Money already works this way too. Some money is universal, like cash in your pocket—you can spend it anywhere. Other money is public, issued for specific purposes—food stamps, housing vouchers, childcare credits, training subsidies. Both circulate in the same economy, but they serve different purposes.

The shift to digital sharpens this distinction. Universal money becomes faster, cheaper, and easier to move across borders—as we see with stablecoins. Public money becomes smarter and more flexible—conditions like where it can be spent, how long it lasts, or who can use it can be written directly into code. Digital money doesn’t just replicate what money already does; it opens up a new design space.

Stablecoins are the clearest example of universal money gone digital—cars for the open road, flexible and user-directed. But the public side is misframed. Central banks are designing CBDCs as if they too should be cars—just digital versions of cash—when the real opportunity lies elsewhere. What governments need isn’t another car. They need buses: digital public money programmed for specific routes, collective goals, and destinations private money doesn’t reach.

Digital Economies Don’t Exist in a Vacuum

Modern economies are not stable systems humming along on their own. Waves of disruption routinely reshape them. Automation displaces whole categories of work. Climate disasters strike with increasing frequency, leaving households and regions scrambling to rebuild. Public health challenges—from obesity to pandemics—erode participation in the workforce and drive up long-term costs. Migration flows test both sending and receiving economies, demanding new forms of support and integration.

In every one of these cases, governments already intervene. They provide unemployment insurance, retraining subsidies, disaster relief checks, housing support, food assistance, and healthcare programs.

These disruptions are multiplying and intersecting. That means the fiscal tools governments rely on will need to scale, adapt, and deliver far more effectively than they do today. That is the real backdrop for thinking about digital public money.

What Digital Public Money Is

Governments deliver public money today through fiscal tools like food stamps, housing vouchers, childcare subsidies, education credits, and disaster relief checks. These programs help citizens participate in the economy, but they are slow, fragmented, and hard to scale. People wait weeks for paper checks, navigate systems that don’t talk to each other, and lose benefits through administrative friction.

Digital public money would modernize this by embedding conditions directly into code—where funds can be spent, how long they last, who can use them. Instead of clunky vouchers, support could be delivered instantly, transparently, and at scale. These are not replacements for cash or competition for bank deposits. They are complements: purpose-built fiscal instruments designed for collective goals.

Digital public money could be:

• Purpose-bound: usable only for food, childcare, or training.

• Time-bound: expiring if unused, creating stimulus with built-in velocity.

• Location-bound: spendable in specific regions to support recovery and local growth.

• Identity-bound: targeted to students, veterans, or unemployed workers.

Consider disaster relief. When hurricanes hit, households wait weeks for FEMA checks while they need food, shelter, and medical care immediately. Delays like this put people at risk. Digital public money could change this completely. Instead of waiting weeks for a paper check, households could receive support within hours—money that works right away for food, temporary housing, or medical needs in the affected areas. No checks to mail, no fraud to chase, no gap between crisis and support.

Crucially, governments don’t need to build everything themselves. Just as cities don’t manufacture buses but still design the routes, digital public money could be issued on top of regulated stablecoin infrastructure. Private actors are better suited to build the infrastructure—wallets, payment systems, and digital rails—because they have the talent, capital, and incentives to iterate quickly. Public institutions are better suited to design the programs—who qualifies, what funds cover, and how they advance collective goals—because they operate with public mandates.

Some worry this could give governments too much control. But restrictions already exist in today’s programs, only in slower and less transparent ways. A paper voucher that can be redeemed only at certain grocery stores is restrictive too, just clunky, leakier, and harder for citizens to use. The goal is to make collective resources reach their intended purpose quickly and effectively.

And accountability runs both ways. Programmable disbursements on transparent digital rails can hold governments to their commitments as well, creating a baseline for how public funds are spent.

Beyond Issuance

Most debates about digital money have been trapped on a different axis: who should issue it—private firms or public authorities? Stablecoins or CBDCs? That framing misses the deeper question: how should money be programmed, and to what ends? When the conversation stays focused on issuance, it circles around deposit caps, interest rates, and bank intermediation. These details matter for monetary policy, but they miss what digital money could enable for citizens.

Programmability changes what money can be. It allows private actors to build conditional transactions—escrow, automated payouts, bundled transfers—that reduce complexity in commerce and finance. And it allows governments to direct fiscal support with new precision—funds that are purpose-bound, time-bound, or location-bound. One side drives innovation in markets, the other advances collective goals.

Stablecoins extend the universal layer of money—cars on the open road. Digital public money extends the public layer—buses on programmed routes. The deeper divide is about purpose. What should each kind of money actually do?

Closer to Citizens

Government experiments around digital public money to date have largely focused on CBDCs, led by central banks in partnership with commercial banks and industry. Most of these pilots emphasize wholesale applications. Their frameworks are designed for monetary settlement, not fiscal delivery, which means experimentation happens far from the citizens these systems are ultimately meant to serve.

Agencies and municipalities work differently. They already run the programs that touch people’s lives most directly—childcare subsidies, housing assistance, job training, public health campaigns. They know how to target support, measure outcomes, and adjust programs. More importantly, they already share what works across borders: cities compare approaches to homelessness, employment offices trade retraining models, health agencies coordinate on vaccination campaigns. This is the natural learning environment for digital public money.

Some of this is already happening. In parts of Latin America and East Africa, NGOs have piloted digital disbursements to cut corruption and speed aid to households. These programs aren’t perfect, but they show how close-to-citizen delivery produces faster learning and clearer results than central-bank pilots.

And the economics are compelling. Public transit delivers up to a 5-to-1 return on investment because it expands access to jobs, schools, and healthcare. Digital public money operates on the same principle: by enabling broader participation in the economy, it multiplies private opportunity. Studies of subsidy programs in emerging markets show that digitizing delivery not only cuts leakage but also improves uptake, creating measurable gains in household welfare and productivity.

Why Both Layers Matter

The future of money will need two layers. Universal money spreads: it lowers friction, connects markets, and gives people a way to transact freely across borders. Public money reaches: it targets the places, people, and problems that universal markets leave behind.

Migration shows why both are essential. For migrants, universal money is a lifeline: remittances today are slow and costly, with fees often 6–7% and transfers taking days. Stablecoins can make them faster and cheaper, preserving more of what workers send home.

At the same time, migrants depend on public money in their host countries—housing assistance, food programs, job training, childcare. These programs exist, but they are fragmented and slow. Digital public money could deliver them instantly and programmatically, making integration faster and contributions stronger.

The same applies to other disruptions. Workers displaced by automation need retraining subsidies that arrive quickly, not through months of forms and reimbursement. Regions hit by climate disasters need relief in hours, not weeks. Families struggling with healthcare costs need programs that integrate seamlessly across providers. Digital public money could deliver this directly, instantly, and in ways traditional subsidy systems cannot.

This future won’t arrive automatically. Consumers will need to embrace wallets as everyday tools, learn new behaviors with programmable money, and trust that these systems work for them. Businesses will need to integrate programmable payments into commerce. Governments will need to shift focus from monetary policy to fiscal delivery, putting experimentation in the hands of the agencies and municipalities closest to citizens.

Aligning this shift with benefits makes adoption easier. People already understand the purpose of unemployment insurance, childcare subsidies, or disaster relief. Delivering those through digital public money provides a clear baseline for comparison and experimentation. By contrast, the retail case for CBDCs as “digital cash” is far harder to explain, and even harder to measure.

Modern economies need both layers of money. Universal money powers exchange; public money anchors support. We already know how this works in transit: when cars and buses run together, the whole city moves better.

Stablecoins may finally change that. They represent money's first real mainstream digital format—capable of being programmable, portable, and user-controlled. And if other format shifts are any guide, their impact will be less about reinforcing existing systems and more about unlocking new financial experiences that help people answer the most basic questions: Do I have enough? When will I have more?

The opportunity lies in treating money as a digital product. Properly built, it can collapse complexity, ease the stress of irregular income, and give people more control over when and how they get paid. Money that adapts to an increasingly services-based economy defined by irregular work and fragmented income becomes the foundation for security, mobility, and freedom in everyday life.

