This is Part 7 of The AI Reckoning: A Future of Trust Series

The most digitally fluent generation in history is quietly declining the most powerful technology ever handed to them. Almost everyone is misreading why.

She said it with one word.

My niece was in town, and I had taken her and two of her friends to lunch. They were curious about what I was working on, so I told them a little about the new business, about the AI coach my co-founder and I were building, designed around empathy, built to support people through change. I was still shaping how I talked about it then. This was before the TEDx talk, before the language had hardened into anything polished. I was just a person at lunch, describing the thing she believed in.

One of my niece’s friends looked at me and said, “Creepy.”

Not hostile. Not even particularly interested in debating it. Just a verdict, delivered with the casual confidence of someone who had settled the question long before she sat down at that table. The conversation moved on. I didn’t.

Because here is what I knew about that young woman that made the word impossible to dismiss. She was not afraid of technology. Nobody in that generation is. She has never known a world without it, never had to learn it the way the rest of us did, never experienced it as new. Her “creepy” wasn’t coming from someone who didn’t understand AI or was evaluating the technology. It came from someone who was evaluating the relationship.

I didn’t know it yet, but that lunch was the beginning of a question I would eventually be unable to put down: whether the loudest misread in this entire industry might be generational. The story we keep telling ourselves is that young people will lead AI adoption the way they led every technology wave before it, and that skepticism is something age does to you. But that is not what the evidence is showing us. The evidence is showing something stranger, and I think far more important.

The Misread

Inside most organizations, there is a standing assumption about who will struggle with AI. The older workers, the story goes, will resist. The young ones will lead. It’s a reasonable assumption, because it has been true for every technology wave in living memory. Teenagers drove the adoption of personal computers, video games, social media, and smartphones, and skeptical parents were dragged along behind them.

With AI, for the first time, the pattern has inverted.

The survey data emerging over the past year tells a story almost nobody predicted. Gallup’s 2026 study of more than fifteen hundred Americans between fourteen and twenty-nine found that excitement about AI among Gen Z has dropped fourteen points in a single year, to just 22 percent, while anger toward the technology rose nine points, to 31 percent.[i] A separate consumer study found that among Gen Z non-users, a majority say they are simply not open to adopting it, a higher refusal rate than their grandparents’ generation.[ii] Read that again. Older Americans are now more open to AI than young ones. The generation that was supposed to lead this adoption is the one most visibly declining it.

But there is a contradiction that breaks the easy explanation: their usage hasn’t collapsed. Roughly half still use these tools weekly. They haven’t fled the technology. They use it, fluently, while trusting it less and less. Their skepticism is not rising because they don’t understand AI. It appears to be rising in the places where they understand it best. Employed Gen Z workers were three times as likely to say the workplace risks of AI outweigh its benefits than to say the reverse. Only three percent said they would fully trust work generated entirely by AI. Given the choice, nearly seven in ten said they would rather have the fully human version.

The industry reads this as an adoption lag. Something training will fix. Better onboarding. More time. I want to suggest a different reading, and it’s the reason this piece sits where it does in the series. In Part 5 of this series, The Human Adoption Gap, I wrote about the difference between whether people can adopt a technology and whether they will. This generation is the purest expression of that distinction we have. Their can is total. They are the most digitally capable cohort ever to enter the workforce. It’s their will that is saying no. And when the people most able to use something are the most reluctant to engage, that is not a lag. That is a signal.

They’re asking whether it’s worthy of their trust.

What the No Is Protecting

If you listen to what our younger generation is actually saying about AI, rather than what gets said about them, the refusal starts to organize around three things and none of them are fear of the technology.

The first is creativity, and more precisely, the fear of losing the struggle that produces it. In the Gallup research, Gen Z consistently declined to believe that AI enhances creativity or critical thinking, and a striking eight in ten students said that using AI tools now would likely make learning harder for them later. One college student put it in terms that stopped me: AI removes the friction from learning, and the friction was the point.[iii] I understand that instinct in my bones. I learned to water ski in the ocean and to snow ski in a blizzard, and what those experiences taught me had nothing to do with technique. They taught me that we learn by pushing against something. Friction is not the obstacle to growth. It is the mechanism. A generation raised to believe that is now watching a technology whose entire promise is the removal of friction, and they are asking the question their elders keep skipping: what happens to the muscle when nothing pushes back?

The second is authenticity. This generation came of age inside a culture that prizes the genuine above almost everything, and they are watching a flood of synthetic content wash through every feed they grew up on. They can usually spot it. They increasingly resent it. And they have started to treat “made by a person” as a marker of value in itself, the way an earlier generation might have treated handmade or original. When seven in ten prefer the fully human version of a piece of work, that is not nostalgia. It is a statement about what they believe work is for.

I relate to this as a creator, not just an observer. I wrote my book waking at five every morning for a year, each word, each phrase, each thought mine. I used an editor at the end to fine tune, but the writing was mine, and the difference between those two things was never confusing to me. As a sculptor and painter I know the process of adding and subtracting and adjusting is not preparation for the work. It is the work. In business, as a creative director I went through version after version, but the vision came from somewhere within me, and no round of revisions ever changed whose it was.

And I will tell you something in the spirit of this piece: I write with AI now. I start with my own drafts, my own arguments, my own stories, and I use it the way I once used that editor, to pressure test and refine. The vision is still mine. The boundary I hold is the same one I hear this generation describing: I know exactly what it can do, and that is why I am careful about what I let it do for me. If that boundary sounds familiar, it should. It turns out the question they are asking is the right question for all of us.

The third thing the no is protecting is the ladder itself. Every previous generation entered the workforce at the bottom, doing the routine tasks through which a craft is actually learned, the first drafts, the research grunt work, the entry-level analysis. Those tasks are precisely what AI automates first. This generation is being asked to climb a ladder whose bottom rungs are being removed while they are standing on them, and then being told their hesitation is a mindset problem. The numbers behind that feeling are real: unemployment among recent college graduates is running well above the rate for workers overall, and competition for entry-level postings has climbed sharply year over year.[iv] One recent graduate put it plainly in a local news interview: the hard part isn’t accepting that AI exists. It’s accepting that it’s taking the opportunities they trained for. Their skepticism about whether this technology serves them is not paranoia. It is a reasonable reading of their own position.

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The Receipts

There is a reason this generation’s “no” arrives faster and more confidently than anyone expected.

They have seen this movie before. They are the only generation that lived the entire lifecycle of the social media promise from inside their own childhoods: the launch, the euphoria, the connection that curdled into performance, the engagement that turned out to be extraction, the apology tours. They were the product the last time a world-changing technology promised to empower them. The lesson they took from the smartphone era, that moving fast and breaking things includes breaking people, is documented in their behavior, not just their sentiment. A recent Deloitte survey found nearly a third of Gen Z had deleted a social media app in the past year.[v] The market for deliberately limited phones is growing fastest among buyers under thirty, and they describe the downgrade not as nostalgia but as boundary setting.

So when the next transformative technology arrived promising to empower them, they did something no generation before them had the experience to do.

They priced the promise against the last one.

The reframe I keep arriving at, that connects directly to the last piece of this series, Part 6, Signal vs Noise is this: the industry is filing this generation’s refusal under noise, sentiment to be managed, an adoption lag, or a training gap. But I believe their refusal is closer to signal than almost anything else in the market. They are the users with the most fluency and the least sunk cost, and they are the ones asking the eighteen-month questions the rest of the market keeps skipping. What does this do to me over time? What does it cost that isn’t on the invoice? Who benefits from my adoption? Those are the questions I spent the last piece urging builders, investors and enterprises to ask. An entire generation got there first and we are missing it because we’ve been calling it resistance.

They may be the only constituency in this story practicing foresight, and they are the ones being told they don’t understand.

The No My Algorithm Hides

Here is a confession that took me longer to arrive at than it should have. I live inside the world of AI. My feeds are full of builders and investors, people for whom this technology is genuinely thrilling, and the algorithms that shape what I see have learned to give me more of what I already give my attention to. The generational refusal this piece describes was invisible from where I stood, not because it was hidden, but because I was never shown it.

The first time someone put it in front of me directly, I pushed back. One of the women on my team shared the insight with me. She sent me an article making the case that this generation was turning away from AI, and my first reaction was the industry’s reaction, almost word for word. They’ll come around. It’s an adoption curve. They don’t fully understand it yet. I reached for every available explanation except the one on the page. It took me longer than I would like to admit to notice what I was doing: I was reading a signal and filing it under noise, which is the exact failure I had just spent an entire piece describing in other people. Even the lunch I opened this piece with had been sitting in my memory as an anecdote, one word from one young woman, until someone I trusted connected it to a pattern and made me look again.

Then the evidence stopped being quiet, siloed and easy to miss. At the University of Central Florida this Spring of 2026 a commencement speaker told graduates that AI was the next industrial revolution. The arena booed. At Middle Tennessee State, a record executive told the class that AI was rewriting their industry as they sat there; when they booed, he told them to deal with it, and they booed louder. At the University of Arizona, one of the most powerful figures in the history of the technology industry was booed repeatedly for saying AI would touch every profession and every relationship they would ever have.[vi] These were not activists who bought tickets to protest. They were graduates, at their own ceremonies. And this class matters more than any other: they started college the same Fall ChatGPT launched. They are the first graduating class whose entire education happened inside this technology. They know it better than the people at the podium. And their answer, delivered in the most public setting their lives have offered so far, was “no.”

If you are a builder or an investor and none of this has reached your feed, that is worth sitting with. I know exactly how the reflex feels from the inside. The signal was there. I filed it under noise myself, until someone made me look twice.

What I Don’t Know

This is the point in the piece where I would normally consolidate the argument. Instead I want to be direct about the limits of what the data can currently support, because this topic deserves that care.

We are still in the early days of AI, and the research on this topic is also early and thinner than the confident takes on all sides suggest. I don’t know whether this is a durable values position or partly a life-stage effect that softens when this generation has mortgages and deadlines and less room for principle. And I don’t know how evenly the refusal is distributed; it may be concentrated among students and creative workers and much weaker elsewhere.

I also know the boundary of this refusal is not a clean generational line, because I live with the evidence. Someone close to me, a generation older than the people this piece is about, has been telling me for years that AI is “just an algorithm.” He works with the special needs community, and his whole professional life is built on the conviction that nothing replaces human presence. He is also unfailingly supportive of me and of what I’m building, which means his skepticism arrives gently, and I suspect he softens it more than he feels it. And yet, the handful of times I’ve used AI to help him with something real, he has been genuinely glad for the result. He holds both positions at once, the principled rejection and the practical appreciation, and he is not confused. He is conflicted, which is different, and I have come to think it may be the most common relationship anyone has with this technology right now. The surveys capture it as a contradiction. Up close, it looks more like a person trying to protect something while still living in the world.

What I am also discovering as of late is that the standard explanation, that they’ll come around once they understand it better, has the evidence exactly backwards. Understanding is not what they lack. Understanding appears to be where the skepticism comes from.

The Seat, Not the Pitch

So rather than resolve the question, I want to open it up to discussion. If you are part of this generation, I would genuinely like to hear how you decide what AI is allowed to touch and what it isn’t. If you manage people from this generation, I’d like to hear what their selective refusal actually looks like inside your organization. The comments are open, and this is the one piece in this series where the conversation may matter more than the argument.

And if you are building or deploying this technology, the way forward is not a better message. It is a seat. This generation is not asking to be persuaded. They are asking to be included in the decisions being made about the work, the tools, and the future they will inhabit longer than anyone else in the room. The organizations that bring them into the planning, and visibly change course based on what they hear, will earn something no adoption campaign can buy. The ones that keep trying to convert them will keep manufacturing the resistance they are trying to cure.

I have thought about that lunch, a couple of years ago, more than almost any conversation this year. And I no longer hear “creepy” as a door closing. I hear it as the price of admission being named. My niece’s friend was not telling me the thing I am building should not exist. She was telling me what it would take for her to trust it: that it listen without harvesting, that it support without replacing, that it never pretend the relationship is something it is not. Those are not unreasonable demands. They are the most precise product requirements I have ever been handed, and they came from a young woman over lunch, for free. The question is whether anyone building this technology is willing to treat the generation that said “no” as the standard to build toward, rather than the audience to win over.

Every technology wave has had its skeptics. What it has never had, until now, is a skeptic class made up of its most capable users.

They aren’t afraid of the future. They’ve already lived in one version of it.

Their “no” isn’t the absence of an answer. It’s an answer.

The question now is whether the rest of us are listening.

The next piece turns from the people moving slower than the market wants to the market moving faster than its own safeguards can follow. Because while human trust struggles to keep pace with AI, something else is falling even further behind: security. And the gap between those two speeds is where the next reckoning is already forming.

Read more from The AI Reckoning: A Future of Trust Series

Part 1: The Hidden Cost of Intelligence, The Trust Story Hiding in Plain Sight

Part 2: The Reckoning Behind the Revenue: When the Numbers Don’t Add Up

Part 3: Rented Intelligence: Building on Borrowed Ground

Part 4: The Fragmentation Tax: Death by a Thousand Tools

Part 5: The Human Adoption Gap: We Built the Technology, We Forgot the Human

Part 6: Signal, Noise, and Judgment: The Trust Debt Nobody Is Measuring

[i] https://news.gallup.com/poll/708224/gen-adoption-steady-skepticism-climbs.aspx

[ii] https://fortune.com/2026/05/20/why-do-kids-hate-ai-gen-z-backlash/

[iii] https://www.usnews.com/news/national-news/articles/2026-04-09/gen-zs-ai-use-remains-stable-as-skepticism-grows-gallup-finds

[iv] https://www.cnbc.com/2026/05/21/new-graduates-booing-commencement-speakers-ai.html

[v] https://fortune.com/2026/05/20/why-do-kids-hate-ai-gen-z-backlash/

[vi] https://www.npr.org/2026/05/20/nx-s1-5822419/ai-colleges-commencement-booing

This is Part 6 of The AI Reckoning: A Future of Trust Series

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For most of human history, the challenge was finding enough information to make a good decision.

Today the challenge is surviving the tsunami.

