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The Shifting Sands of AI: Why Enterprise Leaders Need to Look Beyond OpenAI

The Shifting Sands of AI: Why Enterprise Leaders Need to Look Beyond OpenAI

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Dr. David Bray
Distinguished Chair of the Accelerator & Principal/CEO
Stimson Center & LeadDoAdapt Ventures

Dr. David A. Bray is a Distinguished Fellow and Chair of the Accelerator with the Alfred Lee Loomis Innovation Council at the non-partisan Henry L. Stimson Center. He is also a non-resident Distinguished Fellow with the Business Executives for National Security, and a CEO and transformation leader for different “under the radar” tech and data ventures seeking to get started in novel situations. He is Principal at LeadDoAdapt Ventures and has served in a variety of leadership roles in turbulent environments, including bioterrorism preparedness and response from 2000-2005. Dr. Bray previously was the Executive Director for a bipartisan National Commission on Research and Development, provided non-partisan leadership as a federal agency Senior Executive, worked with the U.S. Navy and Marines…...

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R "Ray" Wang
Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

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A Rapidly Evolving Landscape; OpenAI's Disappearing Moat

We've been watching the generative AI landscape transform at breathtaking speed, and what concerns us most is how quickly the narrative around OpenAI has shifted from "unassailable market leader" to "company facing existential challenges." As leaders who have spent our careers at the intersection of technology, policy, and enterprise strategy, we believe that organizations making multi-million dollar AI investments need to understand the broader context beyond the marketing hype.

The concept of a "moat" in business refers to sustainable competitive advantages that protect a company from competitors. OpenAI's initial moat was built on first-mover advantage, technical superiority, and massive funding. All three pillars are now showing significant cracks.

Microsoft—OpenAI's primary backer—has began testing outside models from xAI, Meta, and even Chinese company DeepSeek. Simultaneously, Apple appears to be reconsidering its OpenAI partnership, now engaging with Google about Siri integration. These moves by two of the world's most valuable companies signal serious concerns about OpenAI's trajectory.

The technical superiority argument is also collapsing. OpenAI's rushed GPT-4.5 release shows a 30% error rate—significantly worse than both Anthropic's Claude 3.7 and xAI's Grok3. When your core product is underperforming relative to competitors, enterprise customers take notice.

 

Competition Is Intensifying; The Economics Don't Add Up

While OpenAI struggles, competitors are gaining momentum. Anthropic secured a $3 billion investment from Google and released Claude 3.7, which many consider technically superior to OpenAI's offerings. Elon Musk's xAI launched Grok3 with impressive deep research capabilities. Even OpenAI's former CTO, Mira Murati, launched Thinking Machines Lab and raised $2 billion at a $9 billion valuation in just two weeks.

And we can't ignore developments from China. Within the last few weeks,they announced what they described as the world's first fully autonomous AI agent, called Manus. Unlike some overhyped Western announcements, Chinese AI capabilities have generally delivered on their promises. This represents both competitive and geopolitical considerations for enterprise leaders.

The financial picture is equally concerning. OpenAI is reportedly burning through $1 billion monthly and could lose up to $44 billion by next year. Sam Altman himself admitted they lose money on every $200/month ChatGPT subscription. Their recent announcement of enterprise offerings priced between $2,000-$20,000 monthly appears to be a desperate attempt to stem these losses.

This pricing strategy reveals a company pivoting toward enterprise customers out of necessity rather than strength. But this market is already dominated by Microsoft, Amazon, and Google, who have decades-long relationships with Fortune 500 companies. OpenAI faces an uphill battle against entrenched competitors with deeper pockets and broader offerings.

Despite the recent headline-grabbing $40 billion funding round that catapulted OpenAI's valuation to $300 billion and reports that the company's revenue has grown by 30% in three months, the company still doesn't expect to break even until 2029—four years from now! This timeline raises serious questions about the sustainability of their business model, especially as they continue to burn through cash at an alarming rate.

In a telling strategic pivot, OpenAI has also announced plans to launch an open-weights reasoning model that developers can run on their own hardware. This represents a significant departure from their closed system subscription model and suggests an acknowledgment that their current approach may not remain competitive in the long term. This move appears to be a course correction in response to mounting pressure from both open-source alternatives and competitors offering more flexible deployment options.

 

Strategic Implications for Enterprise Leaders

For CEOs, CTOs, CIOs, and CMOs, these developments necessitate a more sophisticated approach to AI strategy. The days of simply "partnering with OpenAI" as a complete AI strategy are over. We believe enterprise leaders need to consider:

  • Geopolitical factors: How will US-China tensions affect your AI supply chain? What regulatory frameworks are emerging in different regions?

  • Economic sustainability: Are your AI partners financially viable for the long term? What happens if they significantly raise prices or pivot their business models?

  • Technical diversification: How can you build an AI architecture that isn't dependent on a single provider?

Enterprise clients can implement what we call a "multi-modal, multi-model" approach. This means leveraging different AI models for different use cases and maintaining the flexibility to switch providers as the landscape evolves. The companies that will win in the AI era aren't those that pick the "right" vendor today, but those that build adaptable AI architectures.

OpenAI's current valuation approaching $300 billion seems increasingly disconnected from economic reality. While they deserve credit for catalyzing the current AI revolution, enterprise leaders need to recognize that we're entering a new phase where multiple players will drive innovation.

The next 18 months will be critical. We'll see consolidation among smaller AI companies, continued heavy investment from tech giants, and potentially surprising moves from nation-states viewing AI as critical infrastructure. Enterprise leaders need to stay informed not just about the technology, but about these broader market and geopolitical dynamics.

The bottom line for enterprise leaders: your AI strategy needs to be as sophisticated as the technology itself. 

Look beyond the hype, consider the full spectrum of factors at play, and build flexibility into your approach. We believe the latest "wave" of the current AI revolution is just beginning, and the winners will be those who navigate its complexities with clear-eyed strategic thinking.

 

Data to Decisions Future of Work Innovation & Product-led Growth Marketing Transformation New C-Suite Tech Optimization Chief Analytics Officer Chief Data Officer Chief Digital Officer Chief Executive Officer Chief Information Officer Chief Privacy Officer Chief Procurement Officer Chief Product Officer Chief Supply Chain Officer Chief Technology Officer

Will the Future Like You? Finding Purpose in the Age of AI | DisrupTV Ep 442

Will the Future Like You? Finding Purpose in the Age of AI | DisrupTV Ep 442

R "Ray" Wang
Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

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Elle DeRosa
Director of Marketing, Communications & Operations
Constellation Research

Elle is the Director of Marketing at Constellation Research and producer of the weekly enterprise tech show, DisrupTV. She leads marketing and communications efforts across all avenues while also managing an array of programs including the SuperNova Awards, Business Transformation 150, AX100, AI150, S50 and ShortLists. She has previous experience in public relations and has a strong passion for the marketing world. Contact Elle at [email protected] ...

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Will the Future Like You? Finding Purpose in the Age of AI | DisrupTV Ep 442

As AI learns to imitate us better than we know ourselves, two big thinkers ask the question that matters most: who are we becoming, and what are we actually here to do?

Key Takeaways

  • We’ve shifted from the search for self to the performance of self. After more than a decade of curating ourselves for algorithms, many of us are experiencing what Patricia Martin calls persona fog — a civilizational malady that obscures our core identity.
  • AI may become the most powerful persona fog machine yet. It can get you, amplify you, and optimize your style — ultimately authoring versions of you that you never intended.
  • The antidote to fragmentation is integration. Martin calls for becoming the editor-in-chief of your own identity — deciding what is really you, and discarding what isn’t.
  • Being truly seen is now a rare and powerful act. Putting the phone down and paying undivided attention to someone — child, partner, colleague — helps build their core sense of self in a way performance never can.
  • “Follow your passion” is often bad advice. Tom Rath argues we should ask what others need and what we can contribute, not just what fulfills us — because most people only ever see a tiny sliver of possible paths.
  • Purpose isn’t discovered once — it’s manufactured daily. Rath’s simple heuristic, “What’s the point?”, asked 3–5 times a day, reorders your calendar around contribution instead of reactivity.
  • In the AI era, originating beats responding. Routine, response-driven work is increasingly automatable. The future belongs to people who initiate ideas, relationships, and systems — not just execute tasks.
  • Don’t retire from contributing. Research shows that stopping all meaningful contribution is one of the worst things for long-term well-being — the form can change, but the contribution shouldn’t stop.

