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Why an AI Soft Landing Might Look Like Victorian England

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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

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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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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Win Your Way to an AI Job

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Anduril’s AI Grand Prix: Racing for the Future of Work

LAST UPDATED: January 28, 2026 at 2:27 PM

Anduril's AI Grand Prix: Racing for the Future of Work

GUEST POST from Art Inteligencia

The traditional job interview is an antiquated artifact, a relic of a bygone industrial era. It often measures conformity, articulateness, and cultural fit more than actual capability or innovative potential. As we navigate the complexities of AI, automation, and rapid technological shifts, organizations are beginning to realize that to find truly exceptional talent, they need to look beyond resumes and carefully crafted answers. This is where companies like Anduril are not just iterating but innovating the very hiring process itself.

Anduril, a defense technology company known for its focus on AI-driven systems, recently announced its AI Grand Prix — a drone racing contest where the ultimate prize isn’t just glory, but a job offer. This isn’t merely a marketing gimmick; it’s a profound statement about their belief in demonstrated skill over credentialism, and a powerful strategy for identifying talent that can truly push the boundaries of autonomous systems. It epitomizes the shift from abstract evaluation to purposeful, real-world application, emphasizing hands-on capability over theoretical knowledge.

“The future of hiring isn’t about asking people what they can do; it’s about giving them a challenge and watching them show you.”

— Braden Kelley

Why Challenge-Based Hiring is the New Frontier

This approach addresses several critical pain points in traditional hiring:

  • Uncovering Latent Talent: Many brilliant minds don’t fit the mold of elite university degrees or polished corporate careers. Challenge-based hiring can surface individuals with raw, untapped potential who might otherwise be overlooked.
  • Assessing Practical Skills: In fields like AI, robotics, and advanced engineering, theoretical knowledge is insufficient. The ability to problem-solve under pressure, adapt to dynamic environments, and debug complex systems is paramount.
  • Cultural Alignment Through Action: Observing how candidates collaborate, manage stress, and iterate on solutions in a competitive yet supportive environment reveals more about their true cultural fit than any behavioral interview.
  • Building a Diverse Pipeline: By opening up contests to a wider audience, companies can bypass traditional biases inherent in resume screening, leading to a more diverse and innovative workforce.

Beyond Anduril: Other Pioneers of Performance-Based Hiring

Anduril isn’t alone in recognizing the power of real-world challenges to identify top talent. Several other forward-thinking organizations have adopted similar, albeit varied, approaches:

Google’s Code Jam and Hash Code

For years, Google has leveraged competitive programming contests like Code Jam and Hash Code to scout for software engineering talent globally. These contests present participants with complex algorithmic problems that test their coding speed, efficiency, and problem-solving abilities. While not always directly leading to a job offer for every participant, top performers are often fast-tracked through the interview process. This allows Google to identify engineers who can perform under pressure and think creatively, rather than just those who can ace a whiteboard interview. It’s a prime example of turning abstract coding prowess into a tangible demonstration of value.

Kaggle Competitions for Data Scientists

Kaggle, now a Google subsidiary, revolutionized how data scientists prove their worth. Through its platform, companies post real-world data science problems—from predicting housing prices to identifying medical conditions from images—and offer prize money, and often, connections to jobs, to the teams that develop the best models. This creates a meritocracy where the quality of one’s predictive model speaks louder than any resume. Many leading data scientists have launched their careers or been recruited directly from their performance in Kaggle competitions. It transforms theoretical data knowledge into demonstrable insights that directly impact business outcomes.

The Human Element in the Machine Age

What makes these initiatives truly human-centered? It’s the recognition that while AI and automation are transforming tasks, the human capacity for ingenuity, adaptation, and critical thinking remains irreplaceable. These contests aren’t about finding people who can simply operate machines; they’re about finding individuals who can teach the machines, design the next generation of algorithms, and solve problems that don’t yet exist. They foster an environment of continuous learning and application, perfectly aligning with the “purposeful learning” philosophy.