Every morning brings another model, another benchmark, another study, another prediction, another expert explaining where AI is taking us next. Articles are summarized before we’ve read them. Research is synthesized before we’ve had time to consider it. Opinions arrive polished, confident, and increasingly difficult to distinguish from one another. Artificial intelligence has compressed the distance between questions and answers so dramatically that information is no longer our constraint.

Discernment is.

Somewhere inside this torrent are extraordinary insights. There is also speculation, marketing, repetition, bias, and enough convincing language to support almost any conclusion we want to reach. The challenge is no longer access to knowledge. It is deciding what deserves our attention, what deserves our skepticism, and what deserves the patience of a second look. That feels like a very different kind of leadership challenge than the ones we’ve spent the last two decades preparing for.

Every technological revolution changes the muscles society exercises. Calculators reduced the need for mental arithmetic. GPS changed how we navigate and remember geography. Search engines shifted our relationship with memory itself, replacing recall with retrieval. None of those technologies made us less capable. They changed where we invested our cognitive effort.

I find myself wondering whether AI is doing something similar with judgment. Not replacing it. Changing how often we exercise it.

Judgment has always been one of our defining human capabilities. I suspect we’re entering an era where it must become a deliberate discipline rather than an unconscious habit. The more information arrives at machine speed, the more valuable it becomes to slow down long enough to ask whether we’re seeing signal or simply responding to whatever demanded our attention first.

That thought finally crystallized for me during a demo day a couple of years ago. The founder before the break began with his previous company. It had been acquired by a name everyone in the room recognized, for a number everyone repeated to each other over coffee. He hadn’t said a word about the company he was pitching that afternoon, yet the room was already leaning forward. The questions afterward were warm, almost collegial.

Very few people asked what the business depended on, how resilient the architecture was, or what would happen if the market beneath it changed.

The founder who followed couldn’t tell that story. She spoke instead about customer retention, infrastructure decisions, and the choices she had made to avoid becoming dependent on a single platform. Her product solved one decidedly unglamorous problem, but it solved it completely. The questions she received were shorter, more skeptical, and noticeably less generous.

If I were placing a bet today, I would choose her company every time.

I’ve replayed that afternoon more often than I expected because it revealed something I now recognize almost everywhere. We have become remarkably efficient at evaluating the signals that are easiest to see, and surprisingly inconsistent at recognizing the ones most likely to predict whether something will endure. Under pressure, the impressive résumé often outweighs the resilient architecture. The polished demonstration eclipses the thoughtful design decision. Familiarity quietly becomes a substitute for judgment.

As I’ve written this series, something unexpected has happened. Each article began by exploring a different challenge surrounding AI: the infrastructure, the economics, the platforms, the fragmentation, the human adoption gap.

Yet after each one, I found myself coming back to the same question. We have more information than any generation before us. Why does good judgment sometimes feel harder rather than easier? I don’t think technology created that problem. I think it revealed it.

The Discipline of Judgment

Over the last year, I’ve noticed something changing in myself.

I trust my first reaction less than I used to, and it’s not because I’ve become more skeptical. In fact, quite the opposite. I still find myself excited by what AI is making possible. But I’ve also become aware of how easily certainty can now be manufactured. Every day brings another remarkable demonstration, another confident prediction, another analysis that feels complete before I’ve had time to consider whether it’s actually true.

I’ve realized the challenge is no longer finding answers. It’s deciding which answers deserve my trust and that feels like a different kind of work than I was doing even a few years ago. For most of my career, good judgment meant gathering enough information before making an important decision. Increasingly, I find myself doing the opposite. I spend less time looking for additional information and more time deciding what deserves my attention in the first place. Information has become abundant. Attention has not.

I don’t think I’m alone.

When we say we trust someone’s judgment, we’re rarely talking about intelligence. We all know brilliant people whose decisions we wouldn’t follow. We also know quieter leaders whose advice somehow carries unusual weight, and it’s not because they always have the answer. It’s because they’ve developed the habit of seeing one layer deeper than everyone else.

Perhaps that’s what discernment really is. Not judgment itself, but the practice that comes before judgment. The willingness to pause, to doubt, and to ask one more question before accepting the first convincing answer.

I’ve spent much of my career helping leaders navigate uncertainty, and I’ve come to believe trust follows good judgment far more often than confidence. Confidence persuades. Discernment earns trust. AI hasn’t diminished the importance of judgment, rather it has magnified it. The easier answers become to produce, the more valuable it becomes to recognize which ones deserve our attention and trust.

Sometimes I wonder whether judgment is like any other human ability. The capabilities we practice become stronger, while the ones we quietly outsource begin to fade. Calculators didn’t make us incapable of arithmetic, but most of us no longer calculate in our heads. GPS changed how often we rely on our own sense of direction. Search engines reshaped what we remember because retrieval became easier than recall.

I don’t know yet what AI will do to judgment.

I do know it’s worth asking before we stop exercising it.

The Shortcuts We Reward

Last year the word slop entered the mainstream. Officially, it describes low-quality content produced at extraordinary scale through generative AI. The definition is accurate, but I’ve come to think it captures only the smallest expression of a much larger pattern. Content was simply where we noticed it first.

The same dynamic now appears almost everywhere decisions are made quickly. Product demonstrations optimized for attention rather than resilience. Strategy decks polished enough to survive the board meeting but unable to survive implementation. Procurement processes that reward feature lists before understanding workflow. Investment decisions influenced by familiarity because familiarity is easier to evaluate than architecture.

The common thread isn’t artificial intelligence. It’s our growing tendency to reward whatever performs well during the first evaluation instead of asking whether it will still hold up during the second. AI didn’t create that instinct. It simply accelerated the consequences.

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The Builder’s Dilemma

Imagine a founder we’ll call Jordan.

Jordan is talented, deeply technical, and building at a pace that would have seemed impossible only a few years ago. What once required an engineering team and months of development can now be accomplished in a long weekend with the right combination of models, tools, and determination. That is one of the genuine marvels of this moment. The barriers separating imagination from execution have fallen farther and faster than almost anyone predicted.

Technology changed, and markets changed with it. The signals that attract attention changed as well.

Jordan discovers very quickly that architectural resilience rarely earns the first meeting. Platform independence doesn’t generate headlines. A carefully considered approach to long-term maintainability isn’t what gets shared on social media or featured in product launches. Attention follows novelty. Investment follows momentum. Recognition follows whatever appears to be moving fastest.

None of that makes anyone irrational. Jordan gets it. Markets reward what they can evaluate quickly, and speed has become one of the easiest things to recognize. So he adapts.

The product demonstration becomes a little more polished. The roadmap stretches a little further into the future than the engineering team would privately promise. Questions about infrastructure become conversations for another meeting. Human adoption becomes something to solve after product-market fit.

One compromise is rarely significant. Thousands of similar compromises begin to shape an entire ecosystem.

I’ve come to believe that many founders aren’t optimizing for durability nearly as much as they’re optimizing for investability. They’re responding rationally to the incentives placed in front of them. When the market consistently rewards what looks finished, it shouldn’t surprise us that so much effort goes into appearing finished, because every system eventually produces the behavior it rewards.

That observation extends well beyond founders.

When Familiar Feels True

Investors like to believe they recognize exceptional companies, and they often do. Experience matters. Pattern recognition matters. The ability to quickly identify talent is one of the reasons successful investors become successful in the first place.

Experience, however, has a quiet companion. Familiarity.

Behavioral researchers have spent decades documenting how uncertainty changes the way people make decisions. As ambiguity increases, the human brain begins searching for anchors. Something recognizable. Something that resembles a previous success. Something that reduces the discomfort of not knowing. Pedigree becomes reassuring. A familiar logo. A prestigious university. A previous exit.

Even physical resemblance has been shown to influence investment decisions more than most people would like to admit. Research from UCLA found that investors expressed greater interest in founders whose faces subtly reminded them of their own, particularly when objective information about the company was limited.[i]

None of this reflects dishonesty. It reflects humanity and we can even say biology. Our primitive brains see familiar as safe, and under pressure and overwhelm familiarity begins masquerading as evidence.

That distinction feels increasingly important in today’s AI market because uncertainty has never been higher. Nearly every company can produce an impressive demonstration. Every week introduces another breakthrough, another valuation, another announcement that seems to redefine the competitive landscape all over again.

When everything looks extraordinary, extraordinary stops being a reliable signal. The temptation is to lean more heavily on whatever still feels familiar, but ironically, those may be the very moments that require us to do the opposite.

The slower questions rarely generate excitement. They generate clarity. How dependent is this business on infrastructure it doesn’t control? What assumptions have to remain true for this model to work three years from now? What happens if those assumptions change?

Those questions are less satisfying than a compelling founder story. And they are also remarkably difficult to answer quickly.

Perhaps that’s exactly why they deserve more attention.

The Quiet Cost of Fast Decisions

Enterprise leaders face the same challenge from a different direction. The pressure is to avoid becoming the company that moved too slowly. Every board meeting now contains some version of the same conversation. Competitors are adopting AI. Customers expect it. Analysts are talking about it. Employees are experimenting with it whether policy allows it or not. Waiting feels risky. Moving quickly feels responsible.

Yet speed has a way of changing the questions we ask. The demonstration becomes more important than the deployment. Capabilities become easier to compare than outcomes. The procurement conversation revolves around features, while the more consequential questions remain surprisingly quiet. Will people actually choose to use this once the rollout is over? Does it reduce complexity or simply relocate it? Will this still fit the way our organization works six months from now, after the excitement has faded and the novelty has worn off? Those questions rarely appear in product demonstrations. They reveal themselves only after implementation begins.

Stanford and BetterUp recently described one consequence of this dynamic with a term that immediately resonated with me: workslop. Their research found that AI-generated work often appears complete while quietly increasing the amount of effort required by everyone downstream.[ii] Documents become longer but less thoughtful. Summaries require verification. Emails sound polished while communicating remarkably little. Employees spend time deciphering work that initially appeared finished.

The cost isn’t simply productivity. It’s confidence. Close to half of employees who received this kind of work said it made them see the sender as less capable, less creative, and less reliable. Forty-two percent said it made them trust that person less. Nearly a third said they’d be less willing to work with them again.[iii] Trust erodes in ordinary ways: a manager quietly starts double-checking someone’s work, a colleague grows hesitant to collaborate after one draft required too much rewriting. An employee who adopted AI hoping to appear more productive unintentionally becomes perceived as less careful, less creative, or less reliable.

The researchers were careful to point out that this wasn’t laziness. It was largely the predictable outcome of organizations encouraging greater AI adoption without investing equal effort in helping people use it well. That observation stayed with me because it echoes a pattern we’ve seen throughout this series.

Technology rarely creates the deepest problems. It exposes the assumptions we were already making.

What Signal Actually Looks Like

Noise has one remarkable advantage. It announces itself. Signal rarely does. It usually arrives quietly, without urgency or spectacle, asking questions that feel almost disappointingly ordinary.

Builders often recognize signal in the decisions no customer will ever notice. Architecture. Dependencies. Maintainability. The work that never appears in a product announcement but quietly determines whether a company can survive its second and third chapters.

Investors find signal in different places. Not in the certainty of a forecast, but in the quality of the assumptions beneath it. They ask what happens if the model provider changes direction, if infrastructure costs rise faster than expected, or if the competitive landscape looks entirely different eighteen months from now. The companies worth backing are rarely the ones with all the answers. More often, they’re the ones asking the most thoughtful questions.

Enterprise leaders face perhaps the most practical version of all. Product demonstrations eventually end. Adoption begins the following Monday morning. Will people still choose this tool after the rollout? Has it reduced complexity or simply relocated it? Does it disappear into the work, or does the work begin revolving around it? Those questions are quieter than feature comparisons. They are also far better predictors of whether technology becomes capability or simply another expense.

I’ve noticed something else while writing this series. The signals that matter most almost always require patience. The market rewards speed. Signal rewards attention. Those aren’t the same investment.

One Debt. Three Ledgers.

Looking across builders, investors, and enterprises, I no longer see three separate stories. I see one pattern expressing itself in different ways. Builders borrow against future relevance in exchange for today’s momentum. Investors borrow against future certainty by relying on familiar patterns when uncertainty becomes uncomfortable. Enterprises borrow against future adoption by assuming impressive demonstrations naturally become meaningful organizational change.

Each decision makes sense on its own. Together they accumulate something few organizations measure. Trust debt.

Trust debt is rarely dramatic. It accumulates quietly through hundreds of small decisions that optimize for the immediate over the enduring. Every shortcut taken before understanding is complete. Every assumption left unexamined because there wasn’t time. Every impressive signal accepted before someone asked whether it reflected substance or simply appearance.

Eventually the invoices begin arriving. A product people stop using. An investment thesis that quietly unravels. A strategy that looked compelling until reality introduced variables no presentation anticipated. By then the opportunity to make a different decision has already passed.

The debt wasn’t created when the outcome failed. It was created when judgment yielded to urgency.

The Advantage of Looking Further Ahead

One of the questions I’ve been asking throughout this series is why we seem so willing to pay for hindsight while resisting the smaller investment foresight requires.

Perhaps it’s because hindsight arrives with certainty. Foresight asks us to sit with uncertainty long enough for understanding to emerge. Markets rarely reward that kind of patience in the short term. Leadership often does.

I’ve spent much of my career helping organizations navigate transformation, and one lesson has surfaced again and again. The leaders who consistently earn trust are rarely the fastest people in the room. They’re the ones who know when speed creates advantage and when it quietly becomes liability. They understand that moving quickly and thinking carefully are not opposing ideas. They are complementary disciplines.

The pace of AI isn’t slowing, nor should it. The opportunities are extraordinary. The question I keep returning to is whether our capacity for discernment is expanding at the same rate as our capacity to generate answers. I’m not certain it is. That doesn’t make me pessimistic. It makes me attentive. Because I suspect the next competitive advantage won’t belong exclusively to organizations with better models, larger budgets, or faster infrastructure. It will belong to those that become unusually good at distinguishing signal from noise while everyone else is still reacting to whatever arrives first.

Artificial intelligence has dramatically expanded what we can know. The future may be shaped just as much by what we choose to trust.

The next article turns to a generation that has lived inside this digital environment and tsunami of information longer than anyone else. While much of the market interprets their hesitation as resistance, I wonder whether they’re practicing something we’ve become too busy to notice.

Not fear. Discernment.

