Is OpenAI About to Go Bankrupt?

LAST UPDATED: December 4, 2025 at 4:48 PM

Is OpenAI About to Go Bankrupt?

GUEST POST from Chateau G Pato

The innovation landscape is shifting, and the tremors are strongest in the artificial intelligence (AI) sector. For a moment, OpenAI felt like an impenetrable fortress, the company that cracked the code and opened the floodgates of generative AI to the world. But now, as a thought leader focused on Human-Centered Innovation, I see the classic signs of disruption: a growing competitive field, a relentless cash burn, and a core product advantage that is rapidly eroding. The question of whether OpenAI is on the brink of bankruptcy isn’t just about sensational headlines — it’s about the fundamental sustainability of a business model built on unprecedented scale and staggering cost.

The “Code Red” announcement from OpenAI, ostensibly about maintaining product quality, was a subtle but profound concession. It was an acknowledgment that the days of unchallenged superiority are over. This came as competitors like Google’s Gemini and Anthropic’s Claude are not just keeping pace, but in many key performance metrics, they are reportedly surpassing OpenAI’s flagship models. Performance parity, or even outperformance, is a killer in the technology adoption curve. When the superior tool is also dramatically cheaper, the choice for enterprises and developers — the folks who pay the real money — becomes obvious.

The Inevitable Crunch: Performance and Price

The competitive pressure is coming from two key vectors: performance and cost-efficiency. While the public often focuses on benchmark scores like MMLU or coding abilities — where models like Gemini and Claude are now trading blows or pulling ahead — the real differentiator for business users is price. New models, including the China-based Deepseek, are entering the market with reported capabilities approaching the frontier models but at a fraction of the development and inference cost. Deepseek’s reportedly low development cost highlights that the efficiency of model creation is also improving outside of OpenAI’s immediate sphere.

Crucially, the open-source movement, championed by models like Meta’s Llama family, introduces a zero-cost baseline that fundamentally caps the premium OpenAI can charge. Llama, and the rapidly improving ecosystem around it, means that a good-enough, customizable, and completely free model is always an option for businesses. This open-source competition bypasses the high-cost API revenue model entirely, forcing closed-source providers to offer a quantum leap in utility to justify the expenditure. This dynamic accelerates the commoditization of foundational model technology, turning OpenAI’s once-unique selling proposition into a mere feature.

OpenAI’s models, for all their power, have been famously expensive to run — a cost that gets passed on through their API. The rise of sophisticated, cheaper alternatives — many of which employ highly efficient architectures like Mixture-of-Experts (MoE) — means the competitive edge of sheer scale is being neutralized by engineering breakthroughs in efficiency. If the next step in AI on its way to artificial general intelligence (AGI) is a choice between a 10% performance increase and a 10x cost reduction for 90% of the performance, the market will inevitably choose the latter. This is a structural pricing challenge that erodes one of OpenAI’s core revenue streams: API usage.

The Financial Chasm: Burn Rate vs. Reserves

The financial situation is where the “bankruptcy” narrative gains traction. Developing and running frontier AI models is perhaps the most capital-intensive venture in corporate history. Reports — which are often conflicting and subject to interpretation — paint a picture of a company with an astronomical cash burn rate. Estimates for annual operational and development expenses are in the billions of dollars, resulting in a net loss measured in the billions.

This reality must be contrasted with the position of their main rivals. While OpenAI is heavily reliant on Microsoft’s monumental investment — a complex deal involving cash and Azure cloud compute credits — Microsoft’s exposure is structured as a strategic infrastructure play. The real financial behemoth is Alphabet (Google), which can afford to aggressively subsidize its Gemini division almost indefinitely. Alphabet’s near-monopoly on global search engine advertising generates profits in the tens of billions of dollars every quarter. This virtually limitless reservoir of cash allows Google to cross-subsidize Gemini’s massive research, development, and inference costs, effectively enabling them to engage in a high-stakes price war that smaller, loss-making entities like OpenAI cannot truly win on a level playing field. Alphabet’s strategy is to capture market share first, using the profit engine of search to buy time and scale, a luxury OpenAI simply does not have without a continuous cash injection from a partner.

The question is not whether OpenAI has money now, but whether their revenue growth can finally eclipse their accelerating costs before their massive reserve is depleted. Their long-term financial projections, which foresee profitability and revenues in the hundreds of billions by the end of the decade, require not just growth, but a sustained, near-monopolistic capture of the new AI-driven knowledge economy. That becomes increasingly difficult when competitors are faster, cheaper, and arguably better, and have access to deeper, more sustainable profit engines for cross-subsidization.

The Future Outlook: Change or Consequence

OpenAI’s future is not doomed, but the company must initiate a rapid, human-centered transformation. The current trajectory — relying on unprecedented capital expenditure to maintain a shrinking lead in model performance — is structurally unsustainable in the face of faster, cheaper, and increasingly open-source models like Meta’s Llama. The next frontier isn’t just AGI; it’s AGI at scale, delivered efficiently and affordably.