This essay builds on Finance's Format Moment, where I argued that stablecoins open the door to the kind of outsider creativity that gave us Netflix, YouTube, and Spotify in their industries. Here, I turn from industry to individual. I explore what this format shift could address: the basic questions people live with—Do I have enough? When will I have more? And I show how a digital product approach to money could better serve a services economy defined by irregular work and fragmented income.

Say “Cheese”

When I take a photo of my children in a crowded park on my phone, I can instantly remove the crowd with just my fingers. Would I have even taken the shot with film? Probably not. Every frame was scarce, saved for special occasions and staged, 'say cheese' moments. But the smartphone collapsed those limits. The camera, the album, and the editing tools all merged into one app, and the scarcity of film gave way to the abundance of digital. Photos became casual, searchable, and shareable, giving me more agency not just in how I edit, but in what I choose to capture in the first place.

Programmable money could offer a similar leap: financial experiences that can be reconfigured, timed, and combined in ways that were impossible with analog systems. The equivalent of the camera app is the digital wallet.

Like early smartphone camera apps, today’s wallets point toward what's possible. Venmo adds social context to spending; Apple Wallet brings location awareness. Crypto wallets add options for enhanced privacy and custody, while digital currencies enable fractional payments for micro-transactions. These are building blocks for modernizing money.

Tomorrow's wallets would give people more agency over their money by collapsing functions that today are scattered across banks, payroll systems, billing platforms, and budgeting apps. They could show upcoming bills as simple heads-up cards, stream earnings as work is completed, and carry your financial history with you across institutions. Just as the camera app turned photos into something casual, editable, and personal, digital wallets could turn money into something timely, predictable, and responsive.

Enough and More

The same logic applies to money's core questions. A wallet that only moves funds is like a camera that only takes pictures. What people need is a way to collapse the effort it takes to answer the most basic questions they live with every day: Do I have enough? When will I have more?

Today’s systems make those questions harder, not easier. They force people to coordinate across multiple banks, payment schedules, and account balances—complexity baked into infrastructure that no interface improvement can fully solve.

Two things would help: First, taking the mental math out of "enough." This means knowing at all times what I owe from past transactions, how much I have now, what I will owe soon, and what income is coming in. A wallet that sees all inflows and outflows could manage this like Outlook manages my calendar, always knowing where I've been, where I am today, and where I need to be tomorrow.

Second, giving people more control over when they get "more." Digital money could offer workers more choice in payment timing, with some preferring continuous streams for immediate needs, others choosing weekly or project-based payouts for easier budgeting. What matters is reducing the mismatch between when bills are due and when income arrives.

The limits of today’s systems make this hard to fix. Even well-designed banking apps and faster payment rails can’t resolve timing mismatches or give true real-time cash flow visibility. They sit on infrastructure where money still moves in batches between institutions rather than flowing directly to users.

This points to an overlooked area in financial services. It remains a post-paycheck industry with little to say about how people earn money and much to say about how they store and use it. Most of the retail conversation around stablecoins today is about payment acceptance. But people's challenge isn't choosing between cash, debit, credit, or stablecoins. It's having enough money to pay bills in the first place.

Instead of competing with card networks on transaction efficiency, stablecoins could help address the very challenge traditional financial services ignore: earnings.

Earnings at the Core

Stablecoins already play this role in developing economies. Where domestic currencies lose value weekly, the same work completed today buys less a week later. By letting people denominate and store earnings in dollars, stablecoins help guard what matters most: that their work today retains its worth tomorrow.

The opportunity for developed economies is to treat earnings as a core retail use case. The challenge may differ—inflation there, irregular income here—but the need is the same: money that protects and stabilizes earnings.

This would direct stablecoins toward the irregular work economy: gig, freelance, creator, and service jobs that fall outside the steady paycheck model. Today's services economy has more people working outside traditional employment promises, requiring money that can move continuously for some, in weekly bursts for others, and in project-based payouts for others.

Banks have multiple reasons for avoiding this segment: irregular income creates higher default rates and more complex underwriting, often with lower balances that reduce profitability. But part of the challenge is also the technical mismatch between existing money systems designed for paychecks and what services workers actually need. "Income smoothing" or "early pay" stretches paychecks rather than redesigning payment timing entirely.

As money becomes digital, the opportunity extends beyond speeding up institutional payments to redesigning money for actual economic conditions. The paycheck era created money for industrial regularity. The services era demands money designed for flexible, real-time earnings, and digital money makes that redesign possible.

Digital Rules for Digital Money

The debate over digital money is often framed as a technical one: do we really need it when faster payments are already here? The more powerful lens is a human one: what problems can a digital format solve for people that no analog system ever could?

To view money this way is to see it as a digital product that must evolve to address evergreen pain points and serve a changing economy. The best digital products collapse, bundle, and streamline, turning what was once effortful into something intuitive. For money, that means solving the daily challenge of "enough and more"—where wallet, currency, and financial tools evolve together to extend the problem-solving power of modern tech into the core of earnings and obligations.

When a format moves from pre-digital to digital, it removes old constraints and opens new possibilities. Infrastructure emerges from user need. Amazon built its logistics to support one-click shopping; Netflix built delivery networks to support seamless streaming. The system becomes whatever it takes to unlock the new user experience.

Finance, by contrast, still tends to start with the system: faster settlement, decentralized ledgers, programmable money. The assumption is that better infrastructure will naturally lead to better user experiences. But decades of digital transformation show the opposite: the winning user experience defines what infrastructure gets built, not the other way around.

Seen this way, stablecoins are not just a payments tool but the enabling format shift. What matters is less the infrastructure itself and more what new experiences it allows. This piece argues that many of those new experiences will center on resolving the everyday pain points of earnings—knowing if you have enough, and making it easier to get more—and on building the infrastructure to support people in an increasingly services-based, irregular work economy.

The history of digital format shifts reveals more about stablecoin potential than debates about whether stablecoins are "real money." Focusing on monetary properties is like imagining the future of music by staying locked in arguments over MP3 audio quality. The breakthrough insights come from understanding what kinds of businesses digital formats enable.

Format changes create outsider moments. Hollywood couldn't imagine Netflix because they thought in theatrical windows, not on-demand streaming. Record labels couldn't imagine iTunes because they thought in albums, not songs. Finance has never had a true digital format change, so innovation has been constrained by existing infrastructure—limiting the kind of creative repattern by outsiders that transformed music and video.

Stablecoins let the outsiders in. This doesn't mean digital money is identical to digital media—money is regulated, and people experience earning, spending, and losing money very differently than consuming content. But digital money will need to go through the same fundamental steps: changing customer behavior and building enduring businesses around new user experiences.

Like all format changes, success depends on building the right foundations—user adoption, regulatory relationships, developer ecosystems, and behavioral patterns—around the format. The winners will be those who see past today's constraints to build something entirely new.

This analysis focuses on retail applications of stablecoins - how they might reshape everyday financial experiences for individuals rather than institutional treasury or B2B payment use cases.

Beyond Speed and Cost

To understand what will drive stablecoin adoption, we need to look beyond format features to what actually makes new systems stick. Transaction speed, cost reduction, and settlement improvements matter, but they don't build lasting advantage on their own.

Value accrues when companies build superior user experiences enabled by the new format. The MP3 succeeded because Apple created iTunes and the iPod ecosystem, while Spotify built recommendation algorithms, curated playlists, and social features. The format enabled these innovations, but building compelling experiences determined who captured value.

This pattern appears consistently across successful digital businesses. Netflix transformed video through on-demand streaming and recommendation algorithms. YouTube enabled user-generated content through upload tools and discovery features. TikTok created vertical video culture through editing tools and algorithmic feeds. Each company accumulated different capabilities than incumbents —the foundations required to deliver new user experiences.

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Current stablecoin discourse obsesses over format features—speed, cost reduction, settlement transparency, and cross-border efficiency. But successful digital companies show that format advantages alone don't determine winners. Instead, superior user experiences transform industries by redefining what's required to deliver them.

The opportunity remains open for companies who focus on creating new financial experiences with programmable money. Success will come from developing trust, adoption, and developer ecosystems around these new financial experiences.

