Pegasystem's Ken Stillwell on Tokenomics and Outcome-Based AI

Liz Miller

Vice President & Principal Analyst
Constellation Research

About Liz Miller: Liz Miller is Vice President and Principal Analyst at Constellation, focused on the org-wide team sport known as customer experience. While covering CX as an enterprise strategy, Miller spends time zeroing in on the functional demands of Marketing and Service and the evolving role of the Chief Marketing Officer, the rise of the Chief Experience Officer, the evolution of customer engagement, and the rising requirement for a new security posture that accounts for the threat to brand trust in this age of AI. With over 30 years of marketing experience, Miller offers strategic guidance on the leadership, business transformation, and technology requirements to deliver on today’s CX strategies. She has worked with global marketing organizations to transform everything from…...

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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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Somewhere along the way, the AI industry started keeping score by the wrong number. Token leaderboards, token maxing, ever-larger consumption as a badge of sophistication. In a recent conversation with Constellation Research, Pega's COO and CFO, Ken Stillwell, argued that this scoreboard was rigged from the start and that the smarter path forward is to stop measuring activity and start measuring value.

Speaking with Liz Miller, VP and Principal Analyst at Constellation Research, Stillwell laid out a refreshingly blunt view of AI economics from the seat of the person who has to answer for both cost and results.

Nobody should be surprised that AI is expensive

Stillwell's starting point is almost disarmingly simple: you have to pay for the capacity you use, and that capacity is not cheap. The industry is spending trillions on infrastructure that will need to be refreshed every year or two. Many assumed the cost would fall quickly. Instead, it has climbed.

But expensive is not the same as wasteful. Things that cost money can still deliver enormous value. The problem is not the price tag. The problem is paying for value you never actually receive.

Who built the leaderboards, and why

Here, Stillwell makes his most pointed observation. Token leaderboards were created by the very companies that profit when you consume more tokens. It should surprise no one that AI model providers want you to use a lot of their product. And it should surprise no one that the companies with nothing to gain from token maxing were never the ones promoting it.

Those leaderboards faded fast once a few voices in the market pointed out how backward the whole exercise was. The lasting question is the more useful one: how do you connect the value of AI to its cost?

AI is a utility, so treat it like one

Stillwell's preferred analogy is electricity. AI is a powerful utility, and like any utility, you use it when you need it. You don't leave the air conditioning running with the windows open. You don't cool an empty house to 65 degrees for two weeks when no one is home. These are intuitions people already carry through their daily lives, and Stillwell believes organizations will develop the same instincts for tokens and AI.

That mindset leads to a more deliberate approach. In design work, for example, token use is heavy but concentrated and short-term, because you are not designing forever. There, it can make sense to lean in hard, because faster iteration and faster decisions are worth the spend. The value of the acceleration justifies the cost.

Right-size the model to the task

Not every job needs a frontier model. Using the most advanced, most expensive model for a simple probabilistic task is its own form of waste. Part of a disciplined AI strategy is knowing when a more static, less costly model will do the job perfectly well.

Just as important is knowing where AI does not belong at all. Stillwell draws a clear line: anywhere a human would otherwise make a judgment, do analysis, or reason is in scope for AI. But in a deterministic workflow built specifically to remove human judgment, introducing that judgment back in does not help. The entire reason the system exists is to avoid it.

The confusion at the heart of the market: outputs vs. outcomes

The conversation's sharpest point is a distinction that Stillwell and Miller both see being blurred across the industry: outcomes versus outputs. Plenty of vendors promise outcome-based pricing, then define the outcome as something like the model returning an answer without hallucinating. Stillwell argues that it is not an outcome at all. It is an output, and frankly, one you should expect to be correct every single time simply by virtue of doing business with them.

A real business outcome is something you have to step back and define. For Pega, that means a completed case: a loan origination, a client onboarding, a dispute, a fraud investigation. The kernel of work that actually needs to get done, ideally automated end-to-end. The value is in completing that work, not in racking up activity along the way.