OpenAI must pivot from a model of monolithic, expensive black-box development to one that prioritizes efficiency, modularity, and a true ecosystem approach. This means a rapid shift to MoE architectures, aggressive cost-cutting in inference, and a clear, compelling value proposition beyond just “we were first.” Human-Centered Innovation principles dictate that a company must listen to the market — and the market is shouting for price, performance, and flexibility. If OpenAI fails to execute this transformation and remains an expensive, marginal performer, its incredible cash reserves will serve only as a countdown timer to a necessary and painful restructuring.

Frequently Asked Questions (FAQ)

  • Is OpenAI currently profitable?
    OpenAI is currently operating at a significant net loss. Its annual cash burn rate, driven by high R&D and inference costs, reportedly exceeds its annual revenue, meaning it relies heavily on its massive cash reserves and the strategic investment from Microsoft to sustain operations.
  • How are Gemini and Claude competing against OpenAI on cost and performance?
    Competitors like Google’s Gemini and Anthropic’s Claude are achieving performance parity or superiority on key benchmarks. Furthermore, they are often cheaper to use (lower inference cost) due to more efficient architectures (like MoE) and the ability of their parent companies (Alphabet and Google) to cross-subsidize their AI divisions with enormous profits from other revenue streams, such as search engine advertising.
  • What was the purpose of OpenAI’s “Code Red” announcement?
    The “Code Red” was an internal or public acknowledgment by OpenAI that its models were facing performance and reliability degradation in the face of intense, high-quality competition from rivals. It signaled a necessary, urgent, company-wide focus on addressing these issues to restore and maintain a technological lead.

UPDATE: Just found on X that HSBC has said that OpenAI is going to have nearly a half trillion in operating losses until 2030, per Financial Times (FT). Here is the chart of their $100 Billion in projected losses in 2029. With the success of Gemini, Claude, Deep Seek, Llama and competitors yet to emerge, the revenue piece may be overstated:

OpenAI estimated 2029 financials

Image credits: Google Gemini, Financial Times

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Small Flaws Can Taint the Entire Customer Experience

Details Count

Small Flaws Can Taint the Entire Customer Experience

GUEST POST from Shep Hyken

Have you ever walked into a restaurant bathroom and found paper towels scattered on the floor or an overflowing trash can? What immediately crossed your mind? What did you think about the restaurant? For most of us, our thoughts jump to, “If they can’t keep their bathroom clean, what is their kitchen like?”

I call this the Bathroom Experience, a powerful metaphor for how seemingly minor details can dramatically impact customers’ perceptions of a business. A clean bathroom goes unnoticed because it’s expected. But a dirty one? That sends customers a message that the restaurant might be neglecting other details.

This concept extends far beyond restaurants. Before moving into my current office, I toured the building and specifically checked the bathrooms on multiple floors. The way the building maintained its bathrooms told me what I needed to know about how the property management company handled details throughout the rest of the building.

The concept also extends beyond restrooms. Recently, I checked into a higher-end hotel, and as I was relaxing on my bed, I looked up and noticed thick dust coating the air vents. I found myself wondering what I would breathe in throughout the night. We could refer to this as the Vent Experience.

Dirty Bathroom Shep Hyken Cartoon

These mismanaged details are oversights that create a ripple effect. When a customer picks up a rental car and discovers the glove compartment won’t stay closed, they might wonder, “If they missed this, I wonder if they checked to make sure the brakes were working properly.”

Many years ago, my assistant sent a performance agreement to a client who booked me for a speech. The client called me to discuss canceling the booking. It turns out the agreement had a number of typos and punctuation errors. I was shocked and embarrassed. It turns out my assistant accidentally sent the draft she was working on instead of the final version. I apologized and explained what happened. Fortunately, the client accepted the explanation, but I’ll never forget his comment, which made me realize how important little details are. He said, “I am hiring someone who is supposed to be a good communicator. The document you sent had so many errors, I questioned your ability to do the job.” Ouch! That hurt, but he was 100% correct.

Here’s the point: Details that seem insignificant to you might concern your customers. For some, these examples cause customers to make assumptions about other things that they can’t see.

So, what’s your version of the Bathroom Experience? What small detail is your team overlooking that customers notice and use to judge you and your business? Finding and fixing these details doesn’t just solve small problems; it prevents customers from imagining bigger ones.

