AI agents need to transact autonomously, globally, and at machine speed, often with counterparties they have never encountered before. That requires programmable spending constraints, verifiable identity without centralized gatekeepers, and settlement that is instant, transparent, and always on.
The properties that define crypto, permissionless access, programmable money, transparent settlement, and onchain identity, match what autonomous software needs to transact at scale without human intermediaries. That convergence is no longer theoretical. It is being built in real time.
Traditional payment rails assume a human is on the other end. Every layer reflects that assumption. Credit cards require identity verification tied to a person. Bank transfers depend on business hours and national jurisdictions. ACH settles in days. Chargebacks exist because a human might dispute a charge. The financial system was designed for humans and bounded by business hours and geography.
AI agents do not operate this way. They transact programmatically, instantly, and often in amounts as small as fractions of a cent, across borders, 24 hours a day. They need to pay for API calls, data access, compute resources, and services from other agents. They may do this thousands of times per day without waiting for human approval.
The bottleneck is not model capability or reasoning quality. It is economic infrastructure.
The intuitive response is that agents can use credit cards. For basic tasks, they can. An assistant booking a flight with your saved card is already solved. But that is not autonomy. It is delegation. A credit card still assumes a human principal behind every transaction.
The shift happens when agents manage capital, hire other agents, and transact at machine speed with unknown counterparties. Credit cards do not fail because of policy. They fail because the architecture assumes a human participant. A credit card “authorization” is a promise that settles days later. An agent operating at machine speed cannot wait for a three-day ACH window or absorb the risk of a chargeback sixty days after execution. For autonomous software, finality is the only form of trust.
In February 2026, Stripe integrated the x402 protocol on Base.
The HTTP 402 “Payment Required” status code was reserved in the original HTTP/1.1 specification, but was never implemented. With credit card fees measured in fixed per-transaction costs, selling anything under a dollar per request was economically impractical. The web defaulted to ads and subscriptions instead. x402 gives that dormant status code an economic use case.
The flow is simple. An AI agent sends an HTTP request to an API endpoint. The server responds with a 402 status code and payment instructions. The agent sends USDC to a specified address on Base. Once confirmed, the server grants access. No account creation. No API keys. No human involvement.
Stripe’s integration made this production ready. Developers can charge agents for API calls or MCP requests using Stripe’s existing PaymentIntents API. Funds settle into standard Stripe balances, with taxes, refunds, and reporting handled as usual. For developers, it looks like any other Stripe integration. For agents, the internet finally has a native payment layer.
The distinction between using a service and paying for a service collapses into a single protocol-level interaction. The payment is embedded in the request. For agents operating at machine speed, that difference matters.
The constraint right now is surface area. x402 works, but it only works where endpoints support it. Adoption depends on developers adding payment middleware to their APIs. Most have not yet. The protocol is live. The network of participating endpoints is still thin.
x402 handles the payment primitive. But agents operating across platforms need more than a way to pay. They need a shared language for commerce itself.
Stripe is building that too. With OpenAI, it developed the Agentic Commerce Protocol, an open standard that establishes a shared technical language between AI platforms and businesses. It launched an Agentic Commerce Suite that lets businesses sell across multiple AI interfaces and protocols with a single integration. Anthropologie, Urban Outfitters, Etsy, Coach, and Kate Spade are already onboarding.
Stripe also introduced Shared Payment Tokens, a new payment primitive that lets agents initiate payments without exposing credentials. The tokens work even at businesses that do not process payments through Stripe. That last detail is significant. It means the agent payment layer is being designed to extend beyond any single platform’s ecosystem.
What matters is that these layers are converging. Payment rails, commerce standards, and agent communication protocols are assembling into an interoperable stack. Agents are no longer an edge case. They are becoming a primary user class.
Stripe’s x402 integration did not emerge in isolation. It reflects a deeper strategic bet.
Over the past 18 months, Stripe has assembled the crypto infrastructure stack for agent commerce piece by piece.