Stillwell frames it as the difference between activity-based and outcome-based measures. Activity-based metering, counting microtransactions at various skew levels, makes sense when you are selling raw cloud compute. But value-added work is different. He points out how telecom and internet pricing evolved away from counting minutes and toward simply providing access. The lesson for AI is the same. Tokens measure how much processing is happening, not the value being created. And that disconnect is exactly the trap.

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The takeaway

Stillwell's message lands as a useful corrective for any leader feeling pressure to prove AI maturity through sheer consumption. Pay for value, not activity. Match the tool to the job. Know where AI helps and where it doesn't. And above all, get clear on the difference between an output you should take for granted and an outcome worth paying for.

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The Invisible Infrastructure of Commerce: OpenText's John Radko on Business Networks and the Rise of AI Agents

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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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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Every day, companies exchange an enormous flow of information with their partners. Purchase orders, sales orders, shipping notices, invoices, and countless other documents move between organizations in a constant stream. It is, as OpenText's John Radko puts it, the lifeblood of commerce. And most of the time, it happens invisibly.

In a recent conversation with Constellation analyst Chirag Mehta, John Radko, Senior Vice President of Product Development at OpenText, explained what makes that flow possible, why it is so much harder than most people assume, and how AI is beginning to change the picture.

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The problem business networks solve

The core challenge is simple to state and difficult to solve: companies need to talk to each other, but no two companies are alike. They run different systems, different generations of technology, and different data formats. Even when two organizations use the same document standard, they often apply it differently, including omitting values or relying on entirely different versions.

The result is that a single process, something as routine as placing an order, can look ten different ways across ten different partners. A business network exists to mediate all of that complexity, connecting organizations and keeping information moving reliably between them.

And the complexity does not hold still. As Radko notes, the moment a company changes its ERP system or adjusts how it handles receipt of goods, that change can cascade across its entire partner ecosystem. The network has to absorb that constant motion.

Onboarding and the build-versus-buy question

Getting partners connected is one of the central capabilities OpenText offers. Smaller companies without communication gateways might connect via a web interface, while large enterprises connect directly to SAP or Oracle. Increasingly, mid-market companies are connecting via cloud ERP platforms such as NetSuite and Microsoft Dynamics.

So why not just build all of this in-house? Radko is refreshingly candid here. There is very little, he says, that clients could not do for themselves, just as they could run their own logistics or cater their own events. The real question is whether it makes business sense. For most organizations, B2B integration is not anyone's full-time job. For OpenText's business network team, it is the only job. That focus, combined with purpose-built technology like the Trading Grid platform and years of refined processes, is where the economies of scale come from.

Managing at scale

When customers are processing hundreds of thousands of data exchanges a day, things inevitably go wrong. A partner goes offline, then comes back and tries to retry thousands of pending transactions at once. Handling that without overwhelming the system requires more than connectivity and data transformation.

That is why a large share of OpenText's software investment goes into what Radko calls community management: software that monitors transaction flow, lets customers manage their partners, and surfaces problems quickly. Their TG Insights product, for example, lets customers search through millions of documents to find a specific transaction, while automatically alerting them to failures across their system.

From acceleration to transformation with AI

On AI, Radko frames the journey in two stages: it begins with acceleration and ends with transformation. Today, AI is speeding up nearly everything OpenText does, from onboarding partners and translating data to resolving issues and detecting changes across the network.

The bigger shift is still ahead. OpenText is deploying AI agents that help clients manage their communities, onboard partners faster, and resolve issues more quickly. Looking forward, Radko sees agents acting as managers of the community and a genuine first line of defense. He points to a recent capability rolled out with a major automaker, where the system detects a file failure and automatically resends the file once the underlying issue is resolved. Simple on the surface, but it requires real knowledge of what is happening across the network to know when the retry will actually succeed.

Radko believes the majority of e-commerce and B2B activity will eventually be performed by agents, a view increasingly shared across the software industry as the conversation shifts from end users to agents.