It’s What’s Up Front That Counts

Successful digital format changes shift who controls access to new possibilities. Netflix, YouTube, and Spotify didn't just digitize content—they became the interfaces through which people accessed all movies, videos, and music. The "everything wrapper" became the source of power: whoever controlled the universal access point controlled the user relationship.

Stablecoins enable the same interface shift in finance: moving from account-based systems to wallet-native experiences. Accounts represent institutional custody where banks control rails, own customer relationships, and determine functionality. Money exists as ledger entries accessible through institutional interfaces, subject to their rules and operating hours.

Wallet-native systems flip this dynamic. Users control their own financial interface rather than accessing money through bank websites and apps.

Apple Wallet demonstrates this transition by storing payment credentials, loyalty cards, and identity documents directly on devices, enabling new experiences without requiring bank partnerships for every feature. Instead of logging into separate apps for each service, users access everything through a single interface they control.

But Apple Wallet still relies on underlying account-based infrastructure for payments. Stablecoins could enable mainstream wallet-native experiences where users control actual financial value, not just payment credentials. The wallet becomes the primary financial interface.

Traditional banks built capabilities around institutional custody, regulatory compliance, and customer account relationships. A dominant financial wallet would need entirely different capabilities. Just as Spotify needed different capabilities than record labels to become the music interface, the requirements will follow from what's needed to control the financial interface - but they'll be fundamentally different from what banks built.

When value moves to the front end, it rarely accrues to those who issue or control the format itself. Record labels didn't capture the value from MP3s - Apple and Spotify did by building superior interfaces. Movie studios didn't dominate digital video - Netflix did by controlling how people accessed content. Similarly, stablecoin issuers may enable the format, but the lasting value will flow to whoever builds the most compelling financial interfaces around wallet-native money.

Let’s Get Physical

Digital format changes don't just happen in software. They succeed by embedding into physical experiences that people can see, touch, and understand. Compare successful digital format introductions:iPod offered '1,000 songs in your pocket.' Kindle provided thousands of books on a 'paper-like display' you could read at the beach. These were tangible, immediate benefits people could understand.

This physical requirement is especially important for retail payments, where stablecoins face their biggest adoption challenge. Successful payment innovations have always combined digital capabilities with physical interfaces that people can trust and understand.

Square's breakthrough illustrates how digital payment innovation requires physical thinking. The white dongle transformed any smartphone into a payment terminal, giving small merchants tangible tools they could understand and trust.

Apple Pay succeeded by leveraging existing physical infrastructure—phones in hands, card readers at merchants. WeChat Pay and Alipay recognized phone cameras as payment infrastructure, using QR codes to bypass card networks entirely.

Stablecoins will need the same physical thinking. Digital format changes succeed when they unlock new physical experiences or leverage existing physical capabilities in new ways.

Much of current stablecoin payment discourse emphasizes flows—instant settlement, low fees, cross-border efficiency—over the physical payment interfaces needed to make these benefits useful in retail settings.

Bring on the Hits

Digital formats create initial abundance that consistently gives way to market concentration. Music streaming consolidated around Spotify and Apple Music. Video platforms concentrated around Netflix and YouTube. Even supposedly decentralized crypto markets show dramatic concentration, with most DeFi assets living on Ethereum despite many alternative blockchains.

This concentration happens even when different platforms serve different contexts. Netflix dominates video streaming, YouTube dominates user-generated content, and TikTok dominates short-form video. The key is that each context develops its own concentrated platform rather than fragmenting across many equal competitors.

Stablecoins carry similar expectations of limitless innovation and broad distribution. But like other digital formats, the reality will be more focused.

Stablecoin value propositions tend toward context-specific applications: cross-border transfers without correspondent banking, access to dollar-denominated savings in inflationary economies, or programmable money for developers building financial applications. This suggests concentration will happen within contexts rather than one universal winner.

Within each context, the winners won't be determined by technical superiority, but by creating the most compelling user experiences. These superior experiences drive the network effects, trust accumulation, and ecosystem integration that create concentration—the same dynamics that made Netflix, Spotify, and YouTube the leaders in their domains.

Concentration will follow use. Dominant platforms will emerge within each context, not because of the stablecoins themselves, but because of the experiences built around them. And as with other format shifts, the winners may be outsiders who build what industry insiders couldn't see coming.

Finance's Format Moment

Finance is finally experiencing its format moment. The question is how companies will approach format changes with the potential to reshape entire industries.

Format changes follow a consistent pattern, but not the one most people expect. Success doesn't come from modernizing existing industries but from enabling entirely new categories of behavior.

Netflix didn't emerge from Hollywood's efforts to digitize movies. YouTube didn't come from television's attempts to move online. TikTok didn't arise from anyone trying to improve video sharing. Each created entirely new categories of behavior - binge-watching, user-generated content, vertical video culture - that pulled their industries forward.

Much of the narrative around stablecoins assumes transformation happens by upgrading financial infrastructure. Faster settlement, lower fees. But that’s not how digital formats change industries. Success comes from building new user behaviors, controlling interfaces, and embedding into physical experiences people can understand and trust.

Stablecoins enable wallet-native money - financial value under user control rather than institutional custody. The breakthrough will come from discovering what becomes possible when users control their own financial interface, accumulating the right capabilities around experiences that can only exist in a wallet-native world.

The companies that build around these new possibilities will determine what finance becomes. The future belongs to those creative enough to construct entirely new categories of user experience that pull the industry forward.

Crypto and AI have ushered in a new way of thinking about technology and progress. The old frame—this is faster, better, cheaper—has given way to something else: a language of rupture. Burn down what exists, install something else, and believe that it will be better. Not because it’s been proven in practice, but because it fits the vision. 

In crypto, the vision is to rebuild the architecture of modern finance. Fiat currency and the banking system are seen as constraints to be replaced. In AI, the scope is even broader. Human intelligence is treated as the bottleneck. Superintelligence is imagined as the release. It promises to cure disease, eradicate poverty, and redefine civilization. 

In earlier cycles, technology promised improvements. Now, it demands replacement. We aren’t being invited to try the new. We’re being told to clear the decks.

That shift matters. Technologies like crypto and AI don’t just sit on top of existing systems. They aim to rewire them. The financial system, the labor market, the knowledge economy. They hold a mirror to complex social and economic institutions, and see in its reflection better code. 

Technology used to be measured in users and revenue. Now it’s measured in claims about how life should be organized. Our role changes too. We move from participants to followers. From choosing tools to adopting beliefs.

In this piece I trace how crypto and AI have altered the way we talk about technology, why that change took hold when it did, and whether it signals a lasting realignment in our expectations of what technology is for.

The Loneliness of the IT Enthusiast

Before crypto and AI, there were other promised revolutions.

Voice interfaces. 5G networks. Smart devices that would change how we live. Each had its champions, people who believed that speaking to technology or surfing on low-latency wireless would transform the human experience. Screens were clumsy. LTE was slow. The future would be hands-free, buffer-free, frictionless.

But these champions were often alone. Every company had a few, but never enough. Enough to be known, to nod at one another in meetings, but not enough to shift the culture. Their enthusiasm never scaled. No matter how many devices shipped, it still felt like an IT story, and they still sounded like IT enthusiasts.

Crypto and AI arrived differently. They didn’t start inside companies. They arrived with movements—early, self-organized, and ideologically driven. Not just products, but prophets. Vast communities, internal schisms, divergent visions of the future. The end of fiat. The end of human labor.

Charismatic inventors. World-altering stakes. Entire systems on the table. This is not what happened with 5G or voice. Not with cloud, mobile, social, gaming, or ecommerce. Not even with search.

The closest we’ve come is the internet itself, a technology that grew with our use of it, not just our belief in it. The internet was hyped, but its transformation came through use. It scaled with, and through, us.

Crypto and AI activated belief systems and collective worldviews. They form organized communities with protocols, media channels, and capital networks. What once felt fringe now moves with institutional force. It shapes how we think about the purpose and arc of technology.

