multiple portals, passwords, and fragmented records.

That is not fantasy. It is the promise of AI as an ally, not an agitator.

We are already seeing systems where AI stealthily handles prior authorizations, drafts clinical notes, and surfaces critical data – not to replace clinicians, but to free them. Early adopters report not just time savings, but better patient outcomes. But the real win? Time. Time to listen. Time to notice. Time to care.

Because healing is not transactional. It is relational. It lives in the pause, the eye contact, the hand on the shoulder. And when we automate the mechanical, we make space for the meaningful. The metric should not be how many patients we see – but how deeply we see them.

Designing for Resilience: Spaces that Adapt, Not Just Endure

Now picture the places where care happens.

Too often, they feel like relics – rigid, impersonal, built for a world that no longer exists. The next generation of healing environments must be different. They must be resilient, not just in structure, but in spirit.

We need hospitals that can withstand storms – literal and metaphorical. That can scale during surges, pivot during pandemics, and adapt to the rapid pace of change. Modular walls. Flexible rooms. Infrastructure that evolves.

But resilience is not just about durability – it is about humanity.

It is peaceful zones for staff to decompress. Natural light in every patient room. Wayfinding that feels intuitive, not clinical. It is designing for emotional endurance as much as physical strength.

Because burnout is not just caused by workload – it is shaped by environment. A space that feels cold, chaotic, or dehumanizing wears people down. One that feels calm, connected, and cared for – even in a crisis – helps them endure.

So let us stop building facilities and start creating healing ecosystems. Places that support not just survival, but the fullness of life – where healing and wholeness go hand-and-hand.

Committing to Lifelong Learning: Growing…Together

Even the smartest tools and strongest walls will not matter if we do not equip people with the knowledge, skills, and supportive environment they need to grow.

That is why ongoing education is not just a nice-to-have – it is non-negotiable. But not the kind of training that feels like a box to check. We need learning that is alive, adaptive, and human-centered.

Leaders, clinicians, and designers need to understand not just how to work with AI – but why it matters to their work. It is not about compliance – it is about curiosity. Not just in operating it but partnering with it. We need safe spaces to experiment, explore, grow – and yes, even fail. No innovation happens without change – and no meaningful change happens without real learning.

Micro-learning modules. Peer mentorship. Protected time for reflection. These are not luxuries – they are lifelines of learning and innovation.

And when leaders model learning – when they say, “I don’t know, let’s figure it out together” – they signal that growth matters more than perfection.

Because the future of care is not about mastering technology – it is about forming partnerships. With each other. With patients. With tools that extend our capacity, not replace our judgment.

Transforming Care

Personalizing Care: Seeing the Person, Not the Problem

Finally, imagine care knows you.

Not in a surveillance way – not data hoarded, but wisdom shared. AI that can tailor treatments plans, adjust room settings, and anticipate needs – always with consent, transparency, and control.

This is not about efficiency. It is about dignity.

It is remembering the patient’s name. Honoring their preferences. Adapting to their story. Adjusting to their situation. The most powerful curative is still human attention – and AI can help us focus it.

We are already seeing systems where AI personalizes everything from medication timing to discharge planning – not to automate empathy, but to boost it.

Because when care feels seen and heard, the healing penetrates deeper.

Five Actions for Leaders: From Vision to Practice

So, what can leaders do – right now – to turn this vision into reality?

Redesign Workflows Around Human Dignity: Stop measuring success by speed. Reengineer processes to reduce burnout and restore time for true connection. Use AI to handle the mechanical – documentation, scheduling, billing – and let it also surface critical insights, flag at-risk patients, and streamline workflows so clinicians can focus on what they do best: medicine. Measure moments of care, not mouse clicks – and allow AI to illuminate what truly matters: patient healing and well-being.
Co-Create with Frontline Teams: No more top-down rollouts. Invite nurses, doctors, and support staff into the design of every new tool, space, workflow, and policy. – and use AI to elevate their voices, not override them. Imagine AI that analyzes frontline feedback in real-time, surfaces hidden pain points, and co-generates solutions alongside those who know the work best. Ask: Does this help you provide better care? Their lived experience, supported by intelligent insight, guide what gets built – because the best solutions do not emerge from closed boardroom doors, but from the open collaborative hands and hearts within the community of care.
Build Spaces that Breathe: Invest in modular, adaptable infrastructure – but go further. Design for emotional resilience: tranquil zones, natural light, intuitive layouts, and AI-enhanced environments that respond to human needs in real-time. Imagine rooms that adjust lighting and temperature based on patient stress levels, or corridors that guide staff to moments of respite between high-pressure tasks. A healing space is not just durable – it is humane, alive with invisible intelligence that supports the whole-person: mind, body, heart, and spirit.
Champion Learning as an Act of Care: Make continuous education protected time, not an afterthought. Offer micro-learning, peer mentorship, and collaborative spaces – and harness AI as a dynamic learning partner. Imagine intelligent systems that surface personalized insights, adapt to individualized learning styles, and guide clinicians through real-time decision support that doubles as on-the-job training. When leaders model curiosity and embrace AI not just as a tool, but as a catalyst for growth and innovation, they create cultures where learning is ongoing and invigorating.
Personalize Without Surveillance: Use data to deepen trust, not erode it. Implement AI that personalizes care – predicting needs, tailoring environments, and adapting support – but always with consent, transparency, and patient control. Let personalization mean dignity: remembering a name, honoring a preference, adapting to a story, adjusting to a changing situation, and above all, putting people, not patterns, at the center.

A Future That Feels Human, Beautifully Imperfect

This is not about replacing the system. It is about reimagining it.

From one that manages people to one that sees them.

From one that measures output to one that values presence.

From one that optimizes speed to one that honors slowness – personal focus, deep listening, and the easy moments of connection that no algorithm can replicate.

The tools are here. The insights are clear. The question is no longer can we – but will we?

Will we choose efficiency – or humanity?

Will we build systems that merely function – or ones that truly heal?

The answer lies not in technology, but in where we choose to place our attention – and our intention.

As a Triple Negative Breast Cancer survivor, I have felt firsthand how cold and mechanical care can be – and how profoundly a space can either deepen that pain or help heal it. I have also seen how systems can exhaust the very people meant to deliver care. But I hold onto a belief: healing begins when we return to our humanity. From designers and clinicians to administrators and patients, each of us plays a vital role in co-creating a whole-health environment where care is not just delivered, but genuinely experienced.

And perhaps the most revolutionary act in healthcare today might just be this: to care, deeply, as beautifully imperfect humans – and to let everything else serve a universal truth – one rooted in compassion, true connection, and shared humanity.

Image credits: Kellee M. Franklin

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The Consumption Collapse – When the Feedback Loop Bites Back

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Why the Great American Contraction is leading to a crisis of demand and a re-imagining of the American Social Contract.

LAST UPDATED: April 17, 2026 at 3:58 PM

GUEST POST from Art Inteligencia

The Ghost in the Shopping Mall

In our previous exploration, “The Great American Contraction,” we identified a fundamental shift in the American story. For the first time in our history, the foundational assumption of “more” — more people, more labor, and more expansion — has been inverted. We discussed how the exponential rise of AI and robotics is dismantling the traditional value chain of human labor, moving us from a nation of “doers” to a necessary, albeit smaller, elite class of “architects.”

However, as we move closer to the two-year horizon of the next United States Presidential election, a more insidious shadow is beginning to fall across the landscape. It is no longer just a crisis of employment; it has evolved into a crisis of consumption. This is the “Feedback Loop of Irrelevance.”

The logic is as cold as the algorithms driving it: As increasing numbers of knowledge workers and service providers are displaced by autonomous agents, their disposable income evaporates. When people lose their financial footing, they spend less. When they spend less, the revenue of the very companies that automated them begins to shrink. To protect their margins in a declining market, these companies are forced to cut back even further — often doubling down on automation to reduce costs — which in turn removes more consumers from the marketplace.

We are witnessing the birth of a deflationary death spiral where corporate efficiency threatens to cannibalize the very markets it was designed to serve. Over the next 24 months, this cycle will redefine the American psyche and set the stage for an election year unlike any we have ever seen.

