Every cycle feels noisy while you’re living through it. The signal only becomes obvious later.

The biggest shifts ahead aren’t about more AI, more crypto, or more software. They’re about where intelligence moves, how value flows, and what becomes scarce again.

2025 was the year of abundance. AI gave everyone the ability to create more than ever before. More content. More software. More ideas. Much of it generic. Output exploded faster than judgment.

2026 is the correction. Scarcity returns, not because we create less, but because we choose more carefully. Focus, refinement, and intention replace raw throughput. What matters is no longer how much you can produce, but what survives selection pressure.

The biggest constraint on AI progress is no longer models or compute. It’s physical data.

The internet has been fully consumed. What’s missing are high-quality, real-world interactions: motion, friction, failure, edge cases, and human behavior in physical environments. Companies that can incentivize the capture, labeling, and feedback of real-world data through robotics, sensors, IoT, and human-in-the-loop networks are building the most defensible datasets that exist.

These datasets won’t be scraped. They’ll be earned.

The robotaxi war will heat up as Waymo and Tesla’s Robotaxi receive approval in more cities worldwide. If you’ve ridden in one in California or Texas, you know how life-changing it feels. Every mile driven compounds a data advantage that is nearly impossible to replicate, pushing autonomy closer to a default mode of transportation rather than a novelty.

Beyond vehicles, robotics marks the maturation of agentic systems. Specialized physical AI startups signal a shift from purely digital agents to systems that act in the real world with accountability. Factories, warehouses, and logistics networks become training grounds where agents are forced to deal with physics, constraints, and consequences. What starts in controlled environments steadily moves into everyday life.

At the same time, XR glasses finally get their day. They are no longer awkward or experimental. AI embedded directly into glasses turns the physical world into an interface: contextual memory, real-time assistance, navigation, translation, and perception layered onto daily life. You're already seeing Meta's glasses more frequently in the wild. Google is expected to launch its own in 2026 through partnerships with Gentle Monster and Warby Parker.

This shift extends to the device layer. Expect Apple to make meaningful moves as AI becomes deeply embedded within the iPhone itself. On-device inference preserves privacy, reduces latency, and makes intelligence feel ambient rather than transactional.

This is how autonomy scales across both machines and humans. Vehicles that improve from every mile driven. Robots that learn from every task performed. Wearables that adapt through lived experience. The winners unlock trillion-dollar markets because they own the feedback loop between intelligence and reality.

General-purpose models are impressive yet rapidly commoditizing.

As models converge in capability, differentiation shifts toward data, integration, and execution. The real winners are not the models that can answer everything, but the systems that can do one thing extremely well inside a real workflow.

This is where vertical AI separates itself. Instead of acting as a copilot, vertical AI replaces the entire process. It lives inside the workflow, ingests proprietary data, and makes decisions, not suggestions. It is trained on narrow, high-signal datasets that cannot be scraped or easily replicated.

Legal, healthcare, insurance, supply chain, underwriting. Entire industries where accuracy, compliance, and accountability matter more than novelty. Horizontal tools struggle here because good enough is not acceptable. Vertical AI outperforms by design because it is embedded where work actually happens and fed by proprietary data that compounds over time.

In 2026, incumbents will not be displaced by better models alone. They will be displaced by workflows that simply run themselves.

Legal is the proof point. Companies like Harvey and Ironclad already handle contract analysis, due diligence, and redlining at scale. In 2026, the shift goes further. Vertical AI moves from augmenting lawyers to replacing entire workflows end-to-end. The first firms will operate with a fraction of the headcount on routine work.

But this isn’t a story about job destruction. It rarely is. When tasks become cheaper and faster, demand for them grows. Lawyers freed from document review can handle more matters, advise more clients, and take on work that was previously uneconomical. The resistance to AI-powered legal tools won’t come from lawyers losing jobs. It will come from lawyers whose judgment and client relationships become the only defensible value they have left.