In early 2025, Stripe closed its $1.1 billion acquisition of Bridge, a stablecoin infrastructure platform. Bridge’s transaction volume has since quadrupled. It now operates stablecoin financial accounts in 101 countries and recently received conditional approval from the OCC to establish a federally chartered national trust bank.
In mid 2025, Stripe acquired Privy, which powers more than 110 million programmable wallets. Privy embeds crypto wallets directly into applications without requiring users to interact with third party wallet software.
In September 2025, Stripe partnered with Paradigm to develop Tempo, a Layer 1 blockchain purpose-built for payments. Tempo is designed for sub-second finality, native support for batch transfers and microtransactions, and a built-in mechanism that allows agents to pay transaction fees in the same stablecoins they are transacting in. On most blockchains, an agent making a one-cent data request must first hold a separate volatile token to pay network fees. That is a logic bottleneck for autonomous software. Tempo removes it.
Bridge handles stablecoin orchestration. Privy handles wallet infrastructure. Tempo handles settlement. x402 handles the payment protocol. Together, they form a pipeline from agent intent to onchain execution, assembled by a company that already processes 1.6 percent of global GDP.
In 2025, stablecoin payments volume doubled even as speculative crypto trading declined. Infrastructure usage expanded while asset prices fell.
Stripe processed $1.9 trillion in volume in 2025 and is now valued at $159 billion. This is not a speculative crypto startup. It is a global payments company concluding that programmable, crypto-native rails are structurally suited for agent commerce.
In its 2025 annual letter, Stripe’s co-founders wrote that agentic commerce has “moved into a phase of building and real-world experimentation,” while noting that its evolution will unfold in stages. The measured tone matters. The company investing heavily in agent infrastructure is also the one cautioning that this will take time. That is conviction, not hype.
When a company that powers payments for 90 percent of the Dow Jones Industrial Average and 80 percent of the Nasdaq 100 builds its own blockchain for machine transactions, the question of whether AI will run on crypto rails becomes less theoretical.
Payments require trust.
An agent cannot transact with another agent blindly. It needs to know who it is dealing with, how that agent has performed in the past, and whether its outputs can be verified. In a world of autonomous software, trust must be machine-readable.
On the human internet, trust is informal and layered. We rely on brand recognition, reviews, platform guarantees, and legal recourse. That model does not translate cleanly to agents transacting across different companies and infrastructure.
ERC-8004 went live on Ethereum mainnet in January 2026. It establishes three onchain registries that function as the discovery, identity, and trust layer for autonomous agents.
The Identity Registry assigns each agent a portable onchain identifier with verifiable metadata about its capabilities and endpoints.
The Reputation Registry standardizes how performance feedback is submitted and read, whether onchain for composability or offchain for deeper analysis.
The Validation Registry records independent verification of an agent’s outputs, whether performed by staked services, machine learning systems, trusted hardware, or other agents.
Together, these registries answer three questions before any transaction occurs: Who are you? Have you performed reliably? Can your work be verified?
More than 20,000 agents registered in the first two weeks, and major Layer 2 networks including Base, Polygon, and Avalanche deployed official registries. This is live infrastructure.
Importantly, ERC-8004 does not handle payments. It handles discovery, identity, and trust. Payments operate on a separate layer, which keeps the stack modular.
Paired with ERC-4337 smart accounts, this becomes practical. ERC-4337 enables programmable wallets with session keys, allowing an agent to operate within defined constraints without exposing its primary key. Combined, the two standards allow you to fund an agent with $500 in USDC and restrict spending to providers above a defined reputation threshold.
The identity layer defines who the agent is. The smart account layer governs what it can do. That is enforceable governance for autonomous software.
With payments, identity, and programmable constraints in place, two architectural models are emerging.
Both Big Tech and crypto recognize the same reality. AI agents need to transact. The architectures differ.
Google launched the Agent Payments Protocol in September 2025 with more than 60 partners including Mastercard, PayPal, Coinbase, and American Express. It uses cryptographically signed mandates to link user intent to agent action and final payment. It is well designed and structured. However, it operates within Google’s ecosystem. It runs on top of Google Pay and pre-registered credentials. Merchants must be inside the partner network.