It is time to look beyond the immediate shock of job loss and examine the structural integrity of our economic operating system. If the “Old Equation” of labor-for-income is a sinking ship, we must decide what happens to the passengers before we reach the horizon of 2028.

The Vicious Cycle of Automated Austerity

The transition from a growth-based economy to a Great Contraction is not a linear event; it is a recursive loop. As AI adoption accelerates, we are witnessing a phenomenon I call “Automated Austerity.” This is the process where short-term corporate gains from labor reduction lead directly to long-term market erosion. The cycle progresses through four distinct, overlapping phases:

Phase 1: The First Wave Displacement

We are currently seeing the replacement of both low-skilled physical labor and high-skilled knowledge work by autonomous systems. This isn’t just about factory floors; it’s about the “Architect” roles we once thought were safe. As companies replace $150k-a-year analysts with $15-a-month compute tokens, the immediate impact is a massive surge in corporate profit margins.

Phase 2: The Wallet Effect

The friction begins here. Displaced workers initially rely on savings or severance, but as those dry up, the “gig economy” safety net is nowhere to be found — because AI is already performing the freelance writing, coding, and administrative tasks that used to provide a bridge. Disposable income doesn’t just dip; for a significant percentage of the population, it vanishes. This causes a sharp contraction in discretionary spending.

Phase 3: The Revenue Mirage

This is the trap. Companies that automated to save money suddenly find their top-line revenue shrinking because their customers (the former workers) can no longer afford their products. The efficiency gains are real, but the market size is artificial. We are entering a period where companies may be 100% efficient at producing goods that 0% of the displaced population can buy.

Phase 4: The Secondary Contraction

Faced with shrinking revenues, boards of directors demand even deeper cost-cutting to protect investor dividends. This leads to a second, more desperate wave of layoffs, further reducing the tax base and consumer spending power. This feedback loop creates a Deflationary Death Spiral that traditional monetary policy is ill-equipped to handle.

“When you automate the consumer out of a job, you eventually automate the business out of a customer.” — Braden Kelley

Over the next two years, this cycle will move from the periphery of Silicon Valley to the heart of every American household, forcing a radical re-evaluation of how we distribute the abundance that AI creates.

The Two-Year Horizon: 2026–2028

As we navigate the next twenty-four months, the gap between traditional economic indicators and the lived reality of American citizens will become a canyon. We are entering a period of Economic Bifurcation, where the distance between those who own the “compute” and those who formerly provided the “labor” creates a new social stratification.

The Rise of the ‘Hollow’ Recovery

Expect to hear the term “efficiency-led growth” frequently in the coming months. Wall Street may remain buoyant as AI-integrated corporations report record-breaking margins per employee. However, this is a hollow success. While the stock market reflects corporate optimization, our Alternative Economic Health Measures—like the Genuine Progress Indicator (GPI) — will likely show a steep decline. We are becoming a nation that is technically “wealthier” while the average citizen’s ability to participate in that wealth is structurally dismantled.

The Shift from ‘Doer’ to ‘Architect’ Burnout

The “Great American Contraction” is not just about those losing roles; it is about the immense pressure on those who remain. The survivors — the Architect Class — are tasked with managing sprawling AI ecosystems. This creates a new kind of cognitive load. By 2027, I predict we will see a peak in “Technological Burnout,” where the speed of AI-driven change outpaces the human capacity to design for it. This is where Human-Centered Innovation becomes a survival skill rather than a corporate luxury.

The Mindset of Survivalist Innovation

As the feedback loop of shrinking revenue intensifies, we will see American citizens taking radical actions to decouple from a failing labor market. This includes:

Hyper-Localization: A resurgence in local bartering and community-based resource sharing as a hedge against the volatility of the automated economy.
The ‘Off-Grid’ Digital Economy: Individuals utilizing open-source AI models to create value outside of the traditional corporate gatekeepers, leading to a “shadow economy” of peer-to-peer services.
Consumption Sabotage: A psychological shift where citizens, feeling irrelevant to the economy, consciously reduce their consumption to the bare essentials, further accelerating the contraction.

This period will be defined by a search for meaning in a post-labor world. The American citizen of 2027 is no longer asking “How do I get ahead?” but rather “How do I remain relevant in a world that no longer requires my effort to function?”

Beyond GDP: New Vitals for a Contracting Economy

As the “Old Equation” fails, the metrics we use to measure national success are becoming dangerously obsolete. In a world where AI can drive productivity while simultaneously hollowing out the consumer class, GDP is no longer a compass; it is a rearview mirror. To navigate the next two years, we must shift our focus to alternative economic health measures that prioritize human vitality over transactional velocity.

1. The Genuine Progress Indicator (GPI)

Unlike GDP, which counts the “cost of cleaning up a disaster” as a positive, the GPI factors in income inequality and the social costs of underemployment. As we move toward 2028, we must demand a GPI-centered view of the economy. If AI-driven efficiency creates wealth but destroys the social capital of our communities, the GPI will show we are regressing, providing a much-needed reality check to “hollow” stock market gains.

2. The U-7 ‘Utility’ Rate

Standard unemployment figures (U-3) are increasingly irrelevant. We need a U-7 ‘Utility’ Rate to track those who are “technologically displaced”—individuals whose roles have been absorbed by algorithms or whose wages have been suppressed to the point of working poverty. This metric will highlight the Architect Gap: the growing number of people who have the capacity for high-value human contribution but lack access to the compute resources required to compete.

3. The Social Progress Index (SPI)

The goal of an automated economy should be to improve the human condition. The SPI measures outcomes that actually matter: Access to advanced education, personal freedom, and environmental quality. By 2027, the SPI will be the most honest indicator of whether the Great Contraction is a managed transition to a better life or a chaotic collapse of the middle class.

4. Value of Organizational Learning Technologies (VOLT)

We must begin measuring the “Agility Score” of our nation. VOLT measures how effectively we are using AI to solve complex problems rather than just replacing workers. A high VOLT score paired with a low SPI suggests we are building a “learning machine” that has forgotten its purpose: to serve the humans who created it.

“A high-GDP nation with a crashing Social Progress Index(SPI) is merely a failed state in a gold tuxedo.”

The political battleground of the next two years will be defined by a new set of metrics similar to these (but likely different). The 2028 election will not just be a choice between candidates, but a choice between maintaining the illusion of growth or designing a system of sovereignty for the American citizen.

The Sovereign Tech-Stack & The Localized Pivot

As the “Feedback Loop of Irrelevance” continues to shrink traditional income, we are witnessing a radical grassroots response: The Localized Pivot. When the macro-economy fails to provide value to the individual, the individual stops providing value to the macro-economy and turns inward to their community.

The Rise of the ‘Personal AI’ Infrastructure

By 2027, the barrier to entry for sophisticated production will vanish. We will see a surge in “Sovereign Tech-Stacks” — individuals and small collectives using localized, open-source AI models to run micro-manufactories, automated vertical farms, and peer-to-peer service networks. This is Innovation as a Survival Tactic. These citizens are essentially “unplugging” from the hollowed-out corporate ecosystem and creating a shadow economy that traditional GDP cannot track.

From Global Chains to Hyper-Local Resilience

The contraction of consumer spending will lead to the death of the “long supply chain” for many goods. In its place, we will see the rise of Regional Circular Economies. AI will be used not to maximize global profit, but to optimize local resource sharing. Imagine community AI agents that manage local energy grids or coordinate the bartering of skills — human-centered design at its most fundamental level.

The ‘Architect’ of the Commons

In this phase, the “Architect” role I’ve discussed previously becomes a civic one. These are the individuals who design the systems that keep their communities thriving while the national revenue shrinks. They are the ones building the Human-Centered Guardrails that ensure technology serves the neighborhood, not the shareholder. This shift represents a move from Global Consumerism to Local Sovereignty.

“When the national economic engine stops fueling the household, the household must build its own engine, or it dies.” — Braden Kelley

This localized movement will be the wild card of 2028. It creates a class of “Un-Architected” citizens who are no longer dependent on the federal government or major corporations, creating a profound tension for any political candidate trying to promise a return to the ‘Old Equation’.