One piece of advice

Asked what he would tell organizations to do right now, Radko's answer is direct: treat partner connectivity as a platform. Consolidate as much as possible onto a single infrastructure, because every partner you connect to the network unlocks all the capabilities of that network. As agentic AI rolls out, it will work through the interfaces and APIs available across your services, so the more partners and processes you bring into one place, the more scale and momentum you can build.

In other words, the groundwork organizations lay today will determine how ready they are for an agent-driven future. The business networks that do the invisible work of commerce are about to get much more intelligent.

Matrix Commerce Tech Optimization Data to Decisions Agentic AI AI B2B ERP Enterprise Software Cloud Chief Information Officer Chief Digital Officer On

Winning with AI by Becoming Data Inspired | DisrupTV Ep 443

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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Winning with AI by Becoming Data Inspired | DisrupTV Ep 443

Winning with AI isn't a technology problem. It's a leadership, culture, and strategy problem rooted in how organizations actually use data to drive change.

Part 1: AI Is Not a Tech Program. It's a Strategy and Leadership Test.

Charlene Li opens with a blunt assessment: most organizations underperform with AI because they treat it as a technology rollout, not a strategic, leadership-driven transformation.

Many organizations toss AI over to IT and ask them to go find something to do with it, instead of leading with business strategy and rethinking how decisions and workflows will actually change. Her core argument is that AI is a leadership and strategy challenge, not a tooling challenge. Leaders who abdicate AI to IT without owning the vision and value agenda almost always stall at pilots — because the real work is organizational adaptation, not just tech adoption.

One of her favorite examples of a sound approach is a strategy document that listed six traditional strategic objectives, then added a seventh: we will use AI to achieve the first six. That framing is the shift — AI should serve business strategy, not be a separate AI strategy off to the side.

Kill Your Pilots: Why “Pilot Purgatory” Destroys Momentum

Li doesn’t mince words on the way most enterprises do AI: endless pilots across departments, no real scaling, no enterprise-level value, and leaders declaring progress simply because they’re running lots of pilots.

Her prescription is to stop boasting about pilot volume — that’s just messing around — and instead pick a small number of high-value, cross-functional initiatives tied directly to strategic objectives. Treat these as enterprise bets, not departmental experiments.

She names a widespread AI hesitancy gap: on one side, excitement, FOMO, and pressure from boards, customers, and employees; on the other, fear of risk, speed of change, and uncertainty — what she jokingly labels FOGY, fear of getting in. Leaders caught in this gap become spectators rather than participants. The organizations that win are those whose leaders step into the mess, champion AI personally, and move decisively from pilots to production value.

Speed Is the New Moat: Why First Movers Win (Again)

Citing investor and AI thinker Vikram Mahidhar, Li argues that speed is the new moat in AI. Everyone can access similar cloud infrastructure and foundation models, and many enterprises are already rich in internal data. The differentiator is how fast you can adapt the organization — people, processes, governance, and operating model.

Li connects this to her long-standing work on disruption: disruptive growth is never incremental. Any outsized growth path will be messy, non-linear, and uncomfortable. AI amplifies this dynamic — it forces relational transformation, redefining roles, workflows, KPIs, and skills all at once.

Vala Afshar underscores this from his own vantage point inside a large AI-driven company, noting it is going through the messiest period in its 27-year history — not just because of the technology itself, but because of the sheer scale of reskilling, process redesign, and organizational change required.

In this environment, traditional distinctions between strategy and operations collapse. As R "Ray" Wang puts it, they are now effectively the same thing because the feedback loops are so fast.

Beyond Cost Cutting: AI as Value Reclamation and Capability Expansion

Much of the classic AI narrative focuses on efficiency and headcount reduction: do more with less, expand margins, cut jobs. Both Afshar and Li push back hard on that framing.

Li describes how many leaders initially think: if I can 10x my people, I can fire the other nine. She argues this is entirely the wrong plane of thinking. The better question is: if we can 10x ten people, how do we get 100x value? What new capabilities and services become possible?