Because surviving the tsunami won’t depend on how much information we can generate.

It will depend on how well we recognize what deserves our trust.

And that may become one of the most important human skills of the AI era.

Read more from The AI Reckoning: A Future of Trust Series

Part 1: The Hidden Cost of Intelligence, The Trust Story Hiding in Plain Sight

Part 2: The Reckoning Behind the Revenue: When the Numbers Don’t Add Up

Part 3: Rented Intelligence: Building on Borrowed Ground

Part 4: The Fragmentation Tax: Death by a Thousand Tools

Part 5: The Human Adoption Gap: We Built the Technology, We Forgot the Human

[i] That Silicon Valley Founder Reminds Me of His VC! - UCLA Anderson Review

[ii] AI-Generated “Workslop” Is Destroying Productivity

[iii][iii] https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity

This is Part 5 of The AI Reckoning: A Future of Trust Series

She used to be the person everyone went to.

Not because she held the highest title or had the loudest voice in the room. She was the one people sought out because experience had given her something harder to teach than technical skill. She understood how customers thought. She knew where projects quietly unraveled before anyone else noticed. She knew which shortcuts saved time and which ones created problems months later. Over two decades, she had become the person people trusted when the answer wasn’t obvious.

Let’s call her Kelly.

Then AI arrived. Not all at once, but steadily. One new tool became three. Pilot programs became mandates. Training sessions filled the calendar. Every town hall carried the same message: this was the future, and everyone needed to come along.

No one ever told Kelly she was being replaced. No one needed to. For the first time in her career, she wasn’t sure whether sharing everything she knew made her more valuable or simply made it easier to automate what had once made her indispensable.

That uncertainty changed the way she showed up long before it changed the way she worked. She attended every training session. She experimented with the tools. She completed the exercises and checked every box the organization asked her to check. When people were watching, she used the new system exactly as expected. When they weren’t, she quietly returned to the methods she trusted.

From the organization’s perspective, the rollout was succeeding. The dashboards showed another active user. Another completed training. Another employee adopting AI.

But Kelly hadn’t adopted anything. She had complied. Those two things can look almost identical on a dashboard. Inside a human being, they are profoundly different.

Every article in this series has followed AI through a different layer of its infrastructure: the physical world, the economics, the platforms, and the fragmentation created by an ever-expanding landscape of tools. Every one of those forces ultimately arrives in exactly the same place: a person being asked to change.

The greatest bottleneck to AI has never been the technology. It has always been our ability to understand and support the human being asked to trust it.

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Can vs Will

When organizations struggle to realize value from AI, the conversation almost always turns to the technology. Maybe the models aren’t capable enough. Maybe the data isn’t ready. Maybe the integrations need more work. Those things matter, but they are rarely where transformation succeeds or fails.

After spending decades studying human performance and organizational change, I’ve watched the same pattern repeat itself through mergers, digital transformations, restructurings, and now AI. Leaders naturally focus first on the technology because it’s visible. The human experience is quieter. By the time it becomes visible, it has usually become expensive.

And organizations tend to ask whether people can adopt AI. Do they have access to the tools? Have they completed the training? Do they understand the prompts? Can they use the technology? Far less attention is given to a different question. Will they?

Can is about capacity. Do people have the time, the knowledge, the cognitive bandwidth, and the practical ability to incorporate something new into the way they work?

Will is about trust. Trust is difficult because it isn’t a single emotion. It is built from dozens of perceptions, often invisible to everyone except the person experiencing them. People naturally ask themselves: Do I still have a place here? Am I becoming more valuable or less? Is this change happening with me or to me? If I admit I’m struggling, what will people think?

None of those concerns appear on a dashboard, but every one of them shapes whether someone truly adopts change. One question asks whether adoption is possible. The other asks whether it is likely. Organizations routinely invest in the first while quietly assuming the second will take care of itself. The evidence suggests otherwise. Boston Consulting Group has spent years studying organizations that successfully translate AI investment into business value. Their conclusion is surprisingly consistent: roughly 10 percent of the effort goes into algorithms, 20 percent into technology and data, and 70 percent into people and process.

That framework doesn’t diminish the importance of technology. It simply puts it in context. The algorithm may be the most visible part of an AI transformation. The human being is still the largest determinant of whether it succeeds. And yet, when budgets tighten or timelines compress, it is almost always the human side of the equation that gets treated as optional. The irony is difficult to ignore. We continue investing in making AI more capable while underinvesting in helping people become confident enough to use it. The technology keeps getting better. The bottleneck remains exactly where it has always been.

The Visibility Gap

Leaders are not flying blind because they lack data. They are flying blind because they are receiving the wrong kind of data. The management systems most organizations rely on were designed for a different era. They were built to measure attendance, productivity, utilization, compliance, and output. They tell us whether work was completed, training was finished, systems were deployed, and licenses were assigned.

Leaders measure exactly what the systems they inherited were designed to measure. The challenge is that AI asks leaders to understand something those systems were never built to see: confidence, readiness, cognitive load, psychological safety, and trust. Those are often dismissed as “soft” issues. In reality, they are the conditions that determine whether transformation actually takes hold. They shape whether people experiment, whether they ask questions, whether they share what they know, and whether a new way of working ultimately replaces the old one.

Organizations can tell you how many people completed the training. They can tell you how many licenses have been activated, how many prompts were submitted, and how frequently employees logged into a new platform. Those metrics are useful, but they are only part of the story. A login tells us that someone accessed a system. It tells us almost nothing about what happened after they did. Did they trust it? Did they find it genuinely helpful? Did it become part of the way they naturally work, or did they quietly return to the old process five minutes later? The dashboard cannot tell us.

That is because organizations and employees are often experiencing the same transformation through entirely different realities. Leaders see implementation. Employees experience uncertainty. Leaders see completed training. Employees wonder whether years of hard-earned expertise still matter. Leaders see increasing usage. Employees quietly decide which parts of themselves still feel safe to contribute.

Neither perspective is irrational, and neither perspective is complete. They are each responding to different information. That isn’t a leadership failure, and it isn’t an employee failure. It is a perception gap.

Management systems inevitably reflect the assumptions of the era in which they were designed. Industrial organizations learned to measure output. The knowledge economy expanded that to productivity, utilization, and efficiency. Those measures still matter. But AI is asking organizations to manage something fundamentally different: the human experience of continuous adoption. That shift changes the signals that matter. Some are technological. Others are deeply human. We have become remarkably sophisticated at measuring the first while remaining surprisingly dependent on intuition for the second. As a result, we optimize what we can see while overlooking the forces that ultimately determine whether change takes hold.

The irony is that many leaders sense something is wrong long before they can explain it. The promised productivity doesn’t quite materialize. Once the initial enthusiasm fades, teams quietly drift back toward familiar workflows. The technology works, but the transformation never fully arrives. So organizations respond in the ways they know best: they schedule more training, increase communication, introduce incentives, and encourage managers to reinforce adoption. Those are not the wrong responses. They are simply responses to the wrong diagnosis.

If the underlying challenge is not capability, but trust, then no amount of additional training will solve it. It may simply make people better at appearing to adapt, while leaving the real obstacle untouched. You cannot solve problems you cannot accurately see, and today, the greatest blind spot in most transformations isn’t technological, it’s human. The organizations that will thrive in the AI era won’t be the ones with the most advanced models. They’ll be the ones with the clearest visibility into the human experience of change.

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The Trust Gap

Several months ago, I spoke with a senior business development executive whose company had begun rolling out AI across the organization. Leadership asked him to help train the system by documenting the objections customers raised and capturing the patterns he had learned over years of conversations. They wanted him to teach the AI what had made him successful.

On paper, it was a perfectly reasonable request. The company wasn’t trying to replace him, they said. It was trying to preserve institutional knowledge and make it more broadly available. From a business perspective, the logic was sound.

As we talked, he became quiet. Then he admitted something that has stayed with me ever since. “I wasn’t giving it everything.” He wasn’t refusing to participate. He wasn’t trying to sabotage the initiative. He understood why the company was doing it, and in many ways, he even agreed with the strategy. But he also couldn’t ignore the question quietly running through the back of his mind. What happens if the thing I’m teaching eventually makes me less necessary?

Later he told me something even more revealing. Without ever discussing it, several of his peers had arrived at exactly the same conclusion. Share enough to look cooperative. Hold back enough to stay necessary. At first glance, that sounds like resistance. I don’t think it is. I think it is a deeply human response to uncertainty.

If someone genuinely believes the knowledge they have spent years or even decades developing could become the very thing that diminishes their future value, protecting some of that knowledge isn’t irrational. Whether that perception is ultimately accurate is almost beside the point. People don’t only respond to reality. They respond to what they believe reality might become. That distinction matters because organizations often interpret behaviors like these as a lack of commitment to change. More often, they are signals that people are trying to answer questions no one has helped them answer. Will I still matter? Will my experience still matter? Will my role evolve, or disappear? Is this happening with me, or to me?

Those questions often remain unspoken because the perceived cost of asking can feel uncomfortably high. If I admit I’m struggling, will people assume I can’t adapt? If I question the rollout, will I be labeled resistant? If I say I’m uncertain, will someone quietly decide I’m no longer the right person for the future? Most people never say those things aloud. Instead, they attend the training. They smile in the meetings. They use the expected language. They do enough to demonstrate cooperation while privately trying to make sense of what the change means for them.

I’ve spent much of my career studying organizational change, and one pattern has remained remarkably consistent. People are surprisingly honest when they feel psychologically safe. When they don’t, they become careful. That is an important distinction. Careful people don’t necessarily tell you what is false. They tell you what feels safe. They hold back questions they fear will be misunderstood. They avoid conversations that might make them appear less capable. They quietly preserve options while they wait to see where the organization is actually heading.

None of this makes leaders the problem, and none of it makes employees the problem. It is what happens when two groups of well-intentioned people are trying to navigate profound uncertainty with different information, different incentives, and different perceptions of risk. That is why trust matters so much. Without it, organizations don’t lose intelligence. They lose honesty. And without honesty, even the best technology cannot tell leaders what they most need to know.

The Pace Humans Were Never Built For

Every major technological revolution has required people to adapt. The difference today is not that change exists. It is the speed, frequency, and compounding nature of that change. Before most organizations have fully integrated one new platform, another arrives. Workflows are redesigned. Roles evolve. Expectations shift. New tools appear before old habits have had time to settle. We aren’t simply learning new technology. We are living in a state of continuous adaptation.

Human beings don’t change at the speed of software. Technology can be updated overnight. The human nervous system cannot.

Every meaningful change asks the brain to do something remarkably difficult. It must build new neural pathways while allowing older, deeply practiced ones to gradually become extinct. That doesn’t happen because someone attended a training session or watched a demonstration. It happens through repetition, reinforcement, experience, and, perhaps most importantly, a sense of safety.

Habits are not simply behaviors. They are biological shortcuts. Over time, the brain learns which patterns require the least amount of effort and attention. Those familiar pathways become efficient, automatic, and reassuring. Replacing them requires considerably more than information. It requires enough repeated experience for the unfamiliar to eventually become familiar.

That is one reason transformation often unfolds more slowly than leaders expect. People are not simply learning a new workflow. They are rewiring years of experience. And beneath all of it, the nervous system is asking a remarkably simple question: Is this safe?

When the answer is yes, curiosity expands. People experiment. They ask questions. They tolerate mistakes because they believe learning is part of the process. When the answer is no, the brain behaves very differently. Attention narrows. Familiar routines become more attractive. The old process feels easier to trust, even when everyone agrees the new one is objectively better. Familiar feels safe, even when it isn’t. That isn’t stubbornness, and it isn’t laziness or resistance. It is biology.

What makes this even more complex is that there is no universal timeline for adoption. One person may embrace a new technology immediately while struggling for months after moving to a new city. Another may welcome a major life transition but find a software rollout unexpectedly overwhelming. Someone else may appear calm while experiencing significant internal stress that no one around them ever sees.

My co-founder, Craig Martin, has a phrase he often repeats: “Everyone is an edge case.” His perspective comes from decades of designing complex systems. Mine comes from years spent studying human behavior, organizational change, and cognitive behavioral neuroscience. We arrived at the same conclusion through very different disciplines: people don’t adapt in predictable, standardized ways because no two human beings experience change, or life, in exactly the same way. Every person brings a different history, different experiences, different sources of stress, different levels of resilience, and different perceptions of risk. What feels exciting to one person may feel threatening to another. What feels manageable today may feel overwhelming after months of continuous change.

Yet most transformation strategies are designed as though people adapt in roughly the same way and on roughly the same timeline. They don’t. That doesn’t mean organizations should slow innovation, but it does mean we need to stop expecting human adaptation to follow the same exponential curve as technological advancement.

The challenge isn’t convincing people to change. It’s creating the conditions where change can genuinely take root. Those are profoundly different problems. One asks how we deploy technology. The other asks how we support human adaptation. For decades, we’ve been remarkably good at answering the first question. It’s the second one we’ve largely left unanswered.

The Infrastructure We Never Built

If we step back from AI for a moment, a broader pattern begins to emerge. Over the past several decades, we’ve built extraordinary infrastructure for technology. Infrastructure for compute, data, cybersecurity, workflows, systems of record, and governance. Every time technology introduced a new challenge, we responded by building the systems needed to support it. That is what infrastructure does. It makes progress possible.

Yet every one of those investments ultimately arrives in exactly the same place: a human being deciding whether to trust the change. That is where our thinking begins to break down. For all the sophistication we’ve poured into technology, we’ve continued to assume that human adoption will naturally follow. We communicate the vision. We schedule the training. We deploy the tools. We measure adoption. And we hope people come along.

Sometimes they do, but more often they don’t. And it’s not because the technology failed or because the people failed. It’s because we’ve treated human adoption as an expected outcome rather than something that deserves the same intentional design as every other part of transformation. When organizations struggle to realize value from AI, the response is often to improve the technology, expand the rollout, or increase training. Those efforts are important, but they all begin from the same assumption: that the missing piece is better execution.

What if the missing piece isn’t execution? What if it’s infrastructure? Not infrastructure for technology. Infrastructure for people.