Insurance underwriting and claims processing follow the same arc. The pattern repeats anywhere work is high-volume, document-heavy, and expensive to get wrong.

AI demands new infrastructure. Real-time inference, multimodal data, distributed training, agent orchestration, and edge deployment are no longer edge cases. They are the default.

Today’s infrastructure was built for storage and batch compute, not for continuous reasoning, coordination, and action. This gap is most visible in embodied agents. While models are capable, systems remain fragmented. Workflows do not talk to each other cleanly. State is hard to persist. Latency, cost, and reliability break autonomy before it compounds.

Data lives in too many places, and retrieval is too slow and imprecise to support continuous reasoning, even as context windows grow. Without fast, accurate access to the right information at the right moment, intelligence stalls before it can act.

Agents are not failing because the models are weak. They are failing because the infrastructure beneath them is not designed for long-running, interactive, multi-system execution.

The structural problem is misaligned incentives. Model companies optimize for capability benchmarks. Infrastructure companies optimize for traditional workloads. No one owns the full stack from reasoning to action. Until compute, data movement, identity, and permissions are native to the agent layer, autonomy remains mostly aspirational.

As intelligence moves into browsers, factories, devices, and physical environments, infrastructure must become faster, lighter, and closer to the edge. Inference must run where decisions are made. Relevant data must be retrievable in real time, not stitched together after the fact.

The winners in this layer define how intelligence is created, deployed, and shared globally.

In 2026, expect the first durable agent platforms to emerge. Not demos, but systems that persist state, coordinate across tools, and run unsupervised for hours or days. The gap between “impressive prototype” and “reliable deployment” finally starts to close. The companies that solve orchestration, memory, and cross-system identity become the next infrastructure layer.

Energy and materials are becoming scarce again, and scarcity forces innovation.

Global electrification, AI-driven compute demand, and reindustrialization are colliding with fragile energy systems. Simultaneously, access to critical and precious metals is increasingly constrained by geopolitics, supply chain concentration, and environmental limits. These pressures are structural, not cyclical.

This is why fusion and advanced nuclear move from science projects to strategic imperatives. Reliable, abundant energy is no longer just about cost or climate. It is about sovereignty and economic stability. Breakthroughs in fusion, next-generation fission, and grid-scale energy storage become necessary to support AI, manufacturing, and modern economies at scale.

Countries that cannot secure energy and materials lose leverage across manufacturing, defense, and technology. Deep tech becomes infrastructure for the next century. The inputs to everything else.

Stablecoin supply crossed $300B in 2025. The era of accumulation is ending. The era of utility is beginning.

Regulatory clarity changed everything. The Clarity for Payment Stablecoins Act gave banks a formal path to issue. JPMorgan is scaling JPM Coin. PayPal is expanding PYUSD. Visa is settling in USDC. As licensing standardizes, issuance commoditizes. The competitive advantage shifts from technology to distribution.

The next chapter is usage. Payments, remittances, FX, tokenized assets, yield, and lending. These are behaviors people already perform every day. Stablecoins make them faster, cheaper, and global by default.

This shift accelerates with AI. Standards like x402 allow autonomous agents to make real-time payments for APIs, compute, and services. As software pays software, a machine-to-machine economy emerges that legacy rails cannot support.

Lending is the next major unlock. The first generation of onchain lending required heavy overcollateralization and served only crypto-native traders. What changes now is identity. Advances in onchain identity and zero-knowledge proofs allow users to verify creditworthiness, income, and compliance without exposing sensitive data. This creates a path toward undercollateralized lending where risk is priced on verified behavior rather than blunt collateral.

Yield follows. As the US continues to lower interest rates and inflation persists, people will seek the best possible returns with the least fees. Stablecoin-based accounts that offer transparent, direct access to yield become the next generation of high-yield savings. For consumers, this is where adoption starts.