Up and To the Right

The internet taught us to see progress as an arrow pointing up and to the right. The technologies that took off started small, found product-market fit, and scaled fast. Ecommerce, gaming, social, mobile, cloud. Growth meant more users. Adoption meant success.

Crypto and AI still carry that narrative. Everyone wants to grow. But growth isn’t the only goal. These technologies aim to replace how we move money, do work, structure systems. Displacement is part of the pitch.

In crypto, up and to the right means less fiat and fewer intermediaries. A two-tiered financial system replaced by decentralized tokens, smart contracts, and public blockchains.

In AI, up and to the right means fewer humans. Not computers that assist knowledge work, but ones that do it. People move from the center to the edge. From decision makers to monitors.

When success includes displacement, the meaning of progress shifts. 

It is no longer about scaling new tools. It is about building something else on the other side. And that reshapes what it means to participate in technological change.

Tell Me What It Means

Understanding past tech waves was the work of analysts and journalists. They decoded the ecosystem through daily newsletters, annual internet reports, deep dives into business models and market dynamics. 

If you were building in commerce or gaming or social, you ended up in Mary Meeker’s slides. Everyone—builders, users, analysts—shared a common set of frameworks. You knew what counted, what good looked like.

Crypto and AI don’t fit in those boxes. They’ve created new categories of tech commentator: the translator and the prognosticator. These voices don’t analyze what the technology does so much as interpret what it means. What future it points to. What to believe.

Bitcoin is going to halve. What does it mean? Ethereum completed the merge. What does it mean? The latest language model shows signs of chain-of-thought reasoning. What does it mean? These aren’t product updates. They’re signs to decode.

Prognosticators go further. They narrate the future backward. Bitcoin matters because it will displace fiat. AI matters because it leads to superintelligence. What these technologies are doing today is only relevant in how it gets us to tomorrow. Here’s where we are on that timeline.

There’s still analysis of use cases and business models—especially in AI—but it’s a different conversation, often led by different people. The draw now is interpretation. Translators and prognosticators don’t explain what is. They reveal what’s becoming. What’s coming next. They are meaning-makers for technologies no one quite understands. 

We’re making peace with the idea that technology now sits outside our understanding. We no longer expect shared frameworks. We hope for believable interpretations.

And as the frameworks shift, so do the roles. Technology has always had insiders and outsiders, builders and users. Now, the divide is between believers and nonbelievers. Interpreters and adherents. Those who can shape the narrative, and those who have to trust it.

Exit

Crypto and AI arrived in a moment of accumulated frustration. The institutions that powered earlier waves of innovation had started to feel like constraints. And the stories we told about progress were getting harder to believe.

In finance, regulation insulated incumbents from the kind of disruption that reshaped other industries. Banks upgraded interfaces, not business models. In tech, the biggest platforms absorbed the gains of mobile and cloud. The path to scale and exit for startups grew narrower.

Crypto and AI promised an exit. Not just better products, but new systems. A way around the institutional bottlenecks of finance. A new path to growth, beyond the capture of incumbent platforms.

They also arrived at a time when our narratives of progress had broken down. For believers in crypto, the old story—save up, retire eventually—didn’t make sense in a world shaped by the financial crisis, stagnant wages, and asset inflation. For believers in AI, the breakdown was broader. Progress in politics, climate, and technology all seemed to hit their mirror: hyper-polarization, extreme weather, enshittification.

In both cases, a new story—HODL, AGI—offered something to believe in. Hold on for dear life (HODL) turned being left out of the financial system into a form of resistance, even superiority. Artificial General Intelligence (AGI) promised to break through systemic paralysis with a new form of intelligence that could act where humans no longer could. Both promise a better future if you believe long enough.

In a world of fragmented, contested narratives, where coherence could only be sustained by ignoring lived reality, crypto and AI offered a singular arc. A way out of the world we are in, toward something better.

Reentry

Both crypto and AI are technologies of exit. To deliver on their promise, they have to re-enter. Crypto must transform financial infrastructure and the everyday exchange of value. AI must remake how we work, learn, create, discover, and consume. Their success depends on replacing what exists.

This is where the weight of transcendence begins to show. Movements promise a destination. Technologies need adoption. Eventually, the future has to land.

And so the question becomes: Are we looking for signs that the post-fiat, post-labor future is coming, or content to upgrade the systems we have today?

In AI, the enthusiast storyline still ends in superintelligence.That vision leaves little room for making the tools safe, reliable, or widely usable. What matters most is proximity to the endpoint. Everything else can wait.

Yet many businesses are working from the ground up. Building applications, chasing market share, leaving the big questions to others. Not chasing transcendence. Shipping product.

Crypto faced a version of this earlier. Would any network besides Bitcoin get us to a post-fiat world? Probably not. But stablecoins emerged across chains, enabling new forms of value exchange and the slow march of institutional adoption. In the process, the narrative shifted. Less myth-making, more implementation.

Stablecoins are crypto’s descent into the practical. The application layer is AI’s. Both are finding momentum not in visions of rapture, but in monetizable deployments. They trade transcendence for institutional absorption, exit for upgrade, belief for benefits.

Off Ramps

Exit is largely a feature of advanced economies. In parts of the world where access is the deeper tension, technologies like crypto and AI serve a different role. For many in developing economies, crypto isn’t an end to money but a bridge into global systems. AI isn’t transcendence, it’s a tool for leapfrogging. 

In the West, exit has become the lens through which movements interpret technological change. First with crypto, now with AI, the hype cycle has been recast as a story of transcendence. A story about surpassing the limits of existing systems. And there may be no going back.

Fragmentation, polarization, the breakdown of meaning, and decaying institutional trust—the result of decades of twenty-first century shock—have created fertile ground for narratives of exit. Our most advanced technologies are more than capable of meeting that narrative moment.

Crypto and AI deliver on dreams we haven’t fully made sense of. They build parallel communities where the incoherence of modern life is collapsed into code. 

A new financial system with its own networks, protocols, currencies. Computing power that, in a single prompt, promises to absorb the labor of the knowledge economy. These are technically plausible myths of exit. And they’re believable in a way that the work of restoring institutional trust or rebuilding social cohesion no longer is.In that sense, belief itself becomes the product. It’s how these technologies grow, how they’re funded, how their roadmaps are understood. But it’s also something more. In a culture stripped of coherence, belief is the tool we have left to generate meaning. What started as a way to sell the future has become a way to make sense of the present.

We follow the narratives and participate in them. We repost, debate, retweet. We adopt the interpreter’s frame and share it as if this has always been how we talk about technology. Together we rewrite the story of progress, and our roles in telling it.

There is something thrilling about pursuing wholeness through code. The power, the speed, the feats of strength. They sustain our ability to imagine a future where everything will be different.

And that longing for something different won’t go away. There is too much accumulated fatigue with what is, too much recognition that across industry, economy, society it’s been business as usual for far too long.

Crypto and AI didn’t create that longing. But they have become the clearest expressions of it. They have stepped into a vacuum left by stifled innovation and stalled institutional renewal. If upgrade culture belonged to a previous era, these technologies now offer its replacement: not improvements to what we have, but offramps to something else.

Now, AI agents arrive with capabilities we’ve designed out of our own experience: the freedom to move seamlessly across digital boundaries, to act with autonomy across platforms, to escape the constraints of single ecosystems. These agents can traverse the digital landscape, executing actions without supervision, unbound by the walls that confine human users. We marvel at machines exercising freedoms we’ve never granted ourselves.

This paradox underscores a contradiction in how we’ve built the digital world.

For users, boundaries are intentional and interoperability is achieved only through hacks or mandates. For agents, interoperability exists from day one. 

The emerging “Agentic Era” grants machines a freedom to navigate digital spaces that we have systematically withheld from people.

The irony is striking: we’re building AI agents to overcome the very constraints we’ve designed into our digital systems. 

These were not technical constraints. They were business imperatives that have shaped the internet from its earliest days until now, defining not just products but our very conception of what it means to be human in digital spaces.

Below, I ask why we marvel at the freedoms we’re granting to AI, while accepting as inevitable the limits we’ve long placed on ourselves. I trace how we arrived at a model built on user lock-in, the cultural unease that’s accompanied it from the beginning, alternatives that challenge its assumptions, and the deeper choice now confronting us in the age of agentic AI.