For decades, we’ve built systems that help organizations deploy technology. What we’ve largely overlooked are the systems that help human beings adapt to it. That kind of infrastructure doesn’t exist only during implementation or inside a training session. It exists in the moments when change becomes personal. At three o’clock in the morning when someone lies awake wondering whether AI will eventually replace the career they’ve spent twenty years building. Five minutes before a high-stakes meeting when uncertainty quietly becomes self-doubt. The first week in a new role. The difficult conversation after a restructuring. The moment someone wants to ask for help but worries it might change how they are perceived. The conversation a manager doesn’t know how to begin. Those are the moments where trust is either strengthened or quietly begins to erode.

They are also the moments most organizations never see. Yet those moments determine whether people lean into change or quietly retreat from it. They determine whether experience is shared or protected, whether confidence grows or fear takes hold, and ultimately whether technology becomes integrated into the way people work or simply another tool they learn to work around.

That is the infrastructure we’ve never built. Infrastructure that helps leaders understand how change is actually being experienced, not simply how it was intended. Infrastructure that makes invisible human signals visible before uncertainty becomes resistance, before disengagement becomes attrition, and before trust quietly begins to erode.

The first four articles in this series examined the hidden constraints shaping AI: energy, economics, dependency, and cognitive overload. This article has explored another. The human one. Because every AI initiative, every digital transformation, and every organizational change eventually arrives in exactly the same place. A human being deciding whether to trust what comes next.

Technology may shape the future, but people determine whether it arrives. And those decisions happen through thousands of ordinary moments, one person at a time.

Conclusion

Perhaps the greatest lesson of AI isn’t about artificial intelligence at all. It’s about human intelligence. It’s about understanding how people learn, adapt, trust, and ultimately decide whether a new way of working becomes part of their lives or quietly fades into another failed initiative.

As builders, leaders, investors, and buyers, we have every reason to continue pushing technology forward. We should. But if this series has taught us anything so far, it is that every technological breakthrough eventually reaches the same destination: a human being deciding whether to trust it, use it, and integrate it into the way they work.

Increasingly, I believe the limiting factor in transformation is our ability to help people move through change. The organizations that thrive won’t necessarily be those with access to the most powerful AI. They will be the ones that recognize technology and human adoption are not separate challenges. They are two halves of the same transformation.

We built the technology.

Now we have to build the trust.

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Read more from The AI Reckoning: A Future of Trust Series

Part 1: The Hidden Cost of Intelligence, The Trust Story Hiding in Plain Sight

Part 2: The Reckoning Behind the Revenue: When the Numbers Don’t Add Up

Part 3: Rented Intelligence: Building on Borrowed Ground

Part 4: The Fragmentation Tax: Death by a Thousand Tools

This is Part 4 of The AI Reckoning: A Future of Trust Series

There is a particular kind of tired that has nothing to do with how hard you worked.

You sat down at nine with one thing you needed to think through. By noon you had answered messages in three places, approved something in a tool you open twice a month and have to relearn every time, moved a task from one board to another because two teams use different systems, and asked an AI assistant a question, then asked a different AI assistant the same question because the first one could not see the document the second one wrote. None of it was hard. All of it required you to stop, reorient, remember where you were, and start again.

At the end of the day you are depleted, and you cannot point to the thing that depleted you. The work was not the problem. The work was the small part of the day that survived everything around it. What exhausted you was the space between the tools. The seams.

We have built a working life made almost entirely of seams.

This is the cost no one is accounting for, and it is the one I want to talk about, because it leads somewhere that matters for anyone building, buying, or betting on the next decade of this industry.

Here is where it leads, stated plainly, so the rest of this is the proof and not the suspense. Human behavior rejects complexity and overload. The cognitive cost of switching between disconnected tools is real, it is larger than almost anyone accounts for, and it is poorly understood precisely because it hides in the gaps where no one is measuring. That single fact makes consolidation inevitable, not as a trend that comes and goes with budgets, but as something the math arrives at on its own. And once consolidation is inevitable, the only question left that matters is what you consolidate onto: something built to serve the people depending on it, or something that quietly comes to own them.

This is also, if you have been following the series, a familiar shape. The first piece described how we deploy technology faster than we understand its second-order consequences, and pay later for the costs that were knowable in advance. Fragmentation is that pattern again. The cognitive load of tool sprawl was knowable. We moved too fast to count it. Now it is surfacing as burnout, as stalled adoption, as budgets that produce exhaustion instead of output. The bill was always coming. We just declined to read it in advance.

The last piece in this series looked at a danger beneath the people building right now: the ground they do not own, the platforms that can shift under them without warning. This piece is about a danger above them. The limit of the very human they are building for. And the rush to ship one more point solution, the same rush that sends builders onto borrowed ground, runs straight into it.

This is part of the same reckoning the series has been tracing, and it has a specific cause. AI has made it extraordinarily easy to see a single pain point and ship something that solves it. What took an engineering organization a year now takes a small team a weekend. That is a genuine wonder, and it is also the engine of the problem. When solving one narrow thing becomes this cheap and this fast, the world fills with narrow solutions, each one excellent, each one more surface for a human to carry. The same capability that makes this the most exciting moment in the history of building is the thing quietly manufacturing the fragmentation we are all starting to feel. The reckoning is not that the tools are bad. It is that nobody priced what it costs to live among all of them at once.

If you are reading this nodding, there is a good chance your next thought is about yourself. That you should be more disciplined. Close the tabs, batch the messages, get organized, try harder. Almost everyone reaches for that explanation, and reaching for it is precisely why the real cause stays hidden. So it is worth asking the question directly.

Why Is This Not a Discipline Problem?

It would be easy to file all of this under personal discipline. Close your tabs. Check messages less. Get organized. And there is some truth in that. But the deeper issue is not a habit. It is the architecture of human attention, and it does not bend to willpower the way we wish it would.

When you switch from one task to another, part of your attention stays behind. The psychologist Sophie Leroy named this attention residue: after a switch, your mind keeps processing the thing you just left, and the performance on what you turned to is measurably degraded until the residue clears. The effect is worse when the first task was unfinished, which describes almost every interruption in a normal workday.¹

This is not a small effect at the edges. Researchers tracking knowledge workers have found they switch tasks every few minutes, and that returning to full focus after a real interruption can take as long as twenty-three minutes. Most of the time, the next interruption arrives long before those minutes are up. The result is not a workday with a few costly interruptions in it. It is a workday that never fully assembles a single sustained thought.²

Put a number on the switching itself and it becomes hard to look away from. One analysis of a large workplace dataset found that people toggle between applications and websites on the order of twelve hundred times a day, and lose something like four hours a week purely to reorientation. Four hours lost to finding their focus again.³ A productivity tax quietly waiting for an answer.

Here is the part that matters for where this is going. That cost is not a function of bad tools. Each tool may be excellent. It is a function of the gaps between them, and those gaps multiply with every tool you add. The friction is not in the apps. It is in the human being asked to hold them all together. And the human has a hard limit that no upgrade cycle will raise.

This is why I do not think tool consolidation is a trend that will come and go with budgets. It is a response to something fixed. The number of disconnected surfaces a person can carry has a ceiling, and we have been pretending it does not exist. When a constraint is rooted in human biology rather than market preference, the pressure against it does not ease. It accumulates until something gives.

Consolidation, in other words, is not a preference anyone gets to debate. It is what a fixed human limit produces once enough tools pile up against it.

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The Tax the Enterprise Already Pays

Step back from the individual and the same pattern reappears at the scale of the organization, with zeros attached.

Depending on size and how you count, a company runs somewhere between roughly one hundred and several hundred software applications. Studies consistently find that about half of the licenses paid for go unused, and that the average enterprise wastes a sum measured in the millions every year on software nobody opens.⁴

But the license waste, large as it is, is the part that shows up on a spreadsheet. The more expensive cost is the one we just walked through, now multiplied across every employee. The enterprise pays once for the tool, and then pays again, invisibly, for the human friction of running it alongside forty others. The second bill never appears in procurement. It appears in how long things take, in the quality of thinking people have left after navigating the stack, in the errors that creep in at the seams, and in the quiet erosion of the sense that the work is coherent.

Here is the part that should stop a leadership team cold. Most enterprises adopt these tools chasing two specific outcomes: higher productivity and lower burnout. Those are the words on the dashboard, the goals in the deck, the reasons the budget was approved. And past a certain point, each additional point solution delivers the opposite of both. The switching cost eats the productivity gain the tool was bought to create. The fragmentation feeds the burnout the tool was bought to relieve. The organization is, with the best intentions and a straight face, spending money to manufacture the exact problems it is also spending money to solve.

There is a quieter consequence underneath that one, and it is the one that decides whether any of this technology actually gets used. Human adoption follows the path of least resistance. People take up and keep using the things that reduce friction, and they quietly abandon the things that add it, no matter how capable those things are on paper. Every additional disconnected tool raises the resistance, which is why so much purchased software sits unused and so many rollouts stall after the pilot. A platform that absorbs a domain’s fragmentation into one coherent surface does the opposite. It lowers the resistance, and adoption follows almost on its own. This is the link the spreadsheets miss: the same coherence that relieves the cognitive load is also what makes the technology stick. Fragmentation does not just exhaust people. It is the reason the adoption curve flattens.

This is structural rather than incidental. Most organizations know they have overlapping tools. Their own employees say so. And most have still taken no real steps to consolidate, because every redundant application has an internal champion who chose it and a team that has built its habits around it. The pressure to simplify keeps losing to the easier path, which is to add one more thing.⁵

That standoff cannot hold indefinitely, because the human cost compounds while the political cost of consolidating stays roughly fixed. At some point the first overtakes the second. For investors, this is worth sitting with, because it means the pressure toward consolidation is not sentiment. It is arithmetic with a delay on it.

For the enterprise leader, this reframes the decision on the table. The question is no longer which point solution to buy next. Adding one more best-in-class tool to relieve a pain point is, more often than the business case admits, adding to the very load that is suppressing productivity and driving burnout in the first place. The next defensible move is to look for the platform that owns the whole domain. The one that collapses a dozen scattered tools into a single coherent surface, so the switching cost inside that domain simply disappears.

This is not a call to herd everything onto one provider. A person may still move across several platforms in a day, and that is fine. What matters is that each one is coherent within its domain, and that the few which need to talk to each other do so through connections intelligent enough to carry context across, so knowledge entered once does not have to be entered again. Fewer surfaces, each one whole, connected in ways that remember. That is the elegant version of consolidation, and it is a different thing from both the fragmentation we have now and the single dependency the last piece warned against.

The Ceiling Builders Do Not See

Now turn to the people making the tools, because this is where the story gets quietly difficult, and where I have the most sympathy.

A great many of the most impressive products being built right now solve one problem beautifully. A single workflow. A single friction. A single moment in someone’s day handled better than anyone handled it before. That is a real achievement, and it is often how important companies begin.

And it is, now, the easy thing to do. This is the part worth being clear about. AI has made catching a single pain point and solving it faster and cheaper than at any moment in history. Building a platform, one interface that holds many problems together coherently, is the opposite of easy. It demands wrestling with complexity that a point solution gets to ignore: how the pieces relate, what happens at the seams, how the whole thing stays trustworthy as it grows. Most builders cannot take that on, and many who could, will not. The reason is simple. We are wired to take the easier path, and that natural inclination is reinforced by a market actively cheering for the quick win. A customer in pain wants relief this quarter, not architecture that pays off in three years. Every incentive points toward the narrow solution. Almost none point toward the hard, slow, integrative work that would actually reduce the load. So the narrow solutions multiply, and the bigger picture goes unbuilt, not because no one can see it, but because seeing it and building it are very different commitments.

A point solution carries a ceiling it did not choose and usually cannot see. However well it solves its problem, it is still one more surface for the human to hold. One more login, one more place to check, one more thing that does not quite talk to the others. Which means a product can be successful on its own terms and still be structurally temporary. As the market becomes more aware of the fragmentation cost, standalone tools increasingly face the same outcome: integration, absorption, or abandonment. Not because they failed, but because the human on the other end ran out of room.

This is the trap closing from two sides at once. From below, the ground the builder stands on: a point solution runs on a platform it does not own, which can reprice, compete, or change the terms whenever its own survival calls for it. From above, the human it is built for, who has no capacity left to carry one more disconnected surface. Exposed beneath and exposed overhead. Same builder, same speed, and most of them, moving as fast as this market rewards, see neither limit until one of them gives.

The builders who internalize this early make a different choice. They stop asking only whether their product solves its problem, and start asking whether it reduces the total weight the person is carrying or adds to it. Those are very different questions, and they lead to very different companies.

Consolidate Onto What

So consolidation is coming. The human mind requires it, the enterprise math rewards it, and the builders who ignore it are constructing ceilings over their own work. On that much I am confident.

The mistake is assuming that consolidation means putting everything in one place. It doesn’t. The answer to fragmentation is not one giant platform that tries to become your entire stack. That simply replaces one problem with another. The cognitive load disappears, but the dependency remains. You have reduced the number of seams only to discover that everything now depends on a single provider whose incentives, pricing, roadmap, and priorities are not yours. We have already explored that risk in this series.

A better answer is domain-level coherence. A process platform should own a process. A data platform should own data. A communication platform should own communication. Each should reduce the fragmentation within its domain so completely that the person using it rarely needs to think about the seams inside it. The goal is not fewer capabilities. The goal is fewer surfaces. The distinction matters. People do not experience work as architecture diagrams. They experience it as attention. Every unnecessary transition, every duplicated workflow, every place where context has to be manually reconstructed is a tax paid in attention.

The platforms that win over the next decade will not be the ones with the most features. They will be the ones that ask the least of the human being using them. That is what fragmentation is creating demand for. Not more tools. Not bigger tools. Coherent ones.

Where the Value Actually Lands

Follow that logic all the way out and an investment thesis emerges; one investors are already beginning to ask about.

If point solutions are structurally temporary, exposed from below by the platforms they rent and from above by the human ceiling, then the durable value in this market does not accrue to them. It accrues to whatever becomes the layer that best serves human behavior. The fragmentation that exhausts the worker and taxes the enterprise is, read correctly, a demand signal. It is the market generating need for platform solutions faster than anyone is building them. Every new point solution shipped makes that need slightly more acute, which is a strange thing to realize: the flood of narrow tools is not the competition for these unified solutions. It is the thing creating demand for them.

