Was Your AI Strategy Developed by the Underpants Gnomes?

GUEST POST from Robyn Bolton

“It just popped up one day. Who knows how long they worked on it or how many of millions were spent. They told us to think of it as ChatGPT but trained on everything our company has ever done so we can ask it anything and get an answer immediately.”

The words my client was using to describe her company’s new AI Chatbot made it sound like a miracle. Her tone said something else completely.

“It sounds helpful,” I offered. “Have you tried it?”

“I’m not training my replacement! And I’m not going to train my R&D, Supply Chain, Customer Insights, or Finance colleagues’ replacements either. And I’m not alone. I don’t think anyone’s using it because the company just announced they’re tracking usage and, if we don’t use it daily, that will be reflected in our performance reviews.”

All I could do was sigh. The Underpants Gnomes have struck again.

Who are the Underpants Gnomes?

The Underpants Gnomes are the stars of a 1998 South Park episode described by media critic Paul Cantor as, “the most fully developed defense of capitalism ever produced.”

Claiming to be business experts, the Underpants Gnomes sneak into South Park residents’ homes every night and steal their underpants. When confronted by the boy in their underground lair, the Gnomes explain their business plan:

Collect underpants
?
Profit

It was meant as satire.

Some took it as a an abbreviated MBA.

How to Spot the ~~Underpants~~ AI Gnomes

As the AI hype grows, fueling executive FOMO (Fear of Missing Out), the Underpants Gnomes, cleverly disguised as experts, entrepreneurs and consultants, saw their opportunity.

Sell AI
?
Profit

While they’ve pivoted their business focus, they haven’t improved their operations so the ~~Underpants~~ AI Gnomes as still easy to spot:

Investment without Intention: Is your company investing in AI because it’s “essential to future-proofing the business?” That sounds good but if your company can’t explain the future it’s proofing itself against and how AI builds a moat or a life preserver in that future, it’s a sign that the Gnomes are in the building.
Switches, not Solutions: If your company thinks that AI adoption is as “easy as turning on Copilot” or “installing a custom GPT chatbot, the Gnomes are gaining traction. AI is a tool and you need to teach people how to use tools, build processes to support the change, and demonstrate the benefit.
Activity without Achievement: When MIT published research indicating that 95% of corporate Gen AI pilots were failing, it was a sign of just how deeply the Gnomes have infiltrated companies. Experiments are essential at the start of any new venture but only useful if they generate replicable and scalable learning.

How to defend against the AI Gnomes

Odds are the gnomes are already in your company. But fear not, you can still turn “Phase 2:?” into something that actually leads to “Phase 3: Profit.”

Start with the end in mind: Be specific about the outcome you are trying to achieve. The answer should be agnostic of AI and tied to business goals.
Design with people at the center: Achieving your desired outcomes requires rethinking and redesigning existing processes. Strategic creativity like that requires combining people, processes, and technology to achieve and embed.
Develop with discipline: Just because you can (run a pilot, sign up for a free trial), doesn’t mean you should. Small-scale experiments require the same degree of discipline as multi-million-dollar digital transformations. So, if you can’t articulate what you need to learn and how it contributes to the bigger goal, move on.

AI, in all its forms, is here to stay. But the same doesn’t have to be true for the AI Gnomes.

Have you spotted the Gnomes in your company?

Image credit: AI Underpants Gnomes (just kidding, Google Gemini made the image)

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Three Reasons Change Efforts Fail

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Three Reasons Change Efforts Fail

GUEST POST from Greg Satell

There’s no question we have entered a transformative age, with major shifts in technology, resources, demography and migration. Over the next decades, we will have to move from digital from post-digital, from carbon to zero-carbon and from the Boomer values to those of Millennials and Zoomers. Migration will strain societies’ social compact.

Unfortunately, we’re really bad at adapting to change. We’ve known about the climate threat for decades, but have done little about it. The digital revolution, for all the hoopla, has been a big disappointment, falling far short of its promise to change the world for the better. Even at the level of individual firms, McKinsey finds that the vast majority of initiatives fail.