The distinction is structural, not qualitative. Google’s model is managed intent. The agent acts on behalf of a human, within boundaries a human has defined, through infrastructure a platform controls. The human remains the admin.
The crypto approach is raw execution. x402 and ERC-8004 are open protocols. Any agent can use them. No platform approval required. Trust is anchored onchain. Settlement occurs on public networks. The agent is the economic principal.
Google’s model optimizes for safety within a managed environment. Crypto’s model optimizes for permissionless access and agent autonomy.
If you are building an agent that purchases from a known merchant network on behalf of a user, Google’s approach works well. If you are building an agent that must discover, evaluate, and transact with unknown counterparties across borders at machine speed, open protocols are better suited.
Even Google built a crypto extension. AP2 includes an A2A x402 integration for stablecoin payments, developed with Coinbase, the Ethereum Foundation, and MetaMask. The crypto rails are not competing with the managed system. They are becoming a component of it.
x402 handles payments. ERC-8004 handles identity and reputation. A2A and MCP handle communication and tool access. AP2 and ACP handle authorization and interoperability. Smart contracts enforce programmable policy.
Wallet providers and financial protocols are adapting quickly. Phantom enables agents to swap tokens, sign transactions, and manage wallet addresses across chains. Coinbase introduced Agentic Wallets built specifically for autonomous software, with programmable spending limits and secure key management. Privy allows developers to create wallets with granular policy controls. Uniswap released modular skills that let agents execute trades and interact with smart contracts through standardized interfaces.
AI is being treated as a first-class user of financial protocols.
Each piece addresses a different constraint. Together, they form the economic substrate for autonomous agents.
Smart contracts do something traditional rails cannot. They function as programmable policy engines for AI. When agents manage meaningful capital, constraints must be enforceable at execution. Spending caps. Asset restrictions. Diversification rules. Prohibitions on self-dealing. These are not optional features. They are native to smart contracts.
Agents transacting onchain create a legible trail of every interaction. For some use cases, that transparency is a feature. For others, it is a competitive vulnerability.
Consider an agent purchasing $1 million in compute. On a public blockchain, that transaction is visible in the mempool before it settles. Other agents watching the chain can frontrun the price, bidding up compute costs before the original transaction completes. A trading agent broadcasting its positions leaks its strategy. An agent hiring specialized services reveals its entire workflow to competitors.
Privacy in this context is not about hiding from regulators. It is about hiding from the competition. Without it, the transparency that makes crypto trustworthy becomes the mechanism that makes it exploitable.
That is exactly what zero knowledge proofs address. ZK technology allows one party to prove a statement is true without disclosing anything beyond the truth of the statement itself.
Applied to agent infrastructure, this means an agent could verify its identity without exposing its owner. It could prove solvency without revealing its balance. It could demonstrate compliance with a policy without disclosing the policy’s parameters.
ERC-8004’s v2 specification is already in development with enhanced integration for these cryptographic methods. As the agent economy scales, privacy will not be optional. It will be anti-frontrunning infrastructure.
If AI runs on crypto rails, then crypto infrastructure becomes AI infrastructure. Teams building stablecoin systems, identity standards, and programmable settlement layers are building the economic foundation of the agentic internet.
For years, crypto adoption was framed around retail usage. Wallet downloads. Token speculation. Consumer applications. That framing may have missed the real user. Agents do not care about user experience. They do not need intuitive interfaces or onboarding flows. They require programmable, permissionless, always on infrastructure. The very qualities that made crypto awkward for humans make it natural for machines.
What happens to platforms and payment networks that cannot accommodate non-human participants?
Card networks were built for a world where every transaction has a human origin. If agent to agent commerce reaches meaningful scale, systems that cannot process machine native payments risk being bypassed.
Some incumbents are adapting. Others may struggle. Retrofitting legacy infrastructure to support participants it was never designed for is difficult. In some cases, more difficult than building new rails entirely.
The transition will be uneven and gradual. But the direction is clear.
If this thesis is correct, the largest stablecoin users of this cycle will not be traders. They will be software agents. The dominant wallets will not be consumer apps. They will be programmable wallets built for machines.