Image credit: Pixabay

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The Tax Trap and Why Our Economic OS is Crashing

LAST UPDATED: December 3, 2025 at 6:23 PM

The Tax Trap and Why Our Economic OS is Crashing

GUEST POST from Art Inteligencia

We are currently operating an analog economy in a digital world. As an innovation strategist, I often talk about Braden Kelley’s “FutureHacking” — the art of getting to the future first. But sometimes, the future arrives before we have even unpacked our bags. The recent discourse around The Great American Contraction has illuminated a structural fault line in our society that we can no longer ignore. It is what I call the Tax Trap.

This isn’t just an economic glitch; it is a design failure of our entire social contract. We have built a civilization where human survival is tethered to labor, and government solvency is tethered to taxing that labor. As we sprint toward a post-labor economy fueled by Artificial Intelligence and robotics, we are effectively sawing off the branch we are sitting on.

The Mechanics of the Trap

To understand the Tax Trap, we must look at the “User Interface” of our government’s revenue stream. Historically, the user was the worker. You worked, you got paid, you paid taxes. The government then used those taxes to build roads, schools, and safety nets. It was a closed loop.

The introduction of AI as a peer-level laborer breaks this loop in two distinct places, creating a pincer movement that threatens to crush fiscal stability.

1. The Revenue Collapse (The Input Failure)

Robots do not pay payroll taxes. They do not contribute to Social Security or Medicare. When a logistics company replaces 500 warehouse workers with an autonomous swarm, the government loses the income tax from 500 people. But it goes deeper.

In the race for AI dominance, companies are incentivized to pour billions into “compute” — data centers, GPUs, and energy infrastructure. Under current accounting rules, these massive investments can often be written off as expenses or depreciated, driving down reportable profit. So, not only does the government lose the payroll tax, but it also sees a dip in corporate tax revenue because on paper, these hyper-efficient companies are “spending” all their money on growth.

2. The Welfare Spike (The Output Overload)

Here is the other side of the trap. Those 500 displaced warehouse workers do not vanish. They still have biological needs. They need food, healthcare, and housing. Without wages, they turn to the public safety net.

This creates a terrifying feedback loop: Revenue plummets exactly when demand for services explodes.

The Innovation Paradox: The more efficient our companies become at generating value through automation, the less capable our government becomes at capturing that value to sustain the society that permits those companies to exist.

A Human-Centered Design Flaw

As a champion of Human-Centered Change, I view this not as a political problem, but as an architectural one. We are trying to run a 21st-century software (AI-driven abundance) on 20th-century hardware (labor-based taxation).

The “Great American Contraction” suggests that smart nations will reduce their populations to avoid this unrest. While logically sound from a cold, mathematical perspective, it is a defensive strategy. It is a retreat. As innovators, we should not be looking to shrink to fit a broken model; we should be looking to redesign the model to fit our new reality.

The current system penalizes the human element. If you hire a human, you pay payroll tax, health insurance, and deal with HR complexity. If you hire a robot, you get a capital depreciation tax break. We have literally incentivized the elimination of human relevance.

Charting the Change: The Pivot to Value

How do we hack this future? We must decouple human dignity from labor, and government revenue from wages. We need a new “operating system” for public finance.

We must shift from taxing effort (labor) to taxing flow (value). This might look like:

  • The Robot Tax 2.0: Not a penalty on innovation, but a “sovereign license fee” for operating autonomous labor units that utilize public infrastructure (digital or physical).
  • Data Dividends: Recognizing that AI is trained on the collective knowledge of humanity. If an AI uses public data to generate profit, a fraction of that value belongs to the public trust.
  • The VAT Revolution: Moving toward taxing consumption and revenue rather than profit. If a company generates billions in revenue with zero employees, the tax code must capture a slice of that transaction volume, regardless of their operational costs.

The Empathy Engine

The Tax Trap is only fatal if we lack imagination. “The Great American Contraction” warns of scarcity, but automation promises abundance. The bridge between the two is distribution.

If we fail to redesign this system, we face a future of gated communities guarded by drones, surrounded by a sea of irrelevant, under-supported humans. That is a failure of innovation. True innovation isn’t just about faster chips or smarter code; it’s about designing systems that elevate the human condition.

We have the tools to build a world where the robot pays the tax, and the human reaps the creative dividend. We just need the courage to rewrite the source code of our economy.


The Great American Contraction Infographic

Image credits: Google Gemini

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FLASH SALE – 50% off THE Human-Centered Change Guidebook

48 hours only!

The Human-Centered Change Guidebook - Charting ChangeExciting news!

The publisher of my second book – Charting Change – is having a 48-hour FLASH SALE and so you can get the hardcover, softcover or the eBook for 50% off the list price using CODE FLSH50 until December 5, 2025, 11:59PM EDT. The new second edition includes loads of new content including additional guest expert sections and chapters on business architecture, project and portfolio management, and digital and business transformations!

I stumbled across this and wanted to share with everyone so if you haven’t already gotten a copy of this book to power your digital transformation or your latest project or change initiative to success, now you have no excuse!

