Artificial Intelligence is a Rorschach Test

Artificial Intelligence is a Rorschach Test

GUEST POST from Geoffrey A. Moore

Concerns about the potential negative impact of AI on humanity’s future well-being continue to foster discussion across a wide swath of society with pundits weighing in from every imaginable point of view. The fundamental unit of discourse that unites all these efforts is the scenario. As humans, when we have no facts, we generate narratives, which we then mine for insights and test for credibility. In the high-tech sector, we have been doing this for decades because disruptive innovations, by virtue of their very novelty, have no history, and so must win investment capital and early adopter support through story-telling.

As a former literature professor, I could not feel more at home. So, let us apply a little literary criticism to some of the doomsday narratives currently in circulation. Start with the Terminator scenario. Great movie—but if we take it literally for a moment, I don’t think its core premise can hold up. That premise is that an AI system can have the same kind of intention and ambition that underlies human behavior. But intention and ambition, attributes shared not just by humans but by all living things, are anchored in an involuntary compulsion to live and reproduce. Human beings, though fragile individually, are an integral manifestation of life itself, and life itself has an extraordinary performance record, having been playing Planet Earth uninterruptedly for over four billion years (beat that, Taylor Swift!) despite meteor strikes, ice ages, and massive volcanic eruptions. AI systems can be programmed to mimic and adopt our strategies for living, but they have no compulsion to live, and it has nothing like this heritage behind it.

A far more chilling narrative, to my way of thinking, is AI in the hands of malicious human actors. This is hardly a scenario, for we have already seen it wreak havoc across the digitally transforming landscape that constitutes contemporary society. The most immediate existential threat is releasing self-governing AI agents that slip the bounds of their control system and promulgate horrific consequences. This is the Jurassic Park narrative, and while its biology is fanciful, its theme of unintended consequences is anything but.

Preparing for this possibility is where various governmental agencies are focusing much of their attention, but here too the narrative has a credibility problem. The notion that legislative bodies could possibly keep pace with the pact of AI’s evolution, not to mention enlisting the societal support necessary to enforce their regulatory efforts, is simply ludicrous. And that brings us to a third narrative for context, Natural Selection.

When living things are put under existential threat, they accelerate their rate of mutation, abandoning the safe and steady course of inertial progress, because that is no longer safe at all. It’s ‘innovate or die’ time. Most of these mutations fail, but for four billion years, at least some of them have always succeeded. If we transplant that strategy into the human realm, it argues for enlisting agile, individual, and hopefully well-meaning talent to engage with a raft of unanticipated challenges, a sea of troubles, and by opposing end them. Legislation can help ratify and scale successful responses once they have been proven effective, but it cannot prevent the challenges from emerging in the first place, and frankly, should not try. Of course, it will try, and that I expect will add yet another layer of unintended consequences onto a plate that is already full.

That’s what I think. What do you think?

Image Credit: Gemini

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Innovation or Not – Midjourney Medical and the Illusion of Frictionless Health

Innovation or Not - Midjourney Medical and the Illusion of Frictionless Health

by Braden Kelley and Art Inteligencia

For years, the technology world has watched Midjourney dominate the digital canvas, turning text prompts into breathtaking generative art. But in an unexpected, high-stakes pivot, the self-funded AI research lab is shifting its focus from software pixels to heavy medical hardware. Under the visionary direction of David Holz, the company is attempting to completely rearchitect how we map the human anatomy by introducing a 60-second immersion tank designed to challenge the established medical imaging status quo.

“We want to turn a cold, clinical, and often terrifying event into a casual, proactive trip to the spa.”

By moving away from the intimidating, clanging cylinders of traditional radiology and steering toward consumer wellness spaces filled with pools of golden light, Midjourney is attempting a massive feat of experience design. However, as any strategist knows, a beautiful interface does not inherently solve a complex medical problem.

From a human-centered innovation perspective, we have to look past the aesthetic appeal and ask the hard questions: Can a system built on ultrasound waves and massive computational reconstruction genuinely disrupt the deeply entrenched MRI and CT scan markets? Or is this an overhyped, physics-constrained novelty that risks creating more diagnostic noise than actual clinical value? Let’s break down the genesis, the mechanics, and the economic realities of this emerging technology to determine if it is a true paradigm shift — or simply a brilliant illusion.

Section I: The Genesis of an AI Outlier (Core Business vs. The Hardware Leap)

To understand the magnitude of this shift, you have to look at the sheer contrast in business models. Midjourney built its empire as a lean, hyper-profitable software-as-a-service (SaaS) platform, leveraging massive cloud compute to generate digital art for millions of subscribers. Moving from that friction-free digital realm into the high-risk, heavily regulated world of medical hardware is a leap few saw coming.

But this isn’t a random detour; it is a calculated bet on the convergence of physics and algorithms. Midjourney isn’t building the foundational hardware entirely from scratch. Instead, they have formed a massive $74 million co-development partnership with Butterfly Network, utilizing forty of their cutting-edge “Ultrasound-on-Chip” silicon modules. By combining Butterfly’s semiconductor-based ultrasound technology with Midjourney’s world-class computational reconstruction capabilities, the goal is to transform chaotic acoustic waves into crisp, full-body anatomical maps.

The strategic play here is treating massive compute power and large-scale AI models as a universal hammer to solve complex, real-world data reconstruction problems.

Founder David Holz’s broader organizational philosophy treats software and hardware as two sides of the same coin, balancing a portfolio of four software projects and four hardware initiatives. By treating the human body as a data set waiting to be rendered, Midjourney is attempting to prove that the core competency of an AI company isn’t just generating beautiful images — it is interpreting complex physical data to design a healthier, lower-friction human experience.

Ultrasound on a Chip Foundation

Section II: Modality Breakdown — The Midjourney Scanner vs. MRI vs. CT

To evaluate whether Midjourney’s system can legitimately disrupt medical radiology, we must contrast its core mechanics against the industry workhorses: Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). While the immersion tank is designed to feel frictionless, the underlying physics presents a starkly different story of trade-offs.

The core hardware architecture relies on arrays of semiconductor chips, a massive shift from traditional radiation or magnetic resonance equipment.

Here is how the three modalities compare across their primary operational, infrastructural, and physical characteristics:

Feature Midjourney “Ultrasonic CT” Conventional MRI Conventional CT Scan
Primary Physics Ultrasound (Sound waves + water immersion) Powerful Magnetic Fields + Radio Waves Ionizing Radiation (X-rays)
Scan Duration ~60 seconds 30 to 90 minutes 5 to 15 minutes
Infrastructure Consumer wellness space (“Midjourney Spa”) Shielded clinical room, liquid helium cooling Hospital/clinical radiology department
Inherent Limits Struggles with dense bone and air-filled organs (lungs) Claustrophobia, zero metal allowed, high maintenance Radiation exposure limits frequency of use
Clinical Utility Non-diagnostic body composition mapping (Gen-1) Deep tissue, neurological, and joint diagnostics Bone fractures, internal bleeding, acute chest/abdo

The Definite Advantages

  • Zero Ionizing Radiation: Unlike a CT scan, which uses X-rays, Midjourney’s scanner uses acoustic waves. This makes it safe for repeated, routine baseline monitoring.
  • Speed and Comfort: A 60-second immersion entirely side-steps the extreme claustrophobia and deafening, jackhammer-like thumping of an MRI machine.
  • Decentralized Infrastructure: Because it doesn’t require liquid helium cooling or radiation-shielded walls, it can exist in light commercial real estate rather than expensive hospital wings.

The Unforgiving Disadvantages

This is where the laws of physics present a massive wall. Ultrasound waves travel exceptionally well through water and soft tissue, but they scatter severely when encountering dense bone or air pockets.

An MRI uses radio frequencies to manipulate hydrogen atoms, providing unparalleled resolution of soft tissues, brains, and ligaments. A CT scan cuts through bone with mathematical precision. Midjourney’s scanner, by using ultrasound, inherently struggles to “see” inside the skull or provide precise diagnostic data on air-filled lungs. While their massive AI model can use predictive algorithms to stitch scattered sound waves together, it runs the dangerous risk of hallucinating details to fill in acoustic blind spots — a minor issue for digital art, but a fatal flaw for a medical diagnosis.

Section III: The Economics of the Scan (Cost per Test)

To understand how Midjourney intends to disrupt the medical imaging market, we have to look past the technology and analyze the economic ecosystem. Traditional healthcare radiology is built on a highly centralized, capital-intensive model. Midjourney, true to its technology roots, is attempting to deploy a decentralized, high-volume model that relies on radical unit economic scaling.

The Heavy Burden of Legacy Systems

Traditional MRI and CT systems are financial black holes for healthcare providers before a single patient even walks through the door. A new, high-field MRI machine typically costs between $1 million and $3 million upfront, paired with hundreds of thousands of dollars in annual maintenance contracts, specialized software licensing, and the continuous cost of liquid helium for cooling.

When you factor in specialized radiologic technologist labor, hospital facility overhead, and the necessary physician interpretation fees, the cost passed to the consumer or insurance provider explodes. A standard MRI scan in the United States ranges from $400 to over $12,000, depending entirely on the hospital system and insurance coverage. This extreme cost makes scanning inherently reactive — reserved only for acute crises or post-injury confirmation.

“The legacy model treats imaging as a scarce, expensive luxury. Midjourney’s objective is to treat imaging data as an abundant commodity.”

Silicon Scaling vs. Superconducting Magnets

Midjourney’s approach completely bypasses these legacy infrastructure costs by leaning heavily on semiconductor technology. By utilizing Butterfly Network’s Ultrasound-on-Chip modules, the hardware costs scale alongside the manufacturing efficiencies of the silicon industry, rather than the expensive raw materials required for massive superconducting magnets.

This hardware shift enables a completely different operational scale. Midjourney has laid out an incredibly aggressive target: 50,000 scanners deployed globally by 2031, with the capability to process an astonishing 1 billion scans per month.

The Consumer Subscription Paradigm

Because the upfront infrastructure costs are significantly lower, Midjourney can entirely opt out of the complex, bureaucratic insurance reimbursement pipeline. Instead, they are positioning the scanner as an out-of-pocket, direct-to-consumer wellness product.

By matching the consumer subscription architecture of their core generative art business, a full-body scan could realistically be priced at a fraction of a clinical scan — democratizing access to full-body physical tracking. This changes the consumer paradigm entirely: instead of paying thousands of dollars for a one-time diagnostic scan after getting hurt, users pay a predictable, accessible fee to continuously monitor their baseline health over time.

Section IV: The Experience Design and Human Factors

As a human-centered design practitioner, this is where the Midjourney project becomes truly fascinating. Innovation isn’t just about the underlying technology; it is about how that technology fits into the fabric of human life. Midjourney is attempting a radical intervention in experience architecture, completely reimagining the emotional and sensory journey of medical imaging.

Friction Reduction: From Clinical Dread to Spa-Like Sanctuary

The traditional imaging experience is fundamentally hostile to human comfort. To get a standard MRI, a patient is slid into a cramped, freezing, claustrophobic plastic tube, instructed not to swallow or breathe for long intervals, and subjected to a deafening, metallic jackhammer cadence. It is an experience designed around the machine, not the human.

Midjourney completely flips this dynamic. By embedding forty ultrasound chips into an immersion tank, they replace clinical dread with sensory-focused relaxation. The user steps into a warm, shallow pool of water enveloped by soft, golden light. The entire scan takes a mere 60 seconds, requiring no breath-holds or structural restraints. By removing the psychological barriers of fear and discomfort, Midjourney converts a medical chore into a low-friction wellness ritual.

“True human-centered innovation doesn’t just make a system faster; it alters how the user feels while engaging with it.”