The Anduril AI Grand Prix, much like Google’s and Kaggle’s initiatives, de-risks the hiring process by creating a performance crucible. It’s a pragmatic, meritocratic, and ultimately more effective way to build the teams that will define the next era of technological advancement. As leaders, our challenge is to move beyond conventional wisdom and embrace these innovative models, ensuring we’re not just ready for the future of work, but actively shaping it.

Anduril Fury

Frequently Asked Questions

What is challenge-based hiring?

Challenge-based hiring is a recruitment strategy where candidates demonstrate their skills and problem-solving abilities by completing a real-world task, project, or competition, rather than relying solely on resumes and interviews.

What are the benefits of this approach for companies?

Companies can uncover hidden talent, assess practical skills, observe cultural fit in action, and build a more diverse talent pipeline by focusing on demonstrable performance.

How does this approach benefit candidates?

Candidates get a fair chance to showcase their true abilities regardless of traditional credentials, gain valuable experience, and often get direct access to influential companies and potential job offers based purely on merit.
To learn more about transforming your organization’s talent acquisition strategy, reach out to explore how human-centered innovation can reshape your hiring practices.

Image credits: Wikimedia Commons, Google Gemini

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The Impact of Artificial Intelligence on Future Employment

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The Impact of Artificial Intelligence on Future Employment

GUEST POST from Chateau G Pato

The rapid progression of artificial intelligence (AI) has ignited both intrigue and fear among experts in various industries. While the advancements in AI hold promises of improved efficiency, increased productivity, and innumerable benefits, concerns have been raised about the potential impact on employment. As AI technology continues to evolve and permeate into different sectors, it is crucial to examine the implications it may have on the workforce. This article will delve into the impact of AI on future employment, exploring two case study examples that shed light on the subject.

Case Study 1: Autonomous Vehicles

One area where AI has gained significant traction in recent years is autonomous vehicles. While self-driving cars promise to revolutionize transportation, they also pose a potential threat to traditional driving jobs. According to a study conducted by the University of California, Berkeley, an estimated 300,000 truck driving jobs could be at risk in the coming decades due to the rise of autonomous vehicles.

Although this projection may seem alarming, it is important to note that AI-driven automation can also create new job opportunities. With the emergence of autonomous vehicles, positions such as remote monitoring operators, vehicle maintenance technicians, and safety supervisors are likely to be in demand. Additionally, the introduction of AI in this sector could also lead to the creation of entirely new industries such as ride-hailing services, data analysis, and infrastructure development related to autonomous vehicles. Therefore, while some jobs may be displaced, others will potentially emerge, resulting in a shift rather than a complete loss in employment opportunities.

Case Study 2: Healthcare and Diagnostics

The healthcare industry is another sector profoundly impacted by artificial intelligence. AI has already demonstrated remarkable prowess in diagnosing diseases and providing personalized treatment plans. For instance, IBM’s Watson, a cognitive computing system, has proved capable of analyzing vast amounts of medical literature and patient data to assist physicians in making more accurate diagnoses.

While AI undoubtedly enhances healthcare outcomes, concerns arise regarding the future of certain medical professions. Radiologists, for example, who primarily interpret medical images, may face challenges as AI algorithms become increasingly proficient at detecting abnormalities. A study published in Nature in 2020 revealed that AI could outperform human radiologists in interpreting mammograms. As AI is more widely incorporated into the healthcare system, the role of radiologists may evolve to focus on higher-level tasks such as treatment decisions, patient consultation, and research.

Moreover, the integration of AI into healthcare offers new employment avenues. The demand for data scientists, AI engineers, and software developers specialized in healthcare will likely increase. Additionally, healthcare professionals with expertise in data analysis and managing AI systems will be in high demand. As AI continues to transform the healthcare industry, the focus should be on retraining and up-skilling to ensure a smooth transition for affected employees.