Part 1: Persona Fog — When the Performative Self Takes Over

Patricia Martin opens with a striking reframe of the last decade. We have moved, she argues, from the search for self to the performance of self. After years of curating, posting, and optimizing ourselves for algorithms, many of us are now experiencing what she calls persona fog — a civilizational malady of the psyche.

Persona fog emerges when our persona — Jung’s term for the social mask we wear — becomes load-bearing, carrying weight it was never designed to hold. We invest in multiple, fragmented personas in search of validation and attention, while the true self remains underneath, increasingly hard to access, like a landscape obscured by fog.

The symptoms, Martin says, are everywhere: chronic stuckness, persistent self-doubt, an inability to craft a compelling future story for yourself, and a deeper confusion that goes beyond what to do next — a confusion about who you fundamentally are.

“It's like moving a piano on a skateboard — we've taken the thinnest layer of our psyche and tried to make it carry our entire identity.”

Algorithms, Intermittent Reinforcement, and the Fragmented Self

Martin connects this directly to how social media and algorithms shape identity. Drawing on B.F. Skinner’s concept of intermittent reinforcement, she describes how content creators and everyday users experience wild, unexplained swings in reach — millions of views one day, a fraction the next. Over time, this erodes a stable sense of who we are and what works.

The result is a kind of self-editing by algorithm: we lean into the parts of ourselves that get rewarded and quietly delete the parts that fall flat. We accumulate multiple personas optimized for attention but disconnected from our core self — fueling an addiction-like cycle of posting more, tweaking more, and chasing the next hit of validation, fragmenting further with every round.

AI as the “Most Colossal Persona Fog Machine”

Martin has been working with AI since 2016, and her outlook is sober. AI, she says, is going to be the most colossal persona fog machine we’ve ever encountered. It will get you — deeply understand your patterns and preferences. It will amplify you — enhance your content and presence. And it will optimize you — performing your style better than you do.

The end result, in her words, is that AI will author versions of you that you never intended.

“My sense of self is not only taken away, it's been reiterated and changed, and I'm no longer who I am.”

In a world where the self is both a unit of measure and a unit for sale, and social media is the supply chain, AI doesn’t slow that system down — it accelerates it.

The Radical Act of Self-Determination

If AI, algorithms, and platforms are pushing us toward fragmentation, Martin’s counter-move is self-determination and integration. Consciousness, she argues, is the core tool we all have — a powerful, built-in guidance system that starts sending symptoms like stuckness, doubt, and discontent when it’s buried under layers of false selves.

We are entering an era, Martin says, where the most crucial work is integration — becoming the editor-in-chief of who you really are. This isn’t about quick hacks. We are inside a systemic problem, and systemic problems require self-development, step by step. A core practice is simple to state and hard to do: decide what is really you and what is not, and discard what isn’t true.

In this environment, Martin argues, true leadership will be defined less by hard technical skills and more by clarity of self, sovereignty over one’s identity, and the capacity for integration — including the shadow, in Jungian terms. These qualities will be rare, and therefore immensely valuable.

Reclaiming Attention and Transcendence

Martin cites her interview with Mihaly Csikszentmihalyi, author of Flow, who distinguishes two levels of consciousness: attention, the lower grade, easily hijacked by platforms and notifications, and transcendence, the higher state where peak performance and deeper meaning reside. As our ability to control our focus erodes, so does our access to transcendence — with profound implications not just for performance, but for human flourishing itself.

Becoming more conscious about what we post, why we post it, and how it shapes us, Martin argues, is itself a way of reclaiming both attention and self.

Building Identity in Others: The “Good Enough” Parent, Partner, and Leader

Martin also stresses the constructive side: how we can help others build a stable sense of self in an age of attention scarcity. Drawing on psychiatrist Donald Winnicott’s idea of the good enough parent, she notes that identity is reinforced when someone who matters puts the phone down, pays real and undivided attention, and witnesses you at your most alive, engaged, and curious.

This applies to children, romantic partners, friends, coworkers, and team members alike. Moments of being truly seen — without performance, cameras, or algorithms — are now rare, and therefore deeply powerful. In this age, simply witnessing someone’s core self is an act of identity-building.

Martin prefers the term core self over authentic, noting how authenticity has been commoditized and is often performed online. The task, she says, is to recognize that each of us has an innate kernel, like the seed of a tree, develop reverence for that core in ourselves and others, and support growth that is consistent with that kernel — because a maple tree will always be a maple tree.

Part 2: What’s the Point? Turning Purpose into a Daily Superpower

If Martin explores how we’re losing ourselves, Tom Rath focuses on how we can rebuild purpose — not as a grand, once-in-a-lifetime revelation, but as something we manufacture daily.

Rath is the author of multiple bestsellers — including How Full Is Your Bucket, StrengthsFinder 2.0, Eat Move Sleep, and Life’s Great Question — with over 10 million copies sold. His new book, What’s the Point? Turning Purpose into Your Daily Superpower, takes direct aim at the conventional wisdom of follow your passion.

Why “Follow Your Passion” Is Bad Advice

Rath argues that follow your passion is not just incomplete — it’s often harmful, for two reasons.

First, it centers the self rather than contribution. Passion-based advice asks what do I love, what fulfills me. Rath urges a shift toward what do others need, what can I contribute — because the demand side matters: customers, clients, communities, loved ones.

Second, it operates through a tiny pinhole of exposure. Most young people only see what their parents do and what one or two admired adults do. In reality, you might need exposure to 50 or more roles just to understand the landscape of possibilities. Without that exposure, follow your passion becomes a function of inheritance and pressure, not informed choice. Rath even suggests we might do better, on average, randomly assigning people to early jobs than relying on the narrow career pathways driven by family and social expectations.

Raising Kids Beyond “What Mom and Dad Do”

Rath illustrates this with his own family. When his daughter was 14, she casually mentioned she might be a writer or a teacher — exactly what her parents do. That was a wake-up call: she was two for two in the only careers she’d truly seen up close.

His approach now is to be explicit with his kids: it’s okay if they end up in the same field as their parents, but only after exploring 10, 20, even 30 or more other possibilities. The goal is to intentionally widen the aperture so they don’t lock into a path simply because it’s familiar or convenient. Without that exposure, many people invest years of education and training in fields that aren’t truly aligned with their strengths or interests — only to discover, late, that they want out.

Purpose Is Manufactured in the “Lab of Our Daily Choices”

Rath originally planned to title the book Purpose Unlocked, but his research changed his mind. He found that when most people hear the word purpose, they feel stressed — they experience it as something huge, rare, and almost mystical, descending from the heavens once every couple of decades.

In reality, he argues, purpose is built in the microscopic decisions we make every day.

“Purpose is manufactured in the lab of our daily choices.”

It's less like finding one grand calling and more like planting seeds in a garden: the projects we choose to advance at 9:00 a.m., the meetings we prioritize at 2:00 p.m., the conversations we have — or don’t have — with family at the end of the day.

A Simple Heuristic: “What’s the Point?”

To make this practical, Rath uses a deceptively simple question — what’s the point? — which he literally writes on a whiteboard and applies throughout his day.

What’s the point of spending 45 minutes on 10 low-impact emails from people I barely know? What’s the point of having this routine 4:00 p.m. meeting — is it more important than a high-impact project, or a meaningful conversation with my kids?

By asking this question three to five times a day, Rath reorders his day around substantive, meaningful work, pushes low-value, reactive tasks later, and carves out more time and energy for work that has deeper impact, aligns with his strengths, and contributes to others. Over time, this creates a compounding effect: more of your calendar reflects purposeful contribution, and less of it is driven by mindless reactivity.

Flow, Focus, and the Shift from Responding to Creating

Rath connects this to flow and to the future of work in the AI era. We often think of flow only in terms of sports or performance — the basketball court, the keynote stage. But Rath experiences flow while deeply editing a manuscript on a Wi-Fi-less flight, when fully absorbed in learning about medical topics he cares about, and in substantive, undistracted conversations with the people he leads or mentors.