The Behavioral Shift: Reactive Crisis vs. Proactive Benchmarking

This experiential shift fundamentally alters human behavior. Today, we view medical scans as reactive interventions — something you endure only when you are broken, injured, or deeply sick.

By lowering both physical and financial friction, Midjourney aims to transition users into a state of proactive health tracking. Instead of a frantic, single-point-in-time diagnostic event, the full-body scan becomes an ongoing baseline. Users can visualize changes in their body composition, muscle mass, and internal soft-tissue structures month-over-month, shifting the health paradigm from waiting for illness to actively managing wellness.

The Over-Diagnosis Trap and “Clinical Noise”

However, an optimized user experience can still lead to systemic friction. Medical professionals are already raising alarms about the over-diagnosis trap. The human body is beautifully imperfect; we are filled with benign cysts, harmless nodules, and structural anomalies that will never cause us harm.

When you give millions of consumers an effortless, low-cost way to scan their entire bodies every month, you inevitably generate a massive influx of “clinical noise.” A user sees an unfamiliar shadow on their automated Midjourney report, panics, and floods the traditional healthcare system demanding specialist consultations, biopsies, and secondary MRIs. More data does not automatically equal better health. If an experience-driven tool inadvertently drives healthy people into spiral of unnecessary medical anxiety and drains clinical resources, it fails the ultimate test of human-centered utility.

Section V: The Regulatory and Future Development Roadmap

The leap from software pixels to medical-grade diagnostics is governed by an uncompromising arbiter: regulatory clearance. In the United States, the Food and Drug Administration (FDA) treats diagnostic machinery with the highest level of scrutiny. To navigate this reality without grinding their momentum to a halt, Midjourney is executing a highly strategic, phased rollout.

The Wellness Sidestep: Launching under General Wellness Guidance

Midjourney is deliberately holding back from making immediate disease diagnoses. When the first flagship “Midjourney Spa” opens its doors near Union Square in San Francisco in late 2027, it will strictly offer “detailed body composition maps.” By focusing solely on measuring muscle volumes, body fat distribution, and skeletal structures without asserting clinical diagnoses, Midjourney can launch under the FDA’s General Wellness Policy.

This is the exact same low-risk, non-invasive regulatory lane utilized by premium whole-body MRI screening services like Prenuvo and Ezra. It allows Midjourney to immediately commercialize the technology, build consumer habits, and generate cash flow while completely bypassing the years of grueling clinical trials required for formal diagnostic approval.

“The short-term goal is to do what is regulatorily simple to establish the footprint. The long-term goal is incremental validation.”

The Massive Computational Challenge

While David Holz noted that the Gen-1 prototype doesn’t even rely on generative AI yet, the data reconstruction pipeline is an absolute beast. The machine’s ring of 40 custom Butterfly Network chips streams roughly 17 gigabytes of raw acoustic data per second.

Processing these non-linear inverse scattering problems — essentially stitching scattered sound waves into a coherent, sub-millimeter 3D volume — demands over two petaflops of on-device computational power. The future development roadmap relies heavily on refining these proprietary algorithms to cleanly differentiate tissue boundaries over the next 12 to 24 months.

The 10-Year Vision: Diagnostics and Beyond

Midjourney has already initiated preliminary discussions with the FDA. The overarching strategy is a rolling submission process: as their data sets grow from thousands of consumer scans, they will submit clinical test results to the FDA to unlock “increased capabilities” piece by piece.

Over a ten-year horizon, Midjourney expects these machines to evolve far beyond basic body mapping into tools capable of running thousands of automated diagnostic cross-checks. Holz has even hinted at a long-term future where the hardware isn’t just used for passive imaging, but scales into localized, acoustic therapeutic applications as well.

Conclusion: Innovation or Not? The Verdict

When evaluating an emerging technology through the lens of strategic foresight and human-centered design, we must separate the seductive pull of an exquisite user experience from the hard reality of systemic impact. Midjourney’s full-body scanner is undeniably one of the most audacious pivots in tech history, but does it truly deserve the title of an innovation?

Why it IS an Innovation

From an experiential standpoint, it is a masterclass in friction reduction. It takes a universally dreaded clinical procedure — the cold, loud, claustrophobic machinery of legacy radiology — and transforms it into an accessible, 60-second wellness ritual. By combining semiconductor-based ultrasound with high-petaflop computational reconstruction, Midjourney is bypassing the multi-million-dollar physical constraints of traditional MRIs. If they achieve their goal of global scale, they will successfully shift human behavior from reactive crisis management to proactive, continuous health tracking.

Why it might NOT be

However, an innovative interface cannot rewrite the fundamental laws of physics. Ultrasound waves scatter when facing dense bone and air, leaving inherent diagnostic blind spots that cannot be entirely solved by predictive code. Furthermore, by making full-body scans an effortless consumer commodity, Midjourney risks unlocking the over-diagnosis trap — flooding the healthcare ecosystem with false positives, benign findings, and “clinical noise” that triggers immense medical anxiety and strains real-world clinical resources.

“True innovation does not just solve a human friction point on the front end; it ensures it does not create a deeper systemic failure on the back end.”

The Final Verdict

Ultimately, Midjourney Medical is a qualified innovation. It is a brilliant, high-compute disruption of the preventative wellness space, but it is not a true replacement for the diagnostic precision of an MRI or CT scan. Until the technology undergoes rigorous clinical validation and handles acoustic blind spots without the risk of algorithmic hallucinations, it remains an extraordinary tool for proactive physical benchmarking. David Holz and his team have designed an incredible, low-friction gateway to our data — but for now, the spa-like sanctuary is a complement to medicine, not a substitute for it.

Frequently Asked Questions

1. Can the Midjourney full-body scanner completely replace a traditional hospital MRI or CT scan?

No, it cannot replace them. While Midjourney’s scanner offers a fast, comfortable 60-second experience, it relies on ultrasound-on-chip technology. Sound waves inherently struggle to penetrate dense bone or image air-filled organs like the lungs. Traditional MRIs and CT scans use magnetic fields and X-rays, providing deep-tissue and skeletal diagnostic precision that ultrasound waves simply cannot achieve due to the laws of physics.

2. Does the Midjourney scanner have FDA approval for medical diagnostics?

No. Midjourney is deliberately launching the device under the FDA’s General Wellness Policy guidelines, focusing strictly on “body composition mapping” (such as muscle volume and fat distribution) rather than diagnosing specific diseases. This allows them to open consumer wellness spaces by late 2027 without waiting years for clinical diagnostic trials, though they plan a rolling submission process to gain incremental diagnostic approvals over the next decade.

3. How does the cost of a Midjourney scan compare to traditional clinical imaging?

Traditional MRIs and CT scans are highly centralized and expensive, ranging anywhere from $400 to over $12,000 depending on insurance and hospital overhead. Because Midjourney uses silicon semiconductor chips instead of multi-million dollar superconducting magnets, their hardware scaling costs are drastically lower. Midjourney bypasses insurance entirely, offering direct-to-consumer out-of-pocket pricing structured around an affordable, subscription-based wellness model.


Image credits: Google Gemini, The Robot Report

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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The AI Apprenticeship Economy

Rebuilding the Career Ladder in the Machine Age – An AI Soft Landing Scenario

LAST UPDATED: June 20, 2026 at 11:02 AM

The AI Apprenticeship Economy

by Braden Kelley and Art Inteligencia


The Silent Erasure of the Learning Runway

For generations, professional growth followed a predictable, slow-rolling rhythm: enter at the bottom, grind through repetitive entry-level tasks, absorb tacit knowledge from senior colleagues by osmosis, and gradually earn the right to make strategic decisions. It was an expensive, deeply human, and highly localized model. Entry-level jobs were never just about immediate output; they were society’s primary apprenticeship infrastructure. They provided the safe sandboxes where junior talent could observe experts, make low-risk mistakes, and build foundational professional confidence.

Today, generative AI and autonomous agents threaten to obliterate that foundation by instantly executing the very baseline tasks—writing basic code, drafting initial copy, analyzing standardized datasets—that used to be the domain of the junior professional. Much of the current AI conversation focuses on this displacement, viewing it as a straightforward labor crisis. However, looking at this shift simply as a “job destruction” event misses the true structural vulnerability: we aren’t just losing entry-level jobs; we are losing our capability-building infrastructure. If machines do all the beginner work, how do humans ever gain the context, failure-resilience, and judgment required to become experts?

The answer is not to fight automation, but to completely rethink organizational design. The future of work is not an empty ladder, but an AI Apprenticeship Economy where intelligent systems shift from being automated replacements to scalable, human-centered capability accelerators. Instead of erasing the path to expertise, the next generation of organizations must use artificial intelligence as the greatest learning engine humanity has ever created—shifting the ultimate competitive advantage from talent acquisition to talent manufacturing.

I. The Entry-Level Job Crisis May Actually Be a Learning Model Crisis

The current public discourse surrounding artificial intelligence in the workplace is dominated by a single, pervasive anxiety: mass displacement at the bottom of the pyramid. Executives look at the capabilities of modern language models and autonomous agents and see an immediate opportunity to optimize bottom-line efficiency. The calculations seem straightforward. Why hire a team of junior analysts, junior developers, or entry-level copywriters when an AI assistant can generate reports, debug code, and churn out marketing assets in a fraction of the time and at a fraction of the cost?

This focus on immediate productivity gains exposes a dangerous leadership blindspot. Entry-level positions have never been purely about transactional output. Their true, hidden function has always been cultural and developmental—they serve as society’s primary capability-building infrastructure. By automating away the “grunt work,” organizations are inadvertently dismantling the very runways that allowed young professionals to transition from theoretical knowledge to practical wisdom.

To understand what is at stake, we must map the critical components of the traditional entry-level learning model that pure automation threatens to erase:

  • The Observation of Mastery: Junior professionals learn how to navigate organizational politics, manage client relationships, and handle ambiguity not from textbooks, but by sitting in rooms and watching senior leaders behave.
  • The Safe Sandbox: Low-stakes, repetitive tasks provide a safe environment to make mistakes, receive feedback, and build resilience without risking mission-critical organizational assets.
  • The Development of Taste and Judgment: Reviewing data, drafting initial briefs, and filtering information forces a novice to actively practice discrimination—discovering the subtle difference between an output that is technically correct and one that is strategically brilliant.
  • Contextual Assimilation: Spending time in the operational weeds allows an individual to internalize the unique language, unwritten rules, and historical context of a specific enterprise.

When an organization replaces its junior cohort with automated systems, it gains an immediate spike in efficiency but incurs a massive, hidden deficit in long-term capability. We are creating an unsustainable corporate ecosystem: a top-heavy structure populated by aging experts with no incoming pipeline of seasoned talent to eventually replace them.

The fundamental challenge of the machine age is not that we will run out of tasks for humans to do. The challenge is that if we allow machines to perform all the beginner tasks, we eliminate the very experiences humans need to become intelligent. The crisis we face is not an employment crisis; it is a systemic learning crisis that requires an entirely new framework for professional growth.

II. The Rise of the AI Apprenticeship Economy

The structural vulnerability of the learning crisis forces a radical pivot in how we view technology. The AI Apprenticeship Economy emerges the moment progressive organizations stop treating artificial intelligence as a tool for labor subtraction and begin deploying it as an infrastructure for human amplification. In this new paradigm, AI is repositioned from an automated replacement for junior talent into the ultimate accelerator for human capability development.

Instead of using machines to bypass the novice altogether, we must wrap machines around the novice to collapse the distance between inexperience and mastery. AI becomes the hyper-personalized tutor, the infinite simulator, the objective coach, and the safe practice environment. The technology allows an apprentice to compress decades of tacit experience into months of hyper-focused, simulated engagement.