User Stories

Online, we are everywhere users. Searching, shopping, sharing, creating, listening, watching—users. In digital product design, the word most commonly paired with user is experience. User experience teams are the “champions of the user,” uncovering needs and ensuring products meet them.

But another word takes over when design gives way to business: lock-in. Digital businesses depend on keeping activity within their boundaries to maximize acquisition, retention, and monetization. Lock-in is what prevents a social graph, an ebook, a playlist, or a reputation from moving easily between ecosystems. It defines the space in which user experience teams operate.

Lock-in is how digital businesses scale. If your definition of the user doesn’t support that model, you don’t have a business.

Pure-play digital firms perfected lock-in. Legacy firms adopted it in the name of digital transformation. Together, they brought the tension between user experience and user lock-in to nearly every industry.

No sector has yet developed an alternative at scale. The result is an internet that treats people as inherently bounded, not by need or intention, but by business perimeter.

Efforts to change this—from open social graphs to data portability—remain at the margins. Without lock-in, there is no business model. And once you have lock-in, you have users: people with constrained agency by default.

Our most widely used technology products deliver real value through this model. But in doing so, they reinforce a deeper logic: one that treats constraint as a condition of growth, and people as pathways to scale.

That default stands in stark contrast to what we’re now building for AI agents: systems designed, from the start, to move freely across the very boundaries we’ve constructed to contain ourselves.

Red Pills and Black Mirrors

Popular culture has long captured the unease that comes with treating people as locked-in users.

In The Matrix, the red pill awakens people from a simulated reality controlled by machines. Once awake, they can navigate that system, but they remain bound by its rules. Only agents—autonomous code—can bend or rewrite those rules.

Until Neo. Neo moves like an agent, fights like one, bends the world as they do. To be human and free in The Matrix is to become more like code.

Two decades later, Black Mirror takes the logic further. 

In Common People, a husband signs his comatose wife up for an experimental brain procedure, tethering her consciousness to a cloud service to preserve function. As the company’s business model evolves, their lives degrade, locked into a decaying digital service they can’t opt out of. Recovery of agency requires radical exit.

Between The Matrix and Black Mirror lies a quarter century of digital growth and a growing sense of decay. Cory Doctorow calls it the “enshittification” of the internet: a system that grows in capability even as it erodes in experience. Beneath that erosion is the internet’s foundational tension between user experience and user lock-in.

Neither The Matrix nor Black Mirror offers an alternative vision. That’s the point. They dramatize the cost of escape when systems built on constraint leave no room to imagine another way. Once we accept the premise of people as users, we’ve already limited what kind of digital world is possible. 

To escape that frame, we need new building blocks. A different world—and a different word.

Alt.User

If pop culture captures our unease with being locked in, infrastructure reveals the work of trying to break out. Not everyone accepts the user as the final word. Movements like crypto and digital public infrastructure have begun from a different premise: that people are not just users of digital systems, but participants with agency. Crypto imagines them as sovereign individuals. DPI treats them as citizens.

Crypto replaces user with sovereign individual, and lock-in with decentralization. In its vision of a scaled, decentralized financial system, people can own, store, exchange, and monetize digital assets without relying on intermediaries. 

In crypto, agency is ownership. The sovereign individual holds full custody of assets, identity, activity, and access. The model requires both new infrastructure and a shift in self-perception—from user to custodian of digital presence.

Digital public infrastructure begins elsewhere. Instead of sovereign individuals, it centers citizens. Instead of decentralization, it builds public goods. Through verified identity, shared standards, and data portability, DPI offers the building blocks for access, transparency, and agency at scale. 

In its most expansive form, DPI envisions people moving through digital life as rights-bearing participants—able to carry their data, identity, and reputation across services as easily as a passport crosses borders.

These alternatives rethink how digital systems scale. Crypto seeks it through decentralized coordination. DPI through state-backed standards and public infrastructure. Both move beyond the user as the default mode of participation. 

They remind us that what’s needed is reinvention—a new blueprint for digital life.

The Time Has Come to Make a Choice, Mr. Anderson

The user has had a good run. As the foundation of the internet’s business model, it has helped build an industry that touches nearly every part of life. It can retire comfortably, with a view from the beach of the digital realm it helped construct.

What would take its place? What would technology not centered on users and lock-in look like?

It would begin by separating people from products. 

Both crypto and DPI offer clues: as different as sovereign individual and citizen are, both demand that technology adapt to the person—not the other way around.

In the way people are using AI even today, you can see the contours of expanded agency. People move fluidly between different LLMs, improvising and orchestrating workflows across rather than within providers. To follow that logic would mean replacing the user with new roles: explorer, improviser, orchestrator, artist.

But these roles still map people to technology. We improvise not because the systems are seamless, but because we’ve learned to move across their gaps. This framing narrows our imagination to AI, when what we need to rethink is the digital foundation on which AI now rests.

Because the reality is that so much of what we are asking AI to orchestrate for us is a way around the constraints and lock-in of today’s internet. We are building code with agency to cut across the walls that keep people in.

We face a choice. 

We can continue to build AI inside the old frame: products that treat people as users, businesses that depend on lock-in. Or we can take the vast energy now spent on crafting agency for code, and apply it to how we think about people.

If AI truly is existential-level technology—as we’re told daily—we need a robust concept of the person to match its scale. The only word that can withstand that weight is human: one struggling to break free in The Matrix, and locked in the Black Mirror.

To center technology on the human is to resist narrowing us to tidy narratives of tasks completed inside a system. It is to reintroduce human complexity into the design of technology itself.

Human-centered technology would build upward from new first principles: meaning, dignity, worth, selfhood, trust, growth. These are not use cases. They are conditions for human flourishing. Why would we want our technology to deliver anything less?

Maximizing these conditions depends on freedom of movement. 

Meaning, worth, and trust cannot be contained in one or another digital ecosystem, or aggregated across them. They are strengthened from the freedom to come and go, to cut across digital domains with ease and fluency, to take what works and leave the rest behind. They depend on agency.

And there are signs of change. Many now feel that something fundamental is missing. The early idealism of user-centered design has deepened into a wider search for meaning, dignity, and complexity. A desire to bring the human back in. The talent is there. What’s missing is the model.

Decades into the internet, most businesses still scale through lock-in. The challenge for the next era—the truly agentic one—is to build by expanding human agency. Not by locking people in, but by letting them move.

It’s a story we’ve heard before—but what if it’s the wrong one?

What if AI doesn’t just surpass our capabilities—but reflects back a truth we’ve long avoided: that much of the knowledge economy runs on activity we’ve mistaken for value?

Each week brings fresh proof of AI’s reach: acing math olympiads, generating lifelike video in seconds, replacing layers of customer service. No part of knowledge work feels safe.

The narrative is so dominant, it can feel irresponsible to think beyond protecting jobs. But there’s another way to see what AI is doing—not as a substitute for human work, but as a mirror. Not a tool that shows us what machines can do—but one that reveals what we’ve already become.

AI doesn’t just replicate our capabilities—it reflects the systems we’ve built, the effort we expend, and the value we assume. If we’re willing to look into that mirror, what it reveals is sobering: an oversaturated knowledge economy that absorbs talent without clear impact, while other sectors—equally vital—remain persistently underserved.

But it also reveals an opening: a chance to confront how much of the knowledge economy has grown disconnected from value—and to begin building a more grounded, more diversified economy in its place.

Of course, there are areas where AI genuinely replaces high-value work—tasks with clear stakes and measurable outcomes. But much of what AI reflects back is the opposite: activity we’ve mistaken for value, and systems that reward motion more than results.

Fast Times in the Knowledge Economy

Work in the knowledge economy often produces activity without results—and we know it.

We know it from experience. To move up in the knowledge economy—from entry-level analyst to senior executive—is to produce, consume, and commission an inordinate amount of activity without outcome. Tasks, reports, and studies that lead nowhere.

No one who has spent time in the knowledge economy escapes this feeling. We’ve all built decks no one used, launched strategies that changed nothing. It’s not a failure of effort or ethics. It’s the structure of a system that rewards activity over outcome, and spins through repetition rather than results.