Conclusion

The impact of artificial intelligence on future employment is a complex subject with both opportunities and challenges. While certain job roles may face disruption, AI also creates the potential for new roles to emerge. The cases of autonomous vehicles and AI in healthcare provide compelling examples of how the workforce can adapt and evolve alongside technology. Preparing for this transition will require a concerted effort from policymakers, employers, and individuals to ensure a smooth integration of AI into the workplace while safeguarding the interests of employees.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

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5 Things to Consider When Hiring Corporate Innovators

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5 Things to Consider When Hiring Corporate Innovators

GUEST POST from Stefan Lindegaard

As businesses continue to face unprecedented change and uncertainty, innovation is no longer optional – it’s a must-have for companies looking to survive and thrive. However, finding and developing the right people to drive corporate innovation can be challenging.

In this newsletter, we’ll explore five key ideas for hiring and developing individuals capable of leading corporate transformation and innovation forward, in a world where staying ahead of the curve is essential.

Future Potential vs. Past Competencies:

In the past, companies often hired innovators based on past competencies and results. However, the future of hiring will shift towards potential as a key criterion. Past success in other organizations is no guarantee of success in your own, and companies must adapt their hiring practices to focus on individuals who have shown a proven potential for constant learning, growth, and adaptability. Look for people who are capable of dealing well with ambiguity, adapting quickly to changing circumstances, and who possess the potential to succeed in your organization.

Knowing the Direction of Adaptation:

Organizations and talent alike must know the direction in which they need to adapt. However, it can be challenging to maintain an overview of the internal and external factors and trends impacting innovation efforts and capabilities. To tackle this issue, companies must experiment and develop ways to gauge and maintain an overview and/or direction.

For companies with a strong tradition of relying solely on the knowledge of internal R&D experts, it may require broader tracking of emerging trends, as well as reaching beyond R&D to other parts of the company for ideas on other ways to innovate. Consider all the areas where innovation can occur, including in business models, channels, and customer engagement, to name a few.

The Importance of Community Building:

Innovation is increasingly happening in ecosystems and communities, both internally and externally. Future innovation leaders must be able to create shared purpose, values, and rules of engagement to foster innovation within these communities. To build a successful community, strong networking and communication skills, as well as the ability to inspire people, are essential.

Companies should foster a culture of collaboration, encourage participation from diverse backgrounds and perspectives, and recognize and reward innovation efforts.

Creating the Right Conditions and Frameworks:

To make innovation work in big companies, it’s essential to create the right conditions and frameworks. This means allowing talent to experiment and explore new ideas freely, but also providing the resources, time, and support needed to make innovation efforts successful. Companies must be prepared to take risks and try new approaches, and foster a culture that encourages diversity of thought and collaboration. In addition, creating an inclusive culture that values diversity and recognizes the importance of different types of intelligence can also be beneficial for driving innovation forward.

The Importance of Multiple Intelligences:

Innovation requires a diverse range of skills, not just technical or product expertise. Future innovators must have a broad range of skills and experiences, including creativity, customer-centric thinking, and collaboration skills. Companies should consider different types of intelligence when hiring and developing innovation talent, such as emotional intelligence, social intelligence, and cultural intelligence. By valuing multiple intelligences and creating a culture that encourages diverse perspectives, companies can ensure they have the talent they need to drive innovation forward.

As the business landscape continues to evolve at a breakneck pace, innovation will be the key to survival for many companies. However, innovation is only possible with the right people in place. By shifting the focus from past competencies to future potential, tracking emerging trends and adapting accordingly, building strong communities, creating the right frameworks, and considering multiple types of intelligence, companies can hire and develop the right people for the job. Hiring full teams can also help foster innovation and bring about change faster.

By keeping these ideas in mind, companies can ensure that they have the talent they need to thrive in today’s fast-paced business environment.

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Rise of the Prompt Engineer

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Rise of the Prompt Engineer

GUEST POST from Art Inteligencia

The world of tech is ever-evolving, and the rise of the prompt engineer is just the latest development. Prompt engineers are software developers who specialize in building natural language processing (NLP) systems, like voice assistants and chatbots, to enable users to interact with computer systems using spoken or written language. This burgeoning field is quickly becoming essential for businesses of all sizes, from startups to large enterprises, to remain competitive.