One key factor is that we too often assume that change is inevitable. It’s not. Change dies every day. New ideas are weak, fragile, and in need of protection. If we’re going to bring about genuine transformation, we need to take that into account. The first step is to learn the reasons why change fails in the first place. These three are a good place to start.

1. A Flawed Idea

One obvious reason that change fails is that the idea itself is flawed in some way. Barry Libenson found this out when he was hired to be CIO at the industrial conglomerate Ingersoll Rand. It was his first CIO role and Barry was eager to please the CEO, who he saw as a mentor. So he agreed to aggressive very performance targets for modernizing systems.

Yet while Barry was being financially incentivized to upgrade technology, each of the division leaders were financially incentivized to maximize profit growth. Every dollar they invested in modernizing systems would eat into their performance bonus. Perhaps not surprisingly, Barry’s modernization program didn’t go as well as he’d hoped.

There are a number of tools that can help to troubleshoot ideas and uncover flaws. Pre-mortems force you to imagine how a project could fail. Red Teams set up a parallel group specifically to look for flaws. Howard Tiersky, CEO of the digital transformation agency From Digital and author of the Wall Street Journal bestseller Winning Digital Customers, often uses de Bono’s Six Thinking Hats to help the team take different perspectives.

Most of all, we need to come to terms with the reality that our ideas are always wrong. Sometimes they’re off by a little and sometimes they’re off by a lot, but they’re always wrong, so we always need to be on the lookout for problems. As the physicist Richard Feynman put it.“The first principle is that you must not fool yourself — and you are the easiest person to fool. So you have to be very careful about that.”

2. Failure To Build Trust

Proposed in 1983 by Ira Magaziner, the Rhode Island’s Greenhouse Compact is still considered to be an impressive policy even today, 40 years later. In fact, the bipartisan CHIPS Act is based on the same principle, that targeted, strategic government investments can help simulate economic development in the private sector.

The plan in Rhode Island was to establish four research centers or “greenhouses” throughout the state to help drive development in new technologies, like robotics, medicine and thin film materials, as well as existing industries in which the state had built-in advantages, such as tourism, boat-building and fishing. It quickly gained support among the state’s elite

Yet things quickly soured. There were a number of political scandals that reduced faith in Rhode Island’s government and fed into the laissez-faire zeitgeist of the Reagan era. Critics called the plan “elitist,” for taxing “ordinary” citizens to subsidize greedy corporations. When the referendum was held, it plan got less than a fifth of the vote.

Magaziner’s mistake — one he would repeat with the healthcare plan during the Clinton Administration—was ignoring the need to build trust among constituencies. Getting the plan right is never enough. You need to methodically build trust and support as you go.

3. Identity and Dignity

One of the biggest mistakes change leaders make is assuming that resistance to change has a rational basis. They feel that if they listen to concerns and address them, they will be able to build trust and win over skeptics. Unfortunately, while doing those things is certainly necessary for a successful change effort, it is rarely sufficient.

The simple fact is that human beings form attachments to people, ideas and things and when they feel those attachments are threatened, it offends their identity, dignity and sense of self. This is the most visceral kind of resistance. We can argue the merits of a particular idea and methodically build trust, but we can’t ask people to stop being who they think they are.

Don’t waste your time trying to convince the unconvincible. Your efforts will be very unlikely to succeed and very likely to exhaust and frustrate you. The good news is that irrational resistors, if left to their own devices, will often discredit themselves eventually. You can also speed up the process by designing a dilemma action.

What can be hardest about change, especially when we feel passionately about it, is that at some point, we need to accept that others will not embrace it and we will have to leave some behind. Not every change is for everybody. Some will have to pursue a different journey, one to which they can devote their passions and seek out their own truths.

Change Is Not Inevitable

People like to quote the ancient Greek philosopher Heraclitus, who said things like “the only constant is change” and “no man ever steps in the same river twice, for it’s not the same river and he’s not the same man.” They’re clever quotes and they give us confidence that the change we seek is not only possible, but inevitable.