Not every work environment functions this way. In flight crews, orchestras, emergency rooms, and restaurant kitchens, interdependence is immediate and non-negotiable.

But knowledge organizations—whether thirty-person startups or multinationals, public or private—operate differently. They are home to a cascading rhythm of activity without impact, largely because in today’s knowledge economy, it’s often hard to tell how individuals—from entry level to C-suite—actually create value.

We’ve absorbed that opacity into how organizations function—where activity passes for impact, and effort signals value, even when no real connection exists. The signals of productivity are everywhere, even when the results aren’t. It’s a feature of the knowledge economy, not a bug.

That AI hallucinates—or produces persuasive botshit—shouldn’t be judged solely against whether it fails to match trustworthy human work. It should be seen as a mirror to the broader knowledge economy it was shaped by: one already full of confidently produced nonsense. Quarterly plans, annual roadmaps, five-year strategies—rituals often more performative than productive, and rarely built to withstand scrutiny.

We already know this. We feel it in the projects that stall, the initiatives that never land, the busy days that leave no mark. 

The AI mirror shows us something staggering: an incalculable number of hours where human talent is misallocated—not to creating markets, solving problems, or advancing frontiers, but to keeping the machine in motion.

And still the knowledge economy grows—more roles, more processes, more layers of abstraction. We’ve built a system where managing for results is difficult by design.

OKRs a Go Go

We’ve built systems to measure outcome—but most work escapes their grasp.

One of the core challenges of knowledge work—since the term first appeared—has been figuring out how it creates value. We know our teams are busy, but we’re never quite sure if their work actually drives results. If we’re honest, we’ve all had moments of wondering whether that’s true of our own work, too.

What we want is less activity, more impact. Not: “Last quarter we conducted a study and gained insights.” But: “Last quarter we integrated those insights into the product and saw a 5% increase in retention and a 10% bump in first-time customer spend.”

OKRs—objectives and key results—were meant to force that shift. To translate activity into outcomes. A team that embraces OKRs shouldn’t report completion. It should report change.

But in most organizations, OKRs relapse into glorified to-do lists. The work rarely moves past “study conducted, insights gained.” There’s no clear link to what changed—or whether it mattered.

You could swap in other frameworks—quarterly goals, success metrics, or scorecards—and get the same result. Some teams and organizations do have a clean line from effort to impact. Most don’t. Our tools for measuring knowledge work still fall short of the clarity we crave.

Now contrast that with the promise—and the trajectory—of AI. The more it gets embedded into knowledge work, the more that work exits the ambiguous world of activity and enters the traceable space of digital workflow. And in a digital workflow, everything can be measured.

Your monthly book club might not know you’re bluffing your way through The Odyssey. The meeting imposes structure and accountability, but everyone knows how to fake it—George Costanza-style.

Your Kindle, on the other hand, knows you stopped at page 15. It knows your pace, your session length, your font size. Every interaction is trackable.

The same shift is coming for knowledge work. As tasks move from human-to-human to human-with-AI to AI-to-AI, measurement becomes unavoidable. Not because AI outperforms humans at every task—but because we’ll finally see which tasks were never worth doing in the first place.

The real challenge in managing for results isn’t just about better tools—it’s about the work itself. Too much of what we do today is motion without impact. We’ve tried to fix it with sharper goals and smarter frameworks. But the harder truth is this: a lot of the work may not need doing at all. We’ve allocated more talent to the knowledge economy than it can meaningfully absorb.

Mind the Gap

The knowledge economy isn’t just misallocating effort—it’s misdirecting human potential.

This isn’t just a firm-level problem. And it’s not a critique of who does the work or what it’s directed toward. The knowledge economy clearly skews rewards and serves some customers better than others—across lines of race, gender, age, geography, and other dimensions of social and economic advantage. 

But the deeper issue is structural: too often, no one knows what good looks like—or what value really means. And yet we’ve absorbed more and more talent into that ambiguity.

Part of this has to do with how the knowledge economy grew. As more people became college-educated and digital tools made it easier to produce and distribute ideas, the sector expanded rapidly. It didn’t just grow—it became a status system, attracting a disproportionate share of talent. And like any status system, it built its own flywheel: generating work that validated and reinforced the system itself.

But that growth came at a cost.

It came at the expense of other sectors—care, food, skilled trades, health, infrastructure—where labor shortages persist, markets remain uneven, and essential needs are too often unmet.

The opportunity now is reallocation: into underserved sectors beyond the bounds of today’s knowledge economy. 

Some of that reallocation is already underway. The rise of “tool belt” careers—where value is tangible and the work speaks for itself—is a sign that something is shifting. But when that shift is driven more by fear of job loss than by recognition of imbalance, it shows just how strong the prevailing narrative still is.

Let’s be clear about what that imbalance is—and isn’t:

It’s not that all knowledge work is useless.

It’s not that AI should replace people.

It’s not that the answer is to do less work.

It’s that we’ve overbuilt one part of the economy and underinvested in others. That AI doesn’t just automate what we do—it reveals how narrowly we’ve defined where human effort belongs.

It’s a moment not just to react—but to rethink. To reevaluate where human effort is going—and where it’s most needed.

Beyond Today’s Knowledge Economy

There’s plenty of debate about how far AI will go—how many jobs it will transform or replace, and how soon. But here’s a more immediate truth: AI already shows us how easily much of what we do can be reproduced. That doesn’t mean all of it ought to be replaced. It means we finally have to ask: what’s worth preserving, and what isn’t?

Even if AI never replaces all knowledge work, the shock alone should be enough to confront what we already know: we’ve absorbed too much human talent into an economy built on activity over outcome, on establishing and reproducing status, at the expense of other kinds of needed work. The threat doesn’t need to fully materialize to expose what’s been broken all along.

What would it mean to look into the AI mirror—and walk away from today’s knowledge economy?

For years, we’ve seen labor shortages in sectors like care, trades, infrastructure, food systems, climate and energy, and teaching—while knowledge industries have become oversaturated. High pay, cultural status, and the portability of white-collar skills pulled talent in. Post-COVID, even teachers moved out, opting for more flexible, higher-paid roles.

Today, AI introduces precarity into what once felt like the permanence of knowledge work—and with it, the space to reconsider where human effort is most needed. It offers the possibility of rebalancing: from overserved to underserved, from overgrown industries to neglected ones.

Viewed this way, AI isn’t just a threat to jobs—it’s a catalyst for rethinking where talent belongs. Like pulling a block from a Jenga tower and realizing we don’t need to rebuild upward—we can build outward.

Are we willing to make that choice?

COVID was a preview. The knowledge economy went remote, with little impact on productivity or profit—and real gains in well-being. But it also revealed how dependent we are on the sectors that couldn’t go remote: care, food, delivery, infrastructure. They held the world together.

We’re still negotiating what it means to separate work from place. But the deeper imbalance—between the sectors we value and the ones we rely on—remains unresolved.

And we’ve seen elsewhere that skipping legacy infrastructure can be an advantage. Many emerging economies leapfrogged the PC era to build mobile-first financial systems. Never having developed oversaturated knowledge sectors, they may now be better positioned to adopt AI in ways that diversify rather than concentrate.

In more advanced economies, the opportunity is different. It requires moving beyond the knowledge economy we’ve built—no longer trying to optimize it, but rethinking where human effort truly belongs. And that won’t happen unless we realize that our deepest constraint isn’t technological. It’s narrative.

AI carries not just computational power, but narrative weight. We are in the grip of story as much as automation. And we have a choice: to accept the tale of obsolescence and displacement, or to write a new one—about redirection, and a broader understanding of where human effort belongs.

The story of obsolescence hinges on an understanding of people as finite—in the limits of our intelligence and in the varieties of our needs and wants. But what AI reveals isn’t our limits—it’s how narrowly we’ve defined them.

The AI mirror shows us what we already knew. That the knowledge economy is bloated, uneven, and full of work whose value is hard to defend. 

But it also shows us what’s possible: a future where effort is more connected to need, and where new ladders are built—not just in today’s industries, but in the ones we’ve ignored for too long.