What the smartest investors are starting to ask is not which point solution wins its category. Many will win their category and still be absorbed or abandoned, because winning a category is not the same as being something a human can sustainably carry within the context of a full workday.

The question is, who builds the infrastructure layers that reduce the friction of switching across disconnected tabs, and whether they build it as a neutral peer to the large models.

Those are not the same bet, and only one of them is durable, because only one of them does not eventually turn on the people depending on it.

This is the part that does not fit on the current scoreboard. We are measuring point solutions by adoption and revenue, the metrics that look good right up until the human on the other end runs out of room. What we are not yet measuring is which companies reduce the total load and which ones add to it. That distinction, invisible on today’s dashboards, is where the next decade of value quietly sorts itself out.

What We Were Trying to Protect

It is worth remembering what the goal was, underneath all of this. The point of reducing fragmentation was never efficiency for its own sake. Efficiency is what we say to the CFO. What we actually want is to give the person back the sustained attention the seams have been quietly stealing, hour by hour, for years.

The exhaustion I described at the start is not a personal failing and it is not the price of ambition. It is the predictable result of asking human beings to be the integration layer between tools that were never designed to hold together.

We made the person carry the coherence the systems lacked. Of course they are tired. Consolidation will relieve that weight. The only question that matters is whether we consolidate toward something that serves the human or something that comes to own them.

And that is where we’ll go next. Because every AI initiative ultimately arrives in the same place: A person deciding whether to trust what comes next.

What fragments the day is not the work. It is everything we built around it.

Consolidation is not the risk.

Consolidating onto the wrong thing is.

Read more from The AI Reckoning: A Future of Trust Series

Part 1: The Hidden Cost of Intelligence, The Trust Story Hiding in Plain Sight

Part 2: The Reckoning Behind the Revenue: When the Numbers Don’t Add Up

Part 3: Rented Intelligence: Building on Borrowed Ground

Part 4: The Fragmentation Tax: Death by a Thousand Tools

Endnotes

1. Leroy, S. (2009). Why is it so hard to do my work? The challenge of attention residue when switching between work tasks. Organizational Behavior and Human Decision Processes. https://www.sciencedirect.com/science/article/abs/pii/S0749597809000399

2. Mark, G., Gonzalez, V., & Harris, J. (2005). No Task Left Behind? Examining the Nature of Fragmented Work. Proceedings of CHI. University of California, Irvine.

3. Murty, R. N., Dadlani, S., & Das, R. B. (2022). How Much Time and Energy Do We Waste Toggling Between Applications? Harvard Business Review. Study of 137 users across 20 teams at three Fortune 500 companies over five weeks. https://hbr.org/2022/08/how-much-time-and-energy-do-we-waste-toggling-between-applications

4. Zylo, SaaS Management Index, 2026; BetterCloud, State of SaaSOps, 2025. Application counts vary by company size and counting methodology, with mid-market portfolios commonly cited around 100 to 300 applications and large enterprises higher.

5. Workplace technology overload analyses, 2026, citing internal-champion dynamics and the proportion of organizations that have not undertaken consolidation despite acknowledged tool overlap. Figures are industry estimates.

This is Part 3 of The AI Reckoning: A Future of Trust Series

Imagine Steve Jobs at eighteen years old today.

He walks into a world where the most powerful building tools in the history of technology are available to anyone with a laptop and a credit card. AI models that took billions of dollars and decades of research to create, accessible through an API. Compute that would have required a room full of hardware, running in the cloud for pennies. Frameworks, libraries, deployment infrastructure, all of it there, waiting, free or nearly free.

It would feel like Charlie finding the golden ticket. A world of pure imagination where creativity makes seemingly impossible things possible and where the only limits are his willingness to explore.

Steve Jobs at eighteen in today’s world would have no shortage of imagination, tools, or willingness. What he would need, more than anything else, is an understanding of whose factory he’s building in. Because Willy Wonka’s Chocolate Factory was a place of magic and wonder, but it was also surprisingly dangerous. The inventing room was full of things that hadn’t been fully tested. The chocolate river looked inviting right up until it wasn’t. It’s tempting to spend a weekend vibe coding and call it a business. But where you put down your flag matters as much as what you build. The space is likely leased, and some of the danger zones aren’t visible from inside the excitement.

Jobs may have still built on existing foundations. Many founders do, and it’s often the rational choice. As a founder, I understand the temptation. The tools are extraordinary. The speed is intoxicating. We can build things today that would have required entire engineering organizations a decade ago. But every strategic advantage comes with a tradeoff, and this one deserves more attention than it’s getting. Because the most important decision at this stage isn’t which foundation model to build on. It’s whether you understand what you agree to when you do.

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My co-founder Craig has lived through every major technology cycle since the internet. When we were making the decisions that shaped how we build, he was the one asking the questions that weren’t fashionable yet. Not whether the technology works, but who controls it once it does. Not whether the capability is real, but who is left standing when the market consolidates. He believed in this cycle. He understood that we finally had the compute to make the promise real. What made him cautious wasn’t the technology. It was the dependency.

This forethought ultimately shaped our own architecture decisions. We wanted language models where they added value, but we were unwilling to make the core reasoning layer dependent on technology we did not control. That caution led us somewhere most builders weren’t going, which is what this piece is about.

The danger isn’t that the platforms fail.

The danger is that they succeed.

Because when platforms become powerful enough to define a market, they also gain the ability to redefine the terms for everyone building inside it.

The Kill Zone

In venture capital, the kill zone has a specific meaning. It describes the territory surrounding dominant platform companies where rational investors have quietly stopped funding startups, not because the ideas aren’t good, but because the risk of the platform deciding to build the same thing is too high to justify the bet.

The concept emerged from studying what happened to startups building in the orbit of Facebook, Google, and Amazon. The platforms didn’t need to out-innovate the startups. They just needed to decide the category was worth their attention. When that happened, the startup’s funding dried up, its users migrated to the native version, and its competitive window closed faster than any business plan had modeled.

In the current AI landscape, the kill zone has a sharper edge. The startups building on top of OpenAI, Anthropic, and Google aren’t just building near these platforms. They are building on top of them. Their core capability runs on infrastructure they don’t own. Their competitive differentiation sits on a foundation controlled by companies whose long-term incentives don’t permanently align with theirs.

Imagine building your entire business on Apple’s App Store, only to wake up and find Apple shipping your core feature in iOS.

The platforms are already moving. OpenAI consolidated ChatGPT, Codex, and its browser into a unified desktop application in early 2026, explicitly to compete more directly with enterprise AI providers who had built on its foundation.^[i]

Google’s entire I/O 2026 narrative centered on agentic AI embedded directly into its existing product ecosystem, reducing the need for third-party tools that had been built to fill exactly that gap.^[ii]

Anthropic acquired Stainless, a startup whose SDK generation tools were being used by OpenAI, Google, and Cloudflare, reshaping the developer tooling layer beneath an entire ecosystem of builders who had assumed that infrastructure was neutral.^[iii]

These are not incidental moves. They are a pattern. And the pattern has a direction.

Every time a platform extends its reach into the stack, the builders who depended on that layer discover that the relationship they thought they had was more conditional than they understood. The ground they built on was borrowed. And borrowed ground can be recalled.

The Thin Wrapper Problem

Not all AI startups face equal exposure. The vulnerability scales with how much of the company’s core value proposition lives inside the platform versus on top of it.

At one end of the spectrum are what the VC community calls thin-wrapper startups: companies whose primary contribution is a better interface, a smarter prompt, or a more polished user experience layered over a foundation model. The underlying intelligence is rented. The distribution is rented. In some cases, the data processing and memory capabilities are rented too. What the startup owns is the design and the go-to-market motion. When the platform decides to ship those same design choices natively, the thin-wrapper startup discovers that what it thought was a product was actually a feature.

This is not hypothetical. OpenAI’s move to consolidate its products into a unified desktop application was a direct response to the ecosystem of third-party tools that had built on top of its API to solve exactly the fragmentation problem users were experiencing. The startups that solved it first validated the market. The platform then captured it.

The more defensible position is to build proprietary workflows, domain-specific data advantages, or genuine integrations that create switching costs the platform can’t easily replicate. But even that position is becoming harder to hold as the platforms invest in vertical-specific offerings and enterprise partnerships that go deeper into specific industries.^[iv]

The VC community is noticing. A growing number of investors now require founders to articulate what they call their platform independence thesis: an explicit argument for why their business remains viable if the underlying model provider decides to compete directly. A year ago, that question was rarely asked. Now it is table stakes in term sheet conversations.

There is another dimension to why technology consolidates that goes beyond competitive pressure. Not because dominant platforms force it, but because fragmentation carries its own cost, one paid daily by the people being asked to work across an expanding stack of disconnected tools. That story belongs to the next piece in this series, where I explore why so many promising point solutions are quietly becoming part of the problem they were built to solve.

The Enterprise Dilemma

The platform risk conversation in AI tends to focus on startups. But enterprises face a version of the same exposure, and in many ways the stakes are higher because dependency becomes embedded across people, processes, and customers.

What happens when critical business capabilities depend on providers whose incentives can change faster than your ability to adapt?

Consider what enterprise AI adoption actually looks like at scale. A company selects a foundation model provider. It builds internal workflows, customer-facing products, and operational processes on top of that provider’s API. It trains its people to work with those tools. It integrates them into its existing technology stack. It makes commitments to its own customers based on the capabilities those tools provide.

Then the provider reprices, deprecates the model version the enterprise built on, or launches a competing product in the enterprise’s own industry. None of those scenarios require a catastrophic failure. They simply require the platform to make a rational decision in its own interest. And each of these scenarios has already occurred in the current market. OpenAI deprecated its Assistants API in August 2025, with shutdown scheduled for 2026, forcing enterprises that had built on it to migrate to a new architecture on a timeline they didn’t choose.^[v]

The enterprise that built on that foundation didn’t lose its business. But it absorbed costs that never appeared in the original business case: migration, retraining, disruption to the people doing the work, and credibility spent with its own stakeholders on someone else’s timeline. Leaders who had promised capability delivered disruption instead. Employees who had been trained on one system were asked to start over on another. The business kept moving, but it moved carrying weight it hadn’t planned for.

This is what borrowed ground costs when it shifts. Not always catastrophe. Often something quieter and harder to quantify: the accumulated weight of decisions made on someone else’s terms.

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What the Relationship Actually Costs

There is an asymmetry in platform relationships that rarely gets named in the conversations where it matters most.

The platform knows the enterprise’s usage patterns, its most valuable workflows, its integration dependencies, and the switching costs it has accumulated over time. The enterprise knows the platform’s published pricing, its publicly stated roadmap, and whatever its sales team chose to share. Every conversation about what the relationship costs and what happens when it changes takes place in that gap.

That gap is not neutral. It tilts every negotiation, every pricing conversation, and every decision about what comes next toward the party with more information. The enterprise that has built deeply on a platform has already made its most important concession before it sits down to talk.

What makes this more complicated is that the platforms are not operating from a position of stability. As the previous pieces in this series explored, the entire frontier AI industry is still finding its path to profitability. The pressure to capture more of the value their ecosystems create is not a future risk. It is the present reality of every major provider. The enterprises and founders who validated those markets may find themselves on the wrong side of that math at exactly the moment they are most dependent.

This is not a moral failure on anyone’s part. It is the natural consequence of building interdependence into a market that is still sorting out its own economics. But it is a risk that deserves to be understood before the dependency is built, not after.

What Defensible Actually Means

What, then, does defensible mean in a market where the most powerful foundations are controlled by companies whose interests and yours will not always align?

Defensibility in this environment is not about having a better prompt. It is not about a more elegant interface. It is about whether the value you create can survive a change in the foundation beneath it.

For the startup, that means proprietary data, genuine workflow integration, and switching costs that belong to the customer relationship rather than to the platform.

For the enterprise, it means architecture choices that don’t create single points of dependency, and governance frameworks that account for platform risk alongside security risk.

For anyone building infrastructure rather than applications, it means understanding that the most durable position in this market is not the one closest to the frontier model. It is the one closest to the human being doing the work.

And for anyone building something intended to last, it means sitting with a question that the pace of this market makes easy to skip. Not am I building on something. Every builder builds on something. Jobs built on chips he didn’t fabricate. The transformer itself was built on decades of work that came before it. The question is sharper than that: does the thing I’m building on have interests of its own?

A protocol doesn’t decide to compete with you. It doesn’t reprice you or get withdrawn on a timeline you didn’t choose. But a platform owned by a company navigating its own survival can do all three. The distinction is not whether you depend on something. It is whether what you depend on can change its mind about you.

Steve Jobs at eighteen would have had more tools available to him than any builder in history. The factory is real, and it is extraordinary. The mistake would be assuming that because the factory feels permanent, it is. What Craig understood, and what ultimately shaped how we built, is that the most important question is not whether the tools work. They clearly do. It is whether the ground beneath them belongs to you.

The foundations beneath AI’s extraordinary buildout are less stable than most people realize. Not just physically and economically, as the first two pieces in this series explored, but structurally. The platforms that feel permanent are still finding their path to profitability. The incentives are still shifting. The boundaries are still being redrawn.

None of that makes the factory less remarkable.

It simply means builders should understand whose factory they are building in.

Because the most important question isn’t whether the factory is magical.

It’s who owns the keys.

In the next piece, we look at the other side of this consolidation story: what happens when every problem gets a new AI tool, and why the fragmentation those tools create may become its own hidden tax.

Read more from The AI Reckoning: A Future of Trust Series

Part 1: The Hidden Cost of Intelligence, The Trust Story Hiding in Plain Sight

Part 2: The Reckoning Behind the Revenue: When the Numbers Don’t Add Up

Part 3: Rented Intelligence: Building on Borrowed Ground

Part 4: The Fragmentation Tax: Death by a Thousand Tools

Endnotes

ⁱ https://www.mexc.com/news/969584

ⁱⁱ https://jeffreystop.com/news/2026-05-22-0900-ai-tech-news/

ⁱⁱⁱ https://thenewstack.io/anthropic-stainless-sdk-acquisition/

^iv https://www.mindstudio.ai/blog/google-vs-openai-vs-anthropic-momentum-2026-narrative

^v https://www.buildmvpfast.com/blog/which-ai-platform-startups-build-on-2026

This is Part 2 of The AI Reckoning: A Future of Trust Series

AI is in the Red.