Click here to get your copy of Charting Change for 50% off using CODE FLSH50

Of course you can get 10 free tools here from the book, but if you buy the book and contact me I will send you 26 free tools from the 50+ tools in the Change Planning Toolkit™ – including the Change Planning Canvas™!

*If discount is not applied automatically, please use this code: FLSH50. The discount is available through December 5, 2025. This offer is valid for English-language Springer, Palgrave & Apress books & eBooks. The discount is redeemable on link.springer.com only. Titles affected by fixed book price laws, forthcoming titles, and titles temporarily not available on link.springer.com are excluded from this promotion, as are reference works, handbooks, encyclopedias, subscriptions, or bulk purchases. The currency in which your order will be invoiced depends on the billing address associated with the payment method used, not necessarily your home currency. Regional VAT/tax may apply. Promotional prices may change due to exchange rates.

This offer is valid for individual customers only. Booksellers, book distributors, and institutions such as libraries and corporations, please visit springernature.com/contact-us. This promotion does not work in combination with other discounts or gift cards.

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11 Reasons Why Teams Struggle to Collaborate

(Despite Good Intentions)

11 Reasons Why Teams Struggle to Collaborate

GUEST POST from Stefan Lindegaard

Collaboration is a favorite theme in strategy decks and leadership keynotes. Leaders say it’s essential for innovation, agility, empowerment, and execution. But if you’ve worked in or with large organizations, you’ll know something feels off:

Teams want to collaborate and not just within their own team, but across functions and silos, and even with partners or external experts.

The problem is that most organizations aren’t set up for this.

I often argue that many organizational issues start at the top. Leaders talk the talk but don’t walk the walk. And when collaboration is reduced to a value on a poster – or buried under broken structures – teams are left to figure it out in an environment working against them.

So I’ve created this ranked list of reasons why collaboration fails. It’s not to point fingers at teams but to spotlight the real barriers that leaders and organizations need to address.

1. They promote teamwork, yet reward individual KPIs.

You can’t expect collaboration when success is defined individually. When people are measured and rewarded for their solo achievements, they will naturally prioritize their own goals – even when it works against the team.

2. They push for cross-functional alignment, yet still operate in silos.

True collaboration requires more than cross-functional task forces, it demands integrated ways of working. But when organizational structures and incentives are siloed, collaboration becomes optional, not foundational.

3. They push for cross-functional alignment, yet still operate in silos.

Collaboration isn’t just within teams. It depends on how well teams work across functions, departments, and even with external partners. Without integrated goals and decision rights, silos quietly win.

4. They encourage knowledge-sharing, yet overload teams with competing priorities.

Collaboration takes time. When teams are juggling too much, knowledge-sharing becomes a luxury. People protect their time and focus, not because they don’t care, but because they’re trying to survive the chaos.

5. They say collaboration matters, yet measure success in isolation.

If KPIs and OKRs don’t reflect shared goals, collaboration will always take a back seat. People follow the metrics. And when those metrics are narrow or individual, so is the behavior.

6. They ask for collective ownership, yet assign accountability to a single function.

You can’t expect teams to own outcomes together if only one person or team is held accountable when things go wrong. This creates fear, finger-pointing, and passive involvement from others.

7. They talk about shared goals, yet lack clear alignment across teams.

“Shared goals” sound good, but if each team interprets them differently, you end up with misalignment, duplication, or conflicting efforts. Collaboration without alignment leads to confusion, not impact.

8. They encourage open dialogue, yet don’t create psychological safety to speak up.

Without safety, people stay silent. They avoid saying what needs to be said, and collaboration becomes shallow. Open dialogue is only possible when people trust they won’t be punished for honesty or vulnerability.

9. They expect faster execution, yet require too many approvals to move forward.

Even well-aligned, collaborative teams can lose momentum when bogged down in bureaucracy. Endless approvals signal a lack of trust and slow down the very agility leaders are asking for.

10. They want proactive teams, yet reward those who play it safe and stay in their lane.

Proactivity means taking initiative, stepping into grey zones, and owning outcomes. But when the system rewards safety and punishes stretch behavior, people stay in their box – and so does the organization.

11. They invest in collaboration tools, yet don’t invest in team dynamics or leadership behaviors.

Slack, Miro, Teams, Asana. Tools are helpful, but they don’t create trust, alignment, or clarity. Collaboration starts with people, not platforms.

The Bottom Line

Collaboration isn’t broken – what’s broken is the system surrounding it.

People want to work together. Most teams are willing, capable, and motivated. But collaboration fails when leadership behaviors, organizational structures, and incentives quietly undermine it.

So the question isn’t:

“Why don’t our teams collaborate better?”

It’s:

“What’s making it harder for them to collaborate in the first place?”

Fix the system. Collaboration will follow.