The Road to 2028: The Politics of Human Relevance

As we approach the next Presidential election, the political discourse will undergo a seismic shift. The traditional “Left vs. Right” battle lines over tax rates and social issues will be superseded by a more existential debate: The Individual vs. The Algorithm. The 2028 election will likely be the first in history centered entirely on the consequences of a post-labor economy.

The ‘Humanity First’ Tax and Sovereign Solvency

The most contentious issue will be how to fund a shrinking state as the labor-based tax system collapses. We will see the rise of the “Compute Tax” — a proposal to tax AI tokens and robotic output rather than human hours. This isn’t just about revenue; it’s about sovereign solvency. When companies reinvest profits into compute rather than wages, the “Economic OS” crashes. Expect candidates to run on a platform of Universal Basic Everything (UBE) — providing the results of automation (healthcare, housing, and energy) directly to the people as the tax base from labor vanishes.

The Death of Traditional Immigration Debates

As I noted in our initial look at the Contraction, the old argument about immigrants “taking jobs” or “filling gaps” is dead. In 2028, the focus will shift to “Strategic Talent Acquisition.” The debate will center on how to attract the world’s few remaining irreplaceable “Architect” minds while managing a domestic population that is increasingly surplus to the needs of capital. This will create a strange political alliance between protectionists and humanists, both seeking to shield human value from digital devaluation.

Mindset and Likely Actions of the Citizenry

By the time voters head to the polls, the American mindset will have shifted from aspiration to preservation. We are likely to see:

The Rise of ‘Neo-Luddite’ Activism: Not a rejection of technology, but a demand for “Human-Centered Guardrails” that prevent AI from cannibalizing the last remaining sectors of human connection.
The Search for Non-Monetary Meaning: A surge in candidates who focus on “Quality of Life” metrics rather than fiscal growth, appealing to a class of people who no longer derive their identity from their “job.”
Algorithmic Populism: Politicians using AI to personalize fear and hope at scale, creating a feedback loop where the technology used to displace the worker is also used to win their vote.

The central question of the 2028 election will be simple but devastating: “What is a country for, if not to support the thriving of its people — even when those people are no longer ‘productive’ in a traditional sense?” The winner will be the one who can design a new social contract for a smaller, more resilient, and truly innovative nation.

Conclusion: Designing a Thrivable Contraction

The Great American Contraction is no longer a theoretical “what-if” for futurists to debate; it is an active restructuring of our reality. As the feedback loop of automated austerity begins to bite, we are discovering that a country built on the relentless pursuit of “more” is fundamentally ill-equipped to handle the arrival of “enough.”

The next two years will be a period of intense friction as our legacy systems — our tax codes, our education models, and our social safety nets — grind against the frictionless efficiency of the AI era. We will see traditional economic metrics fail to capture the quiet struggle of the consumer, and we will watch as the 2028 election turns into a referendum on the value of a human being in a post-labor world.

But contraction does not have to mean collapse. If we shift our focus from transactional velocity to human vitality, we have the opportunity to design a new version of the American Dream. This new dream isn’t about the quantity of jobs we can protect from the machines, but the quality of the lives we can build with the abundance those machines create. It is about moving from a nation of “doers” who are exhausted by the grind to a nation of “architects” who are inspired by the possible.

“The goal of innovation was never to replace the human; it was to release the human. We are finally being forced to decide what we want to be released to do.” — Braden Kelley

The road to 2028 will be defined by whether we choose to cling to the wreckage of the growth-based model or whether we have the courage to embrace a smaller, smarter, and more human-centered future. The contraction is inevitable, but the outcome is ours to design.

STAY TUNED: On Tuesday my friend Braden Kelley (with a little help from me) is publishing an article featuring one hypothesis for what an AI SOFT LANDING might look like.

Image credits: Google Gemini

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The Agentic Paradox

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Why Giving AI More Autonomy Requires Us to Give Humans More Agency

LAST UPDATED: April 10, 2026 at 7:11 PM

The Agentic Paradox

by Braden Kelley and Art Inteligencia

The Rise of the Machine “Doer”

For the past few years, we have lived in the era of Generative AI — a world of sophisticated chatbots and creative assistants that respond to our prompts. But as we move deeper into 2026, the landscape has shifted. We are now entering the age of Agentic AI. These are not just tools that talk; they are autonomous systems capable of executing complex workflows, making real-time decisions, and acting on our behalf across digital ecosystems.

On the surface, this promises the ultimate efficiency. We imagine a future where the “busy work” vanishes, leaving us free to innovate. However, a troubling Agentic Paradox has emerged: as we grant machines more autonomy to act, many humans are finding themselves with less agency. Instead of feeling liberated, workers often feel like they are merely “babysitting” algorithms or reacting to a relentless stream of machine-generated outputs.

This disconnect creates a high-stakes leadership challenge. If we focus solely on the autonomy of the machine, we risk creating an “algorithmic anxiety” that stifles the very human creativity we need to thrive. To succeed in this new era, leaders must realize that the more powerful our AI agents become, the more we must intentionally “upgrade” the agency, authority, and strategic focus of our people.

The Thesis: The goal of innovation in 2026 is not to build the most autonomous machine, but to build a human-centered ecosystem where AI agents manage the tasks and empowered humans manage the intent.

The Hidden Cost: The Cognitive Load Crisis

The promise of Agentic AI was a reduction in workload, but for many organizations, the reality has been a shift in the type of work rather than a reduction of it. This has birthed the Cognitive Load Crisis. While an autonomous agent can process data and execute tasks 24/7, it lacks the contextual wisdom to understand the nuances of organizational culture or ethical gray areas. This leaves the human “orchestrator” in a state of perpetual high-alert.

Instead of performing deep, meaningful work, leaders and employees are becoming trapped in the Supervision Trap. They are forced to manage a relentless firehose of machine-generated notifications, approvals, and “check-ins.” This creates a fragmented mental state where the human mind is constantly context-switching between different agent streams, leading to a unique form of 2026 burnout — digital exhaustion without the satisfaction of tactile achievement.

Furthermore, as AI agents take over more of the “doing,” we see an erosion of Deep Work. When every minute is spent verifying the output of an algorithm, the quiet space required for radical innovation and strategic foresight vanishes. We are effectively trading our long-term creative capacity for short-term operational speed.

Notification Fatigue: The mental tax of being the constant “emergency brake” for autonomous systems.
Loss of Intuition: The danger of becoming so reliant on agentic data that we lose our “gut feel” for the market.
The Feedback Loop: A system where humans spend more time managing machines than mentoring people.

To break this cycle, we must stop treating AI agents as simple productivity tools and start treating them as entities that require a new architecture of human attention. If we don’t manage the cognitive load, our most talented people will eventually shut down, leaving the “Magic Makers” of our organization feeling like mere cogs in a machine-led wheel.

Agentic Paradox Spectrum Infographic

Redefining Roles: From “The Conscript” to “The Architect”

As the landscape of work shifts, so too must our understanding of how individuals contribute to the innovation ecosystem. In my work on the Nine Innovation Roles, I’ve often highlighted how different archetypes fuel organizational growth. In this agentic age, we are seeing a dramatic migration of these roles. If we are not intentional, our best people will default into the role of The Conscript — those who are merely drafted into service to support the AI’s agenda, performing the monotonous tasks of verification and data cleanup.

The goal of a human-centered transformation is to automate the role of the “Conscript” and elevate the human into the role of The Architect or The Magic Maker. When the AI handles the heavy lifting of execution, the human is finally free to focus on Intent. This is where true agency resides. Agency is not the ability to do more; it is the power to decide what is worth doing and why it matters to the human beings we serve.

However, there is a dangerous “Agency Gap” emerging. If an organization implements AI agents without redefining human job descriptions, employees lose their sense of ownership. When the machine becomes the primary creator, the human “spark” is extinguished. We must ensure that AI serves as the support staff for human intuition, not the other way around.

The Migration of Value

The AI Agent Role	The Human Agency Role
The Conscript: Handling repetitive execution and data synthesis.	The Architect: Designing the systems and ethical frameworks for the AI.
The Facilitator: Coordinating schedules and managing basic workflows.	The Revolutionary: Identifying the “radical” shifts the AI isn’t programmed to see.
The Specialist: Performing deep-dive technical analysis at scale.	The Magic Maker: Applying empathy and storytelling to turn data into a movement.