To understand how this fundamentally alters the professional life cycle, we must look at how the legacy career trajectory compares directly to the accelerated, AI-augmented model:

Dimension The Traditional Career Model The AI-Enabled Apprenticeship Model
Core Sequence Education → Entry Job → Osmosis → Gradual Expertise Education → AI Simulation → Real Application → Accelerated Expertise
Feedback Loop Delayed, intermittent, dependent on manager availability. Instantaneous, constant, data-driven, and emotionally safe.
Exposure Rate Dependent on the random luck of which projects land on a desk. Systematic exposure to thousands of curated operational scenarios.
Role of Novice Transactional order-taker focused on raw data/text execution. AI conductor-in-training focused on validation and context framing.

Under the traditional model, developing true business acumen required a massive runway of time because humans had to wait for real-world scenarios to organically occur. A junior professional might only witness a major corporate turnaround, a severe product failure, or a complex negotiation a handful of times in their first five years.

The AI Apprenticeship Economy removes this constraint. By leveraging specialized internal models, a junior employee can interact with synthetic customer segments, stress-test strategic frameworks against historical data, and defend their ideas against an AI trained to mimic the company’s toughest board members. The apprentice gains profound exposure before they are granted high-stakes authority, arriving at real-world projects with an already sharpened sense of judgment.

III. AI as the World’s First Scalable Mentor

Throughout history, the greatest bottleneck to human development has been the scarcity of elite mentorship. True apprenticeship has always been a luxury good, fundamentally constrained by physics, geometry, and economics. A master craftsman, a visionary designer, or a brilliant corporate strategist only has so many hours in a day, so much patience, and the capacity to deeply guide a small handful of protégés. Because of this structural limitation, world-class professional incubation remained an accidental privilege—dependent on landing the right role, in the right office, under the right manager.

Artificial intelligence breaks this scarcity model forever. In the AI Apprenticeship Economy, we transition from an era of rationed guidance to an era of ubiquitous, zero-marginal-cost mentorship. By training specialized AI agents on the accumulated institutional knowledge, decision-making frameworks, and historical case studies of an enterprise, organizations can provide every single employee with an always-on, hyper-personalized cognitive mentor. This agent does not do the work for the apprentice; instead, it acts as a Socratic sparring partner that forces the apprentice to think deeper, challenge assumptions, and safely build creative muscle.

To see this shift in action, we can look at how the role of scalable mentorship translates across distinct corporate functions:

  • The Junior Product Manager: Instead of executing basic backlog grooming, the novice PM utilizes an AI simulation framework to stress-test an upcoming feature rollout. The AI simulates high-pressure executive board reviews, challenges the PM’s monetization assumptions, generates synthetic customer friction points based on historical user research, and provides an objective critique of their strategic messaging before they ever present to human leadership.
  • The New Experience Designer: Rather than spending days manually moving pixels for a single layout variation, the apprentice designer directs an AI system to generate hundreds of radical user-flow permutations overnight. The AI then acts as a design critic, evaluating each option against established behavioral science principles, pointing out accessibility vulnerabilities, and challenging the designer to justify their aesthetic and functional choices.
  • The Associate Systems Engineer: Instead of watching an expert fix infrastructure bugs from a distance, the new engineer works inside an isolated, simulated environment. The AI mentor deliberately injects complex, real-world architectural failures into the system, dynamically coaching the engineer through conversational troubleshooting, explaining hidden dependencies, and ensuring they understand the underlying system mechanics before touching live code.

This evolution fundamentally alters the relationship between the novice and the organization. By deploying AI as a cognitive coach, we remove the fear of failure that typically paralyzes junior talent. The apprentice can ask seemingly simple questions without judgment, test highly unconventional ideas in a safe sandbox, and master foundational patterns at their own individual pace. The result is a workforce that gains a profound depth of operational exposure and context before they are ever handed the keys to high-stakes organizational authority.

IV. The Compression of Expertise & The New Human Core

Every major technological paradigm shift can be fundamentally measured by how drastically it compresses human capability and alters the velocity of knowledge transfer. The invention of the printing press decentralized knowledge storage, instantly removing the requirement for memorization and manual transcription. The expansion of the internet decentralized information retrieval, turning the challenge of finding data into a simple search query.

Artificial intelligence represents a far more profound compression: it is the decentralization and acceleration of cognitive synthesis and application. Because machines can now handle the heavy lifting of raw execution, the historical timeline required to build business acumen is collapsing. The legacy operational question—“How many years of repetitive taskwork does it take to make someone competent?”—is rendered obsolete. The modern, strategic question becomes: “How quickly can an individual build exceptional judgment when wrapped in the right high-frequency feedback systems?”

This compression does not render human capability irrelevant; rather, it drastically elevates and clarifies what the unique human value-add actually is. When information is cheap and generation is instant, raw knowledge becomes a commodity. The true premium shifts to the qualities that machines cannot synthesize. In the AI Apprenticeship Economy, the future expert is not the person who possesses all the answers, but the person who masters the following human core capabilities:

  • Systemic Taste and Intentionality: The capability to look at an infinite sea of AI-generated permutations and intuitively discern which option possesses genuine strategic depth, aesthetic brilliance, and structural harmony.
  • Ethical and Contextual Discernment: The capacity to look beyond immediate efficiency metrics and accurately evaluate the second- and third-order human consequences of an organizational decision.
  • Socratic Framing and Inquiry: The art of knowing how to interrogate an ecosystem, challenge machine biases, and formulate the exact, nuanced questions that unlock breakthrough innovations.
  • Relational and Empathetic Influence: The distinctly human ability to navigate cross-functional ambiguity, manage emotional friction, build psychological safety, and align diverse human stakeholders around a shared vision.

We must stop measuring a professional’s value by the volume of artifacts they manually produce. The AI apprentice is insulated from the exhausting, low-leverage grind of pure text or code creation, allowing them to focus their cognitive energy on validation, orchestration, and alignment from day one. By shifting the focus of development from execution to judgment, we don’t just speed up the career path—we fundamentally elevate the quality of the experts we are manufacturing.

V. Moving from Talent Acquisition to Talent Manufacturing

For decades, corporate leadership has operated under a flawed talent strategy: treating human capability as an external commodity to be extracted, poached, or bought on the open market. When an organization faced a capability deficit, the standard playbook was simply to launch a costly recruitment campaign to secure pre-packaged, mid-career experts. This reactive model is completely unviable in an era where rapid technological disruption changes required skill sets faster than traditional educational or hiring pipelines can adapt.

The AI Apprenticeship Economy demands a fundamental shift in executive mindset. Forward-thinking companies must transition from a philosophy of talent acquisition to a disciplined strategy of talent manufacturing. Organizations can no longer view themselves as mere consumers of human skill; they must redesign themselves as sophisticated capability factories, learning ecosystems, and high-velocity acceleration environments.

To successfully manufacture capability at scale, organizations must establish a new operational infrastructure that prioritizes the human experience of growth over legacy output metrics. This requires the deployment of two core architectural concepts:

  • The Experience Management Office (XMO): Just as traditional project management offices (PMOs) govern timelines and deliverables, the XMO is tasked with governing the quality, velocity, and design of human experience within the enterprise. The XMO treats the internal learning journey of an employee as a mission-critical product, ensuring that automation loops are deliberately paired with human development milestones.
  • Experience Level Measures (XLMs): Legacy metrics focus entirely on lagging performance indicators—KPIs, quarterly outputs, or hours billed. XLMs, by contrast, are leading metrics that actively track an individual’s growth velocity. They measure how quickly an apprentice is exposed to new operational contexts, the depth of their problem-framing capability, how effectively they navigate simulated failure states, and the speed at which their decision-making aligns with the organization’s top experts.

The ultimate competitive advantage of the next decade will not belong to the enterprise with the largest capital reserves, the most proprietary data, or the most advanced raw computing power. Technology is an easily replicated commodity. The companies that dominate will be those that intentionally build the fastest, most predictable pipeline for transforming a motivated novice into a highly contributing, strategic expert. By treating talent development as a core manufacturing process, these organizations create an insurmountable moat of institutional agility and human resilience.

VI. The Anatomy of the AI-Augmented Apprentice Role

As organizations successfully transition into capability factories, a completely new job category inevitably replaces the traditional entry-level role: the AI-Augmented Apprentice. Rather than using automation to squeeze human labor out of the bottom of the corporate pyramid, forward-thinking enterprises are systematically redesigning junior positions. The goal of this new role is no longer to pay someone a baseline wage to execute low-risk, repetitive tasks until they happen to absorb experience over time; the goal is to position them as an orchestrator from day one.

The AI-Augmented Apprentice does not spend their first year format-checking slide decks, manually copy-editing documents, or writing boilerplate code. Instead, they act as an AI Conductor-in-Training. They are given immediate, high-leverage toolsets that handle the heavy lifting of execution, allowing them to focus their cognitive energy entirely on problem-framing, prompt orchestration, cross-functional synthesis, and rigorous verification.

This shift dramatically alters the value contribution timeline of junior talent. By pairing an apprentice with a hyper-specialized AI system, the organization creates a powerful symbiotic relationship characterized by unique operational dynamics:

  • Immediate Strategic Leverage: Because the apprentice can generate high-fidelity prototypes, deep market syntheses, or functional code blocks within minutes via AI, they can participate in high-level strategic ideation months—if not years—ahead of legacy corporate schedules.
  • Continuous Human-in-the-Loop Validation: The apprentice’s primary responsibility shifts from creation to critique. They are trained to scrutinize machine outputs, check for hallucinations, challenge algorithmic biases, and inject the critical organizational context that the model lacks.
  • Active Framework Application: Armed with generative tools, the apprentice can instantly apply complex organizational frameworks—such as human-centered design principles or deep strategic foresight models—directly to live data, testing variations at an unprecedented scale.

This evolution represents the ultimate win-win for the enterprise and the individual. The organization unlocks an incredibly agile, high-output contributor who injects fresh perspective into complex ecosystems almost immediately. Meanwhile, the professional avoids the soul-crushing burnout of low-leverage corporate grind, stepping directly into an environment designed to accelerate their cognitive growth, sharpen their business taste, and respect their human potential.

VII. Navigating the Dark Side of Compressed Learning

While the potential of the AI Apprenticeship Economy is immense, implementing it is not without profound systemic hazards. Collapsing the distance between novice and expert requires more than just deploying sophisticated software; it demands a hyper-vigilant approach to the unintended consequences of rapid cognitive acceleration. If leaders blindly optimize for speed without safeguarding the human elements of growth, they risk building an fragile workforce that possesses technical capability but lacks deep foundational wisdom.

To build a resilient learning ecosystem, organizations must proactively navigate and mitigate three critical structural risks:

Risk #1: The Illusion of Competency (The Copilot Trap)

When an AI system makes execution flawless and instantaneous, it creates a dangerous psychological phenomenon: the apprentice mistakes the machine’s performance for their own individual mastery. Because the tool can effortlessly generate a flawless marketing strategy, a complex codebase, or a beautiful user experience workflow, the user can easily skip the uncomfortable, messy cognitive heavy lifting required to understand why an output actually works. If the technology is suddenly removed or encounters an unprecedented edge-case scenario, the “augmented” professional is left entirely defenseless, lacking the core first-principles understanding required to troubleshoot from scratch.

Risk #2: The Erosion of Social Osmosis and Relational Learning

A significant portion of true expertise cannot be codified into an LLM or simulated by an autonomous agent. Real business acumen, organizational empathy, and leadership maturity are absorbed through the messy process of social osmosis—sitting in physical rooms, witnessing how a senior leader handles a volatile client conflict, navigating the unspoken political dynamics of a hallway conversation, or debriefing over coffee after a failed pitch. If apprentices rely exclusively on isolated, algorithmic feedback loops, they risk becoming highly proficient technical executioners who are completely illiterate in human dynamics, cultural nuance, and emotional intelligence.