Image Credit: Pexels

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Innovation Theater – A Defense

Innovation Theater - A Defense

GUEST POST from Robyn Bolton

I can’t believe that I’m writing this. Honestly, I can’t believe I’m even thinking this. I’m an open-minded person, but I truly never thought that anything would ever change my mind on this topic. And yet, I must confess that I’ve come to the conclusion that…

(deep breath)

Innovation Theater is important.

(Sorry, needed a minute to recover. It’s one thing to think something. It’s another to see it in writing.)

Why We All Hate(d) Innovation Theater.

The term “Innovation Theater” was coined by Steve Blank in a 2019 HBR article to describe innovation activities like hackathons, shark tanks, and workshops that “shape and build culture, but they don’t win wars, and they rarely deliver shippable/deployable product.”

The name stuck because it gave the Innovation Industrial Complex a perfect scapegoat. Innovation efforts weren’t producing results because companies were turning real strategy into theater—events that could be delegated and scheduled instead of the courage, commitment, and willingness to change that actual innovation requires.

And in many cases, this criticism was warranted.

But in our rush to dismiss Innovation Theater, we missed something important.

What I (Almost) Missed.

Recently, I visited a company’s Innovation Center, curious to see what ten years of innovation investments and two floors in a downtown high-rise had produced.

The answer was a framework to think more deeply about equity and inclusion. My immediate reaction was rage.  A decade of investments for this? Millions of dollars spent on the very definition of Innovation Theater? And they’re bragging about it?!?

Once the rage subsided, something remained. Something that I couldn’t shake. An inkling that I had missed something. That inkling became the realization that I was wrong.

Over the past five years, the framework had been used in carefully curated workshops to help teams across the organization see things they had previously overlooked, understand topics that were sensitive or taboo, and envision solutions that no one their heavily regulated industry had even considered.

Not every workshop resulted in action. But over time, something shifted.

Seasons. Not Shows.

Repetition created a shared language. Multiple touchpoints built permission. Small success stories accumulated to make risk feel manageable. The workshops didn’t send off isolated sparks of innovation. They built the conditions where acting on new ideas became progressively safer and more normal.

And after several seasons, enduring value was created. The company now enjoys the highest retention rate of customers in its industry and has attracted more new customers than all its competitors combined. A decade of “Innovation Theater” delivered exactly what innovation is supposed to deliver: measurable competitive advantage and revenue growth.

Don’t Cancel Your Next Innovation Event.

The problem isn’t Innovation Theater itself. It’s how we practice it.

A one-off hackathon? Theater. An annual workshop? Theater. But sustained investment over years, touching dozens of teams, building shared language and accumulated proof points? That’s a strategic bet on transformation that creates lasting competitive advantage.

The question isn’t whether Innovation Theater works. It’s whether you’re willing to commit to the season, not just the show. Are you prepared to invest consistently, measure differently, and wait for compounding effects that won’t show up in next quarter’s results?

Because when you commit to the season, not just the show, it’s the most strategic bet you can make.

Image credit: Pexels

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Top 10 Human-Centered Change & Innovation Articles of November 2025

Top 10 Human-Centered Change & Innovation Articles of November 2025Drum roll please…

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

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

  1. Eight Types of Innovation Executives — by Stefan Lindegaard
  2. Is There a Real Difference Between Leaders and Managers? — by David Burkus
  3. 1,000+ Free Innovation, Change and Design Quotes Slides — by Braden Kelley
  4. The AI Agent Paradox — by Art Inteligencia
  5. 74% of Companies Will Die in 10 Years Without Business Transformation — by Robyn Bolton
  6. The Unpredictability of Innovation is Predictable — by Mike Shipulski
  7. How to Make Your Employees Thirsty — by Braden Kelley
  8. Are We Suffering from AI Confirmation Bias? — by Geoffrey A. Moore
  9. How to Survive the Next Decade — by Robyn Bolton
  10. It’s the Customer Baby — by Braden Kelley

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

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

Build a Common Language of Innovation on your team

Have something to contribute?

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

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

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Bridging Differences to Drive Creativity and Innovation

Bridging Differences to Drive Creativity And Innovation

GUEST POST from Greg Satell

I have a friend who was once ambushed on a TV show panel. Being confronted with a clearly offensive remark, she was caught off-guard, said something that was probably unwise (but not untrue or unkind), and found herself at the center of a media-driven scandal. It would cost her enormously, both personally and professionally.

I often think about the episode and not just because it hurt my friend, but also because I wonder what I would have done if put in similar circumstances. My friend, who is black, Muslim and female, is incredibly skilled at bridging differences and navigating matters of race, gender and religion. If she fell short, would I even stand a chance?

We are encouraged to think about matters of diversity in moral terms and, of course, that’s an important aspect. However, it is also a matter of developing the right skills. The better we are able to bridge differences, the more effectively we can collaborate with others who have different perspectives, which is crucial to becoming more innovative and productive.