She highlights Connect Up, a call-center company, as a case study. Instead of using AI to cut agents, they reduced error rates by 85 to 90 percent with co-pilots, shortened time-to-proficiency for new hires by 30 to 40 percent, freed agents from routine tasks to focus on higher-value engagement, and built entirely new services for clients — like analyzing non-performing loans for law firms in minutes instead of months. Importantly, they committed to increasing headcount by 5% over two years, not shrinking it.

Afshar reinforces this with his own language of value reclamation: AI can reclaim value that used to be ignored for economic or capacity reasons. By offloading low-impact, deterministic work to machines, leaders can finally invest human talent in activities they previously had no bandwidth to pursue. The mindset shift is from subtraction — efficiency only — to addition: new revenue, new experiences, new capabilities.

Building an AI-Fluent Workforce

Li outlines four building blocks for AI fluency. Mindset — curiosity, openness to change, willingness to experiment, and comfort with never being on top of everything as the space evolves. Skill set — knowing what AI can and cannot do, and the ability to frame problems in ways AI can help solve. Tool set — access to AI capabilities embedded in day-to-day workflows, with practical training on the tools employees actually use. And decision set, or governance — clear rules and processes for responsible, ethical, and sustainable usage, with guardrails that are understood and actionable, not just policy documents.

She compares AI fluency to learning chopsticks: at first it feels awkward and unnatural, but over time you stop noticing the tool and simply flow. The inflection point arrives when you start using AI to ask how you should use AI to help you do this task better.

Most organizations, she notes, provide some tools, minimal training, and almost no time to practice. Without deliberate time and psychological safety to experiment, true fluency never emerges.

The Case for an AI Value Owner

Afshar raises a structural question: should companies appoint a Chief AI Officer, or embed AI value owners within each business line?

Li’s concept of an AI value owner is less about title and more about function. Everyone in the organization owns a piece of the AI puzzle — HR owns the future workforce, GTM owns customer experience, Finance owns token and compute economics. But when everyone owns AI, no one truly does.

The AI value owner wakes up every day ensuring there is a coherent, enterprise-level AI roadmap, that functions are aligned and moving at a similar tempo — no jackrabbits sprinting ahead and no turtles dragging behind — and that the roadmap is updated frequently, at least quarterly and often continuously.

This person also helps every employee answer three questions: what future are we heading toward, what is our strategy to get from today to that future, and what is my role in making that strategy a success? In Li’s view, until leaders across the organization can clearly articulate these answers, the AI value owner has more work to do.

Part 2: From Data Frustration to Data Inspiration

In the second half of the episode, Sebastian Wernicke shifts the lens from AI back to its foundation: data and culture.

For years, leaders have been told that data is the new oil, and that big data, analytics, machine learning, and digitalization will transform everything. Yet survey results remain stubbornly consistent: nearly all leaders say data and AI are a top priority, and about two-thirds admit they are not satisfied with the value they’re actually getting.

This persistent gap breeds data frustration — we’re spending a lot but not seeing transformative impact — and data cynicism — we tried that, it never really worked for us. Wernicke argues this is not primarily a tooling issue. It is a cultural and decision-making issue.

Final Thoughts

DisrupTV Episode 443 makes an argument that cuts against a lot of conventional AI messaging: the hardest part of winning with AI was never the technology. It’s whether leaders are willing to do the harder, messier work of organizational change — owning strategy personally, killing the pilots that feel safe but go nowhere, and building a culture where data is trusted enough to actually change minds.

Charlene Li and Sebastian Wernicke approach the same problem from opposite ends and arrive in the same place. Li starts with AI and works backward to the organizational readiness required to use it well. Wernicke starts with data and works forward to the cultural conditions required to let it matter. Both conclude that the technology was never really the constraint — leadership, trust, and culture are.

For organizations willing to do this hard, human work — clarifying strategy, appointing real ownership, investing in fluency, and building a culture where being wrong in public is safe — AI stops being another wave of tech hype and becomes a genuine engine for transformation.