Five Skills to Look for When Hiring a Prompt Engineer

But with the rapid growth of the prompt engineer field, it can be difficult to hire the right candidate. To ensure you’re getting the best engineer for your project, there are a few key skills you should look for:

1. Technical Knowledge: A competent prompt engineer should have a deep understanding of the underlying technologies used to create NLP systems, such as machine learning, natural language processing, and speech recognition. They should also have experience developing complex algorithms and working with big data.

2. Problem-Solving: Prompt engineering is a highly creative field, so the ideal candidate should have the ability to think outside the box and come up with innovative solutions to problems.

3. Communication: A prompt engineer should be able to effectively communicate their ideas to both technical and non-technical audiences in both written and verbal formats.

4. Flexibility: With the ever-changing landscape of the tech world, prompt engineers should be comfortable working in an environment of constant change and innovation.

5. Time Management: Prompt engineers are often involved in multiple projects at once, so they should be able to manage their own time efficiently.

These are just a few of the skills to look for when hiring a prompt engineer. The right candidate will be able to combine these skills to create effective and user-friendly natural language processing systems that will help your business stay ahead of the competition.

But what if you want or need to build your own artificial intelligence queries without the assistance of a professional prompt engineer?

Four Secrets of Writing a Good AI Prompt

As AI technology continues to advance, it is important to understand how to write a good prompt for AI to ensure that it produces accurate and meaningful results. Here are some of the secrets to writing a good prompt for AI.

1. Start with a clear goal: Before you begin writing a prompt for AI, it is important to have a clear goal in mind. What are you trying to accomplish with the AI? What kind of outcome do you hope to achieve? Knowing the answers to these questions will help you write a prompt that is focused and effective.

2. Keep it simple: AI prompts should be as straightforward and simple as possible. Avoid using jargon or complicated language that could confuse the AI. Also, try to keep the prompt as short as possible so that it is easier for the AI to understand.

3. Be specific: To get the most accurate results from your AI, you should provide a specific prompt that clearly outlines what you are asking. You should also provide any relevant information, such as the data or information that the AI needs to work with.

4. Test your prompt: Before you use your AI prompt in a real-world situation, it is important to test it to make sure that it produces the results that you are expecting. This will help you identify any issues with the prompt or the AI itself and make the necessary adjustments.

By following these tips, you can ensure that your AI prompt is effective and produces the results that you are looking for. Writing a good prompt for AI is a skill that takes practice, but by following these secrets you can improve your results.

So, whether you look to write your own AI prompts or feel the need to hire a professional prompt engineer, now you are equipped to be successful either way!

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Elevating the Importance of Construction and Manufacturing

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Elevating the Importance of Construction and Manufacturing

GUEST POST from Mike Shipulski

Restaurants aren’t open as much as they used to be because they cannot hire enough people to do the work. Simply put, there are too few people who want to take the orders; cook the food; deliver food to the tables; clear the tables; and wash the dishes. Sure, it’s an inconvenience that we can’t get a table, but because there are other ways to get food no one will starve because restaurants open. And while some restaurants will go out of business, this situation doesn’t fundamentally constrain the economy.

And the situation is similar with manufacturing and construction: no one wants those jobs either. But, that’s where the similarities end. The shortfall of people who want to work in manufacturing and construction will constrain the economy and prevent the renewal of our infrastructure. Gone are the days of relying on other countries to make all your products because we now know it’s not the most cost-effective way to go. But if there is no one willing to make the products, there will be no products made. And if there is no one willing to build the roads and bridges, roads and bridges will suffer. And if there are no products, no good roads, and no safe bridges, there can be no strong economy.

While there is disagreement around why people don’t want to work in manufacturing and construction, I will propose three for your consideration.

Firstly, the manufacturing and construction sectors have an image problem. People don’t see these jobs as high-tech, high-status jobs where the working environment is clean and safe. In short, people don’t see these jobs as jobs they can be proud and they don’t think others will think highly of them if they say they work in manufacturing or construction. And because of the history of layoffs, people don’t see these jobs as secure and predictable and don’t see them as reliable sources of income. This may not be the case for all people, but I think it applies to a lot of people.

Secondly, the manufacturing and construction sectors don’t pay enough. People don’t see these jobs as viable mechanisms to provide a solid standard of living for themselves and their families. This is a generalization, but I think it holds true.