The Challenge Of Diversity

There is no shortage of evidence that diversity can enhance performance. Researchers at the University of Michigan found that diverse groups can solve problems better than a more homogeneous team of greater objective ability. Another study that simulated markets showed that ethnic diversity deflated asset bubbles.

While those studies merely simulate diversity in a controlled setting, there is also evidence from the real world that diversity produces better outcomes. A McKinsey report that covered 366 public companies in a variety of countries and industries found that those which were more ethnically and gender diverse performed significantly better than others.

However, it takes effort to reap the benefits of diversity. Humans are naturally tribal. In a study of adults that were randomly assigned to “leopards” and “tigers,” fMRI studies noted hostility to out group members. Similar results were found in a study involving five year-old children and even in infants. Group identification, even without any of the normal social cues, is enough to produce bias.

The innate distinctions we make regarding each other carry over to work environments. When researchers at Kellogg and Stanford put together groups of college students to solve a murder mystery, teams made up of students from the same sorority or fraternity felt more successful, even though they performed worse on the task than integrated groups.

We rarely welcome someone who threatens our sense of self. So those outside the dominant culture are encouraged to conform and are often punished when they don’t. They are less often invited to join in routine office socializing and promotions are less likely to come their way. When things go poorly, it’s much easier to blame the odd duck than the trusted insider.

Group Identity And Individual Dignity

In western civilization, since at least the time of Descartes, we have traditionally thought in rational terms about how humans behave. We tend to assume that people examine facts to make judgments and that any disputes can be overcome through discussion and debate, through which we will arrive at an answer that is objectively correct.

Yet what if we actually did things in reverse, intuitively deciding what was right and then coming up with rational explanations for how we feel? Discussion and debate wouldn’t achieve anything. If rational arguments are merely explanations of deeply held intuitions, the “arguments” from the other side would seem to be downright lies or just crazy.

In The Righteous Mind, social psychologist Jonathan Haidt points to decades of evidence that suggest that is exactly how we do things. We rely on social intuitions to make judgments and then design logic to explain why we feel that way. He also makes the point that many of our opinions are a product of our inclusion in a particular group.

Hardly the product of cold logic, our opinions are, in large part, manifestations of our identity. Our ideas are not just things we think. They are expressions of who we think we are.

Talking Past Each Other

Clearly, the way we tend to self-sort ourselves into groups based on identity will shape how we perceive what we see and hear, but it will also affect how we share and access data. Recently, a team of researchers at MIT looked into how we share information — and misinformation — with those around us. What they found was troubling.

When we’re surrounded by people who think like us, we share information more freely because we don’t expect to be rebuked. We’re also less likely to check our facts, because we know that those we are sharing the item with will be less likely to inspect it themselves. So when we’re in a filter bubble, we not only share more, we’re also more likely to share things that are not true. Greater polarization leads to greater misinformation.

The truth is that we all have a need to be recognized and when others don’t share a view that we feel strongly about, it offends our sense of dignity. The danger, of course, is that in our rapture we descend into solipsism and fail to recognize the dignity of others. That can lead us to dangerous and ugly places.

In Timothy Snyder’s masterful book Bloodlands, which explores the mass murders of Hitler and Stalin, the eminent historian concludes that the reason that humans can do unspeakable things to other humans is that they themselves feel like victims. If your very survival is at stake, then just about anything is warranted and cruelty can seem like justice.

Once our individual dignity becomes tied to our group identity, a different perspective can feel like more than just an opposing opinion, but a direct affront and that’s what may have precipitated the public attack on my friend. The verbal assault was probably motivated by her assailant’s need to signal inclusion in an opposing tribe.

Building Shared Identity And Purpose

Our identity and sense of self drives a lot of what we see and do, yet we rarely examine these things because we spend most of our time with people who are a lot like us, who live in similar places and experience similar things. That’s why our innate perceptions and beliefs seem normal and those of others strange, because our social networks shape us that way.

As we conform to those around us, we are setting ourselves apart from those who are shaped by different sets of experiences. While there is enormous value to be unlocked by integrating with diverse perspectives, it takes work to be able to bridge those differences. What we hear isn’t always what others say and what we say isn’t what others always hear.

In his book, Identity, political scientist Francis Fukuyama explains that our identities aren’t fixed, but develop and change over time. In fact, we routinely choose to add facets to our identity, while shedding others, changing jobs, moving neighborhoods, breaking off some associations as we take on others. “Identity can be used to divide, but it can and has also been used to integrate,” Fukuyama writes.

Yet integrating identities takes effort. We first need to acknowledge that our truth isn’t the only truth and that others, looking at the same facts, can honestly come to different conclusions than we do. We need to suspend immediate judgment and devote ourselves to a common undertaking with a shared sense of mission and purpose.