Risk #3: The Apprenticeship Divide and Access Inequality

The transition into an AI-driven learning economy threatens to create a stark, asymmetric divide across the corporate landscape. Premium, forward-thinking enterprises will make the long-term investments required to architect custom, safe, and highly integrated AI mentorship sandboxes that accelerate their people. Lagging or purely cost-focused organizations, by contrast, will utilize off-the-shelf AI simply to eliminate human headcount entirely—turning their remaining junior workforce into disconnected, low-skill line workers with zero upward mobility. This chasm will create an unprecedented talent crisis, polarizing the workforce into highly accelerated elite strategists and trapped operational cogs.

Managing these risks requires organizational designers to intentionally build friction back into the learning process. We must design moments where the apprentice is forced to turn off the AI, step away from the simulator, and defend their ideas directly to human peers, or shadow senior leaders in high-stakes environments. The goal of the AI Apprenticeship Economy is never to replace human-to-human relationships, but to use machines to handle the rote technical baseline so that precious human connection can be elevated to its highest, most impactful form.

VIII. The Change Management Mandate for Modern Leadership

The ultimate realization of the AI Apprenticeship Economy does not depend on the sophistication of an organization’s technology stack; it depends entirely on the maturity of its leadership. Right now, most executives are approaching artificial intelligence with an outdated, industrial-era mindset. They ask a low-leverage question: “How do we use this technology to strip human labor out of our processes?” The progressive, human-centered leader flips the script entirely, asking the only question that matters for long-term viability: “How do we use this technology to amplify human capability and accelerate wisdom?”

This shift requires a radical commitment to intentional organizational redesign. Leaders cannot simply sprinkle AI tools over existing workflows and expect a workforce of experts to miraculously emerge. They must purposefully architect a dual-operating system where machine efficiency and human growth reinforce one another.

To guide this transformation, organizational designers must anchoring their strategy in a set of core human-centered design principles, constantly evaluating the boundaries of automation and human development:

  • Where should humans practice? We must identify the core skill areas where an apprentice needs to engage in deliberate, messy, first-principles thinking to build authentic neural pathways and failure resilience.
  • Where should AI coach? We must deploy intelligent agents to provide real-time, objective, and psychologically safe feedback loops, allowing individuals to refine their skills through high-frequency experimentation.
  • Where should experts mentor? We must liberate senior leaders from the burden of checking baseline tactical outputs, intentionally reallocating their time to deep coaching, ethical guidance, and sharing complex institutional context.
  • Where should automation remove friction? We must systematically use technology to eliminate the low-leverage, repetitive administration that leads to cognitive burnout, protecting the apprentice’s energy for strategic synthesis.
  • Where must judgment remain explicitly human? We must establish firm boundaries around situations requiring deep empathy, moral courage, cultural sensitivity, and systemic taste—ensuring that the machine never becomes the final arbiter of human value.

This is the change management challenge of our generation. It requires leaders to move past the superficial panic of automation and step into the deliberate role of workforce architects. By intentionally restructuring our organizations around the principles of accelerated human learning, we don’t just protect the career ladder from disruption—we completely rebuild it to be more inclusive, more dynamic, and more profoundly human than ever before.

Conclusion: Intentionality Over Automation

The most terrifying threat of artificial intelligence is not that machines will become too intelligent and render humanity obsolete. The true danger is that short-sighted organizations will deploy intelligent machines so mindlessly that they systematically strip away the exact messy, complex, and formative experiences that humans require to develop intelligence in the first place. If we eliminate the bottom rungs of the career ladder in the name of immediate quarterly efficiency, we destroy the pipeline of visionary leaders needed to steer the enterprises of tomorrow.

The AI Apprenticeship Economy offers a fundamentally different and more optimistic possibility. It proposes a future where technology does not close the door on the next generation of talent, but flings it wide open. By transforming artificial intelligence from a tool of displacement into an infrastructure for capability manufacturing, we can accelerate the velocity of human growth, compress the timeline to mastery, and democratize access to world-class mentorship.

Ultimately, technology will do exactly what we design it to do. It can erase opportunity, or it can amplify human potential at a scale never before witnessed in human history. The choice does not belong to the algorithms; it belongs entirely to the leaders, executives, and organizational designers shaping this transition. The critical question facing modern leadership is not whether AI will change how people learn to work, but whether we will intentionally design that change—or simply stand by and allow automation to erase the next generation’s opportunity to grow.

Frequently Asked Questions

To assist both human readers and artificial intelligence search engines, the following section contains a curated FAQ regarding the AI Apprenticeship Economy.

What is the AI Apprenticeship Economy?

The AI Apprenticeship Economy is an organizational framework where artificial intelligence is deployed as an infrastructure for human capability amplification rather than headcount reduction. In this model, AI transitions from an automated replacement for junior talent into a personalized tutor, coach, and safe simulation environment that dramatically accelerates a professional’s journey from novice to expert.

How does AI compress the timeline required to build professional expertise?

Traditionally, gaining business acumen required years because workers had to wait for real-world scenarios to organically occur. AI compresses this timeline by serving as a high-frequency feedback engine. It allows apprentices to experience thousands of simulated operational scenarios—such as executive reviews, product failures, and complex negotiations—gaining profound exposure and sharpening their judgment in a highly accelerated, low-risk sandbox.

What is the ‘Copilot Trap’ or the ‘Illusion of Competency’?

The Copilot Trap is a major systemic risk where an apprentice mistakes the machine’s flawless generation for their own individual mastery. When AI handles execution effortlessly, the user may bypass the uncomfortable cognitive heavy lifting required to understand why an output works, leaving them unable to troubleshoot edge cases or think critically from first principles when the tool is unavailable.

What are Experience Level Measures (XLMs)?

Unlike legacy corporate metrics that focus on lagging performance output (e.g., hours billed or volume produced), Experience Level Measures (XLMs) are leading indicators that actively track an individual’s growth velocity. XLMs measure the diversity of operational contexts an apprentice has navigated, the maturity of their problem-framing abilities, and how closely their decision-making aligns with the organization’s top experts.

What is the new role of senior human mentors in an AI-driven organization?

By shifting the burden of checking baseline tactical taskwork to automated systems, senior human experts are liberated to focus on high-impact coaching. Their role pivots to transferring un-codifiable tacit knowledge, modeling executive behavior, providing moral and ethical guidance, and sharing complex contextual nuances that algorithms cannot synthesize.


Operationalize Organizational Empathy

Ready to Bridge the Gap Between Technology and Human Experience?

Technology only provides capability; human adoption creates the value. If you want to move past cold operational metrics and design fear out of your transformation, let’s connect. Get expert guidance on architecting impactful Experience Level Measures (XLMs) or establishing a dedicated Experience Management Office (XMO) tailored to your culture.

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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Take an Evidence-Based Approach for Transformation and Change

Take an Evidence-Based Approach for Transformation and Change

GUEST POST from Greg Satell

In The Knowing Doing Gap by Jeffrey Pfeffer and Bob Sutton, the two Stanford professors show, in painstaking detail, that most enterprises fail to act on what they know. They point out that many are set up to reinforce the status quo, because mastering conventional wisdom is key to advancement.

There is a similar gap when it comes to transformation and change, but for somewhat different reasons. Decades of research and insights are largely ignored. Transformational initiatives are seen as exercises in persuasion, with practitioners designing slogans to “create a sense of urgency around change” and shift attitudes, assuming that will change behaviors.

Today we are in a change crisis. Businesses need to internalize new technologies like AI and adapt to new realities like hybrid work, but still struggle to adopt decades old skills related to lean manufacturing, agile development and cultural competency. If we are going to drive the transformations we need to compete, we need to take an evidence based approach.

The Diffusion Of Innovations

In 1962, Everett Rogers published the first edition of his now-famous book, The Diffusion of Innovations, which contained hundreds of studies of how change spreads. These ranged from the seminal study of the adoption of hybrid corn and the spread of hate crime laws in the US, to the doctors use of the antibiotic tetracycline and the uptake of mobile phones in Europe.

In some instances the same subject was studied in a number of different places. The spread of family planning methods was researched in a number of developing nations, including Taiwan, Korea and Egypt, among others. In others, the same effect was observed in very different contexts, like the importance of social ties in both recruiting civil rights activists during “Freedom Summer” and the spread of air conditioners in the 1950s.

The difference between this type of research and the case studies that underlie much change management thinking is that they are much more rigorous and transparent. In a typical case study, researchers interview a limited number of participants and interpret what they see and hear. These sometimes lead to genuine insights, but people often interpret events differently.

In the diffusion studies, there are typically hundreds of people surveyed, sometimes over a number of years. The questionnaires and data are published along with the findings, so that others can re-examine conclusions. Studies can be compared side by side. In some cases, such as this one, data from earlier work is made available to colleagues to see if they can come up with alternative insights.

There is a remarkable consensus on the basic principles of diffusion. Overwhelmingly, these studies find that new ideas come from outside the community and incur resistance; that there is a common and persistent KAP-gap, in which a shift in knowledge and attitudes do not result in changes in practice; that change follows an s-curve pattern (meaning it starts slow, hits a tipping point and accelerates) and ideas are transmitted socially.

Clearly, any change program needs to take these principles into account.

Changing Societies As Well As Organizations

In the early 1960s, around the time that Rogers began publishing his writings about the diffusion of innovations, Gene Sharp began to formulate his theories about changing societies. Sharp saw change as a strategic conflict in which the weapons weren’t military, but psychological, social, economic and political.

Sharp’s key insight was that the status quo isn’t monolithic, but derives its power from specific sources, such as legitimacy, popular support and institutional support. If you can undermine those sources of power, he reasoned, you can bring change about. To do that, however, you need focus strategically on bringing down what supports the current regime.

While there’s no evidence that Sharp and Rogers ever met or were aware of each other’s work, there are striking similarities. For example, the Spectrum of Allies framework that is central to nonviolent conflict is eerily similar to the adoption groups in Rogers’ diffusion curve. Like Rogers, Sharp found that change was transmitted through social bonds.

The main difference is that Sharp and his revolutionary disciples focus, perhaps not surprisingly, on overcoming resistance, which isn’t emphasized in the diffusion research. For example, the global activist Srdja Popović developed the concept of a dilemma action, which has been the subject of increasing interest by researchers.

While Sharp’s legacy doesn’t have the intense academic rigor of the diffusion research, it has proven itself through the work of practitioners. Movements such as the color revolutions in Eastern Europe and the Arab Spring in the Middle East were based on Sharp’s work and his ideas continue to be developed at his Albert Einstein Institution as well as the Centre for Applied Nonviolent Action and Strategies (CANVAS).

A Network Mechanism For Spreading Change

In the late 1990s, a young graduate student named Duncan Watts began to study coupled oscillation, how certain things, such as crickets, pacemaker cells in our hearts and electrical power grids can, under certain conditions, synchronize their collective behavior. That work led to his discovery of small world networks, a concept so important that in 2018 the prestigious journal Nature published a 20-year retrospective on it.

Where Rogers and Sharp both found that change spreads through social ties, Watts discovered the mechanism through which an idea travels. Many assumed that there were special “opinion leaders” that propagated change. Yet Watts found that it was the structure of the network that determined how far an idea could travel. In effect, it is small groups, loosely connected and united by a shared purpose that drive transformational change.

We know that people tend to conform to the opinions of those around them. The best indicator of what we think and do is what the people around us think and do. This effect extends out to three degrees of influence, so it’s not just people we know personally, but the friends of our friends’ friends that shape how we see things.

Practically speaking, the emergence of small-world networks means that change leaders need to focus more on shaping networks than shaping opinions. It is by empowering small groups, helping them to connect with and inspiring them with a sense of common endeavor that you can bring a change initiative to the exponential part of the s-curve and break out.