Yet while change in general may be inevitable, the prospects for any particular change initiative are decidedly poor and the failure to recognize that simple fact is why so many transformation efforts fall short. The first step toward making change succeed is to understand and internalize just how fragile a new, unproven initiative really is.

To bring genuine change about you can’t expect to just push forward and have everyone fall in line. No amount of executive sponsorship or program budget will guarantee victory. To move forward, you will need to listen to skeptics, identify and fix flaws in your idea to methodically build trust. Even then, you will have to outsmart those who have an irrational lust to kill change and who act in ways that are dishonest, underhanded and deceptive.

Change is always, at some level, about what people value. That’s why to make it happen you need to identify shared values that reaffirm, rather than undermine, people’s sense of identity. Recognition is often a more powerful incentive than even financial rewards. In the final analysis, lasting change always needs to be built on common ground.

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

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Getting the Most Out of Quiet Employees in Meetings

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Getting the Most Out of Quiet Employees in Meetings

GUEST POST from David Burkus

Getting quiet employees to speak up in meetings can feel like a challenge, but it doesn’t have to be. The truth is silence doesn’t mean disengagement. Often, quiet team members are the most reflective, thoughtful contributors—they just need the right environment to share their insights. If you’ve ever wondered how to help them find their voice, you’re not alone. It’s a question many leaders face, and the answer lies not in fixing the individual but in fixing the environment.

Let’s explore how to create a space where everyone feels confident contributing and where the team benefits from the diverse perspectives that emerge.

What Leaders Often Get Wrong

A common tactic leaders use to engage quiet employees is calling on them directly during meetings. It seems logical—put someone on the spot, and they’ll contribute, right? Wrong. Forcing participation in this way often backfires. When you call someone out with, “We haven’t heard from you, what do you think?” you’re not creating an opportunity; you’re creating pressure. This can leave the individual feeling unprepared or even embarrassed, which only reinforces their reluctance to speak up in the future.

One-on-one conversations with quiet employees can also miss the mark. Phrasing like, “I haven’t heard from you in meetings lately,” may seem supportive, but it can come across as criticism. Employees may interpret it as, “You’re not contributing enough,” which puts them on the defensive. The issue isn’t the individual’s nature; it’s the dynamics of the meeting itself.

Build an Environment That Encourages Input

Instead of focusing on “fixing” the quiet employee, focus on creating a space that naturally draws out their input. The foundation of this approach is psychological safety, a concept championed by researcher Amy Edmondson. Psychological safety ensures team members feel respected and valued, even when sharing dissenting ideas. Leaders play a pivotal role in cultivating this environment.

One powerful tool is asking better questions. Broad, open-ended prompts signal that all perspectives are welcome and needed. For example:

“What perspectives might we not have considered?” This invites team members to think expansively without feeling the pressure to speak directly from their own viewpoint.
“How do you see this issue affecting our team or organization as a whole?” This leverages the natural reflective tendencies of quieter team members, giving them an entry point to share their thoughts.
“What insights from your work could help us solve this?” By focusing on an individual’s expertise, this question creates a comfortable way for them to contribute.
“What have you seen work well in similar situations?” Grounding the conversation in personal experience allows quieter team members to share insights on their terms.

These types of questions help build trust and demonstrate that every voice matters.

Rethink Meeting Dynamics

The structure of your meetings can either foster or stifle participation. Too often, meetings are tailored to the preferences of more vocal team members, leaving quieter employees without a natural space to contribute. To counteract this, vary the formats of your meetings to accommodate different communication styles. Some team members thrive in group discussions, others in chat-based brainstorming, and still others prefer to provide detailed input via email. By alternating your approach, you give everyone an opportunity to engage in the way that suits them best.

Another powerful tactic is structured silence. When you pose a key question during a meeting, instead of opening the floor immediately, give everyone a few minutes to think and jot down their ideas. If you’re meeting virtually, ask participants to type their responses into a shared chat or document. This approach levels the playing field by giving everyone equal time to formulate their thoughts before louder voices dominate the conversation. Research consistently shows that this kind of silent brainstorming not only generates more ideas but also produces better ones.