By clearly delineating these roles, leaders can close the Agency Gap. We must empower our teams to move away from “monitoring” and toward “orchestrating.” This transition is the difference between a workforce that feels obsolete and one that feels essential.

Agentic Workforce Migration Infographic

FutureHacking™ the Cognitive Workflow

To navigate the complexities of 2026, organizations cannot rely on reactive strategies. We must use FutureHacking™ — a collective foresight methodology — to map out how the relationship between human intelligence and agentic automation will evolve. This isn’t just about predicting technology; it’s about engineering the “Human-Agent Interface” so that it scales without crushing the human spirit.

The core of this approach involves identifying the Innovation Bonfire within your team. In this metaphor, the AI agents are the fuel — abundant, powerful, and capable of sustaining a massive output. However, the humans must remain the spark. Without the human spark of intent and empathy, the fuel is just a cold pile of logs. FutureHacking™ allows teams to visualize where the “fuel” might be smothering the “spark” and adjust the workflow before burnout sets in.

By engaging in collective foresight, teams can proactively decide which cognitive territories are “Human-Core.” These are the areas where we intentionally limit AI autonomy to preserve our creative agency and cultural identity. It’s about choosing where we want the machine to lead and where we require a human to hold the compass.

Mapping the Friction: Identifying which agent-led tasks are creating the most mental “drag” for the team.
Defining Non-Negotiables: Establishing which parts of the customer and employee experience must remain 100% human-centric.
Intent Modeling: Shifting the focus from “What can the agent do?” to “What outcome are we trying to hack for the future?”

When we FutureHack our workflows, we move from being passive recipients of technological change to being the active architects of our organizational destiny. We ensure that as the machine gets smarter, our collective human intelligence becomes more focused, not more fragmented.

Framework: The “Agency First” Operating Model

Building a resilient organization in the age of Agentic AI requires more than just new software; it requires a new operating philosophy. We must move away from a model of Machine Management and toward a model of Intent Orchestration. This framework provides three critical steps to ensure that human agency remains the primary driver of your business value.

1. Cognitive Offloading, Not Task Dumping

The goal of automation should be to reduce the mental noise for the employee, not just to move a task from a human to a machine. If a human still has to track, verify, and worry about every step the agent takes, the cognitive load hasn’t decreased — it has merely changed shape.
The Strategy: Design “set and forget” guardrails that allow agents to operate within a defined ethical and operational “sandbox,” only alerting the human when a decision falls outside of those parameters.

2. The “Human-in-the-Loop” Upgrade

We must shift the role of the worker from Monitor to Mentor. In the old model, the human checks the machine’s homework for errors. In the “Agency First” model, the human coaches the agent on why certain decisions are better than others, treating the AI as an apprentice. This reinforces the human’s position as the source of wisdom and authority, preventing the “Conscript” mentality.

3. Intent-Based Leadership

Management must evolve to focus on the Intent rather than the Activity. In a world where agents can generate infinite activity, “busyness” is no longer a proxy for value. Leaders must empower their teams to spend their time defining the “Commander’s Intent” — the high-level objectives and human-centered outcomes that the AI agents must then figure out how to achieve.

Intent Based Leadership Blueprint Infographic

The Agency Audit: Ask your team this week: “Does this new AI agent give you more time to think strategically, or does it just give you more machine-generated work to manage?” The answer will tell you if you are facing an Agentic Paradox.

Conclusion: Leading the Human-Centered Revolution

The true test of leadership in 2026 is not how quickly you can deploy autonomous agents, but how effectively you can protect and amplify the human spirit within your organization. As we navigate the Agentic Paradox, we must remember that technology is a force multiplier, but it requires a human “integer” to multiply. Without a clear sense of agency, even the most advanced AI becomes a source of friction rather than a source of freedom.

By addressing the Cognitive Load Crisis and intentionally moving our teams out of “Conscript” roles and into “Architectural” ones, we do more than just improve efficiency — we future-proof our culture. We ensure that our organizations remain places of meaning, creativity, and purpose.

The “Year of Truth” demands that we be honest about the mental tax of automation. It calls on us to use FutureHacking™ not just to map out our tech stacks, but to map out our human potential. The companies that win the next decade won’t be those with the smartest agents; they will be the ones that used those agents to give their people the time and agency to be truly, radically human.

“Innovation is a team sport where the machines play the support roles so the humans can score the points.”

Are you ready to hack your agentic future?

Frequently Asked Questions

What is the primary difference between Generative AI and Agentic AI?

Generative AI focuses on creating content (text, images, code) based on human prompts. Agentic AI goes a step further by having the autonomy to execute multi-step workflows, make decisions, and interact with other systems to complete a goal without constant human intervention.

How can leaders identify if their team is suffering from the Agentic Paradox?

Look for signs of the “Supervision Trap,” where employees spend more time managing and verifying machine outputs than performing strategic work. If your team feels busier but reports a decline in creative output or “Deep Work,” they are likely experiencing the paradox.

What role does FutureHacking™ play in managing AI integration?

FutureHacking™ is a collective foresight methodology used to visualize the long-term impact of AI on organizational roles. It helps teams proactively define “Human-Core” territories, ensuring that as AI scales, it supports rather than smothers human agency and innovation.

Image credits: Google Gemini

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Top 10 Human-Centered Change & Innovation Articles of March 2026

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Top 10 Human-Centered Change & Innovation Articles of March 2026 Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are March’s ten most popular innovation posts:

Resilient Innovation — by Braden Kelley
Has AI Killed Design Thinking? — by Braden Kelley
Mapping Customer Experience Risk to the P&L — by Braden Kelley
Moral Uncertainty Engines — by Art Inteligencia
Necesita un Diagnóstico de Riesgo de Experiencia del Cliente y Fuga de Ingresos — por Braden Kelley
Layoffs, AI, and the Future of Innovation — by Braden Kelley
Organizational Digital Exhaust Analysis — by Art Inteligencia
You Need a Customer Experience Risk & Revenue Leakage Diagnostic — by Braden Kelley
Stereotypes – Are They Useful and Should We Use Them? — by Pete Foley
Is There Such a Thing as a Collective Growth Mindset? — by Stefan Lindegaard

BONUS – Here are five more strong articles published in February that continue to resonate with people:

The Architecture of Organizational Agility — by Braden Kelley
Neuroadaptive Interfaces — by Art Inteligencia
Innovation Lessons from the 50 Most Admired Companies of 2026 — by Braden Kelley
Why It Matters WHO Conducts Your Customer Experience Audit — by Braden Kelley
Values Always Cost You Something — by Greg Satell

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last five years:

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The Four Psychological Disruptions of AI at Work

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LAST UPDATED: April 3, 2026 at 4:20 PM

The Four Psychological Disruptions of AI at Work

by Braden Kelley and Art Inteligencia

Most AI-and-work frameworks are built around economics – job categories, task automation rates, re-skilling costs. This one is built around something different: the interior experience of the person sitting at the desk. The four disruptions mapped in this infographic were identified not through labor market data, but through a human-centered lens – the same lens used in design thinking and change management to surface the needs, fears, and identity stakes that people rarely articulate out loud but always feel.

The framework draws on three converging sources: organizational psychology research on professional identity and role transition; change management practice, particularly the observed patterns of how workers respond when their expertise is devalued or displaced; and direct observation of how individuals are actually experiencing AI adoption in their workplaces right now – not in surveys, but in the unguarded conversations that happen before and after workshops, in the margins of keynotes, in the questions people ask when they think no one important is listening.

Why these four disruptions

Competence Displacement

The skill that defined you no longer distinguishes you.

Professional identity is heavily anchored in the belief that what I know how to do has value. When AI can replicate a signature competency – even imperfectly – it attacks that anchor directly. The disruption isn’t primarily about job loss. It’s about the sudden, disorienting feeling that years of deliberate practice have been, in some meaningful sense, made ordinary.

This disruption appears earliest and most acutely in knowledge workers whose expertise was previously considered difficult to acquire – writers, analysts, coders, researchers, strategists.

Purpose Erosion

The meaning embedded in the craft begins to hollow out.