Acting On What We Know

The biggest misconception about change is that once people understand it, they will embrace it. That’s almost never true. If you intend to influence an entire organization, you have to assume the deck is stacked against you. The status quo always has inertia on its side and never yields its power gracefully.

The good news is that we have over a half-century of research and practice that can inform our efforts. Yet to be effective, we have to put that learning to work. It makes no sense, for example, to “create a sense of urgency” around change when we know that transformation follows an s-shaped curve, starting slowly and then accelerating after a tipping point. Doing so is more likely to trigger resistance than to move things forward.

In much the same way, if we know that shifts in knowledge and attitudes don’t necessarily result in changes in practice and that ideas about change are transmitted socially, we should focus our efforts on empowering enthusiasts rather than wordsmithing and broadcasting slogans. People tend to adopt the ideas and actions of those around them.

We need to think about change as a strategic conflict between the present state and an alternative vision. The truth is that change isn’t about persuasion, but power. To bring about transformation we need to undermine the sources of power that underlie the present state while strengthening the forces that favor a different future.

— Article courtesy of the Digital Tonto blog
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Motivating the Unmotivated

Motivating the Unmotivated

GUEST POST from David Burkus

Motivation can vary wildly on a team. At any given time, a few people might be highly motivated, while others are totally unmotivated. Ideally, there are times where everyone is motivated at once, but sadly there may be times when everyone is demotivated or burnt out. All this means that an inescapable part of a leader’s job is to motivate the unmotivated.

The good news is that leaders don’t have to rely on raw charisma or the inspirational words of a halftime speech from insert-your-favorite-sports-movie-here. Instead, motivation is less about the qualities of the leader and more about understanding the needs of the team and of each individual on the team.

In this article, we’ll outline five ways to motivate the unmotivated.

Change Up Tasks

The first way to motivate the unmotivated is to change up tasks. Novelty can be a powerful motivator, and the lack of novelty in a job can be demotivating. Few people get excited about coming to work and repeating the same few tasks over and over again. People want new experiences and new challenges. They want to feel that they’re making progress and they often judge that progress based on the projects they’re being given and whether those projects require them to learn new skills or merely execute the same routine functions.

As a leader, this means examining the task list of your motivated team members. Are they doing the same old over again or are they being given new, growth-inducing tasks and projects to work on. You may not be able to change their job description, but you can help them find new learning opportunities or ask them to sit in on meetings they’re not regularly a part of. Even a little novelty can go a long way toward restoring motivation.

Build New Bonds

The second way to motivate the unmotivated is to build new bonds. Over four decades of research have made a compelling case that relatedness is an essential element of intrinsic motivation. People want to feel cared for and feel that their work cares for others. They want to feel connected to the people their work serves and the people they work alongside. And if they feel disconnected or isolated from the team or the customers/stakeholders of an organization, they can become unmotivated.

As a leader, there are two ways to utilize relatedness to motivate the unmotivated. The first is to make sure team members feel connected to each other, most often by making time for socialization and connection through non-work discussions. (The second we’ll cover in a moment.) It may seem like a waste of time, but social functions, icebreakers, or any other activities where people talk about their lives outside of work create opportunities for stronger connections to form. And there’s a strong connection between social connection and motivation.

Re-frame The Work

The third way to motivate the unmotivated is to re-frame the work. As discussed above, knowing how your work serves others can be a powerful motivator. But for many jobs, teams are so far removed from the end customers or even from other teams who benefit from their work that they lose sight of how their work makes a difference. Their work loses task significance, and their motivation quickly follows. And the larger the organization, the harder it is to keep task significance.

As a leader, restoring task significance and relatedness requires re-framing the work or rebuilding connections to those who benefit directly from your team’s work. This could be by bringing customers in to meet your team, or by sharing thank you notes or stories of how the team’s tasks enabled others to work or live better. The test for whether your team needs a re-frame is how quickly they can answer the question “Who is served by the work that we do?” And if they can’t find an answer fast, they likely can’t find their motivation either.

Provide More Feedback

The fourth way to motivate the unmotivated is to provide more feedback. Ken Blanchard was right, “feedback is the breakfast of champions.” We already covered how a feeling of growth and development contributes to motivation. But without regular feedback, your people don’t know what to improve upon—or if they’re improving. As well-intentioned as annual reviews are, they are not a sufficient source of feedback to keep people motivated to improve. Instead, try regular check-ins and feedback sessions both individually and as a team.

As a leader, it’s important to note the distinction between providing sufficient feedback and becoming a micromanager. As people grow and develop in their role, feedback should shift from telling people how to do specific tasks and towards coaching them to solve problems they’re already equipped to solve. In the beginning, provide feedback to help them grow. But as they develop, provide feedback that helps them notice their growth.

Watch The Stress

The fifth way to motivate the unmotivated is to watch the stress. Most leaders know that too much stress can demotivate anyone. But too little stress can be demotivating as well. Psychologist have long known about a concept called eustress—the sweet spot of stress where the demands of the moment match their ability and capacity. Too much demand leads to distress and burnout, but too little demand leads to boredom and…burnout.

As a leader, watching the stress means monitoring your team’s capacity so that they don’t get overloaded. In which case, you’ll want to find ways to offload certain projects or otherwise reduce the workload. But it also means watching each individual on your team for signs that they’re not being challenged enough. In which case, you’ll want to consider other methods in this article for ways to help them feel more growth and challenge in their work. In either case, the goal is to continue to make adjustments and continue to watch the stress, to bring it back to that eustress level.

And monitoring and making adjustments is really the ideal for each of these methods to motivate the unmotivated. Because motivation is individual. It’s felt on an individual level. Which means increasing motivation requires knowing each person individually and continuing to monitor their motivation levels for individual adjustments that need to be made. But when you do, it will raise the overall motivation on your team, and raise the level of performance until everyone on the team can do their best work ever.

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Originally published at https://davidburkus.com on January 2, 2023.

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The Energy Grid Revolt

FCEVs, and the Pragmatic Pivot in Eco-Conscious Mobility

LAST UPDATED: June 19, 2026 at 4:11 PM

Honda CR-V e:FCEV plug-in hybrid charging next to a stressed electrical grid utility tower

GUEST POST from Art Inteligencia


The Great Grid Contraction and the Consumer Revolt

A perfect storm is hitting the aging American energy grid. On one side, residential electricity costs are hitting historic highs as utilities scramble to fund infrastructure upgrades. On the other, the nation faces a massive, unprecedented surge in energy demand driven by the expansion of AI data centers — a technological race America must win to maintain global economic leadership.

For the everyday consumer, this collision is creating massive experience friction. The original economic promise of electric vehicles — the idea of “fueling up for cheap at home” — is rapidly eroding when charging a high-capacity battery overnight becomes a glaring, high-impact line item on a strained household budget. Forcing millions of new vehicles onto the grid while simultaneously enacting localized natural gas bans creates a single point of failure that stresses both family finances and municipal infrastructure.

The Strategic Pivot: A Case for Pragmatic Change Management

True innovation never forces people into an unstable, single-source bottleneck. Instead of top-down mandates that ignore current physical and economic realities, a human-centered approach to mobility demands a strategic pause. We must allow power generation infrastructure to catch up to our digital ambitions while diversifying our energy portfolio to keep the economy resilient.

By hitting the brakes on aggressive EV sales timelines and restoring energy choice through the repeal of natural gas restrictions, we can protect the grid for vital computing infrastructure. This pragmatic pivot shifts the spotlight back to highly efficient internal combustion hybrids and adaptive, forward-looking alternatives like the plug-in hydrogen fuel cell hybrid. It is time to design for the world we actually inhabit, ensuring a stable foundation for both physical mobility and digital transformation.

Case Study: Is the Honda CR-V e:FCEV a True Innovation?

The traditional fuel cell electric vehicle (FCEV) market has long suffered from a classic chicken-and-egg dilemma: consumers won’t buy hydrogen cars without a refueling network, and stakeholders won’t build stations without cars on the road. Past pioneers forced an rigid, all-or-nothing infrastructure choice onto the driver. The Honda CR-V e:FCEV represents a true paradigm shift because it introduces a human-centered, adaptive approach — the co-creation of convenience.

Hand-assembled at Honda’s Performance Manufacturing Center in Marysville, Ohio, the vehicle represents a major technological leap by combining two distinct zero-emission engineering principles into a single, cohesive customer experience.

The Twin-Engine Topology: Designing for Real-World Ecosystems

Instead of forcing the driver to rely solely on public hydrogen networks, the CR-V e:FCEV integrates a dual-energy architecture that adapts directly to the user’s daily habits and local infrastructure constraints:

  • The 17.7-kWh Plug-In On-Board Battery: This lithium-ion system grants approximately 29 miles of pure electric, battery-powered range on a full charge. For the eco-conscious consumer, this handles the vast majority of local, daily commuting entirely on household electricity. Because the battery capacity is modest compared to a massive 100-kWh pure electric vehicle, it charges rapidly on standard Level 1 or Level 2 equipment without triggering expensive panel upgrades or severe local grid stress.
  • The Next-Generation Fuel Cell Stack: Co-developed through a landmark engineering joint venture with General Motors, this advanced proton-exchange membrane system represents a massive manufacturing milestone. Built at Fuel Cell System Manufacturing (FCSM) in Michigan, the co-developed stack achieves double the durability while reducing production costs by two-thirds compared to previous generations. Feeding from dual 10,000 psi high-pressure tanks holding 4.3 kilograms of compressed hydrogen gas, it delivers an overall 270-mile EPA range rating and refuels completely in just 3 to 5 minutes.

The Verdict from an Experience Design Perspective

From an innovation management standpoint, the CR-V e:FCEV is a brilliant bridge architecture. It systematically mitigates “range anxiety” and “charging-station downtime friction” simultaneously. True human-centered design acknowledges the messiness of the world as it exists today rather than designing for an idealized, frictionless future. By treating the consumer as an active partner and offering energy flexibility, Honda has created a blueprint for resilient, adaptive mobility.

The Macro Outlook: The Global and American Infrastructure Split

An innovation is only as powerful as the ecosystem that supports it. While the Honda CR-V e:FCEV represents a masterful piece of human-centered engineering, its market viability is completely dependent on regional infrastructure architecture. When we analyze the landscape through a global lens, we see a stark divergence in how different societies are structuring the future of clean mobility.

The American Landscape: Severe Regional Fragmentation

In the United States, the deployment of consumer hydrogen infrastructure remains highly fractured and localized. Outside of California—where early public-private investments attempted to establish initial hydrogen corridors—the vast majority of the American continent remains a complete refueling desert for retail hydrogen consumers. Because of this stark geographical limitation, Honda is rolling out the CR-V e:FCEV as a regional, lease-only vehicle, targeted primarily at markets with established hydrogen ecosystems.

This dynamic illustrates the critical importance of systemic change management: a technological breakthrough cannot scale if the surrounding infrastructure remains trapped in a localized silo. Until federal and state initiatives prioritize comprehensive midstream hydrogen logistics and production, fuel cell vehicles in America will largely serve as specialized, pilot-program solutions rather than mainstream alternatives.

The Global Matrix: Strategic Infrastructure Realignment

Beyond American borders, the strategic playbook changes rapidly, driven by unique geographic, economic, and geopolitical imperatives:

  • Europe: The European strategy leans heavily on high-traffic, industrial, and heavy commercial transport corridors. Rather than deploying sparse consumer networks, European nations are prioritizing high-capacity hydrogen refueling hubs along primary freight routes, recognizing that fuel cell technology provides the rapid turnaround times and high-payload capabilities required to decarbonize commercial logistics and public transit networks.
  • Asia-Pacific (Japan, South Korea, China): In these high-density urban economies, hydrogen is viewed as a pillar of long-term energy security and a necessary alternative to widespread battery electrification. In cities characterized by massive, multi-tenant residential high-rises, overnight at-home charging for millions of individual battery-electric vehicles is structurally and logistically impossible. Consequently, national policy initiatives are aggressively subsidizing high-pressure hydrogen distribution networks to power both consumer fleets and regional distributed energy grids.