That’s not surprising. Building the future has never been inexpensive. What surprises me is not that people aren’t asking about a path to profitability. They are. It’s that the assumption seems to be that if enough money is invested, enough people adopt, and enough capabilities emerge, the economics will eventually sort themselves out.

Maybe they will.

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But assumptions are not business models. And when I started putting the numbers in the same room, I found a story that looks very different from the one most people are telling. The story isn’t that AI is expensive. The story is that the risk of all that spending is quietly moving from the people who created it to the people least equipped to see it coming. The losses are not the headline. Who ends up holding them is.

There is a number that should be sitting at the center of every boardroom conversation about AI right now. It isn’t the productivity gain. It isn’t the efficiency multiple. It isn’t the competitive moat being built or the talent being freed up for higher-value work. It’s this: OpenAI, the company that started this revolution, projects $14 billion in losses in 2026 alone. Cumulative losses between 2023 and 2028 are expected to reach $44 billion.^[i] It also has 900 million weekly users and $20 billion in annualized revenue. A path to profitability exists on paper. It just doesn’t arrive until 2030, and it depends on several things going right in a market where nothing stays fixed long enough to model with confidence.

I’m not sharing this to suggest the technology doesn’t work or that the companies building it aren’t extraordinary. They are. The foundational work being done at OpenAI, Anthropic, Google, and others is remarkable, and what has been built in the last decade is what makes the next chapter possible. We stand on those shoulders. What we’re building wouldn’t exist without them.

And precisely because we are building in this space, I think the economics deserve an examination that most conversations are still avoiding.

Who Is Actually Carrying the Risk?

Here is what happens when you put the numbers together.

The AI providers are losing money despite massive and growing revenue. The enterprises buying their services are overpaying for something with no clean ROI framework. And here is the detail that almost never makes it into the same conversation: the prices enterprises are paying today don’t reflect the true cost of inference. The providers are absorbing a significant portion of that cost inside their losses. Enterprises are being onboarded at subsidized rates that the market hasn’t fully priced yet. When these companies eventually reach the profitability they’re projecting, through architectural improvements, pricing adjustments, or both, enterprise bills will almost certainly increase. The current pricing is not stable long-term pricing.

Everyone is spending more. The true cost of what they’re buying is higher than what they’re being charged. And the returns aren’t materializing at the rate the business cases promised.

This is not a crisis. It is a redistribution of risk. And anyone who has watched technology markets long enough will recognize it. Every market has uncertainty. The question is not whether uncertainty exists, the question is who is carrying it.

Right now, AI providers are carrying it through losses. Investors are carrying it through increasingly ambitious valuations. Enterprises are carrying it through spending commitments made before clear ROI frameworks exist. Employees are carrying it through workforce reductions justified by outcomes that have not always materialized.

The risk has not disappeared. It has simply been distributed across the system.

And the redistribution is not finished. Both OpenAI and Anthropic are moving toward public markets, with OpenAI already valued at $852 billion in private markets and Anthropic in early IPO discussions potentially targeting late 2026. In practical terms, this means the next wave of AI infrastructure may be financed by public market investors betting on profitability that the companies themselves don’t expect for three to four years. That is not unprecedented in technology. But it does mean the risk currently carried by providers, institutional investors, and enterprises is about to find a new home. Public market participants will be the next layer in this redistribution, and they deserve to understand what they are being asked to fund.

We Have Been Here Before

In the late 1990s, AOL and Yahoo defined the internet. They had the users, the revenue, the brand recognition, and the infrastructure. What neither of them had was a fundamentally better way to solve the core problem.

Google didn’t win because it had more resources. It won because it approached the problem from a different starting point entirely. PageRank treated hyperlinks as votes of authority rather than simply indexing keywords, and the results were noticeably better at a level ordinary users could feel immediately. That architectural difference, the decision to measure relevance differently at the foundation, made everything that came before look like a prototype. Yahoo’s portal strategy, AOL’s distribution dominance, none of it could compensate for building on a foundation that was less accurate at the thing that mattered most.

BlackBerry had enterprise email locked up. The security, the keyboard, the relationships with IT departments. Apple was a computer company. Until it wasn’t.

The pattern is consistent: early dominance does not protect against displacement when a competitor approaches the core problem from a fundamentally different foundation. What the incumbents have, distribution, enterprise relationships, capital, and brand, buys time. It does not buy permanence.

We are early. The infrastructure of the next chapter of AI is being built right now. Not in the gleaming campuses of the incumbents, but in the places where builders who have seen the structural limits clearly enough to approach the problem from a completely different starting point are doing the work. In garages. In university labs. And in our case, in a basement data center we call Firefly.

Most of what will take this industry forward has not been built yet. That is not a warning. That is the opportunity.

The question for anyone paying attention, whether investor, enterprise leader, or builder, is not whether AI has a future. It is which architecture that future runs on. And the companies currently dominating may not be the ones that define it.

What the Numbers Are Actually Saying

The provider losses are structural, not temporary growing pains. The reason is the same one we explored in the last piece: the transformer model was designed for research, not for the economics of running billions of conversations daily at civilization scale. The architecture creates costs that revenue, however impressive, cannot currently outrun.

Anthropic’s trajectory is meaningfully different from OpenAI’s and worth understanding. The company passed OpenAI in annualized revenue in April 2026, reaching $30 billion against OpenAI’s $24 to 25 billion, having grown 30 times in 15 months.^[ii,iii]

Anthropic projects positive cash flow by 2027, three years ahead of OpenAI. The divergence reflects a fundamental strategic difference: Anthropic’s enterprise-first focus generates more revenue per dollar of training spend. That is a meaningful signal about where the economics of this industry are heading.

But even Anthropic is still losing money. The entire frontier AI industry is, by design, betting that future revenue will justify present losses.

The path to profitability they’re projecting relies on several things happening simultaneously: revenue scaling faster than compute costs, inference getting dramatically cheaper through next-generation hardware, and agentic workflows generating higher revenue per user as AI moves from chat to complex multi-step tasks. It’s a plausible path. Amazon and Netflix took similar roads. But it requires multiple things going right in a market moving faster than any single company can fully control.

The 2029 and 2030 profitability timelines also carry weight for the capital markets question already noted above. As those public offerings approach, the assumptions underlying the valuations, about token costs, competitive dynamics, enterprise adoption curves, and architectural stability, were modeled in a landscape that may look unrecognizable by 2027.^[iv]

In practical terms, the next phase of the AI buildout may be financed by public market investors betting on profitability timelines that the companies themselves acknowledge are three to four years out. That is not unprecedented in technology. Amazon and Netflix lost money for years before the model worked. But those bets were placed in markets where a year felt like a year. In the current AI landscape, a month can feel like a year. That is not cynicism about the technology, but rather an accounting of the nature of the bet being placed. And the people being asked to fund it deserve to understand that clearly.

On the enterprise side, the numbers tell their own story. 92% of enterprises plan to increase AI spending over the next three years. Only 1% consider their AI strategies mature.^[v]

Average enterprise AI spend is projected to reach $11.6 million in 2026, a 65% jump from 2025,^[vi] yet only 29% of organizations report significant ROI from generative AI. Forrester already projects enterprises will defer 25% of planned 2026 AI spend into 2027 as the bills arrive ahead of the returns.^[vii]

Before going further, one thing needs to be clear, because it is the most misread part of this entire story. I am not arguing that AI costs will stay high or that they won’t fall. They are falling, fast, and they will keep falling. The point is stranger than that. Unit costs are dropping and total risk is rising at the same time. Both are true. Understanding why is the whole game.

This is where it gets counterintuitive. Let’s look at the dramatic reduction in token prices. The average cost per million tokens across major providers dropped from roughly $10 to $2.50 in a single year. Epoch AI research suggests inference costs are declining at rates approaching 200 times per year when accounting for both pricing and efficiency improvements. Andreessen Horowitz has coined the term LLMflation to describe this deflationary curve, drawing a parallel to Moore’s Law in semiconductors.^[viii]

NVIDIA’s next generation Rubin platform targets a ten-times reduction in inference costs compared to its current architecture.^[ix]

And yet enterprise AI bills are going up.

The reason is behavioral. When tokens get cheaper, teams run larger prompts, longer agent loops, more tool calls, more retries. A 99.7% token price decline has not reduced total AI spend, because cheaper calls encouraged much larger workflows, and most production costs moved outside the model invoice entirely into orchestration, vector databases, data egress, observability, compliance, and engineering time that never appears on a token bill.^[x]

Sam Altman acknowledged this directly in May 2026, saying the spending problem had become “kind of a meme now” among enterprise clients who had burned through their entire annual AI budgets in the first quarter.^[xi]

The frontier of this paradox sharpened further in June 2026. Anthropic launched Claude Fable 5 on June 9, its most capable publicly available model. Priced at $10 per million input tokens and $50 per million output tokens, double the previous top tier, it carries a one million token context window and up to 128,000 output tokens per request. The early feedback was exceptional. Independent evaluators reported breakthrough performance on complex, long-running tasks. The capability was clearly there.

What the early reviews didn’t capture, because there wasn’t time, is what Fable-class capability costs at scale. At that context window and output capacity, a single complex agentic task doesn’t just cost more per token. It consumes tokens at a scale that makes the LLMflation savings functionally irrelevant. When enterprise teams deploy frontier models on agentic workflows, the bill doesn’t increase linearly. The combination of larger contexts, extended reasoning chains, multi-step tool use, and retry loops means costs compound in ways that no quarterly budget modeled in advance. At frontier capability levels, you generate a great many tokens before you find out whether the task was worth it.

Fable 5 was pulled from general availability on June 12, three days after launch, following a U.S. government export directive. The directive was geopolitical in nature, not a response to performance, safety concerns, or anything the enterprise community did or could have influenced. The question of who controls access to frontier AI capability, under what conditions, and with how much notice, is one this series will return to. What matters here is the trust dimension: the infrastructure enterprises are being asked to build on can be withdrawn overnight, for reasons entirely outside their control, with no committed timeline for return. The bill and the availability are both unpredictable. That is a new kind of risk, and it doesn’t have a line item in most enterprise AI budgets yet.

Tokens are getting cheaper. The bill is going up. The model can disappear. That is the economic reality most enterprises are discovering after the fact.

The experimentation era is over. The accountability era has begun. And the CFO is being asked to make a trillion-dollar category decision with a framework designed for software licensing.

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The Layoff Equation That Isn’t Adding Up

There is a human dimension to this economic story that deserves its own examination. It is the same pattern as everything above, with a human face on it. The risk doesn’t vanish when a role does. It moves. And it tends to land on the people who had the least say in the decision.

Through the first quarter of 2026, approximately 20% of confirmed tech layoffs were explicitly linked to AI and automation by the companies themselves.^[xii]

That is a dramatic increase from 2025, when AI was cited as a factor in fewer than 8% of layoff announcements. The list is extensive and growing. Microsoft cut more than 15,000 workers while its CEO confirmed AI was writing 30% of the company’s code. Amazon cut 14,000 corporate roles citing AI-driven efficiencies. Klarna replaced 700 customer service workers with AI. Citigroup is targeting 20,000 reductions as automation handles middle-office functions.^[xiii]

Here is what the business case for these decisions consistently assumed: that the cost savings from displacement would materialize faster than the costs of transition. In many cases, that assumption is not holding.

A Gartner study published in May 2026 found that while 80% of companies that piloted AI or automation technology reported workforce reductions, the businesses cut jobs regardless of whether the technology was actually generating returns.^[xiv]

They cut first and measured later. In some cases, they are now measuring and finding the math does not work the way the business case promised.

The rehiring costs are real. The institutional knowledge lost is real. The time required to rebuild capability after a premature displacement is real. And there is a cost that almost never appears in an ROI calculation: the damage to organizational trust.

When employees watch colleagues displaced for a technology that doesn’t deliver on its promises, the invisible agreement that the system is worth showing up for begins to fracture. The people who survived the cuts are not relieved. They are watching. And what they are watching is whether the organization they work for makes decisions they can trust.

Most workforce reduction models calculate salary savings. Very few calculate trust loss. Yet trust may be the more expensive variable. When people believe decisions are being made thoughtfully, they will tolerate uncertainty. When they believe decisions are being driven by pressure, trends, or incomplete information, something far more difficult to rebuild begins to erode.

The balance sheet rarely captures that cost. That fracture doesn’t recover through a town hall or a memo from HR. It accumulates. It surfaces in engagement scores and retention numbers and the quality of work from people who are present in body but have mentally begun their exit. This is the third level of trust, organizational trust, being stress-tested in real time by decisions made without full information.

This is what it costs to move faster than the evidence supports.

Risk doesn’t disappear when jobs disappear. It changes hands. The organization may reduce payroll expense, but employees absorb uncertainty. Teams absorb disruption. Leaders absorb credibility risk. And when the expected gains fail to materialize, everyone discovers that the original business case transferred far more risk than it eliminated.

What Comes Next: Reckoning or Reassessment?

Will enterprises use this moment to ask different questions, or will they simply defer spending and return to the same approach when the pressure eases?

My take is that a bifurcation is already underway. Enterprises that found specific high-value use cases and built clear measurement frameworks around them are doubling down. Enterprises that deployed broadly without a strategy are quietly pulling back, trying to figure out what they actually bought. The reckoning isn’t one dramatic moment. It’s already happening incrementally, in budget reviews and board conversations where someone finally asks for the ROI evidence and the room goes quiet.

The ones who use this moment to ask different questions, about architecture, about what AI is actually for, about what responsible deployment looks like, will be better positioned for what comes next. The ones who wait for the technology to improve enough to paper over the current gaps will find themselves in the same conversation again in three years.

This is not a break-the-glass moment. It is an inflection point. And inflection points are where the next chapter gets written.

The companies building that next chapter aren’t optimizing the transformer. They aren’t wrapping existing models in a new interface. They are building relational intelligence infrastructure designed from the ground up for the human and organizational realities that current AI consistently fails to address. Infrastructure that understands that the most expensive token is the one that didn’t need to be generated. That the most valuable AI interaction is the one that builds trust rather than eroding it. That the measure of a successful deployment is not efficiency gained but capability grown, in the people and organizations the technology serves.