Work is not only instrumental – it is ritual. The process of doing difficult things carefully, over time, is itself a source of meaning. When automation removes the friction, it can also remove the satisfaction. This is subtler than competence displacement and slower to surface, but ultimately more corrosive. People find themselves producing more output and feeling less connected to it.

This disruption is particularly acute for people who chose their profession not just for income but for intrinsic love of the work – and who built their identity around that love.

Belonging Disruption

The social fabric of work shifts when AI enters the team.

Work teams are social ecosystems built on complementary expertise, shared struggle, and mutual reliance. AI changes those dynamics in ways that are easy to overlook. When an AI tool makes one team member dramatically more productive, or when collaborative tasks are partially automated, the invisible social contracts of the team – who depends on whom, who contributes what – are quietly renegotiated. Belonging depends on feeling needed. When that changes, isolation can follow.

This disruption tends to surface not as explicit conflict but as a gradual withdrawal – people collaborating less, sharing less, protecting their remaining territory.

Status Anxiety

The professional hierarchy is being redrawn by AI fluency.

Workplace status has always been tied to expertise scarcity – the person who knew things others didn’t held power. AI is redistributing that scarcity rapidly. Early and confident AI adopters gain speed, output, and visibility. Those who resist, or who are slower to adapt, find themselves losing ground in ways that feel both unfair and disorienting. The new status question – are you someone who uses AI, or someone AI is used on? – is already being asked in organizations, even when no one says it explicitly.

This disruption is uniquely uncomfortable because it combines external threat (status loss) with internal shame (the fear of being seen as behind).

How to read the framework

These four disruptions are not sequential stages – they are simultaneous and overlapping. A single professional can be experiencing all four at once, with different intensities depending on their role, their organization, and how rapidly AI is being adopted around them. The infographic presents them as discrete panels for clarity, but the lived experience is messier and more entangled.

They are also not uniformly negative. Each disruption contains within it the seed of a corresponding renewal: competence displacement can become an invitation to lead with judgment rather than task execution; purpose erosion can prompt a deeper reckoning with what the work is ultimately for; belonging disruption can surface the human connection that was always the real foundation of team cohesion; status anxiety can motivate the kind of deliberate identity authoring that makes professionals more resilient over the long term.

The framework is designed to give leaders and individuals a common language for conversations that are currently happening in fragments — in one-to-ones, in exit interviews, in the silence after a difficult all-hands. Named things can be worked with. Unnamed things can only be endured.

This framework is a practitioner’s model, not a peer-reviewed clinical instrument. It is designed for use in workshops, coaching conversations, and organizational change programs as a starting point for honest dialogue — not as a diagnostic or classification system. It will evolve as our collective understanding of AI’s human impact deepens.

Image credits: Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Claude AI to clean up the article and add citations.

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Will New Jobs Replace Those AI Wipes Out?

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Will New Jobs Replace Those AI Wipes Out?

GUEST POST from Robert B. Tucker

For years, economists and technologists have comforted the public with a familiar refrain: as new technologies destroyed jobs, new ones arose even faster. The tractor displaced farm laborers, yet factories absorbed them. Computers replaced typewriters but created programmers.

The pattern seemed reassuringly predictable. Creative destruction, we were assured, always has a job-producing rainbow at the end of the storm. But artificial intelligence is not simply another tool like the computer or the tractor.

For starters, AI doesn’t just augment human capability in a narrow domain. It is a multi-faceted system that learns, adapts, writes, designs, diagnoses, analyzes, composes, and increasingly decides. In other words, AI is not replacing a single category of work. Rather, it is encroaching simultaneously on dozens. White-collar, creative, analytical, and technical roles are all within its expanding reach.

The first loud alarm bell of mass job displacement came in 2025, when Anthropic CEO Dario Amodei warned in an Axios interview that AI could eliminate “roughly 50% of entry-level white-collar jobs within 1–5 years, and that unemployment could spike to 10–20% within one to five years.”

To be sure, new jobs are appearing. According to LinkedIn’s Economic Graph—the world’s largest real-time map of jobs and skills, over 1.3 million AI-related job opportunities have appeared in the past two years alone. Many of these jobs did not even exist five years ago. But many of these jobs are specialized, technical, or niche. Meanwhile, large-scale occupations employing millions are shrinking.

“Something big is happening,” noted AI investor and CEO Matt Shumer, in an influential post in February 2026, read by 80 million people. “I am no longer needed for the actual technical work of my job. I describe what I want to be built, in plain English, and it just appears. Not a rough draft I need to fix. The finished thing. I tell the AI what I want, walk away from my computer for four hours, and come back to find the work done better than I would have done it myself.”

Citrini Research added to the with a new strain of fears about AI, painting what The Wall Street Journal called a “dark portrait of a future in which technological change inspires a race to the bottom in white-collar knowledge work. “For the entirety of modern economic history, human intelligence has been the scarce input,” Citrini noted. “We are now experiencing the unwind of that premium.” The Dow dropped 820 points on the post.

As AI models are becoming capable of building AI models, the pace of progress in AI has become exponential rather than linear. As the implications of recent advances cascade throughout the economy, stock markets gyrate, and career anxiety pervades the white-collar sector.

This new reality should prompt us to question the breezy optimism that “new jobs will appear.” Of course they will. The real question is: what kind of jobs?

Gig economy jobs have exploded over the past two decades. In 2005, only about 10% of the U.S. workforce participated in gig or independent work. Today that share has surged to roughly 35–38% of workers—about 60–70 million Americans—and still growing. In one sense, gig work offers freedom: flexibility, autonomy, and the ability to diversify income streams. For many workers it’s a hedge against layoffs and economic volatility. But the downsides are equally real. Gig workers often lack employer benefits, job security, retirement plans, and predictable income—and many earn less per hour than in traditional roles.

Yet another occupation often cited as evidence of this “new jobs will appear” optimism is the rise of the social media influencer. In theory, it represents a new category of work born of the digital economy—individuals building audiences, shaping tastes, and monetizing attention. Some sources have suggested that those who manage to accumulate over 50,000 followers could pull in an income of between $40,000 and $100,000 a year.

But the reality of this new job category, at least for some, hides a darker reality. Wellness influencer Lee Tilghman built a large Instagram following and earned hundreds of thousands from brand-sponsored posts. Yet behind the bright lights, she battled anxiety, loneliness, and disordered eating while spending up to ten hours a day online chasing validation. The constant pressure to post content became a ball and chain, which Tilghman later called “performing your life for content.” Suffering from stress and the recurrence of an eating disorder, she quit and now works a traditional 9-5 job which stops at the end of the day. As she told The New York Times, “When you’re an influencer, then you have chains on.”

A growing share of our economy may be shifting from producing tangible value to competing for attention inside algorithm-driven platforms. Millions of aspiring influencers chase likes, followers, and brand partnerships, yet only a tiny fraction earn a stable living. The rest exist in a precarious ecosystem of constant posting, self-promotion, and digital performance. “The information economy that we are currently building is really a new form of feudalism,” notes technologist Jaron Lanier.

In other words, the “new jobs” created by the technological revolution is often not a profession at all; it is a lottery. And even here, AI is moving rapidly. Synthetic influencers, automated content creation, and algorithmically generated personalities are already beginning to crowd the space.

The deeper issue is not simply employment but meaning. A society in which vast numbers of people struggle to find work that is steady, economically viable, socially valued, and personally fulfilling will face pressures far beyond the labor market.

In the Age of Acceleration, the question is no longer whether technology creates or destroys jobs. The question is how fast we adapt.

This article originally appeared in Forbes

Image credit: Pexels

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Humans and AI BOTH Hallucinate

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Humans and AI BOTH Hallucinate

GUEST POST from Shep Hyken

One of the reasons customers are concerned about or even scared of artificial intelligence (AI) is that it has been known to provide incorrect answers. The result is frustration and concern over whether to believe any AI-fueled technology. In my annual customer service and customer experience research, I asked more than 1,000 U.S. consumers if they ever received wrong or incorrect information from an AI self-service technology. Fifty-one percent said yes.

No, AI is not perfect. Even though the technology continues to improve, it still makes mistakes. And my response to those who claim they won’t trust AI because of those mistakes is to ask, “Has a live customer support agent ever given you bad information?”