A more realistic story begins here: many of our needs are still unmet. Our wants remain limitless—and often unpredictable.

The question AI raises isn’t obsolescence—it’s recognition: of how much human capacity we’ve overlooked, and how much human need and want we’ve yet to meet.

But in transitional periods—when the foundations are shifting—change often comes from elsewhere: from new technologies, shifting values, and a reshaping of the economic order. When we view these forces through a firm-centric lens, we constrain our ability to make sense of them—and to lead.

Firms tend to interpret change through familiar questions: What markets should we enter? What customers should we target? What products should we build? How should we price? The shifts underway now are different. They don’t just affect what firms offer—they reshape what firms can do, and what they are for.

Today, firms sense that the terrain is shifting, but not how or where it will settle. AI is poised to reshape business—but how, and on what timeline, remains unclear. People’s attitudes toward work—and their expectations of business—are changing, beyond the reach of firm policy. And state intervention in the economy—from industrial policy to great power rivalry—is reordering global markets. 

These forces don’t always move together, but their cumulative effect is to challenge how business is done and what firms are for.

In moments like this, business escapes the firm. It no longer unfolds solely within organizations or markets, but across a wider terrain—shaped by dynamics that firms respond to, but do not control. 

We don’t yet know what will replace the old foundations. But the urge to name something too quickly—whether a model, a playbook, or a purpose—often obscures more than it reveals. The challenge, and the opportunity, is to see business through a lens wide enough to make sense of the transition as it unfolds.

Beyond the Roadmap

In transitional periods, technologies don’t just change what firms do. They change what business is.

Much of today’s business conversation around artificial intelligence focuses on productivity: how firms can automate tasks, reduce costs, or accelerate workflows. This framing casts AI as an enterprise tool—something to be evaluated, adopted, and implemented. Inside many firms, the dominant questions are operational: Can we trust the output? Will it replace jobs? Where do we find use cases?

But this frame risks missing the bigger picture. AI is not just a tool for firm-level efficiency. It’s a general-purpose technology that reshapes the conditions under which business takes place—redefining how knowledge is created, how decisions are made, and how institutions coordinate action. It alters who gets to participate in value creation, what skills matter, and where the boundaries of the firm should be drawn.

Many of AI’s most significant effects are surfacing outside the firm—not because firms are slow, but because individuals face fewer constraints. People are using AI to learn, create, and connect in real time, reshaping how knowledge is gained, how authorship is defined, what interaction means. Through open source models, agent tools, and viral culture—like the recent Studio Ghibli image wave—the public is actively shaping how AI is used and what it means.

At the same time, early decisions about what AI is—what it can do, what it should do—are being made by labs and builders who are often at a distance from how work functions or how institutions create value. That leaves a growing gap: between how AI is defined, how it’s adopted, and what it will ultimately mean for society. Firms that focus only on enterprise use cases may miss this broader reshaping—and lose the ability to engage with it.

At times like this, firms can’t afford to be passive consumers of emerging technology. But in the race not to be left behind, too many have defaulted to implementing AI capabilities developed elsewhere—rather than engaging with the deeper questions AI raises. That means not just experimenting with tools, but asking what knowledge counts, how trust is earned, and which futures we choose to make real. 

If the challenge for firms in disruptive periods is digital transformation, in transitional times it’s technological participation—moving beyond adoption to help shape the assumptions these technologies carry.

Leaders guiding firms through this shift need to engage with the broader transformation AI is enabling: looking past tools and workflows to the assumptions they build in, and tracking how AI is reshaping the environment around the firm—how people learn, how work is coordinated, how trust is built, and how decisions get made.

Firms don’t need to have all the answers. But they do need to pay attention. The most important impacts of AI won’t be found in product roadmaps, but in the world beyond the firm’s walls. 

Cultural Drift

The split between firm culture and broader societal shifts is widening.

Recently, we’ve swung from a high-water mark of corporate purpose—symbolized by the Business Roundtable’s 2019 statement—to a wave of retrenchment: from flexible work to inclusion to sustainability—initiatives once framed as core to corporate purpose. What seemed like a turning point now feels more like a brief pause—an attempt to redefine the purpose of business from within, without changing the conditions around it.

But shareholder primacy didn’t originate inside firms. It took hold through economic theory, gained momentum in capital markets, spread through business schools, and was locked in by decades of policy and practice. It was a system-wide shift. The idea that stakeholder capitalism could take hold through firm-led declarations ignored how real change moves: not by consensus within companies, but through changes in the wider landscape they inhabit. 

Today, that landscape is shifting again. Generational expectations around work, meaning, and equity are taking shape outside formal institutions. Remote work, still in its early forms, is changing how we experience time, use space, and understand control. Norms are forming in places firms don’t oversee: on social networks, in side hustles, and in the slow, steady exit from traditional career arcs—toward more flexible, independent, or project-based work. These are early cultural signals of how work and value will be redefined.

And beneath these shifts lies a deeper divergence. Across many countries—including the U.S., U.K., Germany, and South Korea—young men and women are no longer moving through the world in sync. They are splitting in values and in outcomes, forming distinct worldviews, economic trajectories, and expectations. 

We tend to think of demographic change as slow, but this realignment has been sharp and fast—accelerated by digital environments that fragment social experience and reinforce separate spheres. This drift is global and still unfolding. Most firms, still built around generational archetypes and one-size-fits-all policies, are unprepared to absorb how this generation of workers is taking shape. 

The result is a growing dissonance—one dramatized by Severance, but visible in boardrooms and Slack threads alike. Inside firms, one language and logic. Outside, another. And in that space between them, the relevance and authority of the firm begins to slip. 

We talk about the future of work as if it will arrive through corporate policy. But much of it is already unfolding elsewhere—in the drift between generations, in norms shaped online, in quiet exits that look like apathy but may be something else entirely. For leaders navigating this shift, it may be less about offering answers and more about listening for what’s emerging—before the disconnect becomes a divide.

Shifting Grounds

How economic ideas outside the firm are redrawing its terrain.

Business doesn’t operate in a vacuum. It moves within an economic order—shaped by ideas, institutions, and now, more visibly again, by political will.

Globalization was one such order: it opened domestic firms to foreign markets, restructured supply chains, and redefined where and how companies could organize, build, and compete. It was a multi-decade transition that changed how business saw its purpose and played its role.

We’re entering another such shift today, shaped by forces beyond the firm.

Industrial policy has re-entered the mainstream, reframing how nations pursue growth, allocate capital, and compete globally. Once dismissed as outdated or inefficient, it’s now central to economic strategy in the U.S., the EU, and parts of Asia..

Meanwhile, great power rivalry—especially between the U.S. and China—has leapt from the pages of Foreign Affairs. It’s restructuring supply chains, redirecting investment, and challenging the logic of openness that defined the last era of globalization. 

Recent tariff shocks underscore how much strategic direction now originates at the national level—not within firms. The return of state-led economic intervention—sometimes strategic, sometimes chaotic—reminds us that firms don’t define the rules of engagement. They respond to them.

The ideas behind these shifts aren’t new—debates over strategic interdependence, national competitiveness, and the risks of great power conflict have circulated for years. What’s changed is their movement: from theory into policy, and from policy into the daily decisions firms must make. 

There’s a temptation to name this as the start of a new order. But we’re still in the early stages—where old assumptions are being undone faster than new ones can cohere.

These shifts emerge from places firms don’t always track—policy papers, law journals, economic theory—but ultimately reshape what’s viable, valuable, and vulnerable. Their effects now show up everywhere—from capital allocation to product strategy, hiring, and market access.

In moments like this, leadership becomes less about directing what’s known and more about engaging with what’s unfolding. 

Leaders will need to develop the awareness—and the relationships—that allow them to move in step with the ideas shaping tomorrow’s terrain.

Moving Forward

In stable periods, business tends to move from the inside out—driven by strategy, scaled through execution, and absorbed into norms. In disruptive periods, bold firms take the lead—challenging incumbents, creating new categories, reshaping markets. 