That category doesn’t have a widely accepted name yet. That is usually a sign you’re early to something important.

We don’t have to ask if AI is creating value. It is.

What remains less certain is how that value will ultimately be distributed, what risks it depends upon, and who absorbs the consequences when expectations outrun reality.

Every technology wave creates winners. The question is whether it also creates understanding. Right now, we are measuring adoption, investment, valuation, and spending. What I am less certain we are measuring is who is left holding the risk when the assumptions underneath those numbers stop holding.

That is the real reckoning. Not whether AI works. Whether we are asking enough questions before we build the future on top of it.

In the next piece, we look at what happens to the founders and enterprises building on top of the current AI platforms when those platforms consolidate. Because the ground beneath the current buildout is less stable than most people realize.

Trust isn’t built on promises. It’s built on understanding the tradeoffs before the bill arrives.

Because when the returns come, everyone celebrates.

When they don’t, someone still pays.

Read more from The AI Reckoning: A Future of Trust Series

Part 1: The Hidden Cost of Intelligence, The Trust Story Hiding in Plain Sight

Part 2: The Reckoning Behind the Revenue: When the Numbers Don’t Add Up

Part 3: Rented Intelligence: Building on Borrowed Ground

Part 4: The Fragmentation Tax: Death by a Thousand Tools

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Endnotes

ⁱ https://europeanbusinessmagazine.com/sam-altmans-openai-is-burning-billions-most-users-pay-nothing-as-anthropic-closes-in/

ⁱⁱ https://www.the-ai-corner.com/p/anthropic-30b-arr-passed-openai-revenue-2026

ⁱⁱⁱ https://www.saastr.com/anthropic-just-passed-openai-in-revenue-while-spending-4x-less-to-train-their-models/

^iv https://tech-insider.org/anthropic-vs-openai-2026/

^v https://onereach.ai/blog/what-shapes-enterprise-ai-agents-in-the-future/

^vi https://writer.com/blog/enterprise-ai-adoption-2026/

^vii https://bizzdesign.com/blog/enterprise-ai-adoption-balancing-innovation-and-roi-2026

^viii https://www.artefact.com/blog/is-ai-really-getting-cheaper-the-token-cost-illusion/

^ix https://www.investing.com/analysis/the-ai-token-pricing-crisis-behind-openai-and-anthropics-revenue-race-200680777

^x https://www.navyaai.com/reports/ai-cost-report-token-prices-vs-ai-bill

^xi https://www.tomshardware.com/tech-industry/artificial-intelligence/openai-ceo-sam-altman-admits-ai-token-costs-are-becoming-a-huge-issue

^xii https://tech-insider.org/tech-layoffs-2026-ai-workforce-impact/

^xiii https://tech.co/news/companies-replace-workers-with-ai

^xiv https://fortune.com/2026/05/11/ai-automation-layoffs-gartner-study-roi/

This is Part 1 of The AI Reckoning: A Future of Trust Series

There is a trust story hiding in plain sight, and there’s nothing artificial about it.

I am writing this as a builder, not a critic. I am inside this industry, developing what we see as the answer. That context matters because what follows isn’t pessimism or alarm. It’s the view from within, and it’s the same view that led us to build something fundamentally different.

If you’re an investor, an enterprise leader, or someone trying to understand where this is actually going, I’d encourage you to read this carefully. The reckoning is already here.

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There is much to be excited about. What we are experiencing is extraordinary. AI is delivering on promises that would have seemed implausible a decade ago. The capability is real. The momentum is real. The investment flowing into it is the largest concentration of capital in the history of technology.

So is the cost. And it’s hiding in plain sight.

Water. Energy. Infrastructure. Quantities that the business case almost never includes. The bill is real, and it is growing. What is also real is that it is being paid by people who were never given the invoice.

That gap is where trust breaks down.

Trust rarely collapses because people disagree with a decision. More often, it erodes when people discover they were never given the full picture in the first place.

The issue is not that AI consumes water, energy, and infrastructure. Every transformative technology consumes resources. The issue is whether those costs are visible, understood, and included in the decisions being made. When costs remain hidden, confidence becomes fragile. Not because anyone intended deception, but because transparency and understanding failed to keep pace with adoption.

The Physical Reality Nobody Budgeted For

Let’s start with water, because water makes this tangible in a way that terawatts and gigabytes don’t.

AI data centers are currently withdrawing 550 million gallons of water every single day. That’s roughly the same rate as the entire global bottled water industry.ⁱ In 2025 alone, AI data centers broke 264 billion gallons of water withdrawal for the year, the equivalent of the annual water usage of 1.8 million Americans. This was happening while 63% of the United States was experiencing drought conditions as of early 2025.ⁱⁱ

A single Google data center in Council Bluffs, Iowa consumed 1 billion gallons of water in 2024. One data center. One year. That’s enough water to supply all of the state’s residential users for five days.^{iii iv} Microsoft increased its water consumption by 34% in a single year.^[v] The training run for GPT-4 consumed 11.5 million gallons of water in July 2022 alone, in one location, in one month.^vi Accelerated AI adoption alone could result in an additional 4.2 to 6.6 billion cubic meters of water withdrawal by 2027. That is four to six times the annual water withdrawal of Denmark. ^vii

Why so much water? Because of heat. Servers generate extraordinary amounts of it, and water is the most efficient way to absorb and dissipate it. In evaporative cooling systems, water absorbs heat from the servers and converts to steam that is vented into the atmosphere, not returned to the source. It doesn’t disappear from the water cycle, but it leaves the local watershed as humidity, unavailable to the communities and ecosystems that depended on it. The compressor-based alternative uses refrigerant instead of water but requires roughly one watt of cooling for every watt of compute. At the scale of modern data centers spanning hundreds of thousands or millions of square feet, it simply isn’t physically viable.

Water cooling isn’t a greedy choice. At this scale, it is the only viable one.

Here is what that means in practice. Data centers are predominantly sited where water is cheapest and most accessible, which puts them in direct competition with agriculture, municipalities, and the people who live nearby. In much of the American West and Midwest, groundwater aquifers are already being drawn down faster than they recharge. A data center doesn’t just use water, it joins a queue that was already oversubscribed. The communities downstream don’t lose access because of malice, they lose it because the data center got there first, and the permitting process never asked who else was depending on it.

Some will point to desalination as an answer. It is worth examining. Desalination is extraordinarily energy-intensive, which increases electricity demand, which requires more cooling, which loops back to water. It also produces concentrated brine discharge with its own environmental consequences. It is not a solution to this problem. It is a relocation of it.

The more compute, the more heat. The more heat, the more water drawn from watersheds that were never part of the business case. It is a compounding loop, and it scales with every query, every conversation, every token generated. That is not a side effect. That is the deal. The costs are environmental. The consequences are human. And the people absorbing both were never included in the conversation that created them.

Now add energy.

AI’s appetite for electricity is growing even faster than its appetite for water. In 2025, electricity demand from data centers grew by 17%, compared to just 3% growth in global electricity demand. AI-focused data centers climbed even faster. By 2030, electricity consumption from data centers is set to double, and power use from AI-specific facilities is poised to triple.^viii The implications extend far beyond utility bills. New transmission infrastructure, grid upgrades, and power generation capacity are required to support that demand. Those costs don’t disappear. They are distributed across communities, ratepayers, and public infrastructure long before they appear in an AI business case.

The communities surrounding these facilities will feel those demands first. Grid strain, infrastructure upgrades, and rising energy costs are not abstract future concerns. They are the practical consequences of scaling a technology whose resource requirements continue to grow. And we are deploying it at extraordinary speed while still learning what those second-order consequences are.

The Decision That Started All of This

So what’s driving the scale of these demands?

The answer lies in a decision made in 2017. That year, a team of researchers at Google published a paper introducing what they called the transformer. It was a genuine breakthrough. A new way of processing language that outperformed everything that came before it. It became the foundation of every major AI language model that followed. GPT. Claude. Gemini. All of them.ix

That architecture might have remained a research breakthrough, powerful but contained, were it not for a parallel development. Nvidia’s GPU advances, particularly the V100 released that same year and the A100 that followed in 2020, provided the computational infrastructure to train and run these models at previously unimaginable scale.

The transformer gave AI its engine. The GPU gave it a highway. What neither provided was a map of where that highway was going, or who was paying for the road.

The moment those two forces converged in a product the world could touch, everything accelerated. ChatGPT launched publicly in November 2022 and became the fastest-growing consumer application in history within weeks. Investors who had been funding research could suddenly see the destination. Capital followed at a scale the industry had never seen. The largest concentration of investment in the history of technology didn’t begin with a boardroom strategy. It began with a public demo that made the future feel immediate.

Here is the issue. The transformer was designed for research. It was not designed for the economics of running billions of conversations a day at civilization scale. The standard transformer design is dense, meaning every single parameter in the model is activated for every single token it processes. Every word. Every punctuation mark. Every invisible reasoning step happening behind the scenes before a visible response appears. The entire network fires every time, for everything. At research scale, this is manageable. At the scale we are now asking it to operate, the compounding costs of that density are the bill nobody budgeted for.

The Token Economy Nobody Explained to the CFO

Talk is cheap. Tokens are not.

Inference, the process of running a model to generate a response, now accounts for more than 60% of total AI computation among major providers.^x Training a model is a significant but bounded cost. You can plan for it, budget it, and measure it against a defined outcome. Running it at scale is different. Inference costs are ongoing, variable, and increasingly difficult to predict as usage patterns shift, reasoning demands grow, and agentic workflows multiply the token consumption of every interaction. Every word generated costs real energy, real water, real money. That unpredictability is where enterprise budgets are breaking down.

Now factor in reasoning mode.

When AI models engage extended chain-of-thought reasoning, the kind that makes them genuinely better at complex tasks, token consumption increases dramatically. The model thinks through a problem step by step before producing a visible response, and those internal thinking steps generate tokens you pay for even though you never see them. You send 50 tokens, receive 100 tokens back, and are charged for 650 tokens total because 500 reasoning tokens ran silently in between.^xi Agentic workflows involving planning, tool use, and multi-step reasoning have caused token consumption per task to increase 10 to 100 times since late 2023.^{xii xiii}

The hidden multiplier that most enterprise buyers never see until the bill arrives.

Here is the counterintuitive detail that the next piece examines more closely: token prices are actually falling. Dramatically. And enterprise bills are still going up. That paradox is the economic story underneath this one.

For enterprises deploying these systems, the reckoning is already underway. Average enterprise AI spend hit roughly $7 million in 2025 and is projected to jump 65% to $11.6 million in 2026.^xiv Yet only 29% of organizations report seeing significant ROI from generative AI. Forrester predicts a market correction, with enterprises deferring 25% of planned 2026 AI spend into 2027.^xv The bills arrived before the returns did. This is where the trust conversation begins to shift from environmental costs to organizational costs.

When expectations are set higher than outcomes can realistically support, people start searching for explanations. Employees question leadership. Leaders question vendors. Investors question assumptions. Everyone begins looking for someone to blame when the deeper issue may be that the underlying economics were never fully understood. Trust is often lost in the space between promise and reality.

This is not a technology problem in isolation but rather a structural one, rooted in the mismatch between what the underlying model was built for and what we are asking it to do. Structural problems require structural thinking, not more spending on the same foundation. A different foundation entirely.

The Proposed Solutions, and What They’re Missing

The industry is not sitting still, and some of what is being tried is genuinely promising.

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On water, the most significant development is the shift to closed-loop cooling. Microsoft’s Fairwater facility in Wisconsin, one of its most advanced AI campuses, has filled its cooling system with water once during construction and now recycles it continuously. Microsoft CEO Satya Nadella claimed at Microsoft Build 2026 that the facility’s annual water consumption is comparable to that of a single local restaurant. Google has pledged to replenish 120% of the water it consumes by 2030.^xvi These are meaningful moves in the right direction.

But here is the context that can be easy to miss: these innovations apply to new facilities. The vast majority of existing data center infrastructure uses evaporative cooling and will continue doing so for years. And even the most water-efficient new designs still require enormous energy, which brings us to the bigger question.

On energy, nuclear is the most credible long-term answer being pursued at scale. Microsoft is reviving Three Mile Island. Amazon has secured a 17-year nuclear power purchase agreement. Meta has partnered with Oklo for a 1.2 gigawatt campus in Ohio with 16 small modular reactors.^xvii The logic is sound: nuclear provides reliable, around-the-clock, low-carbon power that renewables alone cannot guarantee at the scale AI demands.

But here is what is not being said alongside the enthusiasm.

Nuclear power plants are among the most water-intensive energy sources that exist. They consume 20 to 83% more water than coal-fired plants of the same capacity. A typical 1,000 megawatt reactor requires roughly 35 to 65 million liters of water per day for cooling, depending on design.^xviii Nuclear plants have already been forced to reduce output or shut down during drought conditions in Europe and the United States because river temperatures rose too high or water levels fell too low.^xix

We are proposing to solve AI’s water problem with an energy source that has its own significant water dependency. That is not a solution to the water crisis.

Nuclear also carries vulnerabilities that deserve acknowledgment: unresolved waste disposal, physical security and cyber risk when reactors are concentrated near high-value data campuses, and regulatory safeguards being quietly dismantled to accelerate deployment. Those safeguards were built from the hard lessons of Three Mile Island, Chernobyl, and Fukushima. Removing them in the name of speed is precisely the kind of second-order consequence we should be paying attention to.^xx

Nuclear is not the wrong answer. It is an incomplete one.

Then there is the space proposal. In early 2026, Elon Musk merged SpaceX and xAI and announced a vision for one million orbital, solar-powered data centers circling the Earth. Others have suggested similar futures. At first glance, it sounds elegant. Move computation into space where solar energy is abundant and many of the terrestrial constraints disappear. The physics of low Earth orbit communication are more viable than science fiction suggests, and the solar energy argument has genuine logic behind it.

But I find myself wondering whether this is another example of a question we are not asking. The challenges facing AI are not answered by simply changing where we place the infrastructure. Moving data centers into orbit does not change the amount of computation required. It does not change the underlying architecture. It does not change the growing demand for tokens, reasoning, and increasingly complex AI workloads. What it does is relocate the consequences.