Thirdly, the manufacturing and construction sectors require specialized knowledge, skills, and abilities skills that are not taught in traditional high schools or colleges. And without these qualifications, people are reluctant to apply. And if they do apply and a company hires them even though they don’t have the knowledge, skills, and abilities, companies must invest in training which creates a significant cost hurdle.

So, what are we to do?

To improve their image, the manufacturing and construction trade organizations and professional societies can come together and create a coordinated education program to change what people think about their industries. And states can help by educating their citizens on the importance of manufacturing and construction to the health of the states’ economies. This will be a long road, but I think it’s time to start.

To attract new talent, the manufacturing and construction sectors must pay a higher wage. In the short term, profits may be reduced, but imagine how much profits will be reduced if there are no people to build the products or fix the bridges. And over the long term, with improved business processes and working methods, profits will grow.

To train people to work in manufacturing and construction, we can reinstitute the Training Within Industry program of the 1940s. The Manufacturing Extension Partnership programs within the states can be a center of mass for this work along with the Construction Industry Institute and other construction trade organizations.

It’s time to join forces to make this happen.

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Top 10 Human-Centered Change & Innovation Articles of January 2023

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Top 10 Human-Centered Change & Innovation Articles of January 2023Drum 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 January’s ten most popular innovation posts:

  1. Top 40 Innovation Bloggers of 2022 — Curated by Braden Kelley

  2. Back to Basics: The Innovation Alphabet — by Robyn Bolton

  3. 99.7% of Innovation Processes Miss These 3 Essential Steps — by Robyn Bolton

  4. Top 100 Innovation and Transformation Articles of 2022 — Curated by Braden Kelley

  5. Ten Ways to Make Time for Innovation — by Nick Jain

  6. Agility is the 2023 Success Factor — by Soren Kaplan

  7. Five Questions All Leaders Should Always Be Asking — by David Burkus

  8. 23 Ways in 2023 to Create Amazing Experiences — by Shep Hyken

  9. Startups Must Be Where Their Customers Are — by Steve Blank

  10. Will CHATgpt make us more or less innovative? — by Pete Foley

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

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 three years:

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The Five Gifts of Uncertainty

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The Five Gifts of Uncertainty

GUEST POST from Robyn Bolton

“How are you doing?  How are you handling all this?”

It seems like 90% of conversations these days start with those two sentences.  We ask out of genuine concern and also out of a need to commiserate, to share our experiences, and to find someone that understands.

The connection these questions create is just one of the Gifts of Uncertainty that have been given to us by the pandemic.

Yes, I know that the idea of uncertainty, especially in big things like our lives and businesses, being a gift is bizarre.  When one of my friends first suggested the idea, I rolled my eyes pretty hard and then checked to make sure I was talk to my smart sarcastic fellow business owner and not the Dali Lama.

But as I thought about it more, started looking for “gifts” in the news and listening for them in conversations with friends and clients, I realized how wise my friend truly was.

Faced with levels of uncertainty we’ve never before experienced, people and businesses are doing things they’ve never imagined having to do and, as a result, are discovering skills and abilities they never knew they had.  These are the Five Gifts of Uncertainty

  1. Necessity of offering a vision – When we’re facing or doing something new, we don’t have all the answers. But we don’t need all the answers to take action.  The people emerging as leaders, in both the political and business realms, are the ones acknowledging this reality by sharing what they do know, offering a vision for the future, laying out a process to achieve it, and admitting the unknowns and the variables that will affect both the plan and the outcome.

  2. Freedom to experiment – As governments ordered businesses like restaurants to close and social distancing made it nearly impossible for other businesses to continue operating, business owners were suddenly faced with a tough choice – stop operations completely or find new ways to continue to serve. Restaurants began to offer carry out and delivery.  Bookstores, like Powell’s in Portland OR and Northshire Bookstore in Manchester VT, also got into curbside pick-up and delivery game.  Even dentists and orthodontists began to offer virtual visits through services like Wally Health and Orthodontic Screening Kit, respectively.