That question gets a surprised look, and then a smile, and then an acknowledgement, something like, “You’re right. I never thought about that.”

When AI gives bad information, I refer to that as Artificial Incompetence. It’s just as frustrating when we experience bad information from a live agent, which I call HI, or Human Incompetence. I doubt – I actually know – that the AI and the human aren’t trying to give you bad information.

I once called a customer support number to get help with what seemed like a straightforward question. I didn’t like the answer I received. It just didn’t make sense. Rather than argue, I thanked the agent, hung up, and dialed the same customer support number. A different agent answered, and I asked the same question. This time, I liked the answer. Two humans from the same company answering the same question, but with two completely different answers. And we worry about AI being inconsistent!

AI Hallucination Cartoon Shep Hyken

AI and Humans Make Mistakes

The reality is that both AI and humans make mistakes, and both will continue to do so. The difference is our expectations. We don’t expect humans to be perfect, so when they are not, we may be disappointed, maybe even angry. We may or may not forgive them, but usually, we just chalk it up to being … human. But it’s different when interacting with AI. We expect it to be reliable, and when it makes a mistake, we often assume the entire system is flawed.

Perhaps we should treat both with the same reasonable expectations and the same healthy skepticism we apply to weather forecasters, who use sophisticated technology and have years of training yet still can’t seem to get tomorrow’s forecast right half the time. Well, it seems like half the time! That doesn’t mean we won’t be checking the forecast before we plan our outdoor activities. AI, too, is sophisticated technology that can make life easier.

Image credits: Gemini, Shep Hyken

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Layoffs, AI, and the Future of Innovation

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Efficiency Breakthrough or Creative Bankruptcy?

LAST UPDATED: March 21, 2026 at 10:24 PM

Layoffs, AI, and the Future of Innovation

by Braden Kelley and Art Inteligencia

Framing the Debate: Signals or Symptoms?

A new wave of layoffs across technology companies has reignited a familiar but increasingly urgent question: what exactly are we witnessing? On the surface, the explanation seems straightforward — companies are tightening costs, responding to macroeconomic pressures, and recalibrating after years of aggressive hiring. But beneath that surface lies a deeper and more consequential debate about the future of innovation, the role of engineers, and the impact of artificial intelligence on knowledge work itself.

Two competing narratives have quickly emerged. The first frames these layoffs as a rational and even necessary evolution. In this view, advances in AI-powered development tools — ranging from large language models to code-generation systems — have fundamentally altered the productivity equation. Engineers equipped with tools like Claude or OpenAI Code can now accomplish in hours what once took days. The implication is clear: if output can be maintained or even increased with fewer people, then reducing headcount is not a sign of weakness but a signal of maturation. Companies are becoming leaner, more efficient, and ultimately more profitable.

The second narrative is far less optimistic. It suggests that layoffs are not a leading indicator of a smarter, AI-augmented future, but a trailing indicator of something more troubling — an innovation slowdown. According to this perspective, many technology companies have already harvested the most accessible opportunities within their existing platforms. What remains is incremental improvement rather than transformative change. In such an environment, cutting engineering talent becomes less about efficiency gains and more about a lack of compelling new problems to solve. The cupboard, in other words, may not be empty — but it may be significantly less full than it once was.

What makes this moment particularly complex is that both narratives can be true at the same time. AI is undeniably increasing productivity in certain domains, compressing development cycles and enabling smaller teams to deliver meaningful results. At the same time, innovation has never been solely a function of efficiency. Breakthroughs emerge from exploration, from cross-functional collisions, and from a willingness to invest in uncertain futures. Layoffs, especially when executed at scale, can disrupt the very conditions that make those breakthroughs possible.

This tension forces us to confront a more nuanced question: are these layoffs a signal of transformation or a symptom of stagnation? Are organizations courageously embracing a new model of AI-augmented work, or are they retreating into cost-cutting as a substitute for bold thinking? The answer matters, because it shapes not only how we interpret today’s decisions, but how we design organizations for tomorrow.

For leaders, the stakes extend beyond quarterly earnings. The choices being made now will determine whether AI becomes a catalyst for a new era of human-centered innovation or a tool that accelerates efficiency at the expense of imagination. For engineers, the implications are equally profound. Their roles are being redefined in real time — not just in terms of what they produce, but in how they create value within increasingly AI-mediated systems.

Ultimately, this is not just a debate about layoffs. It is a debate about what organizations choose to optimize for: productivity or possibility, efficiency or exploration, output or insight. And in that choice lies the future trajectory of innovation itself.

The Case for “Smarter, Leaner, More Profitable”

For many technology leaders, the recent wave of layoffs is not a retreat — it is a re-calibration. The argument is grounded in a simple but powerful premise: the economics of software development have fundamentally changed. With the rapid advancement of AI-assisted coding tools, the amount of output a single engineer can produce has increased dramatically. What once required large, specialized teams can now be accomplished by smaller, more versatile groups augmented by intelligent systems.

Tools such as Claude and OpenAI Code are not merely incremental improvements in developer productivity; they represent a shift in how work gets done. Routine coding tasks, boilerplate generation, debugging assistance, and even architectural suggestions can now be offloaded to AI. This allows engineers to spend less time writing repetitive code and more time focusing on higher-value activities such as system design, problem framing, and integration across complex environments.

In this emerging model, the role of the engineer evolves from builder to orchestrator. Instead of manually crafting every line of code, engineers guide, refine, and validate the outputs of AI systems. The result is a compression of development cycles — features are built faster, iterations occur more rapidly, and time-to-market shrinks. From a business perspective, this translates into a compelling opportunity: maintain or even increase output while reducing labor costs.

This logic is not without precedent. Across industries, waves of automation have consistently redefined the relationship between labor and productivity. In manufacturing, the introduction of robotics did not eliminate production; it scaled it. In many cases, it also improved quality and consistency. Proponents of the current shift argue that AI represents a similar inflection point for knowledge work. The companies that adapt fastest will be those that learn to pair human creativity with machine efficiency.

From a financial standpoint, the incentives are clear. Reducing headcount while sustaining output improves margins, a priority that has become increasingly important in an environment where growth-at-all-costs is no longer rewarded. Investors are placing greater emphasis on profitability and operational discipline, and companies are responding accordingly. Leaner teams are not just a byproduct of technological change — they are a strategic choice aligned with evolving market expectations.

There is also a strategic argument that goes beyond cost savings. By automating lower-value tasks, organizations can theoretically redeploy human talent toward more innovative efforts. Engineers freed from routine work can focus on solving harder problems, exploring new product ideas, and experimenting with emerging technologies. In this view, AI does not replace innovation capacity; it expands it by removing friction from the development process.

Smaller teams can also mean faster decision-making. With fewer layers of coordination required, organizations can become more agile, responding quickly to changing market conditions and customer needs. This agility is often cited as a competitive advantage, particularly in fast-moving technology sectors where speed can determine success or failure.

Ultimately, the “smarter, leaner” argument rests on a belief that efficiency and innovation are not mutually exclusive. Instead, they are mutually reinforcing. By leveraging AI to increase productivity, companies can create the financial and operational headroom needed to invest in the next wave of innovation. Layoffs, in this context, are not an admission of weakness — they are a signal that the underlying system of value creation is being rewritten.

The Case for “Innovation Is Running Dry”

While the efficiency narrative is compelling, an equally important — and more unsettling — interpretation of recent layoffs is gaining traction: that they reflect not technological progress, but an innovation slowdown. In this view, companies are not simply becoming leaner because they can do more with less, but because they have fewer truly novel problems worth investing in. The layoffs, therefore, are less a signal of transformation and more a symptom of diminishing opportunity.

Over the past decade, many technology companies have scaled around a set of highly successful platforms and business models. These platforms have been optimized, expanded, and monetized with remarkable effectiveness. But maturity brings constraints. As systems stabilize and markets saturate, the number of greenfield opportunities naturally declines. What remains is often incremental improvement — refinements, extensions, and efficiencies — rather than the kind of breakthrough innovation that requires large, exploratory engineering teams.