This is no easy task. It takes significant effort. However, it is at this nexus of identity and purpose that creativity and innovation reside, because when we learn to collaborate with others who possess knowledge, skills and perspectives that we don’t, new possibilities emerge to achieve greater things.

— Article courtesy of the Digital Tonto blog
— Image credit: Pexels

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The Evolution of Trapped Value in Cloud Computing

The Evolution of Trapped Value in Cloud Computing

GUEST POST from Geoffrey A. Moore

Releasing trapped value drives the adoption of disruptive technology and subsequent category development. The trapped part inspires the technical innovation while the value part funds the business. As targeted trapped value gets released, the remaining value is held in place by a secondary set of traps, calling for a second generation of innovation, and a second round of businesses. This pattern continues until all the energy in the system is exhausted, and the economic priority shifts from growth to maintenance.

Take cloud computing for example. Amazon and Salesforce were early disrupters. The trapped value in retail was consumer access anytime anywhere. The trapped value in SaaS CRM was a corporate IT model that prioritized forecasting and reporting applications for upper management over tools for improving sales productivity in the trenches. As their models grew in success, however, they outgrew the data center operating model upon which they were based, and that was creating problems for both companies.

Help came from an unexpected quarter. Consumer computing, led by Google and Facebook, tackled the trapped value in the data center model by inventing the data-center-as-a-computer operation. The trapped value was in computers and network equipment that was optimized for scaling up to get more power. The new model relentlessly focused on commoditizing both, with stripped-down compute blocks and software-enabled switching—much to the consternation of the established hardware vendors who had no easy place to retreat to.

Their situation was further exacerbated by the rise of hyperscaler compute vendors who offered to outsource the entire enterprise footprint. But as they did, the value trap moved again, and this time it was the hyperscaler pricing model that was holding things back, particularly when switching costs were high. That has given rise to a hybrid architecture which at present is muddling its way through to a moderating norm. Here companies like Equinix and Digital Realty are helping enterprises combine approaches to find their optimal balance.

As this norm takes over more and more of the playing field, we may approach an asymptote of releasable trapped value at the computing layer. If so, that just means it will migrate elsewhere—in this case, up the stack. We are already seeing this in at least three areas of hypergrowth today:

  1. Cybersecurity, where the trapped value is in patching together component subsystems to address ongoing exposure to catastrophic risk.
  2. Content generation, where the trapped value is in time to market, as well as unfulfilled demand, for fresh digital media, both in consumer markets and in the enterprise.
  3. Co-piloting, where the trapped value is in low-yielding engagement with high-value digital services due to topic complexity and the lack of sophistication on the part of the end user.

All three of these opportunities will push further innovation in cloud computing, but the higher margins will now migrate to the next generation.

The net of all this is a fundamental investment thesis that applies equally well to venture investing, enterprise spending, and personal wealth management. As the Watergate pair of Woodward and Bernstein taught us many decades ago, Follow the money! In this case, the money is in the trapped value, so before you invest in any context, first identify the trapped value that when released will create the ROI you are looking for, and then monitor the early stages to determine if indeed it is getting released, and if so, that a fair share of the returns are coming back to you.

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

Image Credit: Pixabay

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Why 4D Printing is the Next Frontier of Human-Centered Change

The Adaptive Product

LAST UPDATED: November 29, 2025 at 9:23 AM

Why 4D Printing is the Next Frontier of Human-Centered Change

GUEST POST from Art Inteligencia

For centuries, the pinnacle of manufacturing innovation has been the creation of a static, rigid, and perfect form. Additive Manufacturing, or 3D printing, perfected this, giving us complexity without molds. But a seismic shift is underway, introducing the fourth dimension: time. 4D Printing is the technology that builds products designed to change their shape, composition, or functionality autonomously in response to environmental cues.

The innovation isn’t merely in the print, but in the programmable matter. These are objects with embedded behavioral code, turning raw materials into self-assembling, self-repairing, or self-adapting systems. For the Human-Centered Change leader, this is profoundly disruptive, moving design thinking from What the object is, to How the object behaves across its entire lifespan and in shifting circumstances.

The core difference is simple: 3D printing creates a fixed object. 4D printing creates a dynamic system.

The Mechanics of Transformation: Smart Materials

4D printing leverages existing 3D printing technologies (like Stereolithography or Fused Deposition Modeling) but uses Smart Materials instead of traditional static plastics. These materials have properties programmed into their geometry that cause them to react to external stimuli. The key material categories include:

  • Shape Memory Polymers (SMPs): These materials can be printed into one shape (Shape A), deformed into a temporary shape (Shape B), and then recover Shape A when exposed to a specific trigger, usually heat (thermo-responsive).
  • Hydrogels: These polymers swell or shrink significantly when exposed to moisture or water (hygromorphic), allowing for large-scale, water-driven shape changes.
  • Biomaterials and Composites: Complex structures combining stiff and responsive materials to create controlled folding, bending, or twisting motions.