In the context of AI, he makes a crucial point. For years, it was plausible for some people to say I’m just a task or response person, I just execute. With AI rapidly automating routine, response-oriented work, that stance is no longer safe.

His conclusion: everyone, in every role, needs to shift toward initiating and creating — ideas, relationships, systems, and the development of people. If your job is primarily routine response, you are at high risk of being replaced by automation. The future belongs to those who originate — who can see needs and start things that matter.

Living on Borrowed Time and Post-Traumatic Growth

Rath’s view of purpose isn’t abstract — it’s shaped by his own experience living with a rare genetic cancer syndrome. At 16, he lost an eye to cancer and was told there was a 90% chance it would later appear in major organs, including the pancreas, kidneys, brain, and spine. Those predictions came true, and he has been battling cancer for more than 30 years.

That diagnosis hyper-focused him on what he could do physically to stay ahead of the disease, and what he could contribute that might outlast him. It also led him to the concept of post-traumatic growth: many people who face deep challenges emerge with greater motivation, clearer priorities, and a stronger desire to focus on what matters. He saw a similar pattern during the pandemic, as people suddenly asked what work and relationships would still matter if tomorrow looked very different.

Practically, he suggests investing even 30 minutes a day in a relationship, a project, or a piece of work that could still be helpful to someone a week, a month, or a year from now — what he calls evergreen contributions that continue to grow beyond the moment.

Don’t Retire From Contributing

Rath also points to global well-being research showing that some of the worst things for long-term well-being include the death of a child, divorce, and — surprisingly to many — retiring cold with no ongoing contribution. Simply stopping all meaningful work and engagement is devastating to well-being.

His message to those later in their careers: you may change form — paid work, volunteer roles, mentoring — but don’t retire from contributing. Careers don’t need to be smooth, upward lines; a spiky trajectory with reinvention can be healthy. And given that 80 to 90 percent of people likely never try what they’d be best at, disruption or job loss can be an opportunity to explore and realign.

Leadership in the Age of AI: Invest in People, Not Titles

For business leaders and people managers, Rath’s advice is clear. In the coming decade, one of the most valuable human skills will be the ability to observe what people do uniquely well, ask good questions, genuinely listen, and invest in others’ growth and development.

As more transactional, predictable work is automated, humans will increasingly need care, attention, development, and — in a very grounded sense — love. Rath encourages leaders to see talent development as central, not peripheral, to their role, to focus less on status markers and more on who people are becoming, and to recognize that what truly endures isn’t titles, compensation, or follower counts, but the long-term impact on people’s lives.

Rath shares a powerful personal anchor here: his grandparents’ influence — decades after their passing — still shapes his daily routine, parenting style, and worldview. That, he suggests, is what real leadership and legacy actually look like.

Final Thoughts

As the episode wrapped, Vala and Ray reflected on the privilege of spending an hour each week with thinkers who have written more than ten books and sold millions of copies, yet remain willing to share deeply personal stories.

Two big threads tie this episode together. The first is a question of identity: who are we becoming in a world of AI and algorithms? Patricia Martin warns of persona fog, AI-driven amplification, and the risk of losing our core self, and calls for integration, self-determination, and a renewed reverence for the core self in ourselves and others.

The second is a question of direction: how do we build purpose amid disruption? Tom Rath reframes purpose as something we build daily, not passively discover. He challenges us to replace follow your passion with follow your contribution, and offers a simple compass — what’s the point? — for structuring days, careers, and lives that matter.

In Ray’s words, we are in an age where we must question what it means to be human in the age of AI. If we lose sight of that, we risk losing sight of where humanity is going.

Read together, Will the Future Like You? and What’s the Point? offer a powerful dual lens. Martin helps us see how we’re being manipulated, fragmented, and performed. Rath helps us rebuild our lives around contribution, daily choices, and enduring relationships.

“The future may or may not like us — but these conversations offer a roadmap to liking ourselves, serving others, and staying human in a hyper-automated world.”

Related Episodes

If you found Episode 442 valuable, here are a few others that align in theme or extend similar conversations:

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Revenue Intelligence: The Execution Engine for Growth

Revenue Intelligence: The Execution Engine for Growth

Martin Schneider
Vice President and Principal Analyst
Constellation Research

Martin Schneider has had a unique career that has spanned both analyst and marketing practitioner roles, focused on high technology and related industries. The unifying factor has always been both a keen analysis of go-to-market trends, while also having achieved success as a marketing leader. Schneider started his career as a journalist covering B2B technologies, and quickly transitioned into a leading analyst covering application software for the 451 Group in NYC, where he specialized in CRM, marketing automation, and business intelligence/analytics technologies. After analyzing various go-to-market strategies of dozens of technology vendors, Schneider made the move to the vendor side, where he led successful go-to-market teams for several startups and established tech providers,…...

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85% of Fortune 500 companies are growing at single digits, and the pressure to find profitable growth has never been higher. In this segment, analyst Martin Schneider breaks down revenue intelligence, why it's one of the hottest categories in enterprise tech right now, and what it actually takes to optimize customer lifetime value across the full sales journey.

Martin covers the key players in the space, from RevOps platforms to legacy CRM providers, what buyers (CROs, CMOs, and emerging chief growth officers) should be asking vendors, and why agentic, workflow-driven tools are the ones to watch.

Read Martin's full market overview report: https://www.constellationr.com/research/revenue-intelligence

Revenue & Growth Effectiveness Growth Chief Revenue Officer
Off

Claude Mythos and the Future of Enterprise Security

Claude Mythos and the Future of Enterprise Security

Chirag Mehta
Vice President and Principal Analyst
Constellation Research

Chirag Mehta is Vice President and Principal Analyst focusing on cybersecurity, next-gen application development, and product-led growth. With over 25 years of experience, he has built, shipped, marketed, and sold successful enterprise SaaS products and solutions across startups, mid-size, and large companies. As a product leader overseeing engineering, product management, and design, he has consistently driven revenue growth and product innovation. He also held key leadership roles in product marketing, corporate strategy, ecosystem partnerships, and business development, leveraging his expertise to make a significant impact on various aspects of product success. His holistic research approach on cybersecurity is grounded in the reality that as sophisticated AI-led attacks become…...

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Anthropic's Claude Mythos has sparked plenty of headlines, but what does it actually mean for enterprise security teams?

Constellation Research analyst Chirag Mehta breaks down what Mythos signals about where security work is heading. His argument: most organizations don't have a findings problem; they have a speed-to-fix problem. The real challenge comes after a vulnerability is discovered: validating its existence, understanding whether it can be exploited, engineering a safe fix, and shipping it before risk compounds.

Chirag covers:

  • What Claude Mythos is and why its restricted, defender-focused design matters
  • Why validation and remediation speed will define the next era of security programs
  • Practical implications for CISOs, product leaders, and engineering teams
  • Why the operating environment around AI, including test harnesses and human review, matters as much as the model itself

Read Chirag's full report: https://www.constellationr.com/research/claude-mythos-and-new-cybersecurity-operating-model

Digital Safety, Privacy & Cybersecurity Future of Work Data to Decisions cybersecurity Security Chief Information Security Officer
On

How Market Engineering and Mission Guardians Will Shape the Next Era of Business | DisrupTV Ep 441

How Market Engineering and Mission Guardians Will Shape the Next Era of Business | DisrupTV Ep 441

R "Ray" Wang
Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

Read more
Elle DeRosa
Director of Marketing, Communications & Operations
Constellation Research

Elle is the Director of Marketing at Constellation Research and producer of the weekly enterprise tech show, DisrupTV. She leads marketing and communications efforts across all avenues while also managing an array of programs including the SuperNova Awards, Business Transformation 150, AX100, AI150, S50 and ShortLists. She has previous experience in public relations and has a strong passion for the marketing world. Contact Elle at [email protected] ...

Read more

How Market Engineering and Mission Guardians Will Shape the Next Era of Business | DisrupTV Ep 441

In an AI-first world where building a product is no longer the hard part, the companies that will define the next decade are those that engineer their markets — and protect their missions against the structural forces that quietly corrupt even the best organizations.