Related Episodes

If you found Episode 443 valuable, here are a few others that align in theme or extend similar conversations:

• What Happens When AI and Geopolitics Become Inseparable? | DisrupTV Ep 440
• How Market Engineering and Mission Guardians Will Shape the Next Era of Business | DisrupTV Ep 441
• Will the Future Like You? Finding Purpose in the Age of AI | DisrupTV Ep 442

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>

On-Prem or Cloud for AI? Outcome-Based Pricing and Spotting Agentic AI Washing

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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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 AI strategy keeps circling back to the same hard questions: where should workloads actually run, how should vendors get paid for outcomes, and how can buyers tell real AI capability from a marketing label. Episode 132 of ConstellationTV takes on all three.

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On-Prem or Cloud? The Debate Continues

Host Holger Mueller opened the episode with the Big Debate, joined by Michael Ni and Esteban Kolsky, on whether AI has to run in the cloud or can live on-premises. Michael argued that AI follows its own kind of gravity, pulling compute toward wherever inference demand and real-time decisions actually happen, which points toward a distributed model spanning cloud, edge, and on-prem layers. Esteban pushed back with a data-gravity and elasticity argument, comparing cloud economics to the historical shift away from companies generating their own electricity. Holger argued for the edge on cost and efficiency grounds, citing Apple's on-device AI push as evidence that enterprises don't need to run everything through the server. The debate landed where these things usually do: it depends on the layer, and the next few years will be about getting that placement right rather than picking a single side.

Personalization Is a Means, Not the Outcome

Liz Miller used her Marketing Minute to share takeaways from Pega World, Pegasystems' annual event. Her core point: personalization is a tactic, not the business outcome itself. The real goal is a more durable, profitable customer relationship, and Pega's customer decision hub has been quietly doing that work for years, well before "agentic" became the term of the moment. Liz also referenced comments from Pegasystems CEO Alan Trefler on deliberately testing and deploying agentic AI, without losing sight of why the work matters in the first place.

Outcome-Based Pricing: Great on the Whiteboard, Hard in Practice

Larry Dignan covered the renewed push toward outcome-based pricing, with Oracle, Pegasystems, HubSpot, and UiPath all referencing it recently. His take is skeptical. Outcome-based pricing runs into the same problems revenue-share models always have: nobody agrees on how to measure the outcome, nobody wants to give up their share once there's something to share, and it's unclear who bears the cost if an outcome takes more time or tokens than expected. Larry expects the topic to come up heavily on Q2 earnings calls, but his advice for now is to tread carefully.

Five Ways to Spot Agentic AI Washing

Holger closed the episode with a preview of his new Best Practice report on agentic AI washing, which extends the same skepticism that applied to cloud washing a decade ago. Of the 18 questions in the full report, he highlighted five: whether the capability existed before generative AI took off in 2023, whether it's just a chatbot with a new label, whether it actually runs in the cloud and can scale elastically, whether it can access third-party data rather than just the vendor's own, and whether the underlying capabilities are extensible rather than fixed. Vendors that fail these tests, in Holger's view, are most likely repackaging something that already existed.

Tech Optimization Data to Decisions Next-Generation Customer Experience Marketing Transformation Agentic AI AI Quantum Computing Chief Information Officer Chief Technology Officer Chief Marketing Officer Chief Revenue Officer On ConstellationTV

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?

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.

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.

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.

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.

Related Episodes

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

• What Happens When AI and Geopolitics Become Inseparable? | DisrupTV Ep 440
• How Market Engineering and Mission Guardians Will Shape the Next Era of Business | DisrupTV Ep 441
• The Human Edge in an Age of Agentic AI | DisrupTV Ep. 439

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>

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

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

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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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.

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.

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.

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.

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.

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.

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.

Related Episodes

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

• What Happens When AI and Geopolitics Become Inseparable? | DisrupTV Ep 440
• What Do You Really Stand For in the Age of AI? | DisrupTV Ep. 438
• The Human Edge in an Age of Agentic AI | DisrupTV Ep. 439

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

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.

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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.

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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.

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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.

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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