In this context, layoffs can be interpreted as a rational response to a shrinking frontier. If there are fewer bold bets to pursue, there is less need for the capacity required to pursue them. The risk, however, is that this becomes a self-reinforcing cycle. As organizations reduce investment in exploration, they further limit their ability to discover the next wave of opportunity. Over time, efficiency begins to crowd out possibility.

Compounding this dynamic is an increasing reliance on metrics that prioritize productivity over potential. Organizations are becoming exceptionally good at measuring what is already known — velocity, output, utilization — but far less adept at valuing what has yet to be discovered. When success is defined primarily by efficiency gains, it becomes harder to justify the uncertainty and longer time horizons associated with breakthrough innovation.

The rise of AI tools adds another layer of complexity. While these tools can accelerate development, they do not inherently generate new insight. They are trained on existing patterns, which means they are exceptionally effective at extending the present but less equipped to invent the future. This creates the risk of an “illusion of progress,” where output increases but originality does not. More code is produced, but not necessarily more meaningful innovation.

There are also significant cultural consequences to consider. Layoffs, particularly when they affect engineering and product teams, can erode trust and psychological safety within an organization. When employees perceive that their roles are precarious, they are less likely to take risks, challenge assumptions, or pursue unconventional ideas. Yet these behaviors are precisely what fuel innovation. In attempting to optimize for efficiency, companies may inadvertently suppress the very creativity they depend on for long-term growth.

Another often overlooked impact is the loss of institutional knowledge. Experienced engineers carry not just technical expertise, but contextual understanding of systems, decisions, and past experiments. When they leave, they take with them insights that are difficult to codify or replace. This loss can slow future innovation efforts, even as short-term efficiency metrics appear to improve.

Ultimately, the concern is not that companies are becoming more efficient — it is that they may be becoming too narrowly focused on efficiency at the expense of exploration. Innovation requires slack, curiosity, and a willingness to invest in uncertain outcomes. When organizations begin to treat these elements as expendable, they risk signaling something far more significant than cost discipline: a diminishing appetite for invention itself.

Paths to AI-Driven Engineering Outcomes

The Human-Centered Tension: Productivity vs. Possibility

Beneath the surface of the efficiency versus stagnation debate lies a deeper, more human tension — one that cannot be resolved by technology alone. At its core, innovation has never been just about output. It has always been about the quality of thinking, the diversity of perspectives, and the collisions between ideas that spark something new. When organizations focus too narrowly on productivity, they risk overlooking the very conditions that make possibility achievable.

Innovation does not emerge from isolated efficiency; it emerges from interaction. It is the byproduct of cross-functional curiosity — engineers engaging with designers, product managers challenging assumptions, customers re-framing problems, and leaders creating space for exploration. These interactions are often messy, inefficient, and difficult to measure. But they are also where breakthroughs live. When layoffs reduce not just headcount but diversity of thought and opportunities for collaboration, the innovation system itself becomes less dynamic.

The rise of AI-augmented work introduces a new layer to this tension. As engineers increasingly rely on AI tools to generate code, suggest solutions, and optimize workflows, their role begins to shift. They move from hands-on builders to orchestrators of machine-assisted output. While this shift can increase speed and efficiency, it also raises an important question: what happens to deep craft? The tacit knowledge developed through wrestling with complexity — the kind that often leads to unexpected insights — may be diminished if too much of the process is abstracted away.

There is also a cognitive risk. AI systems are designed to identify and replicate patterns based on existing data. This makes them powerful tools for scaling what is already known, but less effective at challenging foundational assumptions. If organizations become overly dependent on these systems, they may unintentionally standardize thinking. The range of possible solutions narrows, not because people lack creativity, but because the tools they use guide them toward familiar patterns.

Trust plays a critical role in navigating this tension. In environments where employees feel secure, valued, and empowered, they are more likely to experiment, take risks, and pursue unconventional ideas. Layoffs, particularly when they are frequent or poorly communicated, can erode that trust. The result is a more cautious workforce — one that prioritizes safety over exploration. In such environments, productivity may remain high, but the willingness to pursue breakthrough innovation often declines.

Curiosity is the other essential ingredient. It is the force that drives individuals to ask better questions, challenge the status quo, and seek out new possibilities. Yet curiosity requires space — time to think, room to explore, and permission to deviate from immediate objectives. When organizations optimize relentlessly for efficiency, that space tends to disappear. Every moment is accounted for, every effort measured, and every outcome expected to justify itself in the short term.

This creates a paradox. The same tools and strategies that enable organizations to move faster can also constrain their ability to think differently. Speed without reflection can lead to acceleration in the wrong direction. Efficiency without exploration can result in incremental progress that ultimately limits long-term growth.

For leaders, the challenge is not to choose between productivity and possibility, but to intentionally design for both. This means recognizing that innovation systems require balance — between execution and exploration, between structure and flexibility, and between human judgment and machine assistance. It requires protecting the conditions that enable creativity even as new technologies reshape how work gets done.

Ultimately, the question is not whether AI will make organizations more efficient — it already is. The question is whether leaders will use that efficiency to create more space for human ingenuity, or whether they will allow it to crowd out the very behaviors that make innovation possible in the first place.

The Future of Innovation in the Age of AI: Augmentation or Abdication?

As organizations navigate layoffs, AI adoption, and shifting expectations around productivity, the future of innovation is not predetermined — it is being actively shaped by the choices leaders make today. The central question is no longer whether artificial intelligence will transform how work gets done, but how that transformation will be directed. Will AI serve as an amplifier of human ingenuity, or will it become a mechanism for narrowing ambition in the pursuit of efficiency?

Three distinct paths are beginning to emerge. The first is an augmentation-led renaissance, where organizations successfully combine human creativity with machine capability. In this scenario, AI handles the repetitive and computationally intensive aspects of work, freeing humans to focus on problem framing, experimentation, and breakthrough thinking. Innovation accelerates not because there are fewer people, but because those people are empowered to operate at a higher level of abstraction and impact.

The second path is the efficiency trap. Here, organizations become so focused on optimizing output and reducing cost that they gradually lose their capacity for exploration. AI is used primarily to streamline existing processes rather than to unlock new possibilities. Over time, these organizations become highly efficient at executing yesterday’s ideas, but increasingly disconnected from tomorrow’s opportunities. What appears to be strength in the short term reveals itself as fragility in the long term.

The third path is a bifurcation of the competitive landscape. Some organizations will lean into augmentation, investing in both AI capabilities and the human systems required to harness them effectively. Others will prioritize efficiency, focusing on cost control and incremental gains. The result is a widening gap between companies that consistently generate new value and those that primarily replicate and optimize existing models. In such an environment, innovation becomes a defining differentiator rather than a baseline expectation.

What separates the leaders from the laggards will not be access to AI alone — those tools are increasingly commoditized — but how organizations integrate them into their innovation systems. Leading organizations will invest not just in AI infrastructure, but in what might be called curiosity infrastructure: the cultural, structural, and leadership practices that encourage questioning, exploration, and cross-functional collaboration. They will recognize that technology can accelerate execution, but only humans can redefine the problems worth solving.

This shift will require a redefinition of roles. Engineers, for example, will need to move beyond execution and into areas such as systems thinking, ethical judgment, and interdisciplinary collaboration. Their value will be measured not just by what they build, but by how they frame problems, challenge assumptions, and integrate diverse inputs into coherent solutions. Similarly, leaders will need to become stewards of both performance and possibility, ensuring that the drive for efficiency does not crowd out the pursuit of innovation.

Organizations that thrive will also be those that intentionally protect space for exploration. This does not mean abandoning discipline or ignoring financial realities. It means recognizing that innovation requires a portfolio approach — balancing investments in core optimization with bets on uncertain, high-potential opportunities. AI can make this balance more achievable by reducing the cost of experimentation, but only if leaders choose to reinvest those gains into discovery rather than solely into margin expansion.

Ultimately, the future of innovation in the age of AI will be defined by whether organizations treat these tools as a substitute for human thinking or as a catalyst for it. The real risk is not that AI replaces engineers — it is that organizations stop asking the kinds of questions that require engineers to think deeply, creatively, and collaboratively in the first place.