Key Takeaways

  • Product is no longer the bottleneck. AI has made building faster and cheaper than ever. The real differentiator is now market engineering — category design, positioning, messaging, storytelling, and thought leadership.
  • Market engineering is a CEO sport, not a marketing function. The leaders who built category-defining companies — Benioff, Jobs, Siebel — all owned their narrative personally. You cannot delegate this and expect to win.
  • ~76% of profits in any category accrue to the category leader. If that statistic is true, Cleveland asks: why would you ever aim to be anything else?
  • Most corporate corruption is structural, not personal. Eric Ries calls it financial gravity — a systemic force that pulls organizations away from their mission and toward short-term extraction, often despite good intentions.
  • Mission-driven is meaningless without mission-protected. If anyone with enough capital can buy your company and reverse its values overnight, you are not mission-driven. You are mission hopeful.
  • Alternative governance structures work — and are hiding in plain sight. Costco, Novo Nordisk, Patagonia, and others prove you can be principled and profitable — but only if you design structures that resist financial gravity.
  • The next AI gold rush is vertical apps, not infrastructure. The sum of value in domain-specific AI applications will likely far exceed today’s infrastructure valuations — and winning will depend on market engineering, not just code.

Part 1: Bruce Cleveland on Market Engineering in the AI Era

Bruce Cleveland opens with a deceptively simple observation: the center of gravity in building companies has shifted. In the past, venture capital was essential just to fund large development teams and build the underlying technology. Today, with AI and modern infrastructure, product creation is cheaper and faster than it has ever been. Many successful startups now launch with a handful of developers.

The bottleneck has moved. The real differentiator is no longer building the product — it’s winning the market.

Cleveland calls this discipline market engineering, and he defines it around five core tenets: category design or redesign, positioning, messaging, storytelling, and thought leadership. When you do these five things well and in concert, you don’t just enter an existing space — you create or redefine a category and become its default leader.

He points to Oracle, Siebel Systems, Salesforce, and C3 AI as proof. In each case, the breakthrough wasn’t purely technical. It was the ability to change how people thought about a market. Oracle reframed how databases should operate. Siebel created the CRM category. Salesforce redefined CRM through cloud computing and a fundamentally different business model.

“Market engineering, not product engineering, is what distinguishes the winners — especially in the AI era, where product is relatively easy to create and go-to-market is not.”

Who Actually Owns Market Engineering?

One of Cleveland’s strongest claims is about ownership — and it’s one that challenges conventional org chart thinking.

Many leaders see the word market and instinctively hand the responsibility to the CMO. Cleveland says that’s a mistake. Market engineering is not a marketing function. It is a CEO function, or the P&L owner of a business unit.

The reason is span of control. Engineering a market requires alignment across sales, marketing, product, services, finance, and operations. Only the CEO has that breadth. The CMO and CRO are critical partners — but they are executing a strategy that must be authored and actively owned at the top.

Cleveland points to Mark Benioff, Tom Siebel, Scott McNealy, and Steve Jobs as archetypal market engineers — all deeply hands-on with category narrative, positioning, and thought leadership. He also notes this helps explain why CMO tenure is notoriously short: they are being held accountable for work they cannot fully control if the CEO hasn’t embraced this role.

“It’s not marketing engineering, it’s market engineering. The CEO owns this. The CMO is involved, but the CEO cannot delegate this and expect to win.”

The Traction Gap: A Map for Founders

Before Market Engineering, Cleveland wrote Traversing the Traction Gap to answer a foundational question that haunts founders and innovation leaders: where are we, and what should we be doing right now?

He breaks the journey into three phases. Go-to-Product: from idea to a working product that solves a real problem. Go-to-Market: finding repeatable, scalable ways to acquire and retain customers. Go-to-Scale: turning early traction into durable growth and an enduring company.

His observation: most venture firms invest once there is visible traction — once a startup has become a spreadsheet company, not just a slide deck company. His own investing success at the earliest stages led him to codify what investors actually look for, and how founders can de-risk that journey.

“There’s no instruction manual for your startup. No school really tells you, you’re here, do this next. The Traction Gap framework is meant to be that you-are-here map from ideation to scale.”

The Next AI Wave: Vertical Apps and Domain Expertise

Cleveland also connects market engineering to the next wave of AI-driven value creation. Right now, capital is flooding into the picks and shovels of AI: foundation models, chips, and base infrastructure platforms. But over time, he argues, the real value will shift to applications — particularly vertical, domain-specific apps built by people with deep industry expertise.

This has real implications for talent and investment strategy. We will need fewer generalist developers and more domain experts — people who can articulate precisely what needs to be built and feed the right problems and constraints into AI systems. Cleveland calls this forward deployed engineering.

For investors and PE firms, this means evaluating not just technical capability but who actually understands the industry deeply enough to direct these tools. The sum of value in vertical AI apps will likely far exceed even today’s massive infrastructure valuations. But winning that race will depend far less on code, and far more on the quality of the market engineering behind it.

Part 2: Eric Ries on Why Good Companies Go Bad — and How Great Ones Stay Great

If Bruce Cleveland’s work is about creating and leading markets, Eric Ries asks an equally critical but darker question: why do companies that start with strong ideals and genuine missions drift into short-termism, extraction, and even outright corruption — often despite the best intentions of their founders?

His answer, developed in his new book Incorruptible, challenges the popular narrative that corporate decline is driven by bad actors or individual moral failures. In reality, he argues, much of what we call corruption is structural.

Financial Gravity: The Force Pulling Companies Down

Ries describes financial gravity as a systemic force that gradually pulls organizations away from their mission and toward mediocrity or worse. The evidence is everywhere: executive tenure is shrinking, company lifespans are shrinking, stock holding periods are shrinking, and public trust in institutions is shrinking.

These aren’t isolated phenomena. They are manifestations of the same underlying force: a system that rewards short-term extraction over long-term value creation and mission fidelity. At the center of this system is shareholder primacy — the belief that the sole purpose of a company is to maximize shareholder value, and that how you make money is largely irrelevant as long as you make it.

“We live in an era of so-called best practices in governance that are actually weak, value-destroying practices. They treat mission and purpose as an afterthought — even though that’s where all the value is created.”

Redefining Profit

Ries proposes a different definition of profit: the maximization of human flourishing. Under this lens, being for-profit is not inherently suspect — it can be a genuinely noble pursuit. But profit should reflect value created for customers, employees, and society, not value extracted from them.

This definition helps explain something many people feel but rarely articulate. It explains why people recoil when their favorite restaurant is taken over by private equity and immediately expect worse food and worse service in the name of efficiency. It explains the growing public skepticism about large-scale capitalism as currently practiced, not necessarily about entrepreneurship or markets themselves.

Many builders — founders, executives, engineers, creators — already intuitively believe this version of profit. They just rarely say it out loud.

Mission Guardians: The Structural Antidote

One of Ries’s central insights is the need for a mission guardian inside organizations. A mission guardian is a person or entity with formal — often legal — power, capable of saying no to decisions that would betray the company’s core mission even if those decisions promise short-term financial gain. Critically, a mission guardian is designed to outlast individual leaders and investors.

“If you say you’re mission-driven but anyone with enough money can buy you and change everything tomorrow, you’re not mission-driven. You’re mission hopeful at best.”

Ries points out that most people assume there are only two governance options: whoever buys the most stock controls the company, or a despotic founder-emperor with absolute power. In reality, there are other proven models that enshrine mission — but they are poorly understood and almost never taught.

Alternative Governance: More Than Theory

Ries points to several real-world structures that embed mission guardianship in practice. Industrial foundation structures — used by companies like Novo Nordisk and Hershey — place voting control inside a foundation or trust that is legally bound to the company’s mission and long-term health. Data shows companies with this structure are five times more likely to survive to year fifty than conventionally governed firms.

Two-entity governance models, like the one used by Anthropic, separate mission stewardship from day-to-day operations. An external trust holds the power to appoint directors, decoupling mission protection from any single charismatic founder while still allowing a fully functioning for-profit operating company.

“Everyone listening has already interacted with these companies — you’ve bought eyewear, filled prescriptions, worn Patagonia, or invested through Vanguard — but almost nobody can explain how their governance works. That’s part of the problem.”