But in transitional periods, change often begins elsewhere. What matters most to business may not emerge within firms, or even within markets. It takes shape in ideas that gain traction—in law, policy, technology, culture—and gradually redraw the boundaries of what firms can be.

The forces reshaping business are already at work: in AI, in generational behavior, in politics. But they haven’t settled into a structure we can fully name. Moving too quickly to certainty can limit our perspective—and obscure the signals that will matter most.

These signals aren’t hidden. But they’re easy to miss when we’re focused only on what fits inside of firms. Ideas that seem marginal or impractical at first often prove foundational in hindsight. 

The challenge isn’t prediction. It’s perception: developing the range to notice what’s changing and the patience to follow it before the path is clear.

This requires leadership that looks beyond the firm, tracks the edges of change, and engages with emerging ideas—even if they take years to develop. Leaders who do this well are often the most curious—those willing to embrace ambiguity and hold competing perspectives.

Often, the shape of what’s next is already there—just not where we’re used to seeing it.

When business escapes the firm, leaders have to meet it on new ground.

How we imagine the future shapes how we think about business. When societies think in moonshots, firms think in audacious goals. When our understanding of the future changes, so must our approach to growth.

The 20th-century imagination was filled with visions of progress—flying cars, the end of disease, domestic robots, abundant leisure. But those dreams never materialized. Instead, the 21st century has been shaped by shocks rather than moonshots. Nostalgia for an era when the Western world could “put a man on the moon” has given way to uncertainty about what progress even looks like.

The forces shaping today’s world do not follow the linear paths of progress we once imagined. Financial crises, pandemics, extreme climate events, political polarization, and technology acceleration now drive change more than deliberate planning. In this new “Age of Shock,” traditional strategic planning—built around envisioning, organizing, and marshalling resources toward fixed goals—will increasingly fall short.

Succeeding in today’s interconnected world requires a shift from controlling change to navigating and adapting to it. Without acknowledging the uncertainty of transformational forces like AI or climate change, firms will fail to ask the open-ended questions that unlock new markets and growth. Those that remain siloed cannot see how today’s shocks ripple across domains, leaving them vulnerable. Today’s challenge is learning how to grow when the path forward is unknown.

Growth in the Age of Shock means moving beyond plan-and-execute strategies and embracing opportunism and entrepreneurialism. Traditional planning is limiting, weighed down by practices that assume a linear path forward. Instead, firms must recognize shock as an opening for value creation—and act on it. Shocks loosen the grip of existing structures, reorder norms and values, and create futures shaped by adaptation rather than past predictions.

The pandemic triggered a global reshuffling of where and how work gets done. Polarization has redefined the boundaries of what is politically and economically possible across nation-states worldwide. Artificial intelligence is dismantling the divide between knowledge work and automation, with consequences we have yet to fully grasp. In this environment, organizations that seize the moment and act without a playbook will outperform those relying on traditional strategic planning.

If this view of the 21st century holds—as the growing success of opportunistic, entrepreneurial actors and the repeated failure of rigid long-term planning suggests—then companies and boards need to reassess their approach to growth. They will need new expertise, a different approach to uncertainty, and leadership that embraces plural perspectives. 

New Expertise for a New Era

Recommendation #1: Integrate non-traditional expertise into core business functions.

Firms should start by bringing in the missing expertise needed to make sense of and act upon the 21st century. Measuring what they can control is no longer enough—they must develop insight into external forces beyond their control. Leading financial institutions now hire climate scientists and geopolitical analysts, recognizing that market success depends on understanding non-market forces. The rising demand for cross-domain sensemaking reflects this shift in thinking. 

To be effective, this new expertise must be fully integrated into how companies assess and serve customers and markets. The functional separation that defines many firms today—for example, policy on the periphery, product and sales at the core—creates blind spots in a shock-driven world. When AI ethicists help shape product decisions or geopolitical analysts inform market entry strategies, organizations gain the ability to navigate complex terrain. 

Boards should assess management’s grasp of the knowledge gaps they need to fill and their readiness to break down silos that prevent interdisciplinary expertise from shaping business strategy. The history of digital transformation—and the challenges firms faced in attracting and integrating digital leaders—offers a preview of what’s ahead. Today’s task is even greater, as much of the expertise firms now need lies beyond traditional business domains, in areas like climate science, energy transitions, demographics, and emerging fields such as AI ethics and digital trust. 

Reimagining Uncertainty as Opportunity

Recommendation #2: Transform managing uncertainty from downside mitigation to opportunity creation.

Organizations retooling for growth in the Age of Shock must approach uncertainty differently. Unplanned change naturally puts firms in a defensive, reactionary posture. The instinct is to assess and contain uncertainty as if it were quantifiable risk, which works when shocks are rare, the intervals between them long, and their impact contained. But in the 21st century, we face not just risk but uncertainty—where shocks are frequent, consequences unpredictable, and traditional models inadequate. 

When unplanned change is no longer an interruption of the business cycle but a regular feature of it, firms that treat uncertainty as a constraint will be constrained by it. Instead of reacting defensively, they must develop the internal conditions for agility, experimentation, and rapid execution when openings emerge.

Adaptive firms will delegate more decision-making to opportunistic, entrepreneurial teams that recognize and act on emerging opportunities. They learn to embrace ambiguity and operate without a playbook, equipping leaders and talent with the capabilities and culture needed to do both. These teams have proven successful in high-pressure military environments, where devolving authority to those closest to the action often outperforms traditional planning. Their decentralized decision making, rapid adaptability, free-flow of information, and bias toward action are qualities businesses can adopt in today’s increasingly unpredictable environment.

Boards should interrogate not just how management mitigates downsides—such as jobs lost to AI—but how they will create value in uncertain times. What new customer needs are emerging? What markets are being created? What unconventional partnerships should we pursue? A proactive stance toward uncertainty sees the reordering of norms and values as openings for growth that reactive firms will miss.

Leading in a World of Many Futures

Recommendation #3: Build leadership teams for a world of multiple, distinct market futures.

One key reason the 21st century differs from Western expectations is the arrival of alternative visions of the future. China’s transformation into an economic powerhouse and India’s digital identity initiatives have created development paths that don’t follow Western models. Social commerce, digital public goods, and transportation networks from Asia now shape global practices, while emerging market approaches to technology adoption often leapfrog Western patterns entirely.

The 21st century now reflects a broader imagination, where lingering Western visions share the stage with the economic and technological ambitions of larger populations and capable states. The ability of these populations and states to reroute or constrain paths to the future is itself a shock—to Western labor markets, business growth, and technological supremacy.

This plurality of futures calls for new leadership approaches. Western companies have discovered that success in non-Western markets comes from recognizing them as distinct ecosystems with their own rules, rather than as extensions of home markets. As products and services can no longer be built once and exported everywhere, management teams need experience across multiple systems. A background exclusively in a single, dominant market becomes a constraint rather than an advantage.

Too many firms still assign regional rather than global scope to non-Western market leaders. It is rare for insights from outside the home country to shape and drive global strategy. Few companies recruit global functional leaders from beyond the home market—even in fields where non-Western approaches excel, such as mobile commerce, digital payments, social media, travel, and hospitality. 

Even within traditional home markets, plurality is increasingly evident. The rural-urban divide in many Western nations has created distinct economic, cultural, and political ecosystems that function according to different rules and values. As a result, for many companies, home is no longer a singular but plural market. Boards should assess whether management is adapting to plural markets as they evolve or remains committed to a one-size-fits-all playbook.

Growth in the Age of Shock

A quarter of the way into the 21st century, we have a clearer understanding of how it differs from the past. Our interconnected, digital, networked, and global world challenges the linear model of progress that defined the 20th century. The Age of Shock—where change begins in one place and extends everywhere—calls for a new approach to growth.

For firms and boards, this means:

• Integrating the expertise needed to act in an unpredictable world

• Building opportunistic and entrepreneurial capabilities to navigate uncertainty as the new terrain for value creation

• Reshaping management teams for an era of plural, rather than singular, markets

Firms that develop these strengths will operate with a deeper understanding of the 21st century as it is—not as we once imagined it—and will shape the many possibilities for change and growth that the Age of Shock presents.