Support Contributions in the Moment

When a quiet employee does speak up in meetings, how you respond matters. A positive reaction reinforces their willingness to participate again. Start by praising their contribution and ensuring it gets the attention it deserves. Avoid allowing others to immediately dismiss or talk over their idea. Instead, amplify it by saying something like, “That’s an interesting perspective. Let’s explore that further.”

This approach sends a clear message: their input is valued, and this team appreciates diverse ideas. Over time, these affirming responses build confidence and encourage more frequent participation.

Amplify Voices Outside the Meeting

Sometimes, even with the right environment, a quiet employee may hesitate to contribute in the moment. In these cases, follow up with them privately after the meeting. Instead of framing the conversation as a critique, approach it as an opportunity. For example, you might say, “I’d love to hear your thoughts on what we discussed today. What’s your perspective?”

When they share, praise their ideas and encourage them to bring them up in future meetings. If they do, reinforce their contribution publicly. Highlight the value of their insights to the team, ensuring they feel recognized and respected. This two-step process—private encouragement followed by public amplification—builds their confidence and strengthens their connection to the team.

Create Space for Every Voice

Quiet employees aren’t a problem to be fixed; they’re a strength waiting to be unlocked. By shifting your focus from “Why won’t they speak up?” to “How can I create an environment where they feel comfortable contributing?” you’ll foster a more inclusive and innovative team dynamic. Start by rethinking your meeting structures, asking better questions, and supporting contributions both in and out of the meeting room. Over time, you’ll see not just one employee speaking up more but a cultural shift where every voice is heard—and valued.

By encouraging everyone to speak up in meetings, you’ll unlock the full potential of your team. After all, the best ideas don’t come from the loudest voices. They come from the collective brilliance of the group. It’s your job as a leader to make sure every voice has its chance to shine.

This article originally appeared on DavidBurkus.com

Image credit: Pixabay

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Embodied Artificial Intelligence is the Next Frontier of Human-Centered Innovation

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LAST UPDATED: December 8, 2025 at 4:56 PM

Embodied Artificial Intelligence is the Next Frontier of Human-Centered Innovation

GUEST POST from Art Inteligencia

For the last decade, Artificial Intelligence (AI) has lived primarily on our screens and in the cloud — a brain without a body. While large language models (LLMs) and predictive algorithms have revolutionized data analysis, they have done little to change the physical experience of work, commerce, and daily life. This is the innovation chasm we must now bridge.

The next great technological leap is Embodied Artificial Intelligence (EAI): the convergence of advanced robotics (the body) and complex, generalized AI (the brain). EAI systems are designed not just to process information, but to operate autonomously and intelligently within our physical world. This is a profound shift for Human-Centered Innovation, because EAI promises to eliminate the drudgery, danger, and limitations of physical labor, allowing humans to focus exclusively on tasks that require judgment, creativity, and empathy.

The strategic deployment of EAI requires a shift in mindset: organizations must view these agents not as mechanical replacements, but as co-creators that augment and elevate the human experience. The most successful businesses will be those that unlearn the idea of human vs. machine and embrace the model of Human-Embodied AI Symbiosis.

The EAI Opportunity: Three Human-Centered Shifts

EAI accelerates change by enabling three crucial shifts in how we organize work and society:

1. The Shift from Automation to Augmentation

Traditional automation replaces repetitive tasks. EAI offers intelligent augmentation. Because EAI agents learn and adapt in real-time within dynamic environments (like a factory floor or a hospital), they can handle unforeseen situations that script-based robots cannot. This means the human partner moves from supervising a simple process to managing the exceptions and optimizations of a sophisticated one. The human job becomes about maximizing the intelligence of the system, not the efficiency of the body.

2. The Shift from Efficiency to Dignity

Many essential human jobs are physically demanding, dangerous, or profoundly repetitive. EAI offers a path to remove humans from these undignified roles — the loading and unloading of heavy boxes, inspection of hazardous infrastructure, or the constant repetition of simple assembly tasks. This frees human capital for high-value interaction, fostering a new organizational focus on the dignity of work. Organizations committed to Human-Centered Innovation must prioritize the use of EAI to eliminate physical risk and strain.