Incumbents see this coming. Western Union is experimenting with stablecoin rails. Banks are piloting tokenized deposits. Adaptation is table stakes. But even the ones who move fast lose margin. Interchange, FX spreads, prepaid float, and correspondent banking margins don’t survive programmable money.

In 2026, the question isn’t whether stablecoins replace legacy rails. It’s who owns the distribution when they do.

As AI absorbs global knowledge and stablecoins power daily transactions, privacy becomes non-negotiable.

The next wave of infrastructure enables selective disclosure: proving what matters without revealing everything. Zero-knowledge proofs, secure enclaves, FHE, and MPC are no longer academic. They’re foundational.

Privacy becomes an operational necessity now because the cost of exposure has crossed an existential threshold. AI systems can correlate data across sources at scale. Breaches are no longer embarrassing but existential. Regulation has caught up. And the long-term threat of quantum computing makes today’s encryption a liability.

When anyone can generate anything, trust becomes the bottleneck. Verification infrastructure ensures that people, content, payments, and actions are real. Media provenance, AI-resistant identity, cryptographic attestations, and reputation systems become essential for both humans and agents.

There’s also a new economic layer emerging. People and devices can prove they contributed data, labeling, or expertise without revealing private information and get paid for it. Authentic contribution becomes a monetizable primitive.

In an infinite content world, authenticity is the new commodity.

As AI increases productivity and automation removes friction from daily life, people get something back that has been scarce for decades: time.

That time doesn’t disappear. It gets reinvested. And increasingly, it gets reinvested into extending life itself.

Healthtech in 2026 is not about wellness trends. It is about buying time with time. Wearable devices and continuous biomarker monitoring move from early adopters to defaults. Companies like Function and Superpower make bloodwork actionable and routine rather than annual and reactive. Peptides, precision supplementation, and AI-driven diagnostics enable care tailored to the individual rather than the average.

The feedback loop tightens. Health becomes measurable, adaptive, and proactive.

Reactive healthcare cannot support aging populations, longer working lives, and rising chronic disease. Longevity-focused healthtech shifts care upstream and reframes health as a compounding asset rather than a recurring emergency.

In 2026, investing in health is no longer aspirational. It is a rational strategy for preserving the most valuable resource people have left.

Multimodal AI continues to improve, and entertainment is where the boundaries blur first.

Tools like Sora show how quickly video realism is advancing. Suno did the same for audio. When visuals, voice, and emotion converge, creative output stops feeling synthetic.

This is already happening in music. Walk My Walk by Breaking Rust, a fully AI-generated artist, charted on digital country lists. Listeners didn’t distinguish between human and machine. In Japan, AI music stars are already mainstream. From experience, I've seen entertainment trends start there before moving West. In 2026, an AI-generated track will break into the Billboard Hot 100.

The implications extend beyond music. When anyone can make a hit, a film, or a feed’s worth of content, the bottleneck shifts from creation to selection. What do you listen to? Who do you trust? What even matters?

That question is the bridge to what comes next.

Timelines are flooded with AI-generated slop and hollow content. Influencer feeds increasingly depict lives optimized for engagement rather than reality, creating a constant sense of comparison without substance. People feel the disconnect.

As content becomes infinite and cheap, attention shifts away from scale and toward truth. The human response to abundance is not disengagement. It is curation. People want fewer things that feel real, grounded, and personally meaningful, not more things engineered for dopamine and aspiration theater.

This is where niches grow, not shrink. Communities form around specific tastes, ideas, and identities. What you truly like becomes signal.

We saw early proof of this in 2025. Long-form podcasts grew. Independent writing platforms like Substack expanded. People opted into slower, deeper content because it felt intentional rather than algorithmic. Meaning beat virality.

In 2026, this accelerates. AI does not replace curation. It amplifies it. Personal agents act as curators, not feeds. They ingest podcasts, newsletters, books, and conversations you actually care about, then help you refine what resonates and what does not. Over time, taste compounds.