Augmentation or abdication is not a technological choice. It is a leadership choice. And in making it, organizations will determine whether this moment becomes a turning point toward a more innovative future — or a gradual slide into highly efficient irrelevance.

Frequently Asked Questions

1. Why are technology companies laying off engineers despite using AI tools?

Layoffs may result from a combination of efficiency gains and slowing innovation opportunities. AI tools like
Claude and OpenAI Code allow smaller teams to maintain or increase output, reducing the need for some roles.
At the same time, some companies face fewer breakthrough projects to pursue, which can also drive workforce reductions.

2. Does AI replace human engineers or just augment their work?

AI primarily augments engineers by automating repetitive coding, debugging, and optimization tasks. This allows
engineers to focus on higher-value activities such as system design, problem framing, and creative innovation.
While some roles shift, AI is intended as an amplifier of human ingenuity rather than a replacement.

3. How can companies maintain innovation in the age of AI?

Companies can preserve innovation by investing in curiosity infrastructure, protecting time and space for
experimentation, fostering cross-functional collaboration, and reinvesting efficiency gains into exploratory,
high-potential projects. Balancing productivity with opportunity ensures that humans and AI together drive breakthroughs.

Image credits: ChatGPT

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from ChatGPT to clean up the article and add citations.

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5 Ways to Protect Your Career from AI Job Displacement

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How To Protect Your Career From AI Job Displacement

GUEST POST from Robert B. Tucker

I don’t want to sound like an alarmist, but if your work involves sitting at a computer, your job could be in jeopardy. The pace of progress in AI has become exponential rather than linear, as AI models are becoming capable of building AI models. As the implications of recent advances cascade throughout the economy, stock markets gyrate, and career anxiety pervades the white-collar sector.

As a futurist and innovation expert advising organizations for over three decades, I have had a front-row seat to many varieties of disruptions. This experience has led me to conclude that technological innovations rarely eliminate those who are willing to experiment and adapt. Most at risk are those who are complacent: those who assume they can get by without fundamentally changing how they operate.

“Something big is happening,” noted AI investor and CEO Matt Shumer, in an influential post read by 80 million people. “I am no longer needed for the actual technical work of my job. I describe what I want to be built, in plain English, and it just appears. Not a rough draft I need to fix. The finished thing. I tell the AI what I want, walk away from my computer for four hours, and come back to find the work done better than I would have done it myself.”

The first big warning of mass job displacement came in 2025, when Anthropic CEO Dario Amodei warned in an Axios interview that AI could eliminate “roughly 50% of entry-level white-collar jobs within 1–5 years, and that unemployment could spike to 10–20% within one to five years. “

Following Matt Shumer’s post last week, Citrini Research earlier this week tapped into a new strain of fears about AI, painting what the Wall Street Journal called a “dark portrait of a future in which technological change inspires a race to the bottom in white-collar knowledge work. “For the entirety of modern economic history, human intelligence has been the scarce input,” Citrini noted. “We are now experiencing the unwind of that premium.” The Dow dropped 820 points on the post.

The question on everyone’s mind right now seems to be: What happens when artificial intelligence can do my job faster, cheaper, and perhaps better than I can? But as a futurist and innovation consultant, I believe there’s a better question that one can ask: In what ways do I protect my career when the pace of AI progress is exponential, rather than linear?

My suggestions are below:

1. Stop Trying to Compete with AI on Efficiency. Compete on value

If your primary value add comes from sitting at a computer processing information, summarizing documents, generating reports, or performing predictable analysis, AI systems are intent on making you redundant. My suggestion here is to alter your value proposition.

In the legal arena, AI can conduct research, analyze and draft contracts, and otherwise do the job of entry-level workers. In healthcare, AI can read scans, analyze lab results, review medical journals, and suggest diagnoses. In customer service, genuinely capable AI agents are often more competent than call center workers. In 2023, AI struggled to write code. Today, at a growing number of companies, AI is writing much of the code.

Three years ago, AI could generate text but struggled to reason. In 2026, it solves complex problems step-by-step. In 2022, AI needed constant prompting. Today, agentic systems are planning and executing multi-stage projects on their own. And where AI once missed human nuance entirely, it is beginning to recognize emotion and adapt responses accordingly. You get the idea; AI is assaulting assumptions about what it can and cannot do at every juncture.

Many professionals unknowingly position themselves as competitors to automation. But competing on efficiency or productivity alone is a losing battle. To shift, ask yourself a different question: What do I uniquely contribute when the data is already available?

2. Become AI-fluent, starting today

NVIDIA CEO Jensen Huang warned in May 2025 at the Milken Institute Global Conference, “You’re not going to lose your job to an AI, you’re going to lose it to someone who uses AI.” Why not be that person instead?

In Build a Better Future: 7 Mindsets for Navigating the Age of Acceleration, I describe the Preparedness Mindset as most important of all — proactively anticipating change rather than reacting too late. Preparedness demands that, regardless of any misgivings about AI, we lean in to it, we become experts in it, and we design effective early warning systems to keep us abreast.

My suggestion is: spend time each week using new AI tools to draft communications, analyze data, brainstorm strategy, simulate customer conversations, and stress-test ideas. In doing so, you are not just learning to use new software. You are learning collaboration with a new type of intelligence. Those who understand what AI can and cannot do become indispensable translators between technology and business results. There’s no time to waste in becoming AI-fluent.

3. Hone your innovation skills

When the personal computer arrived, some employees feared it. Others stayed late learning spreadsheets and word processing. Within a few years, the difference in career trajectory was unmistakable. This same dynamic is unfolding again.

Tens of thousands of white-collar jobs are vanishing as AI starts to bite. Yet today organizations are desperately in need of people with an opportunity mindset – the outward focus to “find a (customer) need and fill it,” and to get new projects done, improve customer experience, motivate teams, enter new markets, and achieve unconventional results.

Human agency — the willingness to initiate action rather than await instruction — becomes a career differentiator. That might mean: proposing new AI-enabled services to clients, redesigning workflows, volunteering for experimental projects, or building personal expertise outside formal job descriptions. History shows that disruption rewards proactive learners who act on their ideas.

4. Move Closer to Problems, Not Tasks

AI replaces tasks faster than it replaces responsibility. Professionals who define themselves narrowly — “I prepare quarterly reports” or “I write marketing copy” — face greater exposure than those who own outcomes.

Executives increasingly value people who solve problems rather than execute assignments.

Consider shifting your identity toward improving customer retention, accelerating product innovation, strengthening culture, managing risk, or enabling growth. Tasks may change as AI evolves. Problems remain. This reflects what I call the Adaptability and Human Agency Mindsets — expanding your role faster than disruption can shrink it.

5. Develop A Long View of Value Creation

Periods of technological upheaval tempt people toward short-term survival thinking. Yet careers are marathons measured over decades. The professionals who flourish are those who continually reinvent how they add value.

Three forward-looking questions:

What skills will matter more five years from now?
What emerging problems will organizations struggle to solve?
Where can I become known as a trusted guide?

The Long View mindset encourages investing in capabilities that compound over time: leadership presence, interdisciplinary thinking, ethical judgment, and strategic foresight. Ironically, these human-centered abilities become more valuable as machines grow more capable.

The Opportunity Hidden Inside the Fear

As the futurist Thomas Koulopoulos observed in Gigatrends: Six Forces That Are Changing the Future for Billions, “As a species, we consistently allow the peril of the present to eclipse the promise of the future, and by doing that, we fail to comprehend just how much we can accomplish.”

Artificial intelligence will undoubtedly reshape entry-level work and certain knowledge professions. But history suggests something equally important: entirely new roles emerge alongside disruption. Entirely new opportunities will inevitably arise as well.

The printing press eliminated scribes but created publishers. The internet disrupted travel agents, yet produced digital marketing, cybersecurity, and platform entrepreneurship. AI will do the same.

The essential question is not whether change is coming. It is whether we as individuals choose to become passengers or navigators.

In an accelerated age, the safest career strategy is not hiding from technology but running toward it — with curiosity, agency, and vision. Those who learn fastest, adapt deliberately, and commit themselves to solving meaningful problems will not merely avoid displacement. They will help build the future that others are still struggling to understand.

This article originally appeared in Forbes

Image credit: Unsplash

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