3. The Shift from Digital Transformation to Physical Transformation

For decades, digital transformation has been the focus. EAI catalyzes the necessary physical transformation. It closes the loop between software and reality. An inventory algorithm that predicts demand can now direct a bipedal robot to immediately retrieve and prepare the required product from a highly chaotic warehouse shelf. This real-time, physical execution based on abstract computation is the true meaning of operational innovation.

Case Study 1: Transforming Infrastructure Inspection

Challenge: High Risk and Cost in Critical Infrastructure Maintenance

A global energy corporation (“PowerLine”) faced immense risk and cost in maintaining high-voltage power lines, oil pipelines, and sub-sea infrastructure. These tasks required sending human crews into dangerous, often remote, or confined spaces for time-consuming, repetitive visual inspections.

EAI Intervention: Autonomous Sensory Agents

PowerLine deployed a fleet of autonomous, multi-limbed EAI agents equipped with advanced sensing and thermal imaging capabilities. These robots were trained not just on pre-programmed routes, but on the accumulated, historical data of human inspectors, learning to spot subtle signs of material stress and structural failure — a skill previously reserved for highly experienced humans.

The EAI agents performed 95% of routine inspections, capturing data with superior consistency.
Human experts unlearned routine patrol tasks and focused exclusively on interpreting the EAI data flags and designing complex repair strategies.

The Outcome:

The use of EAI led to a 70% reduction in inspection time and, critically, a near-zero rate of human exposure to high-risk environments. This strategic pivot proved that EAI’s greatest value is not economic replacement, but human safety and strategic focus. The EAI provided a foundational layer of reliable, granular data, enabling human judgment to be applied only where it mattered most.

Case Study 2: Elderly Care and Companionship

Challenge: Overstretched Human Caregivers and Isolation

A national assisted living provider (“ElderCare”) struggled with caregiver burnout and increasing costs, while many residents suffered from emotional isolation due to limited staff availability. The challenge was profoundly human-centered: how to provide dignity and aid without limitless human resources.

EAI Intervention: The Adaptive Care Companion

ElderCare piloted the use of adaptive, humanoid EAI companions in low-acuity environments. These agents were programmed to handle simple, repetitive physical tasks (retrieving dropped items, fetching water, reminding patients about medication) and, critically, were trained on empathetic conversation models.

The EAI agents managed 60% of non-essential, fetch-and-carry tasks, freeing up human nurses for complex medical care and deep, personalized interaction.
The EAI’s conversation logs provided caregivers with Small Data insights into the emotional state and preferences of the residents, allowing the human staff to maximize the quality of their face-to-face time.

The Outcome:

The pilot resulted in a 30% reduction in nurse burnout and, most importantly, a measurable increase in resident satisfaction and self-reported emotional well-being. The EAI was deployed not to replace the human touch, but to protect and maximize its quality by taking on the physical burden of routine care. The innovation successfully focused human empathy where it had the greatest impact.

The EAI Ecosystem: Companies to Watch

The race to commercialize EAI is accelerating, driven by the realization that AI needs a body to unlock its full economic potential. Organizations should be keenly aware of the leaders in this ecosystem. Companies like Boston Dynamics, known for advanced mobility and dexterity, are pioneering the physical platforms. Startups such as Sanctuary AI and Figure AI are focused on creating general-purpose humanoid robots capable of performing diverse tasks in unstructured environments, integrating advanced large language and vision models into physical forms. Simultaneously, major players like Tesla with its Optimus project and research divisions within Google DeepMind are laying the foundational AI models necessary for EAI agents to learn and adapt autonomously. The most promising developments are happening at the intersection of sophisticated hardware (the actuators and sensors) and generalized, real-time control software (the brain).

Conclusion: A New Operating Model

Embodied AI is not just another technology trend; it is the catalyst for a radical change in the operating model of human civilization. Leaders must stop viewing EAI deployment as a simple capital expenditure and start treating it as a Human-Centered Innovation project. Your strategy should be defined by the question: How can EAI liberate my best people to do their best, most human work? Embrace the complexity, manage the change, and utilize the EAI revolution to drive unprecedented levels of dignity, safety, and innovation.