  3. Ability to change – Businesses are discovering that they can move quickly, change rapidly, and use existing capabilities to produce entirely new products. Nike and HP are producing face shields. Zara and Prada are producing face masks. Fanatics, makers of MLB uniforms, and Ford are producing gowns.  GM and Dyson are gearing up to produce ventilators. And seemingly every alcohol company is making hand sanitizer.  Months ago, all of these companies were in very different businesses and likely never imagined that they could or would pivot to producing products for the healthcare sector.  But they did pivot.

  4. Power of Relationships – Social distancing and self-isolation are bringing into sharp relief the importance of human connection and the power of relationships. The shift to virtual meetups like happy hours, coffees, and lunches is causing us to be thoughtful about who we spend time with rather than defaulting to whoever is nearby.  We are shifting to seeking connection with others rather than simply racking up as many LinkedIn Connections, Facebook friends, or Instagram followers as possible.  Even companies are realizing the powerful difference between relationships and subscribers as people unsubscribed en mass to the “How we’re dealing with COVID-19 emails” they received from every company with which they had ever provided their information.

  5. Business benefit of doing the right thing – In a perfect world, businesses that consistently operate ethically, fairly, and with the best interests of ALL their stakeholders (not just shareholders) in mind, would be rewarded. We are certainly not in a perfect world, but some businesses are doing the “right thing” and rea being rewarded.  Companies like Target are offering high-risk employees like seniors pregnant women, and those with compromised immune systems 30-days of paid leave.  CVS and Comcast are paying store employees extra in the form of one-time bonuses or percent increases on hourly wages.  Sweetgreen and AllBirds are donating food and shoes, respectively, to healthcare workers.  On the other hand, businesses that try to leverage the pandemic to boost their bottom lines are being taken to task.  Rothy’s, the popular shoe brand, announced on April 13 that they would shift one-third of their production capacity to making “disposable, non-medical masks to workers on the front line” and would donate five face masks for every item purchased.  Less than 12 hours later, they issued an apology for their “mis-step,” withdrew their purchase-to-donate program, and announced a bulk donation of 100,000 non-medical masks.

Before the pandemic, many of these things seemed impossibly hard, even theoretical.  In the midst of uncertainty, though, these each of these things became practical, even necessary.  As a result, in a few short weeks, we’ve proven to ourselves that we can do what we spent years saying we could not.

These are gifts to be cherished, remembered and used when the uncertainty, inevitably, fades.

Image credit: Pixabay

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Why are so many people quitting?

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Why are so many people quitting?

GUEST POST from Mike Shipulski

People don’t leave a company because they feel appreciated.

People don’t leave a company because they feel part of something bigger than themselves.

People don’t leave a company because they see a huge financial upside if they stay.

People don’t leave a company because they are treated with kindness and respect.

People don’t leave a company because they can make less money elsewhere.

People don’t leave a company because they see good career growth in their future.

People don’t leave a company because they know all the key players and know how to get things done.

People don’t leave the company so they can abandon their primary care physician.

People don’t leave a company because their career path is paved with gold.

People don’t leave a company because they are highly engaged in their work.

People don’t leave a company because they want to uproot their kids and start them in a new school.

People don’t leave a company because their boss treats them too well.

People don’t leave a company because their work is meaningful.

People don’t leave a company because their coworkers treat them with respect.

People don’t leave a company because they want to pay the commission on a real estate transaction.

People don’t leave a company because they’ve spent a decade building a Trust Network.

People don’t leave a company because they want their kids to learn to trust a new dentist.

People don’t leave a company because they have a flexible work arrangement.

People don’t leave a company because they feel safe on the job.

People don’t leave a company because they are trusted to use their judgment.

People don’t leave the company because they want the joy that comes from rolling over their 401k.

People don’t leave a company when they have the tools and resources to get the work done.

People don’t leave a company when their workload is in line with their capacity to get it done.

People don’t leave a company when they feel valued.

People don’t leave a company so they can learn a whole new medical benefits plan.

People don’t leave a job because they get to do the work the way they think it should be done.

So, I ask you, why are people leaving your company?

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