Importantly, this kind of curation gives people time back. By spending less energy sifting through noise, people can be more intentional online and more present offline. Online communities become places to learn and deepen interests, not destinations to stay trapped inside.

That reclaimed attention flows back into real life.

The future isn’t less content. It’s content that helps you live more fully outside the feed.

As AI automates work and decision making, people are forced to confront a new question: what do you do with the time you get back?

In 2025, social clubs made a visible comeback. Many will follow the same traps as the past: broad positioning, status signaling, a lack of clear purpose beyond access. Short-lived hype followed by quiet churn.

We have seen this movie before. Clubs like Zero Bond or Soho House initially succeed by tapping into cultural momentum, but struggle to sustain long-term engagement when identity replaces action.

The communities that endure are built around specificity and shared behavior. Action is the filter. Othership works because it is not just social. It is for people who care about longevity, recovery, and showing up consistently for a physical practice. The activity creates commitment. The niche creates alignment.

This pattern repeats across categories. Board games. Sports leagues. Cooking classes. Pottery studios. Run clubs. Strength training. Communities built around doing something together create real bonds because participation requires effort, not just presence.

This is not escapism. It is maintenance. Online curation helps people find what they care about. Offline communities give them a place to live it. People who believe the future will be longer invest in staying present, capable, and connected inside it.

Vinod Khosla has said that technology ultimately gives humans back time. In 2026, we see people use that time not for status or spectacle, but to build durable relationships and embodied skills anchored in shared action.

Physical data. Trust. Authenticity. Health. Energy. Taste. Time.

These are the new scarcities. Not because they disappeared, but because everything else became abundant.

The coming year belongs to whoever figures out how to create, verify, and compound them.

The opportunity is the same whether you’re building infrastructure or building a life: invest in what compounds. Your health. Your taste. Your relationships. The skills and communities that don’t depreciate when the next model drops.

In an age of infinite output, the scarcest thing is knowing what matters to you.

Thanks for reading Mixed Realities by TJ Kawamura! Subscribe for free to receive new posts and support my work.

Subscribe

Every cycle feels noisy while you’re living through it. The signal only becomes obvious later.

The biggest shifts ahead aren’t about more AI, more crypto, or more software. They’re about where intelligence moves, how value flows, and what becomes scarce again.

2025 was the year of abundance. AI gave everyone the ability to create more than ever before. More content. More software. More ideas. Much of it generic. Output exploded faster than judgment.

2026 is the correction. Scarcity returns, not because we create less, but because we choose more carefully. Focus, refinement, and intention replace raw throughput. What matters is no longer how much you can produce, but what survives selection pressure.

The biggest constraint on AI progress is no longer models or compute. It’s physical data.

The internet has been fully consumed. What’s missing are high-quality, real-world interactions: motion, friction, failure, edge cases, and human behavior in physical environments. Companies that can incentivize the capture, labeling, and feedback of real-world data through robotics, sensors, IoT, and human-in-the-loop networks are building the most defensible datasets that exist.

These datasets won’t be scraped. They’ll be earned.

The robotaxi war will heat up as Waymo and Tesla’s Robotaxi receive approval in more cities worldwide. If you’ve ridden in one in California or Texas, you know how life-changing it feels. Every mile driven compounds a data advantage that is nearly impossible to replicate, pushing autonomy closer to a default mode of transportation rather than a novelty.

Beyond vehicles, robotics marks the maturation of agentic systems. Specialized physical AI startups signal a shift from purely digital agents to systems that act in the real world with accountability. Factories, warehouses, and logistics networks become training grounds where agents are forced to deal with physics, constraints, and consequences. What starts in controlled environments steadily moves into everyday life.

At the same time, XR glasses finally get their day. They are no longer awkward or experimental. AI embedded directly into glasses turns the physical world into an interface: contextual memory, real-time assistance, navigation, translation, and perception layered onto daily life. You're already seeing Meta's glasses more frequently in the wild. Google is expected to launch its own in 2026 through partnerships with Gentle Monster and Warby Parker.