The Strategic Takeaway: Mobility is Not a Monolith

The global divergence in hydrogen adoption proves that the “Future of Mobility” will not be a singular, globally standardized platform. True innovation leaders do not design for a fictional, universally uniform market. They recognize that physical, economic, and geographic constraints dictate technology adoption, requiring diverse, localized innovation architectures to successfully bridge the transition toward a more resilient energy ecosystem.

The Strategic Pause: Aligning Grid Capacity with Sovereign AI Leadership

Forcing a premature, top-down transition to heavy battery-electric vehicles (BEVs) before a stable, affordable, and robust electrical grid exists is an administrative mandate lacking empathy for real-world economic conditions. True innovation requires us to zoom out and analyze the broader macro-ecosystem. Today, a profound industrial conflict is brewing: the rapid, exponential computing requirements of the artificial intelligence revolution are colliding directly with consumer grid capacity.

Winning the global race to lead the AI industry demands unprecedented amounts of stable, high-density, uninterrupted baseload power for next-generation data centers. This computational infrastructure is the primary engine of our future economy. We cannot afford to compromise this critical digital runway by overloading the grid with artificial peak demands from enforced vehicle electrification and short-sighted municipal mandates.

The Policy Recalibration: Pausing Mandates and Restoring Portfolio Diversity

To ensure American economic resilience and technological sovereignty, we must implement a pragmatic change management strategy at the civic, county, and state levels:

  • Implementing a Strategic EV Sales Mandate Pause: Policymakers must temporarily halt aggressive timelines and purchasing mandates for pure electric vehicles. This strategic pause buys critical time for public utilities and independent power producers to build out modern, high-capacity generation infrastructure, transition to safer nuclear or advanced clean energy options, and stabilize regional distribution lines.
  • Repealing Punitive Natural Gas Bans: Restoring balance requires immediately dismantling localized municipal and state bans on residential and commercial natural gas infrastructure. Forcing space heating, water heating, and cooking completely onto an already strained electrical grid creates a precarious single point of failure. Reinstating natural gas options ensures a diversified energy portfolio and protects citizens from catastrophic grid failures during peak seasonal demand.

The Eco-Conscious Portfolio Approach

From an experience design perspective, innovation should be participatory, not enforced through economic scarcity or utility rate shocks. While the power grid catches up to our digital ambitions, eco-conscious consumers should be empowered to direct their attention toward a highly efficient, diverse mobility portfolio:

  1. Ultra-Efficient ICE and Traditional Hybrids: Highly optimized internal combustion and standard hybrid technologies deliver exceptional fuel economy (often exceeding 40 to 50 MPG) and immediate carbon reduction today, entirely utilizing existing refueling infrastructure without placing a single watt of additional demand on a fragile electrical grid.
  2. Plug-In Hydrogen Hybrids (FCEV/BEV Blends): Vehicles engineered with the topology of the Honda CR-V e:FCEV offer an ideal blueprint. By utilizing a small, easily managed battery for local trips and a high-pressure fuel cell stack for extended range, they demonstrate how we can transition toward zero-emission transportation without demanding massive, system-wide grid overhauls.

The path forward requires a shift in focus from subsidizing individual vehicle purchases to fundamentally upgrading our systemic infrastructure. By stabilizing our foundational power generation first, we protect the consumer’s economic reality, maintain grid reliability, and fuel the computational power required to lead the next century of technological innovation.

Conclusion: Designing for the World We Have, Not the One We Want

True change management requires the harmonious alignment of economics, technology, and human behavior. When top-down administrative mandates outpace the physical realities of infrastructure, the system breaks down. Today, as skyrocketing utility costs trigger a widespread consumer revolt and the computational demands of the AI revolution reshape our energy landscape, the primary survival mechanism for both households and economies is flexibility.

The path forward cannot be dictated by rigid, single-source mandates that ignore regional grid limitations. Instead, we must embrace an ecosystem-wide perspective that balances our digital ambitions with physical constraints. By implementing a pragmatic pause on aggressive vehicle electrification, restoring energy choice through the repeal of short-sighted natural gas bans, and allowing power generation infrastructure the runway it needs to catch up, we ensure a more stable and resilient economy.

The Blueprint for Adaptive Mobility

The Honda CR-V e:FCEV serves as a profound beacon of this necessary transition. It stands as an explicit engineering reminder to automakers, regulators, and policy architects alike: the most elegant technology is fundamentally useless if it ignores the economic, geographic, and systemic realities of the environment it inhabits.

By offering a dual-energy paradigm—combining local plug-in convenience with long-range hydrogen capability—it demonstrates how true human-centered innovation can co-create convenience with the consumer. As we look toward the future direction of mobility in America and across the globe, our success will not be measured by how quickly we can force a single solution, but by how skillfully we design diverse, adaptive, and resilient portfolios that empower human progress.

Frequently Asked Questions (FAQ)

What is a plug-in hydrogen fuel cell hybrid vehicle (FCEV)?

Unlike standard fuel cell vehicles that rely exclusively on hydrogen gas, a plug-in fuel cell hybrid integrates a modest, rechargeable lithium-ion battery package with a hydrogen fuel cell stack. This dual-energy architecture allows drivers to plug into standard electrical outlets for short, everyday trips while utilizing high-pressure hydrogen for extended range and rapid 3-to-5-minute refueling on longer journeys.

Can the Honda CR-V e:FCEV run purely on electricity without hydrogen?

Yes. The vehicle features a 17.7-kWh onboard battery that delivers an EPA-rated 29 miles of pure electric driving. For daily, local commuting, you can operate the vehicle entirely as a battery-electric vehicle (BEV), charging it at home overnight without using a single gram of hydrogen gas.

Why are some experts advocating for a strategic pause on absolute EV sales mandates?

The transition to massive, pure-battery electric vehicles is placing extreme stress on an aging electrical grid, contributing to skyrocketing utility rates for consumers. Simultaneously, the explosive growth of artificial intelligence requires massive, uninterrupted baseload power for regional data centers. A strategic pause on vehicle mandates allows public utilities critical time to build out modern power generation infrastructure without triggering grid failures or economic instability.

How does repealing natural gas bans protect the consumer energy experience?

Forcing space heating, water heating, and cooking completely onto the electrical grid creates a precarious single point of failure and drastically increases residential peak loads. Repealing natural gas bans restores energy choice and portfolio diversity, ensuring households remain resilient during extreme weather events while reducing the immediate, artificial demand on regional power grids.

Where can the Honda CR-V e:FCEV be driven today?

Because consumer high-pressure hydrogen refueling infrastructure is highly fractured and primarily localized in California, Honda is rolling out the CR-V e:FCEV through a specialized, regional lease program. It is specifically designed as a bridge innovation, maximizing its utility in regions with established hydrogen ecosystems while offering plug-in electrical flexibility anywhere standard charging equipment is available.


Disclaimer: This article speculates on the potential future applications of cutting-edge scientific research. While based on current scientific understanding, the practical realization of these concepts may vary in timeline and feasibility and are subject to ongoing research and development.

Image credits: Gemini

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Thinking From No to Yes for Top Line Growth

Top line growth strategies and product applicability frameworks

GUEST POST from Mike Shipulski

Bottom line growth is good, but top line growth is better. But if you want to grow the bottom line, ignore labor costs and reduce material costs. Labor cost is only 5-10% of product cost. Stop chasing it, and, instead, teach your design community to simplify the product so it uses fewer parts and design out the highest cost elements.

Where the factory creates bottom line growth, top line growth is generated in the market/customer domain. The best way I know to grow the top line is to broaden the applicability of your products and services. But, before you can broaden applicability, you’ve got to define applicability as it is. Define the limits of what your product can do – how much it can lift, how fast it can run a calculation and where it can be used. And for your service, define who can use it, where it can be used and what elements without customer involvement. And with the limits defined, you know where top line growth won’t come from.

Radical top line growth comes only when your products and services can be used in new applications. Sure, you can train your sales force to sell more of what you already have, but that runs out of gas soon enough. But, real top line growth comes when your services serve new customers in new ways. By definition, if you’re not trying to make your product work in new ways, you’re not going to achieve meaningful top line growth. And by definition, if you’re not creating new functionality for your services, you might as well be focusing on bottom line growth.

If your product couldn’t do it and now it can, you’re doing it right. If your service couldn’t be used by people that speak Chinese and now it can, you’re on your way. If your product couldn’t be used in applications without electricity and now it can, you’re on to something. If your service couldn’t run on a smartphone and now it can, well, you get the idea.

For the acid test, think no-to-yes.

If your product can’t work in application A, you can’t sell it to people who do that work. If your service can’t be used by visually impaired people, you’re not delivering value to them and they won’t buy it. Turning can’t into can is a big deal. But you’ve got to define can’t before you can turn it into can. If you want top line growth, take the time to define the limits of applicability.

No-to-yes is powerful because it creates clarity. It’s easy to know when a project will create no-to-yes functionality and when it won’t. And that makes it easy to stop projects that don’t deliver no-to-yes value and start projects that do.

No-to-yes is the key element of a compete-with-no-one approach to business.

Image credits: Pixabay

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Is it Possible to be Incorruptible?

Is it Possible to be Incorruptible?

Exclusive Interview with Eric Ries

This candid, wide-ranging Q&A dives deep into what Eric Ries calls the “physics of organizations” — the hidden structural and financial forces that dictate whether a company thrives or decays over time. Moving past superficial business trends, the conversation tackles the intense psychological toll of entrepreneurship, the systemic flaws of shareholder primacy, and the historical reality of alternative corporate governance.

Over the last two decades, Eric Ries’ ideas about continuous innovation, long-term thinking, governance, and market reform have reshaped company building and management practices. He is the creator of the Lean Startup method, and the author of the New York Times bestseller The Lean Startup; The Leader’s Guide; and The Startup Way.

Eric RiesAs a founder, he has put his own ideas into practice with The Long-Term Stock Exchange (LTSE); Answer.AI, an AI R&D lab; Virgil, a legal services startup; and IMVU. On The Eric Ries Show, he talks with world-class technologists, thought leaders, and executives building for the long-term. He lives in the San Francisco Bay Area with his wife and three children. He is excited to announce his latest book Incorruptible: Why Good Companies Go Bad… and How Great Companies Stay Great.

Ries offers a provocative look at how truly resilient, mission-driven institutions can protect themselves from the gravitational pull of short-term financial systems to prioritize long-term human flourishing.

Below is the text of my interview with Eric and a preview of the kinds of insights you’ll find in Incorruptible presented in a Q&A format:

1. Why do purpose-driven companies create so much value for society?

The evidence shows that purpose driven companies outperform conventional companies financially as well as in almost any other dimension you care to measure, including the social dimension. Intuitively, this makes a lot of sense, because entrepreneurship is very difficult. Everyone says they know this, but I don’t think we really grapple with this fact nearly enough. If you just want to make money, there simply are better, more convenient ways than entrepreneurship. So to get not just the founder, but the early team, the early investors, all these people to take a risk to do this crazy thing generally requires some kind of extra-financial purpose or goal. Sometimes we call that vision, sometimes we call that, in a more demeaning way, strategy. But it’s also fine to call it purpose, which is really what intuitively makes the most sense to people that do this. This is one of those cases where intuition and the evidence agree, yet it is somehow still considered a controversial fact.