“The future of work is not AI replacing humans; it is EAI eliminating the tasks that prevent humans from being fully human.”

Frequently Asked Questions About Embodied Artificial Intelligence

1. How does Embodied AI differ from traditional industrial robotics?

Traditional industrial robots are fixed, single-purpose machines programmed to perform highly repetitive tasks in controlled environments. Embodied AI agents are mobile, often bipedal or multi-limbed, and are powered by generalized AI models, allowing them to learn, adapt, and perform complex, varied tasks in unstructured, human environments.

2. What is the Human-Centered opportunity of EAI?

The opportunity is the elimination of the “3 Ds” of labor: Dangerous, Dull, and Dirty. By transferring these physical burdens to EAI agents, organizations can reallocate human workers to roles requiring social intelligence, complex problem-solving, emotional judgment, and creative innovation, thereby increasing the dignity and strategic value of the human workforce.

3. What does “Human-Embodied AI Symbiosis” mean?

Symbiosis refers to the collaborative operating model where EAI agents manage the physical execution and data collection of routine, complex tasks, while human professionals provide oversight, set strategic goals, manage exceptions, and interpret the resulting data. The systems work together to achieve an outcome that neither could achieve efficiently alone.

Your first step toward embracing Embodied AI: Identify the single most physically demanding or dangerous task in your organization that is currently performed by a human. Begin a Human-Centered Design project to fully map the procedural and emotional friction points of that task, then use those insights to define the minimum viable product (MVP) requirements for an EAI agent that can eliminate that task entirely.

UPDATE – Here is an infographic of the key points of this article that you can download:

Embodied Artificial Intelligence Infographic

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: 1 of 1,000+ quote slides for your meetings & presentations at http://misterinnovation.com

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Important Questions for Innovation

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Important Questions for Innovation

GUEST POST from Mike Shipulski

Here are some important questions for innovation.

What’s the Distinctive Value Proposition? The new offering must help the customer make progress. How does the customer benefit? How is their life made easier? How does this compare to the existing offerings? Summarize the difference on one page. If the innovation doesn’t help the customer make progress, it’s not an innovation.

Is it too big or too small? If the project could deliver sales growth that would dwarf the existing sales numbers for the company, the endeavor is likely too big. The company mindset and philosophy would have to be destroyed. Are you sure you’re up to the challenge? If the project could deliver only a small increase in sales, it’s likely not worth the time and expense. Think return on investment. There’s no right answer, but it’s important to ask the question and set the limits for too big and too small. If it could grow to 10% of today’s sales numbers, that’s probably about right.

Why us? There’s got to be a reason why you’re the right company to do this new work. List the company’s strengths that make the work possible. If you have several strengths that give you an advantage, that’s great. And if one of your weaknesses gives you an advantage, that works too. Step on the accelerator. If none of your strengths give you an advantage, choose another project.

How do we increase our learning rate? First thing, define Learning Objectives (LOs). And once defined, create a plan to achieve them quickly. Here’s a hint. Define what it takes to satisfy the LOs. Here’s another hind. Don’t build a physical prototype. Instead, create a website that describes the potential offering and its value proposition and ask people if they want to buy it. Collect the data and refine the offering based on your learning. Or, create a one-page sales tool and show it to ten potential customers. Define your learning and use the learning to decide what to do next.

Then what? If the first phase of the work is successful, there must be a then what. There must be an approved plan (funding, resources) for the second phase before the first phase starts. And the same thing goes for the follow-on phases. The easiest way to improve innovation effectiveness is avoid starting phase one of projects when their phase two is unfunded. The fastest innovation project is the wrong one that never starts.

How do we start? Define how much money you want to spend. Formalize your business objectives. Choose projects that could meet your business objectives. Free up your best people. Learn as quickly as you can.

Image credit: Unsplash

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Is OpenAI About to Go Bankrupt?

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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

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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

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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

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48 hours only!

Exciting 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

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(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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