Low Earth orbit currently contains over 14,000 satellites and an estimated 120 million debris fragments, all traveling at roughly 27,000 kilometers per hour.^xxi A January 2026 study calculated that a complete loss of satellite command for just 24 hours carries a 30% chance of triggering Kessler syndrome, the cascade of collisions that renders entire orbital shells permanently unusable.^xxii We do not have a solution to space debris at scale. This is not for lack of effort. Serious work is underway on active debris removal, controlled de-orbit and collision avoidance, and some of it is promising. But the question that matters is one of sequence. Does that mitigation mature and deploy at the speed orbital compute is being proposed, or does the infrastructure go up first while the solutions are still being built? Confidence that the answer will arrive in time is not the same as the answer having arrived.

Removing a single 95 kilogram satellite currently costs 86 million euros.^xxiii Introducing one million orbital data centers into an already critically congested environment is not a bold engineering vision. It is a proposal that carries risks the global space community is already warning about. A cascade failure in low Earth orbit would not just affect AI infrastructure, it would threaten GPS, weather systems, global communications, and human spaceflight for generations.

It is a bold vision. It is still not the answer the future requires. And depending on how it is pursued, it could make several other problems significantly worse.

Second Order Consequences

History is filled with examples of technologies that worked exactly as designed while producing consequences nobody anticipated. The most important decisions are often not about whether something can scale, but whether the foundations beneath that scale remain sustainable once second-order effects begin to emerge.

Can we create systems capable of extraordinary intelligence while remaining accountable to the people, communities, and resources they depend upon?

The conversation about what comes next is happening. It is just not happening loudly enough given the scale of investment already committed to the current path. And that is partly because the people most invested in the transformer model have the least incentive to ask the question seriously.

This is what it looks like to deploy technology before we understand its second-order consequences. Not malice, institutional inertia dressed up as progress. When capital commits at this scale, it doesn’t just fund a direction, it forecloses questions. The people closest to the problem can develop a vested interest in preserving the assumptions that brought them this far because the weight of what has already been built makes certain questions feel too costly to ask. That is something more dangerous than deception. It is the point at which bias and incentive become indistinguishable from each other. It becomes a reaction when what is needed is a pause and a deliberate response that is oriented toward the future. Not scaling a broken system but considering a redesign.

Why do we keep responding to consequences by scaling the system that created them, rather than pausing to consider a better path forward? That is ultimately a trust and leadership question.

What the Future Requires

AI will continue to advance. The upsides are too promising. The deeper question is whether we are willing to challenge the assumptions that got us here.

The rethinking is already happening, quietly, among researchers and builders like us, asking a different question than the one that dominated the last decade. Not how do we power the transformer at greater scale, but how do we build something that inherently makes compute more efficient and responsible as we scale.

Yann LeCun, former Chief AI Scientist at Meta, has argued publicly for years that the transformer is the wrong foundation for the next generation of AI and left Meta in late 2025 to pursue his Joint Embedding Predictive Architecture full time. ^xxiv State Space Models like Mamba offer near-linear computational complexity compared to the transformer’s quadratic demands, reducing the resource requirements of processing long sequences significantly. ^xxv As of late 2025, several of these alternative approaches have transitioned from theoretical research to production deployment and are matching or surpassing transformer performance on key benchmarks. ^xxvi

I mentioned at the beginning of this article that I am a builder. My co-founder and I did not start with the transformer and try to make it cheaper. We started with the problem: how do you build AI that is relational, explainable, and architecturally efficient from the ground up? That question led us to build a fundamentally different architecture. Not a wrapper. Not an optimization. Designed for the cost and resource realities of the world we are actually in, not the research environment of 2017. I am not ready to say we have solved everything. But I am ready to say, with confidence, that the path forward exists and that it will not look like a more expensive version of what we have now.

In the next piece, we go deeper into AI economics. Because the cost of inference is not just an environmental story. It is a business model story. And the math is stranger than most people realize.

AI is here to stay. And so are we. The only question that matters for the future is whether we build it in a way that remains sustainable, accountable, and worthy of the trust being placed in it.

We’re at an inflection point. Eyes wide open is not pessimism, it is the only responsible way to build.

Read more from The AI Reckoning: A Future of Trust Series

Part 1: The Hidden Cost of Intelligence

Part 2: The Reckoning Behind the Revenue: When the Numbers Don’t Add Up

Part 3: Rented Intelligence: Building on Borrowed Ground

Part 4: The Fragmentation Tax: Death by a Thousand Tools

[i] https://www.barchart.com/story/news/2339834/ai-data-centers-water-consumption-breaks-264-billion-gallons-in-2025

ii https://droughtmonitor.unl.edu / https://www.theinvadingsea.com/2025/09/05/data-center-water-consumption-google-meta-amazon-microsoft

[iii] https://www.theinvadingsea.com/2025/09/05/data-center-water-consumption-google-meta-amazon-microsoft-digital-realty-equinix-cooling-system/

[iv] https://www.theinvadingsea.com/2025/09/05/data-center-water-consumption-google-meta-amazon-microsoft-digital-realty-equinix-cooling-system/

[v] https://www.datacenterdynamics.com/en/news/microsofts-water-consumption-jumps-34-percent-amid-ai-boom/

[vi] https://malotastudio.net/ai-data-centre-water-usage/

[vii] https://www.weforum.org/stories/2025/11/data-centres-and-water-circularity/

[viii] https://www.iea.org/news/data-centre-electricity-use-surged-in-2025

[ix] https://arxiv.org/abs/1706.03762

[x] https://arxiv.org/pdf/2603.21690

[xi] https://medium.com/@dhevanmuhamad/understanding-tokens-the-currency-of-ai-thats-costing-you-money-87a87cd0757d

[xii] https://adam.holter.com/ai-costs-in-2025-cheaper-tokens-pricier-workflows-why-your-bill-is-still-rising/

[xiii] https://www.mindstudio.ai/blog/ai-token-cost-crisis-enterprise

[xiv] https://writer.com/blog/enterprise-ai-adoption-2026/

[xv] https://bizzdesign.com/blog/enterprise-ai-adoption-balancing-innovation-and-roi-2026

[xvi] https://introl.com/blog/water-usage-efficiency-wue-ai-data-center-cooling-guide-2025

[xvii] https://www.datacenterknowledge.com/energy-power-supply/how-realistic-is-nuclear-power-for-ai-data-centers

[xviii] https://www.justsecurity.org/138215/nuclear-powered-ai-risks-deregulation/

[xix] https://www.sciencedirect.com/science/article/pii/S0301421525001387

[xx] https://www.justsecurity.org/138215/nuclear-powered-ai-risks-deregulation/

[xxi] https://amplyfi.com/blog/understanding-the-space-debris-dilemma-the-kessler-syndrome/

[xxii] https://www.sciencedaily.com/releases/2026/01/260128075341.htm

[xxiii] https://medium.com/@marc.bara.iniesta/space-debris-and-the-kessler-problem-a-reality-check-99484cfa5e86

[xxiv] https://cacm.acm.org/news/beyond-llms-a-post-transformer-world-emerges/

[xxv] https://www.researchgate.net/publication/399331454_Architectural_Pluralism_in_AI_A_Comprehensive_Analysis_of_Alternatives_to_the_Transformer_Architecture

[xxvi] https://pchojecki.medium.com/going-beyond-llms-transformers-39f3291ba9d8

I’ve spent the last two years in conversations with founders, executives, investors, technologists, and employees trying to make sense of what AI is about to change inside organizations and inside our lives.

What strikes me most is not that things are moving quickly. It’s that moving quickly has become the strategy.

As a founder, I understand why. Speed is being rewarded everywhere right now. Investors are rewarding it. Markets are rewarding it. Companies are rewarding it. Nobody wants to be the organization that missed the shift.

The pressure to adopt AI, show measurable ROI, and demonstrate momentum is enormous. And underneath that pressure is another layer that people talk about far more quietly: the fear executives carry about falling behind, making the wrong bets, or losing relevance if they cannot prove results fast enough.

At the same time, I’m seeing another side of the story emerge inside organizations.

Adoption initiatives that are quietly failing. Employees struggling to keep pace with constant change while trying to maintain performance. Leaders mistaking compliance for alignment. Teams carrying cognitive and emotional loads that traditional productivity metrics do not capture.

And increasingly, I find myself asking whether we are measuring the wrong things while assuming we are making progress.

That’s what led me to this series.

Because underneath the headlines about efficiency, automation, and competitive advantage is a much larger question about trust.

Trust in leadership.
Trust in institutions.
Trust in information.
Trust in our own value and judgment.
Trust that the systems being built are actually serving humans rather than simply accelerating expectations beyond what humans can sustainably absorb.

There’s a difference between speed and velocity.

Speed tells you how fast you’re moving. Velocity tells you how fast you’re moving and where you’re going.

Right now, we have extraordinary speed. What I’m less certain about is whether we’ve spent enough time asking where all of this is actually leading us.

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The AI Reckoning isn’t a technology series. It’s a trust series that happens to be using AI as its lens right now, because AI is where the trust breakdown is most visible, most consequential, and most urgently under-examined.

• The physical costs nobody accounted for.

• The economics that don’t add up the way the industry is presenting them.

• The adoption failures being misreported as adoption successes.

• The security and governance frameworks running years behind the deployment curve.

• The ambient erosion of human confidence that the productivity numbers aren’t capturing.

Each of these is a trust problem, and none of them exist in isolation.

But here’s what I keep coming back to as the thread underneath all of it:

That responsibility isn’t abstract. It shows up in the decisions being made right now by enterprises, investors, founders, and policymakers about what gets built, how fast, for whom, and at what cost. And it shows up in something less visible but equally important: The quiet confidence crisis unfolding inside the people being asked to keep pace with all of it.

Which brings me to the part of this conversation I find most urgent and least examined.

Increasingly, I’m seeing younger generations ask questions that many organizations seem reluctant to confront. Not because they don’t understand the technology. Because they understand it clearly enough to see what’s being traded away.

Authenticity.

Creativity.

The sense that their work means something and that they are the ones doing it.

They are pushing back against a future they didn’t design and aren’t sure they want. That pushback is often being misread as resistance to change. I think it’s something more strategic than that. I think it’s a generation that has grown up with enough information to ask a question the rest of us have been too busy to ask:

They are right to ask it, and we have a responsibility to answer it, not by handing them a roadmap we drew without them, but by bringing them into the design process. You cannot build systems people will trust if the people most affected had no hand in building them. That’s not idealism. That’s how trust actually works.

This series will go into all of it. Some pieces will be reported and researched. Some will be more exploratory. Questions I’m sitting with that I’ll think through out loud and invite you into. I’ll tell you which is which.

What it will always be is a genuine attempt to ask the questions that are getting drowned out by the noise of the ones we’re already answering.

The reckoning isn’t coming. It’s already here. We’re just not calling it that yet.

I’d like this series to be a conversation, not a monologue. If you’re seeing questions that aren’t being asked, assumptions that deserve more scrutiny, or perspectives that aren’t getting enough airtime, I’d love to hear from you. Leave a comment, join the chat, or reply directly.

The future is being shaped right now. Let’s make sure we’re asking the right questions before we decide where we’re going.

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I almost didn’t start this. Not because I don’t have things to say, but because I have too many. Would I be opening Pandora’s box? Would my voice just be a drop in the ocean? Were my thoughts worthy of your time? For a long time, I told myself the internet didn’t need another person adding to the noise. I convinced myself those were good enough reasons not to dive in. They weren’t. They were stories I used to rationalize avoidance. Fear dressed as discernment.

So here I am.

I’ve spent my career working at the intersection of human behavior, leadership, and organizational change. I’ve sat with executives who can’t sleep, teams fracturing under pressure they can’t name, and founders navigating systems that were never designed for the speed they’re being asked to move at. I’ve written about imposter syndrome, the internal architecture of self-doubt, and I’ve watched that same doubt play out at organizational and societal levels in ways we don’t have good language for yet.

The thread running through all of it, always, is trust.

I’ve come to believe that trust operates at four distinct levels simultaneously.

1. There is the trust we have in ourselves, our judgment, our legitimacy, our right to take up space.

2. There is the trust between people, built slowly and lost quickly in the spaces between what we say and what we do.

3. There is the trust inside organizations, the invisible agreement that the system is worth showing up for.

4. And there is systemic trust, our collective faith in the institutions, technologies, and structures that hold society together.

These four levels are not separate. They cascade. When self-trust erodes, it surfaces in teams. When organizational trust fractures, people internalize it as personal failure. When systems move faster than people can adapt, self-doubt rushes in to fill the gap. The person sitting in a meeting wondering if they’re good enough and the enterprise quietly accumulating a trust deficit with every AI promise it can’t keep are experiencing different expressions of the same underlying breakdown.

That connection is what this newsletter is about.

Right now, trust is cracking in ways that aren’t being widely reported.

• We are deploying technology before we understand its second-order consequences.

• We are optimizing for speed to market over responsibility to people.

• We are watching a generation of younger workers push back against a future they didn’t design and aren’t sure they want.

We are calling all of this progress. Some of it is. I’m not a pessimist and this isn’t a doom scroll. After all, I’m a tech founder and CEO. I’m in the middle of it.

But I’m also someone who has spent a long time paying attention to what lives beneath the surface of systems, in individuals, in organizations, in the broader culture. And right now, beneath the surface, something important is shifting. The signal is there, but it’s getting drowned out by noise we’re choosing not to question, because the inconvenient answers would upset markets that have already bet heavily on where we find ourselves today.

The people building this technology are largely talking to each other. The broader population experiencing it is not in that room. That gap is not a communication problem. It’s a trust problem.

Those are some of the questions I’m bringing here. This is where I think out loud.

The Future of Trust will go wherever trust takes us. Some weeks that’s artificial intelligence, the vulnerabilities nobody’s talking about, the adoption failures being misreported, the trust debt being quietly accumulated by an industry moving faster than it’s ready for. Some weeks it’s the internal landscape, imposter syndrome, the stories we tell ourselves about our own legitimacy, the ways self-doubt masquerades as humility. Some weeks it’s leadership, organizational change, or whatever is sitting in front of me that I can’t stop thinking about.

What it will always be is real. And never written to tell you what you want to hear.

If that sounds like something worth reading, I’m glad you’re here.