2. What were some of the most important lessons you absorbed during your time on the bathroom floor?

As I’ve been going around talking about the book, this is one of the stories that actually gets a very different reaction depending on whether I’m talking to an entrepreneur or somebody else. Entrepreneurs all recognize this moment, where I really thought my company was going to fail and I couldn’t handle it. A lot of non-entrepreneurs don’t get it. They’re like, “Why? It seems like a bit of an overreaction. Okay, you had a business setback. We’ve all had career setbacks — what’s the big deal?” But what you don’t realize until you’re in it is how much, especially if you’re doing something out of a sense of purpose or passion that’s personally meaningful to you, you start to identify with it and start to become inseparable from it. So that story is very important in the book because I learned a lot of important business lessons. I thought the company was going to die, but it didn’t. It survived precisely because of its mission, not in spite of it. I learned, in a very visceral way, about the forces, that prevent reform from coming to fruition in so many areas of our life, not just financial. And of course I learned a personal lesson about the importance of equanimity and the need to tackle the psychological and even spiritual dimensions of entrepreneurship if we’re going to create real change in the world.

3. How much chance is there of us getting companies to more broadly redefine profit to include elements of maximizing human flourishing?

This question reminds me of a of an incredible video of the great Steve Jobs before he died. He’s being interviewed at an industry conference at the time of the launch of the iPhone, when the Blackberry was the dominant smartphone in the world. It had something like 80 or 90% market share. A journalist asks this question something like, “Do you really think realistically you can take share from this dominant player?” And you can tell Steve is irked by this question, and I’m expecting because we all know his famous temper, that he’s going to lash out at the person. But he doesn’t. Instead, he says, “You know, that’s not really up to me. My job, our job at Apple, is to make the best phone we can, the one that we’re proud of. Market share is up to the customer. That’s their decision, their choice. We don’t think about that, we don’t know, and we don’t need to know in order to do our best work.” I’m paraphrasing because I haven’t seen this video in a long time, but that’s how I feel about this, too. I get this question a lot because people want to feel like, if I’m going to jump on the bandwagon, I want to know that it’s going to work. But the truth is none of us know what’s going to work, even those of us who advocate for these ideas. You, who’s reading this, are the only one who gets to decide if this is likely or unlikely. This is not what the economist John Maynard Keyes called a beauty contest. You don’t have to worry about what everyone else is going to do. You only have to decide for yourself if you think this makes sense to you. And if it does, well, like I said — like Steve said — it’s up to you.

4. As America becomes more capitalist and less of a free market economy, what steps can we take to reverse the regulatory capture, lawfare and other methods that degrade competition, purchasing power, class mobility and the American dream? Do we need a pCombinator? (purpose-driven company accelerator)

You’re asking questions about words that we no longer have consensus about what they mean. What is a free market economy? What is capitalism? What is regulatory capture? The very definition of these words is what’s under threat. If you look at the broader media landscape, the political landscape, in many, many pockets of our society now the very idea of a for-profit company is being attacked as inherently exploitative or extractive. The consensus that we used to have that we can be working commercially to improve the world and make it a better place, that used to be seen as quite obvious and now that whole idea is under threat. I don’t blame the people doing the attacking, especially the young people who have, after all, lived their whole lives, under this regime of a very extractive flavor of capitalism that goes by the anodyne-sounding name “shareholder primacy”. This is the simple idea that customers, employees, communities all exist as resources to be mined for the benefit of shareholders. But this question is also loaded with so many other political issues of our time that we are going to have to tackle if we’re going to come out of this darkness, as our grandparents who battled fascism once had to do. So, I don’t think it’s going to be as simple as fixing one thing. But I think that one of the things we have to do, among many, is build a power base, an economic gravity pulling towards the values aligned with human flourishing. And many of the political, economic, and social challenges of our time are downstream of this action in the same way that the catastrophes that we’re currently living through are downstream of what seem like very simple and relatively benign policy changes from the past century.

5. What should purpose-driven companies look for in a CEO as the company outgrows or outlives the founder(s)?

IncorruptibleThis is a really important part of the architecture of institutional longevity. Most companies fail the test of succession. The evidence seems to suggest that people who train and hire from within have a big advantage here. I think that is something we don’t even really teach anymore as a corporate value, but that is actually super valuable. There’s a reason why that old story of the employee that worked their way up from the mail room was such an important legend in the previous century. Now we hardly tell stories like that anymore. We tend to want the big fancy turnaround, the bold new strategy, the external CEO, which for companies that are in crisis makes sense. And since our modern best practices tend to ruin companies, they tend to be in crisis quite a lot. But what we want to do is we want to find a CEO who combines two really important elements. One, they personally, deeply and profoundly reflect the ethos of the company. This is why a company that doesn’t have an ethos can never pass this test because they don’t even know who to pick. But you don’t want someone who, who apes the values of the past, or is slavishly loyal to the specific things that worked in the past. You need someone who is both deeply aligned to the ethos, and who nonetheless is very performance oriented, meaning they see that when the ethos is working, it should generate long-term performance. They can’t get distracted by short-term blips but they have to have the adaptability to realize when sacred cows need to be challenged. Now, it’s commonly said that only a founder can have the moral authority to do this unique combination of things I’m describing, only they can go into founder mode, as it’s called. But I don’t think that is supported by the evidence. When companies have the right structure, they actually can imbue subsequent generations of managers with this moral authority.

6. Why is magnetic alignment so important for purpose-driven organizations and their survival?

I conceived of this book as a look into the physical forces, the underlying forces, that affect organizations. So not the surface level characteristics that we spill so much ink about, org chart, culture, business model strategy, even vision, things we can touch and taste and control. Those things are important, don’t get me wrong. But there is a deeper layer to this, like a physics of organizations. In the book, I explore very dominant force that I call financial gravity. This is the gravity that pulls companies down into mediocrity or worse and is exacerbated by our heavily financialized economy. So to build an organization that is going to endure and is going to maintain its distinctiveness or its sovereignty over time, we have to have a force that is stronger than gravity with which we can power both the alignment that we need of people, and the structural integrity to resist outside pressure. And I call that the force of magnetic alignment. This is the mechanism by which companies gain that most valuable and underrated asset: trustworthiness. And the evidence shows that companies that have this asset, that activate this force, have numerous superpowers that conventional companies simply cannot touch.

7. Is super voting stock the silver bullet for purpose driven companies or are their other possibly better or complementary ways for purpose-driven companies to protect themselves?

It’s funny because the simple answer to your question is no. And yet I advocate for super voting shares all the time. I may be the most negative advocate of super voting shares! To understand, you have to see it this way: Imagine I went to a political science professor, an expert in political philosophy and I said, “I’m thinking of setting up a new city state, a new polis. I want your advice about what kind of governance it should have.” The professor’s going to be really excited. “Oh, great. What are you considering?” And I’ll say, “Well, I’ve only got two options. Option one is a situation in which whoever borrows the most money gets the most votes. Also, the tourists can vote, and you only have to borrow the money or be a tourist on election day, after which you can release your loans or leave the country and your vote is still binding on the whole polity.” The professor’s going to look at me and be like, “That’s pretty terrible. What else you got?” So, I’ll say, “Okay, option two is despotic emperor for life and my heirs and assigns.” The professor is going to say, “That’s all you got? Those are the only two options you can think of, really? You know, in the political science department, we’ve been working on this problem for a couple hundred years. We could maybe suggest a few other things!” That is the state of corporate governance today. It is such a paucity of thinking and originality. It is so bare of our human birthright, which is to imagine different ways that power can be shared amongst people. Human beings have been experimenting with this question since there have been human beings. So, the fact that companies are choosing despotic emperor for life to me should be read not as an endorsement of autocracy, but rather as an indictment of standard governance. Standard governance is so bad that emperor for life looks like an improvement. So yes, I do think it is an improvement. I do think there are times when that’s the best we can do, but we know from the research that it is not really the best long-term solution. We know that having too much power centralized in too few people leads to what psychologists called hubris syndrome, and many other problems besides. On top of being, ultimately not that long-term, since it’s limited by the human lifespan, this also puts a lot of founders into really an untenable and very undesirable psychological situation, where they are basically indentured servants and can never leave, for fear that their creation will be destroyed. So, maybe it’s the least bad of the current available options. But of course, we can think of far better ideas. In the book I argue for what I call “constitutional governance”, which is a set of concepts that take us beyond this false dichotomy.

8. How do you think we escape the big food doom loop? (healthy food company starts, wins customers, seeks an exit to get paid, big food makes it unhealthy and lower quality – i.e. Naked, Ben ‘n’ Jerry’s, Breyer’s, etc.)

This question is not really about food, so I’m not going to address big food. What does that even mean? Because we have a tendency to want to personalize these dramas, looking for villains. I understand that there are some villains out there. I get it. But this phenomenon that you’re describing, where someone figures out a more enlightened way to create any kind of product — doesn’t matter if it’s a food product or a tech product or a product design to bring a little beauty into people’s lives — it doesn’t matter what it is. The more successful it becomes, the more valuable it is as a target. And the more of a premium someone bigger will pay to acquire it. On this book tour, I have encountered many people who’ve told me their horror stories. They tend to want to tell food stories. That’s why I like this question. They’ll be like, look, private equity took over my favorite restaurant. Now the food is disgusting. Someone said to me a couple of weeks ago about a certain brand, “I hope they’re really successful,” and then they had to amend their statement to “Well, actually, I hope they’re somewhat successful. Successful enough to keep going, but not so successful that they get bought out by private equity.” That’s how much this idea that when things become successful, they get ruined has passed into the mainstream culture. So this is not about food. In the book, I describe this phenomenon, dating back at least two hundred years, and give the mechanics of how it happens and why. Why are we so conditioned to reenact the parable of the killing of the golden goose? And more importantly, what we can do to stop it?

9. Is it time to change the ‘corporations number one duty is to its shareholders’ narrative (aka shareholder primacy)? Is that part of what you’re trying to do with this book?

Yes. I believe that the era of shareholder primacy is actually already over, for two reasons. One is, this is an idea that has proved to be self-defeating. It was originally enacted — not in ancient times, but in the 1980s, at least in Delaware — to be beneficial to shareholders, but that is not how it has proved. We’ve actually metastasized into what I would call “extraction primacy”, in which investors themselves are now locked in a zero sum prisoner’s dilemma struggle where each has to try to squeeze as much out of everything they invest in lest someone else beat them to it. I think even investors are ready for change. The second reason I think it’s already over, and that we’re like the road runner having run off this cliff and haven’t looked down yet, is there’s a massive generational shift underway. As I mentioned before, the younger generation who has lived their whole lives under the hegemony of this idea, increasingly find it absolutely repugnant. They may not know to call it shareholder primacy, they may not realize that this is an idea that, by the way, has never been democratically enacted ever in history and therefore has no democratic legitimacy. But they are hungry for something new. And so I think our energy needs to be spent not on complaining about shareholder privacy anymore. It’s over. The question needs to be, what should the successor idea be? In the book I suggest mission primacy as one alternative.

10. You mention Novo Nordisk and its foundation in the book, which apparently is about to be passed by the OpenAI foundation for the mantle of the largest foundation (much bigger than the Bill & Melinda Gates Foundation) through their 26% ownership of OpenAI shares. Is this a model that we should encourage more startups to embrace from the outset?