This shift extends to the device layer. Expect Apple to make meaningful moves as AI becomes deeply embedded within the iPhone itself. On-device inference preserves privacy, reduces latency, and makes intelligence feel ambient rather than transactional.

This is how autonomy scales across both machines and humans. Vehicles that improve from every mile driven. Robots that learn from every task performed. Wearables that adapt through lived experience. The winners unlock trillion-dollar markets because they own the feedback loop between intelligence and reality.

General-purpose models are impressive yet rapidly commoditizing.

As models converge in capability, differentiation shifts toward data, integration, and execution. The real winners are not the models that can answer everything, but the systems that can do one thing extremely well inside a real workflow.

This is where vertical AI separates itself. Instead of acting as a copilot, vertical AI replaces the entire process. It lives inside the workflow, ingests proprietary data, and makes decisions, not suggestions. It is trained on narrow, high-signal datasets that cannot be scraped or easily replicated.

Legal, healthcare, insurance, supply chain, underwriting. Entire industries where accuracy, compliance, and accountability matter more than novelty. Horizontal tools struggle here because good enough is not acceptable. Vertical AI outperforms by design because it is embedded where work actually happens and fed by proprietary data that compounds over time.

In 2026, incumbents will not be displaced by better models alone. They will be displaced by workflows that simply run themselves.

Legal is the proof point. Companies like Harvey and Ironclad already handle contract analysis, due diligence, and redlining at scale. In 2026, the shift goes further. Vertical AI moves from augmenting lawyers to replacing entire workflows end-to-end. The first firms will operate with a fraction of the headcount on routine work.

But this isn’t a story about job destruction. It rarely is. When tasks become cheaper and faster, demand for them grows. Lawyers freed from document review can handle more matters, advise more clients, and take on work that was previously uneconomical. The resistance to AI-powered legal tools won’t come from lawyers losing jobs. It will come from lawyers whose judgment and client relationships become the only defensible value they have left.

Insurance underwriting and claims processing follow the same arc. The pattern repeats anywhere work is high-volume, document-heavy, and expensive to get wrong.

AI demands new infrastructure. Real-time inference, multimodal data, distributed training, agent orchestration, and edge deployment are no longer edge cases. They are the default.

Today’s infrastructure was built for storage and batch compute, not for continuous reasoning, coordination, and action. This gap is most visible in embodied agents. While models are capable, systems remain fragmented. Workflows do not talk to each other cleanly. State is hard to persist. Latency, cost, and reliability break autonomy before it compounds.

Data lives in too many places, and retrieval is too slow and imprecise to support continuous reasoning, even as context windows grow. Without fast, accurate access to the right information at the right moment, intelligence stalls before it can act.

Agents are not failing because the models are weak. They are failing because the infrastructure beneath them is not designed for long-running, interactive, multi-system execution.

The structural problem is misaligned incentives. Model companies optimize for capability benchmarks. Infrastructure companies optimize for traditional workloads. No one owns the full stack from reasoning to action. Until compute, data movement, identity, and permissions are native to the agent layer, autonomy remains mostly aspirational.

As intelligence moves into browsers, factories, devices, and physical environments, infrastructure must become faster, lighter, and closer to the edge. Inference must run where decisions are made. Relevant data must be retrievable in real time, not stitched together after the fact.

The winners in this layer define how intelligence is created, deployed, and shared globally.

In 2026, expect the first durable agent platforms to emerge. Not demos, but systems that persist state, coordinate across tools, and run unsupervised for hours or days. The gap between “impressive prototype” and “reliable deployment” finally starts to close. The companies that solve orchestration, memory, and cross-system identity become the next infrastructure layer.

Energy and materials are becoming scarce again, and scarcity forces innovation.

Global electrification, AI-driven compute demand, and reindustrialization are colliding with fragile energy systems. Simultaneously, access to critical and precious metals is increasingly constrained by geopolitics, supply chain concentration, and environmental limits. These pressures are structural, not cyclical.