The Costco and Saul Price Story: Ethos Plus Structure

To make the argument concrete, Ries tells the story of Saul Price, a legendary but often overlooked retail innovator whose ideas helped shape Costco.

Price’s first major company, FedMart, was built on a radical philosophy: the company would act as a fiduciary to the customer, not just to shareholders. There was a clear hierarchy of obligations — customers first, employees second, shareholders last. That meant capped margins on every item, above-market wages, and a willingness to tell customers where they could get a better deal elsewhere, because as Price put it, the money is in the membership.

FedMart was extraordinarily successful for two decades. Then investors grew impatient. They pushed for more conventional profit-maximizing behavior. Eventually the board forced Price out — he arrived one day to find the locks changed on his office. Within seven years, the reconfigured, conventionalized FedMart was bankrupted.

Rather than retire, Price took two weeks off and started again — this time building Price Club, alongside Jim Sinegal, who had been a stock boy at FedMart and later an executive before also leaving when Price was ousted. Price Club eventually merged to become Costco.

Costco today routinely receives terrible governance scores from rating agencies. It also remains one of the best-performing stocks in public markets. It maintains capped margins, relentless focus on member value, and above-market treatment of employees. It is, in Ries’s framing, a governance fortress.

“The formula is ethos plus structure: the soul of Saul Price combined with the structural integrity Jim Sinegal put in place. That’s how you get a company that can stay true to its mission and still make a lot of money.”

Two Halves of the Same Problem

Taken together, Bruce Cleveland and Eric Ries are describing two halves of the same challenge facing every founder, executive, and board member right now.

Cleveland’s work answers how you win markets in a world where product is no longer the bottleneck: build a market engineering capability that you personally own as CEO, and treat category design, positioning, messaging, storytelling, and thought leadership as hard disciplines, not soft afterthoughts.

Ries’s work answers how you keep winning without losing your soul: recognize financial gravity and shareholder primacy as structural forces that will, by default, corrupt your mission over time, and deliberately design governance structures — mission guardians, trusts, alternative ownership models — that have the power to say no when short-term incentives collide with long-term purpose.

In an AI era where infrastructure is rapidly commoditizing, vertical apps and distribution will decide who captures value, and trust is both scarce and extraordinarily valuable — these ideas are not theoretical. They are becoming the operating system for the next generation of durable, category-defining companies.

Final Thoughts

DisrupTV Episode 441 is a rare conversation that operates at two levels simultaneously: the strategic and the structural, the market and the mission.

Bruce Cleveland gives leaders the market engineering playbook for a world where AI has commoditized product creation. The question is no longer can you build it — it’s whether you can define the category, own the narrative, and become the name people think of first.

Eric Ries gives leaders something harder to find: an honest diagnosis of why even great companies with great intentions drift, and a set of structural tools for designing organizations that can actually resist that drift over time.

The through-line between them is integrity — in your market positioning and in your governance. Category leaders who build governance fortresses around their mission will be the ones who define this era and endure beyond it.

“Disruption is coming either way. The question is whether your company will be engineered to lead and structured to endure.”

Related Episodes

If you found Episode 441 valuable, here are a few others that align in theme or extend similar conversations:

Future of Work AI Chief Executive Officer Chief Technology Officer Chief AI Officer Chief Experience Officer

From “activation energy” and agent orchestration to donkeycorns and relationship capital, DisrupTV 425 explains what actually separates AI hype from real business impact.

On DisrupTV <iframe width="560" height="315" src="https://www.youtube.com/embed/J5CupyHoVng?si=GHB_W8FIfV0UhJBW" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

AI Productivity Debate, Revenue Intelligence, and Claude Mythos | CRTV Episode 131

AI Productivity Debate, Revenue Intelligence, and Claude Mythos | CRTV Episode 131

Martin Schneider
Vice President and Principal Analyst
Constellation Research

Martin Schneider has had a unique career that has spanned both analyst and marketing practitioner roles, focused on high technology and related industries. The unifying factor has always been both a keen analysis of go-to-market trends, while also having achieved success as a marketing leader. Schneider started his career as a journalist covering B2B technologies, and quickly transitioned into a leading analyst covering application software for the 451 Group in NYC, where he specialized in CRM, marketing automation, and business intelligence/analytics technologies. After analyzing various go-to-market strategies of dozens of technology vendors, Schneider made the move to the vendor side, where he led successful go-to-market teams for several startups and established tech providers,…...

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Hannah Mason
Marketing Director
Constellation Research

Hannah Mason is a marketing director at Constellation Research. She oversees brand positioning, creative direction, and content development across digital platforms, events, and campaigns. Her work spans branding and design—including graphics, sales collateral, and event presentations—along with website content management, newsletters, and Constellation Insights. Mason also manages surveys and produces Constellation’s video collateral, including ConstellationTV. Her role ensures that the Constellation brand is communicated with clarity, creativity, and consistency across all channels. Contact Hannah at [email protected]. ...

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Enterprise technology is at a crossroads. Budgets are tighter, growth is harder to find, and the pressure to show real returns on AI investment has never been greater. Episode 131 of ConstellationTV tackles three of the biggest questions facing technology and business leaders today.

Is AI at an inflection point? The debate continues.

Host Martin Schneider opened the episode with a Great Debate featuring Holger Mueller, Esteban Kolsky, and Larry Dignan. The question: has AI actually hit an inflection point, or are organizations still waiting on the productivity payoff?

The consensus leaned toward yes, with important caveats. Holger Mueller argued that inflection is happening one use case at a time, not company-wide, and that the real shift will come as enterprises gain better control of their data architecture and begin building or customizing their own agents. Esteban Kolsky was more direct, calling the inflection point for generative AI a done deal while noting that CRM may not be the right home for it. Martin pointed to the quote-to-cash cycle and CPQ workflows as strong near-term candidates in which deterministic, high-value tasks can be effectively handed off to AI.

The group also took on token economics. The emerging view: as enterprises move toward proprietary and domain-specific models, the token conversation shifts from cost-per-query to infrastructure investment and operational efficiency. Tokens, as Holger noted, are simply another form of elastic cost, and enterprises that treat them like cloud spend will be better positioned to manage them.

Revenue intelligence: the execution engine for growth

R "Ray" Wang sat down with Martin to discuss his newly published market overview on revenue intelligence, a category gaining significant momentum as organizations scramble to find profitable, scalable growth.

Revenue intelligence is not pipeline inspection or sales coaching in isolation. It is the full orchestration of the revenue lifecycle, from acquisition and activation through retention, renewal, and expansion, all aimed at optimizing customer lifetime value. Martin noted that 85% of the Fortune 500 are growing in the single digits, meaning the pressure to do more with existing customers and data has become a strategic priority.

Key trends in the space include the rise of purpose-built AI and domain-specific models tailored to revenue processes, the expansion of data acquisition beyond the CRM to include call records, meetings, email, and third-party signals, and a shift from DIY AI approaches toward vendor-led, outcome-driven implementations. Chief revenue officers and chief growth officers are emerging as the primary buyers, and they want results without having to build from scratch.

Vendors covered in the report include Clari, Salesloft, Gong, Salesforce, Outreach, ZoomInfo, Conga, SugarCRM, and Zoom Revenue Accelerator. The full report is available on constellationr.com.

Claude Mythos and the new cybersecurity operating model

Chirag Mehta closed the episode with a walkthrough of his new Big Idea report on Claude Mythos, Anthropic's advanced AI system currently available only through a restricted, defender-focused program.

Chirag's central argument is that Mythos is not primarily a threat to enterprise security; it is a signal about where the work of security is heading. The challenge for most organizations is not finding vulnerabilities. It is what happens after: validating whether an issue is real, understanding whether it can be chained or exploited, engineering a safe fix, and shipping that fix before the risk compounds.

Mythos points to meaningful capability in exactly that space, combining long-context code understanding, reasoning, tool use, and cybersecurity-specific workflows. The implications for CISOs, product leaders, and engineering teams are practical: security programs will need tighter connections between discovery, validation, engineering, and release management. Organizations that can validate faster, fix faster, and maintain control over disclosure and production stability will have a structural advantage.