This allows for the creation of Active Origami—intricate, flat-packed structures that self-assemble into complex 3D forms when deployed or activated.

Case Study 1: The Self-Adapting Medical Stent

Challenge: Implanting Devices in Dynamic Human Biology

Traditional medical stents (small tubes used to open blocked arteries) are fixed in size and delivered via invasive surgery or catheter-based deployment. Once implanted, they cannot adapt to a patient’s growth or unexpected biological changes, sometimes requiring further intervention.

4D Printing Intervention: The Time-Lapse Stent

Researchers have pioneered the use of 4D printing to create stents made of bio-absorbable, shape-memory polymers. These devices are printed in a compact, temporarily fixed state, allowing for minimally invasive insertion. Upon reaching the target location inside the body, the polymer reacts to the patient’s body temperature (the Thermal Stimulus).

  • The heat triggers the material to return to its pre-programmed, expanded shape, safely opening the artery.
  • The material is designed to gradually and safely dissolve over months or years once its structural support is no longer needed, eliminating the need for a second surgical removal.

The Human-Centered Lesson:

This removes the human risk and cost associated with two major steps: the complexity of surgical deployment (by making the stent initially small and flexible) and the future necessity of removal (by designing it to disappear). The product adapts to the patient, rather than the patient having to surgically manage the product.

Case Study 2: The Adaptive Building Facade

Challenge: Passive Infrastructure in Dynamic Climates

Buildings are static, but the environment is not. Traditional building systems require complex, motor-driven hardware and electrical sensors to adapt to sun, heat, and rain, leading to high energy costs and mechanical failure.

4D Printing Intervention: Hygromorphic Shading Systems

Inspired by how pinecones open and close based on humidity, researchers are 4D-printing building facade elements (shades, shutters) using bio-based, hygromorphic composites (materials that react to moisture). These large-scale prints are installed without any wires or motors.

  • When the air is dry and hot (high sun exposure), the material remains rigid, allowing light in.
  • When humidity increases (signaling impending rain or high moisture), the material absorbs the water vapor and is designed to automatically bend and curl, creating a self-shading or self-closing surface.

The Human-Centered Lesson:

This shifts the paradigm of sustainability from complex digital control systems to material intelligence. It reduces energy consumption and maintenance costs by eliminating mechanical components. The infrastructure responds autonomously and elegantly to the environment, making the building a more resilient and sustainable partner for the human occupants.

The Companies and Startups Driving the Change

The field is highly collaborative, bridging material science and industrial design. Leading organizations are often found in partnership with academic pioneers like MIT’s Self-Assembly Lab. Major additive manufacturing companies like Stratasys and Autodesk have made significant investments, often focusing on the software and material compatibility required for programmable matter. Other key players include HP Development Company and the innovative work coming from specialized bioprinting firms like Organovo, which explores responsive tissues. Research teams at institutions like the Georgia Institute of Technology continue to push the boundaries of multi-material 4D printing systems, making the production of complex, shape-changing structures faster and more efficient. The next generation of breakthroughs will emerge from the seamless integration of these material, design, and software leaders.

“4D printing is the ultimate realization of design freedom. We are no longer limited to designing for the moment of creation, but for the entire unfolding life of the product.”

The implications of 4D printing are vast, spanning aerospace (self-deploying antennae), consumer goods (adaptive footwear), and complex piping systems (self-regulating valves). For change leaders, the mandate is clear: start viewing your products and infrastructure not as static assets, but as programmable actors in a continuous, changing environment.

Frequently Asked Questions About 4D Printing

1. What is the “fourth dimension” in 4D Printing?

The fourth dimension is time. 4D printing refers to 3D-printed objects that are created using smart, programmable materials that change their shape, color, or function over time in response to specific external stimuli like heat, light, or water/humidity.

2. How is 4D Printing different from 3D Printing?

3D printing creates a final, static object. 4D printing uses the same additive manufacturing process but employs smart materials (like Shape Memory Polymers) that are programmed to autonomously transform into a second, pre-designed shape or state when a specific environmental condition is met, adding the element of time-based transformation.

3. What are the main applications for 4D Printing?

Applications are strongest where adaptation or deployment complexity is key. This includes biomedical devices (self-deploying stents), aerospace (self-assembling structures), soft robotics (flexible, adaptable grippers), and self-regulating infrastructure (facades that adjust to weather).

Your first step toward adopting 4D innovation: Identify one maintenance-heavy, mechanical component in your operation that is currently failing due to environmental change (e.g., a simple valve or a passive weather seal). Challenge your design team to rethink it as an autonomous, 4D-printed shape-memory structure that requires no external power source.

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 credit: Google Gemini

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