This is why fusion and advanced nuclear move from science projects to strategic imperatives. Reliable, abundant energy is no longer just about cost or climate. It is about sovereignty and economic stability. Breakthroughs in fusion, next-generation fission, and grid-scale energy storage become necessary to support AI, manufacturing, and modern economies at scale.

Countries that cannot secure energy and materials lose leverage across manufacturing, defense, and technology. Deep tech becomes infrastructure for the next century. The inputs to everything else.

Stablecoin supply crossed $300B in 2025. The era of accumulation is ending. The era of utility is beginning.

Regulatory clarity changed everything. The Clarity for Payment Stablecoins Act gave banks a formal path to issue. JPMorgan is scaling JPM Coin. PayPal is expanding PYUSD. Visa is settling in USDC. As licensing standardizes, issuance commoditizes. The competitive advantage shifts from technology to distribution.

The next chapter is usage. Payments, remittances, FX, tokenized assets, yield, and lending. These are behaviors people already perform every day. Stablecoins make them faster, cheaper, and global by default.

This shift accelerates with AI. Standards like x402 allow autonomous agents to make real-time payments for APIs, compute, and services. As software pays software, a machine-to-machine economy emerges that legacy rails cannot support.

Lending is the next major unlock. The first generation of onchain lending required heavy overcollateralization and served only crypto-native traders. What changes now is identity. Advances in onchain identity and zero-knowledge proofs allow users to verify creditworthiness, income, and compliance without exposing sensitive data. This creates a path toward undercollateralized lending where risk is priced on verified behavior rather than blunt collateral.

Yield follows. As the US continues to lower interest rates and inflation persists, people will seek the best possible returns with the least fees. Stablecoin-based accounts that offer transparent, direct access to yield become the next generation of high-yield savings. For consumers, this is where adoption starts.

Incumbents see this coming. Western Union is experimenting with stablecoin rails. Banks are piloting tokenized deposits. Adaptation is table stakes. But even the ones who move fast lose margin. Interchange, FX spreads, prepaid float, and correspondent banking margins don’t survive programmable money.

In 2026, the question isn’t whether stablecoins replace legacy rails. It’s who owns the distribution when they do.

As AI absorbs global knowledge and stablecoins power daily transactions, privacy becomes non-negotiable.

The next wave of infrastructure enables selective disclosure: proving what matters without revealing everything. Zero-knowledge proofs, secure enclaves, FHE, and MPC are no longer academic. They’re foundational.

Privacy becomes an operational necessity now because the cost of exposure has crossed an existential threshold. AI systems can correlate data across sources at scale. Breaches are no longer embarrassing but existential. Regulation has caught up. And the long-term threat of quantum computing makes today’s encryption a liability.

When anyone can generate anything, trust becomes the bottleneck. Verification infrastructure ensures that people, content, payments, and actions are real. Media provenance, AI-resistant identity, cryptographic attestations, and reputation systems become essential for both humans and agents.

There’s also a new economic layer emerging. People and devices can prove they contributed data, labeling, or expertise without revealing private information and get paid for it. Authentic contribution becomes a monetizable primitive.

In an infinite content world, authenticity is the new commodity.

As AI increases productivity and automation removes friction from daily life, people get something back that has been scarce for decades: time.

That time doesn’t disappear. It gets reinvested. And increasingly, it gets reinvested into extending life itself.

Healthtech in 2026 is not about wellness trends. It is about buying time with time. Wearable devices and continuous biomarker monitoring move from early adopters to defaults. Companies like Function and Superpower make bloodwork actionable and routine rather than annual and reactive. Peptides, precision supplementation, and AI-driven diagnostics enable care tailored to the individual rather than the average.

The feedback loop tightens. Health becomes measurable, adaptive, and proactive.

Reactive healthcare cannot support aging populations, longer working lives, and rising chronic disease. Longevity-focused healthtech shifts care upstream and reframes health as a compounding asset rather than a recurring emergency.