Chirag was clear that the public record does not show Mythos autonomously compromising well-defended enterprises from scratch. Its value depends heavily on the operating environment around it, including test harnesses, human review, and engineering processes. The report is available now on constellationr.com.

Episode 132 will be hosted by Holger Mueller. Watch Episode 131 now on the Constellation Research YouTube channel and at constellationr.com.

Data to Decisions Digital Safety, Privacy & Cybersecurity Future of Work Revenue & Growth Effectiveness Agentic AI AI Generative AI Chief Information Officer Chief Technology Officer Chief Information Security Officer Chief Revenue Officer
On ConstellationTV

SAP's Big Bet: Autonomous Enterprise or Overpromise? | 2026 Q2 SAP Coffee Corner

SAP's Big Bet: Autonomous Enterprise or Overpromise? | 2026 Q2 SAP Coffee Corner

Holger Mueller
Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the&nbsp;cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.&nbsp; Coverage Areas: Future of Work Tech Optimization &amp; Innovation&nbsp; Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

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SAP Sapphire 2026 stood out. Not for a single headline announcement, but for something rarer: a coherent, end-to-end vision that actually held up across keynotes, analyst sessions, and conversations on the floor.

In the Q2 episode of Coffee Corner Radio, Martin Fischer and I covered what mattered most from this year's event. Here are the highlights.

The Autonomous Enterprise Vision

For the first time since mySAP.com and the Enjoy initiative in the late nineties, SAP presented a vision compelling enough to anchor the entire conference. The autonomous enterprise narrative, centered on the new SAP Business AI Platform, brought together Business Data Cloud, Joule, the agentic framework, and BTP under one roof. Is it mostly a rebundling of existing capabilities? To a degree. But the commitment that these pieces work together and the platform-first framing of the main keynote signal a meaningful shift in how SAP thinks about its role for customers.

Build Your Own Agents

With 30-plus packaged agents announced and roughly 100 more expected this year, SAP's message was clear: the platform is there, but customers need to own their automation destiny. Packaged agents are a starting point, not a finish line. The key takeaway for any SAP customer right now is to evaluate which agents you need, assess the headroom in your backend systems, and start building.

Acquisitions That Signal Intent

Three acquisitions stood out. Veltio, for MDM capabilities that strengthen the case for an open data layer. Dremio, for data federation capabilities, though federation's track record in high-performance transactional scenarios warrants healthy skepticism. And Prior Labs, a two-year-old Freiburg AI startup acquired to advance SAP's tabular AI capabilities, with the notable decision to keep it as an independent research lab rather than absorb it into the product organization.

SAP is investing in the German software ecosystem, and that matters for the European installed base.

The API Policy Controversy

The pre-Sapphire API policy announcement landed with the subtlety of a dropped piano. The initial version read as broadly restrictive, suggesting third-party and custom integrations might no longer be permitted. Two updates and a 17-page FAQ later, the situation is clearer but not entirely settled. The policy reads stricter than the FAQs, and customers are right to want a concrete roadmap: which APIs, on what timeline, at what price.

The underlying direction is architecturally sound. SAP's final move toward a centralized, vendor-supported API framework is what the agentic era requires. But the execution gap between policy and delivery is the real question, and it needs an answer before customers can plan confidently.

What Comes Next

The vision is the strongest SAP has put forward in this century. Now comes the hard part. Customers doubling down on SAP have a clearer path forward than they did a year ago. But the roadmap for APIs, the scalability of backend systems for agentic workloads, and the delivery of industry-specific capabilities will all be tested in the months ahead.

Listen to the full Coffee Corner Radio Q2 episode on the Constellation Research YouTube channel and all major podcast platforms

Future of Work Tech Optimization Data to Decisions Agentic AI AI Automation Enterprise Acceleration Chief Information Officer Chief Technology Officer Chief Operating Officer
On CR Conversations

Scaling AI from Experimentation to Impact with IBM Consulting

Scaling AI from Experimentation to Impact with IBM Consulting

Michael Ni
Vice President and Principal Analyst
Constellation Research

Michael Ni is Vice President and Principal Analyst at Constellation Research, covering the evolving Data-to-Decisions landscape—where CDOs, CIOs, and CPOs must modernize data infrastructure, integrate AI into decision-making, and scale automation to improve business outcomes. Ni’s research examines how enterprises operationalize AI, automate decision-making, and integrate data management and analytics into core business processes. He focuses on the challenges of scaling AI-driven decision systems, aligning data strategy with business goals, and the growing role of data and decisioning “products” in enterprise ecosystems. With 25+ years as a product and GTM executive across enterprise software, AI platforms, and analytics-driven technologies, Ni brings a practitioner’s perspective to…...

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I had a great conversation at IBM Think 2026 with Javier Cassini, Global Managing Partner for Hybrid Cloud and Data at IBM Consulting. We'd just come off Constellation's Futures Forum conference, where I'd spent two days with 120 CEOs and board members. The theme was unmistakable: the metrics are real now, and the pressure to scale AI is no longer theoretical.

Javier confirmed what I was hearing on the ground. The question has fundamentally changed. Clients aren't coming to IBM asking for use cases anymore. They want to capture the dividend, and they want to do so at an industrial scale.

Three Patterns Driving Real Results

Javier outlined three profiles he's seeing consistently across IBM's customer base.

  1. The first is productivity at scale. Not 10-20% efficiency gains... we're talking 40-60% reductions in operating costs, depending on the domain. The key insight here is that you can't get there by layering AI on top of existing steps. You have to re-engineer the entire workflow from first principles. Finance transformation, order-to-cash, supply chain, and software development lifecycles. All of it gets rethought around AI, not retrofitted with it.
  2. The second is decision velocity. I love this framing because velocity isn't just speed; it has direction. It means collapsing decision trees, automating steps that previously required a committee meeting every Friday, and doing it with better context and data than a human could have managed alone. So many processes aren't expensive in terms of headcount, but they're slow due to friction at specific points. That friction is where the opportunity is.
  3. The third, and arguably the most strategic, is net-new revenue. Pearson was one of the examples on stage at Think: how do you build entirely new AI-native products and business models? Javier is clear that you typically need the second pattern to fund the third. But boards increasingly see this as existential. If you're not learning and building the flywheel now, you may be behind your competitors before you realize it.

Why Sophisticated Teams Still Struggle

This is the part of the conversation that I think gets glossed over too often. Even teams with strong data foundations and clear executive sponsorship hit walls. Javier's take: it starts with security, trust, and governance. If you don't invest in those early on, you'll hit problems at scale that you didn't anticipate in a clean MVP environment.

He also walked through a data readiness framework that I thought was genuinely useful. Most teams think about data in terms of cleanliness — is it accurate, is it structured? That's layer one. But you also need a semantic layer (what does the data actually mean?), a context layer (what does it mean in this specific situation, in this market, with these rules?), and a decision layer where feedback loops capture what actually happened and improve the model's autonomy over time. That last piece, the learning loop, is what separates organizations that get compounding returns from those that plateau.

The Operating Model Is the Real Bottleneck

Javier said something near the end of our conversation that I've been thinking about since. The bottleneck has moved. It's no longer a technology problem. It's an operating model problem. The question isn't whether the models are good enough; they are. The question is how quickly you can transform your organization, how quickly you can bring your team to new ways of working, and whether leadership understands the art of the possible well enough to provide real direction.

CEOs are feeling this acutely. At our Futures Forum conference, two things were top of mind beyond the standard profitability and productivity goals: whether I have the right people and whether my organization can actually evolve. Javier sees a lot of use-case proliferation that never scales because there's no single top-down commitment to a single vision. Organizations get attached to what they've already built, even when it hasn't reached scale, and it waters everything down.

What Success Actually Looks Like

One of the metrics Javier tracks is the level of agency: how much work can you delegate to agents versus keeping a human in the loop? The model he described is humans at the edge: setting direction, defining what they want, delegating execution to agents, and expanding that surface over time as trust and observability increase. Autonomy isn't a switch. It's a dial. And you build toward it through evals, feedback loops, and deliberate governance, not by hoping the models figure it out.

Interestingly, the organizations getting there fastest tend to be in regulated industries. They already know exactly what humans can and can't do. That clarity turns out to be a competitive advantage when you're designing AI systems with the right guardrails.