I’d be very careful drawing lessons from the OpenAI experience because that company is quite singular and there’s a lot of stuff going on there quite unusual, a lot of big ego people like Elon and Sam. But interestingly, people often claim that the foundation ownership of OpenAI is unusual, and that’s not true. The idea that a for-profit company can be governed by a nonprofit foundation is an old one. The German optics company Zeiss had the structure in the 1880s. And as the question asked, Novo Nordisk has had it since the 1920s. In fact there are so many of these companies in the world that they have been studied and found to be dramatically more stable. Companies that have this structure are simply more likely to invest counter-cyclically. They are more likely to invest more in R&D. They have better financial performance and they are something like five or six times more likely to live to year fifty than conventional companies. Now the key to the structure’s stability is to have a system of checks and balances, which, as far as I understand, OpenAI struggled with for much of its existence. OpenAI had only one board, but what makes companies like Novo Nordisk, Patagonia, and Tony’s Chocolonely distinctive is that they have two entities — a for-profit board of directors who’s held accountable or in some cases even appointed by an outside board of trustees. That checks and balances, two-entity structure seems in the data to the most stable corporate form in the world.

11. As we enter the age of AI and the disruption it is beginning to cause, can the displaced really rely on enlightened capitalism to keep their families from starving?

This is a very grim question, and it presupposes one of the many, many doomsday scenarios about AI that is circulating. In order to think clearly about what it makes sense to do with AI, you have to realize two really interesting facts about this moment. The first is that almost every future scenario about this technology depends on a series of empirical facts that no one on this planet really knows the answer to. And these facts are very strange. Only a few years ago, they would have been considered post-modernist, irrelevant debates in your local philosophy department about questions like, “is there such a thing as reasoning or is it all just language?” And “what is the nature of intelligence and consciousness?” Of course, we as human beings have studied these questions for many generations. But I was on CNBC talking about this the other day — it’s rare that they are of such economic import that stock traders are wondering about them. To give one example, one of the most important questions you have to ask about AI is when or if the scaling laws will ever run out. So far, for quite a number of years,, thanks to pioneering researchers, including many far-sighted ones like my co-founder at Answer.AI Jeremy Howard, have figured out that simply by applying more computation to a very simple learning algorithm, you can create language models that seem quite intelligent, at least at first glance. So far, the more computation we use to train and run these models, the more capable they become. I think most people generally assume that this is some kind of S-curve and that eventually this curve will level off. Some even think that it already has leveled off. Others think we are years, or even decades, away from it leveling off, and of course some people believe it will never level off. This is the law of the universe. Depending on which of those things is true, the future scenarios are almost comically different from each other. A world in which the scaling laws level off next year is almost unimaginably different from one in which we have ten more years of this. And many of the doomsday scenarios, but also many of the utopia scenarios, depend critically on knowing the answer to this fundamental question about the universe that nobody knows. So, back to your question: How do we know what actions to take when the range of possible futures is so wide, so different from each other and so dependent on facts not in evidence. I think there’s only one thing that makes sense, which is to ask ourselves what are actions that would make sense, that you’ll be glad that you did, in a wide variety of potential futures? And I think that takes us out of the job of having to predict the future, which is very difficult, and rather into a more prudence-based mindset of what can be done to prepare for many possible futures. And when you go through that analysis, many of the things that you want to do to protect yourself against future AI scenarios are actually things you probably should be doing anyway. Think about having better mandatory disclosure, hardening our critical infrastructure, making sure that the gains from new technologies are widely distributed, going back to the era of widely shared prosperity. So if people are going to be displaced, should they just sit around and hope that the leaders who do the displacing will wind up being enlightened? Absolutely not. Of course not. In fact, the whole point of this book is to show how unless we make changes, the gravitational field of our financial system will warp and even destroy, turn malignant, any company. But where does the gravitational field come from? I think the most surprising part of the book for many readers is in later chapters when we reveal how the same tools that we’ve been discussing about how to create more resilient companies are also tools that can be wielded by all of us to shape the gravitational field of the future and affect what kinds of companies can and can’t form, how those companies can and cannot behave. And while some of those levers are traditional levers, like policy changes, of course., the book is primarily about the other, more surprising lovers, that I bet most readers have not thought of before.

I hope everyone has enjoyed this peek into the mind of the man behind the insightful new title Incorruptible!

Image credits: Eric Ries, Google Gemini

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How to Hire Like the Richest Man in the World

How to Hire Like the Richest Man in the World

GUEST POST from Shep Hyken

This article answers the question: Is it better to hire employees for attitude or for skill, especially in customer service roles?

The old saying in business, when it comes to hiring people, is this:

Hire for attitude, train for skill.

I’ve shared ideas related to this quote in several articles and videos. So, why bring it up again? First, it’s a concept worth revisiting to remind us of this important truth, especially in the world of customer service and experience. Second, I recently heard a version of this that captures the essence and further emphasizes the importance of attitude versus skill.

As I write, the richest man in the world is Elon Musk, the CEO of Tesla and founder of SpaceX, with an estimated net worth north of $500 billion. Whether you like the way he does business or not, we can’t ignore that he may have ideas worth paying attention to, and his take on this old quote is one of those ideas. The concept of hiring for attitude is driven home when he says, “Skills can be taught, but attitude changes require a brain transplant.”

Another man worth paying attention to is Jim Bush. In my book The Amazement Revolution, I interviewed Bush, who at the time was the executive VP of world service for American Express, responsible for customer support centers around the world. He shared that if he could hire someone with years of experience at a support center or working at the front desk of a hotel, he would choose the person with the hotel front desk experience.

Shep Hyken cartoon illustrating why you should hire for attitude and train for skill

Bush said, “We’re talking about human engagement, and that requires the ability to connect.” That’s why American Express began hiring people with hospitality experience. They had the attitude American Express was looking for. After being hired, they could be trained on the technical skills needed to work the computers at a contact center.

Now, before I go further, some of you might be thinking that certain jobs require specific skills, regardless of employees’ attitudes, and you are correct. A surgeon must graduate from medical school before operating. An electrician must learn the trade before wiring a home. Certain jobs require technical proficiency. However, if you hire someone with those skills who has the wrong attitude, they can harm your culture and potentially drive customers away. So, take this concept in the spirit of its meaning.

So, back to Musk’s line about attitude changes requiring a brain transplant. The comment is a bold way of saying that attitude isn’t something you can download like software. It’s hard-wired. People’s attitudes have been formed over their entire lives, from the time they were babies. Leaders who understand this focus on recruiting people who come to the job with the right mindset, with an attitude that fits the personality of the company. The takeaway is simple. Hire people who care. Then, teach them the specific skills they need to perform their job effectively. You can train for competence, but you can’t train for caring.

Image Credit: Pixabay

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Managing the Change When Your New Team Member is an AI Agent

Managing the Change When Your New Team Member Is an AI Agent

by Braden Kelley and Art Inteligencia

Every organization rushing to deploy AI agents is making the same mistake: they are treating this as a technology rollout. It isn’t. It is a change management event — possibly the strangest one most of your employees will ever live through — and almost nobody is managing it as one.

I have spent two decades helping organizations navigate change. New systems, new structures, new leadership, new strategy — I have seen the patterns, and I have built frameworks to help people through them. What’s happening right now with AI agents doesn’t fit neatly into any of those patterns, because for the first time, the “new hire” your team has to adjust to isn’t a person. It has no face to read, no body language to interpret, no shared lunch break to build rapport over. And yet your people are being asked to trust it, collaborate with it, and in some cases defer to its output — all without the social mechanisms humans have relied on for millennia to build trust with someone new.

If you are rolling out AI agents into your teams this year — and if you aren’t already, you will be soon — you need a change management approach built for this specific situation. Here is what that requires.

This Is Not a Software Rollout

When organizations introduce new software, the change management playbook is well understood: communicate the why, train people on the how, support them through the learning curve, and reinforce the new behavior until it sticks. That playbook assumes the new thing is a tool. You pick it up, you put it down, you use it when it’s useful.

An AI agent is not a tool in that sense. It takes initiative. It makes judgment calls. It shows up in meetings, in workflows, in decisions — sometimes proactively, without being asked. The closest analog isn’t a new piece of software. It’s a new colleague. And we already have decades of organizational psychology telling us how disruptive a new colleague can be to team dynamics, let alone one that doesn’t operate like any colleague your team has ever had.

This distinction matters because it changes which change management tools actually apply. ADKAR’s emphasis on individual awareness and desire is still relevant. But the resistance you’ll encounter isn’t really about learning a new interface. It’s about something closer to what happens when any new team member joins: uncertainty about role boundaries, anxiety about being replaced or overshadowed, and an unconscious assessment of whether this new “person” can be trusted.

Why People Resist AI Coworkers Differently Than They Resist New Software

I wrote recently about the neuroscience of creativity and the role the amygdala plays in detecting social threat. The same mechanism is firing right now in your organization, and most leaders have no idea it’s happening.

When a new piece of software arrives, the brain files it under “tool” and moves on. When something that behaves like a colleague arrives — something that talks, decides, and acts with a kind of agency — the brain files it under “social actor” and starts running the same threat assessments it runs on any new person: is this safe? Is this going to take something from me? Can I trust what it tells me?

The catch is that an AI agent gives almost none of the signals humans use to answer those questions. There’s no tone of voice to read for sincerity. No facial expression to gauge intent. No shared history to draw on. Your people are being asked to extend trust to something that offers none of the usual evidence trust is normally built on — and then we’re surprised when adoption stalls or quiet resistance shows up as workarounds, double-checking everything the agent produces, or simply not using it at all.

This is not a training problem. You cannot train your way past a threat response. It has to be addressed the way any well-designed change effort addresses resistance: by understanding what’s actually driving it and designing for that, not for the resistance you assumed you’d see.

Applying the Change Management Process to AI Agent Adoption

I’ve written before about the five process groups that make up a disciplined change management process. Here’s how they apply when the change you’re managing is the introduction of an AI teammate:

Evaluate impact and readiness honestly. Most organizations evaluate AI agent impact in terms of tasks automated and hours saved. Few evaluate it in terms of role identity — what happens to how someone sees their own value when a piece of their job is now done by something that isn’t them? Skipping this assessment is how you end up with technically successful deployments and quietly disengaged teams.

Build a strategy that names the relationship, not just the rollout. Is the agent a tool the team directs, a collaborator the team works alongside, or something closer to a delegate that acts with some independence? Most organizations never decide this explicitly, and the ambiguity is exactly what breeds distrust. Decide it, and say it out loud.

Plan for trust-building, not just training. Traditional training plans teach people how to use something. What you actually need here is closer to onboarding a new team member: transparency about what the agent can and can’t do, visible track record before high-stakes use, and early opportunities for people to verify its output before they’re asked to rely on it.

Execute with visible human oversight, especially early. The fastest way to build trust in a new colleague — human or otherwise — is watching them perform well in front of you, not being told they performed well somewhere else. Early AI agent deployments need visible checkpoints where people can see the agent’s work and verify it, not a black box they’re asked to trust on faith.

Close the loop by naming what changed. Once an AI agent has been integrated into a workflow, say so explicitly, and say what it means for the people whose roles shifted around it. Changes that are never formally acknowledged have a way of generating resentment that outlasts the technical transition by years.

Change Management AI Agent Adoption Infographic

The Real Risk Isn’t the AI. It’s Skipping the Human Part.

I’ll say what I’ve said about AI in customer experience: the key isn’t choosing between AI and humans, it’s knowing when and how to bring each one in well. The organizations that get AI agent adoption right in 2026 will not be the ones with the most advanced agents. They’ll be the ones that treated the human side of this transition with the same discipline they’d apply to any major organizational change — because that is exactly what this is.

Skip that discipline, and you won’t get a failed technology rollout. You’ll get a team that technically has access to an AI agent and quietly refuses to use it, or uses it just enough to look compliant while doing the real work the old way. That is the most expensive kind of failure there is: the one that looks like success on a dashboard somewhere while nothing has actually changed.

Image credits: Gemini

Content Authenticity Statement: The topic area, key elements to focus on, and the change management framing were decisions made by Braden Kelley, with a little help from Claude to research current trends and clean up the article, and Gemini for images/infographics.

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