In 2026, investing in health is no longer aspirational. It is a rational strategy for preserving the most valuable resource people have left.

Multimodal AI continues to improve, and entertainment is where the boundaries blur first.

Tools like Sora show how quickly video realism is advancing. Suno did the same for audio. When visuals, voice, and emotion converge, creative output stops feeling synthetic.

This is already happening in music. Walk My Walk by Breaking Rust, a fully AI-generated artist, charted on digital country lists. Listeners didn’t distinguish between human and machine. In Japan, AI music stars are already mainstream. From experience, I've seen entertainment trends start there before moving West. In 2026, an AI-generated track will break into the Billboard Hot 100.

The implications extend beyond music. When anyone can make a hit, a film, or a feed’s worth of content, the bottleneck shifts from creation to selection. What do you listen to? Who do you trust? What even matters?

That question is the bridge to what comes next.

Timelines are flooded with AI-generated slop and hollow content. Influencer feeds increasingly depict lives optimized for engagement rather than reality, creating a constant sense of comparison without substance. People feel the disconnect.

As content becomes infinite and cheap, attention shifts away from scale and toward truth. The human response to abundance is not disengagement. It is curation. People want fewer things that feel real, grounded, and personally meaningful, not more things engineered for dopamine and aspiration theater.

This is where niches grow, not shrink. Communities form around specific tastes, ideas, and identities. What you truly like becomes signal.

We saw early proof of this in 2025. Long-form podcasts grew. Independent writing platforms like Substack expanded. People opted into slower, deeper content because it felt intentional rather than algorithmic. Meaning beat virality.

In 2026, this accelerates. AI does not replace curation. It amplifies it. Personal agents act as curators, not feeds. They ingest podcasts, newsletters, books, and conversations you actually care about, then help you refine what resonates and what does not. Over time, taste compounds.

Importantly, this kind of curation gives people time back. By spending less energy sifting through noise, people can be more intentional online and more present offline. Online communities become places to learn and deepen interests, not destinations to stay trapped inside.

That reclaimed attention flows back into real life.

The future isn’t less content. It’s content that helps you live more fully outside the feed.

As AI automates work and decision making, people are forced to confront a new question: what do you do with the time you get back?

In 2025, social clubs made a visible comeback. Many will follow the same traps as the past: broad positioning, status signaling, a lack of clear purpose beyond access. Short-lived hype followed by quiet churn.

We have seen this movie before. Clubs like Zero Bond or Soho House initially succeed by tapping into cultural momentum, but struggle to sustain long-term engagement when identity replaces action.

The communities that endure are built around specificity and shared behavior. Action is the filter. Othership works because it is not just social. It is for people who care about longevity, recovery, and showing up consistently for a physical practice. The activity creates commitment. The niche creates alignment.

This pattern repeats across categories. Board games. Sports leagues. Cooking classes. Pottery studios. Run clubs. Strength training. Communities built around doing something together create real bonds because participation requires effort, not just presence.

This is not escapism. It is maintenance. Online curation helps people find what they care about. Offline communities give them a place to live it. People who believe the future will be longer invest in staying present, capable, and connected inside it.

Vinod Khosla has said that technology ultimately gives humans back time. In 2026, we see people use that time not for status or spectacle, but to build durable relationships and embodied skills anchored in shared action.

Physical data. Trust. Authenticity. Health. Energy. Taste. Time.

These are the new scarcities. Not because they disappeared, but because everything else became abundant.

The coming year belongs to whoever figures out how to create, verify, and compound them.

The opportunity is the same whether you’re building infrastructure or building a life: invest in what compounds. Your health. Your taste. Your relationships. The skills and communities that don’t depreciate when the next model drops.

In an age of infinite output, the scarcest thing is knowing what matters to you.

Thanks for reading Mixed Realities by TJ Kawamura! Subscribe for free to receive new posts and support my work.

Subscribe