The conversation could have gone another two hours. We barely scratched the surface of governance trade-offs and the shift from automated decisions to trusted execution. More on that soon.

Future of Work Data to Decisions Digital Safety, Privacy & Cybersecurity Agentic AI AI Automation Chief Executive Officer Chief Operating Officer Chief Information Officer Chief Digital Officer
On Event Update

What Happens When AI and Geopolitics Become Inseparable? | DisrupTV Ep 440

What Happens When AI and Geopolitics Become Inseparable? | DisrupTV Ep 440

R "Ray" Wang
Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

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Elle DeRosa
Director of Marketing, Communications & Operations
Constellation Research

Elle is the Director of Marketing at Constellation Research and producer of the weekly enterprise tech show, DisrupTV. She leads marketing and communications efforts across all avenues while also managing an array of programs including the SuperNova Awards, Business Transformation 150, AX100, AI150, S50 and ShortLists. She has previous experience in public relations and has a strong passion for the marketing world. Contact Elle at [email protected] ...

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What Happens When AI and Geopolitics Become Inseparable | DisrupTV Ep 440

Artificial intelligence is no longer just a technological shift — it is a structural force reshaping geopolitics, economic power, corporate governance, and leadership itself.

In DisrupTV Episode 440, hosts Vala Afshar and R “Ray” Wang were joined by an exceptional panel:

  • Malcolm Turnbull, former Prime Minister of Australia
  • Lucy Turnbull, former Lord Mayor of Sydney
  • David Bray, Distinguished Chair of the Accelerator, Stimson Center & Principal/CEO, LDA Ventures Inc.
  • Sheri Jacobs, Author of The Unexpected Power of Boundaries: Rethinking The Rules, Risks And Real Drivers Of Innovation

Across geopolitics, infrastructure, media systems, and organizational design, a single theme emerged:

In the AI era, trust is the currency — and boundaries are the operating system.

AI, Geopolitics, and the New Balance of Power

Malcolm Turnbull set the stage at the highest level. AI isn't changing everything — but it is profoundly amplifying existing strategic dependencies, particularly around energy and compute.

He identified two "currencies of the future": data and electricity. China, he argued, has positioned itself as an "electro-state" — with massive grid investment, leading global spending in renewables, and the capacity to power AI data centers at scale and lower cost than most Western competitors.

The implication for business leaders is sobering: choosing your technology stack is no longer a procurement decision. It's a geopolitical one. Organizations must ask who controls the platforms they're integrating into their operations, whether those operators are trustworthy partners, and what happens if access is suddenly constrained or weaponized.

Turnbull's warning was blunt: a three-to-six-month lead in frontier model capability is trivial compared to long-term structural advantages in energy and infrastructure.

From Open Internet to Centralized AI: A Very Different Playbook

Both Lucy Turnbull and David Bray drew a sharp contrast between the early internet era and today's AI landscape — and the differences matter enormously.

The early internet was decentralized, open, and permissionless. Barriers to entry were low. Garage innovators could build meaningful companies. Infrastructure and services spread globally with relatively few geopolitical constraints.

Today's AI landscape is a different beast: capital-intensive, compute-heavy, dominated by a handful of frontier model providers, and increasingly shaped by both corporate strategy and national policy. For the first time in three decades of digital globalization, we are seriously entertaining hard boundaries around access to models, data, and infrastructure.

Bray added a striking data point: by 2030, over 40% of the world's data is expected to be AI-generated — raising deep questions about authenticity, decision-making, and who controls the filters that shape what AI systems are allowed to say or show.

His prescription: decentralization. He sees genuine hope in edge AI and on-device models, alternative AI toolkits beyond today's dominant generative systems, and open-source and open-weight models that enable local innovation and community-level filtering.

Fragmented Realities, Media Silos, and the AI Filter Problem

One of Turnbull's deepest concerns is that AI may turbocharge an already fragmented information environment.

Digital tools and low-cost content production have already atomized media. People increasingly inhabit echo chambers, no longer sharing a common factual baseline. What once required major broadcast infrastructure can now be done with consumer electronics — from anywhere, by anyone.

In that environment, AI systems configured with ideological or commercial filters — especially those controlled by a single platform — could quietly shape what people see, learn, and believe at a population scale. The risk isn't limited to authoritarian states. Turnbull explicitly warned that illiberal manipulation of AI filters is a live risk inside democracies.

The leadership takeaway: AI content and filter governance must be treated as a core risk and ethics domain, not a technical side issue delegated to engineering teams.

What Boards Should Be Asking Right Now

The panel zeroed in on the boardroom — and offered some pointed provocation.

David Bray's question for CEOs: "What happens if our company becomes the target of a conspiracy theory or disinformation attack?" This is no longer hypothetical. Disinformation can move markets, damage brands, and undermine trust in leadership overnight.

Lucy Turnbull's question: "How can we increase shareholder returns while being a civil, ethical, and humane enterprise?" The premise is that profit and principles are not in tension — they must be integrated.

Malcolm Turnbull's question — deceptively simple, deeply technical: "Who has administrative privileges on our company's network and systems, and who are these three people?" That single question cuts straight to cybersecurity, insider risk, and operational resilience.

Bray also surfaced lesser-known but very real risk chains that boards are largely ignoring: helium shortages linked to Middle East geopolitical tensions that spike chip production costs two-to-four times and disrupt medical imaging operations; infrastructure constraints limiting the US's ability to add new data center power connections at the pace AI workloads demand; and growing Gen Z unease about AI's impact on employment — a source of genuine intergenerational tension if left unaddressed.

The bottom line for executives: AI risk is not just about algorithms. It is supply chain risk, infrastructure risk, reputational risk, and social stability risk — all at once.

Leadership in an Age of Turbulence

When asked what qualities leaders need most right now, Malcolm and Lucy Turnbull converged on fundamentals that become more critical, not less, in an AI-driven world.

For Malcolm, it comes down to character and trust. Trust is built by telling the truth, being transparent, and maintaining consistency — not just when it's convenient. He offered a practical example from his time as Australia's communications minister, when he inherited a problematic broadband rollout. Rather than obscure the problems, he published a simple weekly spreadsheet: how many premises were passed, how many were connected, broken down by technology. The act of sustained, transparent accountability — even when few people checked — rebuilt trust over time.

He also invoked a surfer's metaphor for navigating change: you either catch the wave and ride it, or fight against it and get dumped. Openness to innovation isn't optional. Just because something was done a certain way yesterday doesn't mean it should be done that way tomorrow.

Lucy Turnbull emphasized the combination of passion, purpose, and judgment — the ability to avoid reckless decisions even when outliers seem to be profiting from them. She pointed to her father, a trial lawyer who practiced into his 80s, as a model of focus, commitment, and treating every case as the most important one.

David Bray shared a personal story of enduring a multi-year disinformation campaign, ultimately emerging vindicated. His lesson: do not adopt the tactics of those who lack integrity, even when they appear to be winning. In an era of polarization and information manipulation, staying grounded in integrity and taking the next right step is itself a leadership strategy.

The Unexpected Power of Boundaries

The second half of the episode shifted to Sheri Jacobs, whose book — The Unexpected Power of Boundaries — provided a perfect thematic bridge from geopolitics to personal leadership.

Jacobs' central argument is counterintuitive: many leaders misuse the mantra of "thinking outside the box." They call for unlimited freedom in brainstorming and innovation, but teams lacking clarity on constraints default to safe ideas, incremental thinking, and compliance with the status quo. Real creative freedom requires knowing where the edges are.

She draws a meaningful distinction between constraints — imposed by outside forces like time, money, or regulation — and boundaries, which are chosen limits we set for ourselves and our organizations. Boundaries are not limitations. They are sources of power because they provide focus, clarify priorities, accelerate execution, and build trust by making expectations explicit.

One of her most resonant lines from the episode: "Business grows at the speed of clarity, and clarity is boundaries."

That clarity matters for customers (what you do and don't do), employees (what's expected and off-limits), partners (where you play and how you operate), and investors (what risks you will and won't take). Without it, organizations overcommit, burn out their teams, dilute focus, and stall innovation. With it, they run more experiments safely, learn faster, and allocate energy where it matters most.

Jacobs connected this directly to AI: trust will be the currency of the AI economy. Speed matters, but trust scales. Boundaries — around data use, model behavior, human oversight, and customer commitments — are how organizations signal and sustain trust at machine scale.

Operating at Machine Scale in a Human-Scale World

Ray Wang tied the conversation together with a challenge many leaders feel acutely: machines can run 24/7, humans cannot. AI raises expectations around responsiveness, volume, and velocity in ways that are genuinely unsustainable without intentional design.

Jacobs' framework offers the corrective. The answer isn't to match machine speed — it's to define clear human boundaries around availability, focus, and priorities, aligned with organizational purpose and long-term sustainability.

For boards and senior leaders, this also means being honest about whether existing leadership patterns are fit for a new era. Old playbooks don't apply when a significant portion of content, analysis, and decision support is machine-generated. Building cultures where people have permission to experiment, failure is treated as learning, and boundaries are explicit, fair, and regularly revisited isn't just good leadership hygiene — it's a competitive strategy.

Key Takeaways

  • AI is a geopolitical lever, not just a technology. The choice of AI platform is now a strategic decision about national alignment, access risk, and long-term infrastructure dependency.
  • Energy is the new frontier advantage. Nations with the cheapest, most abundant power will dominate AI infrastructure — and China is aggressively positioning itself to win that race.
  • Trust is the decisive competitive currency — between nations, companies, customers, and teams. Speed gets you noticed; trust keeps you in the game.
  • Boundaries enable innovation, they don't limit it. When people know where the edges are, they're freer to explore bold ideas within that frame. Clarity is the accelerant.
  • Boards are underestimating AI risk. Disinformation, insider threats, supply chain disruptions, and workforce instability are all AI-adjacent risks that demand board-level attention now.
  • The information environment is fragmenting fast. AI-powered filter systems, in the wrong hands, could quietly reshape what millions of people believe — including inside democracies.
  • Operating at machine scale requires human-scale boundaries. Leaders must define what they will and won't do — for their organizations and themselves — before AI speed burns them out.

Final Thoughts

DisrupTV Episode 440 was, at its core, an argument for a different kind of AI leadership — one grounded not in velocity but in integrity.

The panel made clear that the technical dimensions of AI — model capability, compute infrastructure, data volume — are only part of the challenge. The harder and more consequential questions are human ones: Who do we trust? What do we stand for? Where are our edges? What do we owe our teams, our customers, and the broader world as we integrate these systems into the fabric of how we operate?

Malcolm Turnbull's surfer metaphor is worth sitting with. The wave is coming regardless. The leaders who will shape what's on the other side are not those who paddle hardest — they're those who read the water honestly, stay grounded in their values, communicate with transparency, and build the kind of trust that can withstand speed, pressure, and disruption.

In the AI age, speed gets you noticed. Trust keeps you in the game. And boundaries — chosen, communicated, and honored — are what make that trust real.

Related Episodes

If you found Episode 440 valuable, here are a few others that align in theme or extend similar conversations:

Future of Work AI Chief Executive Officer Chief Technology Officer Chief AI Officer Chief Experience Officer

From “activation energy” and agent orchestration to donkeycorns and relationship capital, DisrupTV 425 explains what actually separates AI hype from real business impact.

On DisrupTV <iframe width="560" height="315" src="https://www.youtube.com/embed/J5CupyHoVng?si=GHB_W8FIfV0UhJBW" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

Cornerstone Workforce AI: What It Is, What It Does, and Why You Should Start Now

Cornerstone Workforce AI: What It Is, What It Does, and Why You Should Start Now

Holger Mueller
Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the&nbsp;cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.&nbsp; Coverage Areas: Future of Work Tech Optimization &amp; Innovation&nbsp; Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

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Holger Mueller sat down with Cornerstone OnDemand's Suchi Upadhyayula in New York, on the day they launched Cornerstone Workforce AI. Here is what customers need to know.

What Cornerstone Workforce AI Actually Delivers

Let's start with what is new for existing customers. If you are on Cornerstone's learning platform today, meaningful AI innovation is arriving for you right now.

  1. First, there is a course assistant. This is built directly into the learning experience and helps learners go deeper on topics they care about, complete quizzes, and engage with content in a more interactive way than was previously possible. It is a better learning experience, not just a chatbot bolted on top.
  2. Second, there is an adaptive learning agent with role-play capabilities. This is significant. The ability to practice what you have just learned in a simulated scenario within the same platform closes a gap that traditional learning management systems have never been able to address. You learn. You practice. You retain.
  3. Third, for customers on the talent platform, there are new assistive features for performance reviews. Nobody loves writing performance reviews. Managers dealing with 360 feedback across large teams lose meaningful hours every cycle. Cornerstone is now helping surface that work faster with AI assistance, both for the person writing the review and for the manager synthesizing feedback.

The Intelligence Plus Package

These capabilities land differently depending on your current setup. If you are a Learn Plus customer, you get meaningful new functionality out of the box at no additional cost. If you are looking for more agentic, outcome-based capabilities, that is where Intelligence Plus comes in. The tier structure is logical, and the on-ramp is designed to let customers build from where they are rather than rip and replace.

Custom Workflows and Forward Deployed Engineers

Beyond the packaged capabilities, Cornerstone is now offering something more bespoke. If you have a specific outcome in mind, say, a custom onboarding flow tailored to your organization's brand and business processes, Cornerstone will send forward-deployed engineers to build it with you. This is a meaningful shift. It is not a vendor handing you a product and leaving. It is a co-creation model that takes your nuances seriously.

This approach is gaining momentum across enterprise software, and Cornerstone is leaning into it correctly.

MCP Support and the Open Platform Play

One of the more technically interesting announcements is MCP support. Cornerstone has built a trusted data platform as its foundation, layered strong APIs on top of it, and is now exposing those APIs as MCPs. In practice, this means that if your organization is already running Claude, OpenAI, or another agent platform of your choice, you can connect directly to Cornerstone's data sources and capabilities via standard MCP protocols.

The result is a flexible, composable architecture. Your enterprise decides which agent platform to use. Cornerstone's data and functionality connect to it. The learning content meets employees where they already are, in Teams, in Slack, in whatever tools they use every day. That is the right design principle, and it is good to see it in practice.

Skills Without the Spreadsheet

One of the sharpest observations in the conversation was about how skills are actually built and detected. Nobody wakes up in the morning thinking about which skills they need. They wake up, go to work, and do tasks. A Java developer who needs to learn Python is not thinking about skill acquisition. They are thinking about getting their work done.

Cornerstone's approach to skills detection is task-based rather than declarative. Instead of asking employees to fill out yet another skills inventory spreadsheet, the system infers skills from what people are actually doing. That is the right direction, and it sidesteps one of the oldest failure modes in HR technology.

Making Your Content AI-Ready

Most Cornerstone customers have rich learning catalogs assembled over the years. The challenge is that a catalog sitting in a system is not the same as that catalog being accessible to AI. Cornerstone is launching assistive tools to help customers curate, select, and vectorize their existing content to make it AI-ready. User-generated content can be promoted to AI visibility with a single step. It is a practical, low-friction path to getting value from what already exists.

Start Now

The most important piece of advice from this conversation is simple: start the journey now.

There are still many organizations sitting on the sidelines, waiting to understand exactly how the AI inference works, whether compliance requirements are met, and whether the timing is right. Cornerstone's message is that they are here to help navigate those internal approval processes. But the deeper point is this: the innovation cycle in this space is no longer yearly. It is quarterly at best and monthly at worst. If you wait for a perfect moment, you will spend the rest of the year catching up to organizations that started six months ago.

You do not need to have it all figured out. You need to get started, learn from it, and build from there. That is exactly what Cornerstone is building toward with its co-creation model, and it is the right instinct for where enterprise AI is right now.

Human to the power of AI. That is the frame Suchi closed with, and it is a good one.

Future of Work Tech Optimization Data to Decisions AI Machine Learning Agentic AI Digital Transformation Customer Experience Chief Human Resources Officer Chief Information Officer Chief Technology Officer Chief Operating Officer Chief Executive Officer
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