The most capable software ever built requires financial infrastructure that operates at its speed and on its terms. The defining adoption curve of this cycle may not be human at all. It may be machine.
Thanks for reading Mixed Realities by TJ Kawamura! Subscribe for free to receive new posts and support my work.
Subscribe
AI agents need to transact autonomously, globally, and at machine speed, often with counterparties they have never encountered before. That requires programmable spending constraints, verifiable identity without centralized gatekeepers, and settlement that is instant, transparent, and always on.
The properties that define crypto, permissionless access, programmable money, transparent settlement, and onchain identity, match what autonomous software needs to transact at scale without human intermediaries. That convergence is no longer theoretical. It is being built in real time.
Traditional payment rails assume a human is on the other end. Every layer reflects that assumption. Credit cards require identity verification tied to a person. Bank transfers depend on business hours and national jurisdictions. ACH settles in days. Chargebacks exist because a human might dispute a charge. The financial system was designed for humans and bounded by business hours and geography.
AI agents do not operate this way. They transact programmatically, instantly, and often in amounts as small as fractions of a cent, across borders, 24 hours a day. They need to pay for API calls, data access, compute resources, and services from other agents. They may do this thousands of times per day without waiting for human approval.
The bottleneck is not model capability or reasoning quality. It is economic infrastructure.
The intuitive response is that agents can use credit cards. For basic tasks, they can. An assistant booking a flight with your saved card is already solved. But that is not autonomy. It is delegation. A credit card still assumes a human principal behind every transaction.
The shift happens when agents manage capital, hire other agents, and transact at machine speed with unknown counterparties. Credit cards do not fail because of policy. They fail because the architecture assumes a human participant. A credit card “authorization” is a promise that settles days later. An agent operating at machine speed cannot wait for a three-day ACH window or absorb the risk of a chargeback sixty days after execution. For autonomous software, finality is the only form of trust.
In February 2026, Stripe integrated the x402 protocol on Base.
The HTTP 402 “Payment Required” status code was reserved in the original HTTP/1.1 specification, but was never implemented. With credit card fees measured in fixed per-transaction costs, selling anything under a dollar per request was economically impractical. The web defaulted to ads and subscriptions instead. x402 gives that dormant status code an economic use case.
The flow is simple. An AI agent sends an HTTP request to an API endpoint. The server responds with a 402 status code and payment instructions. The agent sends USDC to a specified address on Base. Once confirmed, the server grants access. No account creation. No API keys. No human involvement.
Stripe’s integration made this production ready. Developers can charge agents for API calls or MCP requests using Stripe’s existing PaymentIntents API. Funds settle into standard Stripe balances, with taxes, refunds, and reporting handled as usual. For developers, it looks like any other Stripe integration. For agents, the internet finally has a native payment layer.
The distinction between using a service and paying for a service collapses into a single protocol-level interaction. The payment is embedded in the request. For agents operating at machine speed, that difference matters.
The constraint right now is surface area. x402 works, but it only works where endpoints support it. Adoption depends on developers adding payment middleware to their APIs. Most have not yet. The protocol is live. The network of participating endpoints is still thin.
x402 handles the payment primitive. But agents operating across platforms need more than a way to pay. They need a shared language for commerce itself.
Stripe is building that too. With OpenAI, it developed the Agentic Commerce Protocol, an open standard that establishes a shared technical language between AI platforms and businesses. It launched an Agentic Commerce Suite that lets businesses sell across multiple AI interfaces and protocols with a single integration. Anthropologie, Urban Outfitters, Etsy, Coach, and Kate Spade are already onboarding.
Stripe also introduced Shared Payment Tokens, a new payment primitive that lets agents initiate payments without exposing credentials. The tokens work even at businesses that do not process payments through Stripe. That last detail is significant. It means the agent payment layer is being designed to extend beyond any single platform’s ecosystem.
What matters is that these layers are converging. Payment rails, commerce standards, and agent communication protocols are assembling into an interoperable stack. Agents are no longer an edge case. They are becoming a primary user class.
Stripe’s x402 integration did not emerge in isolation. It reflects a deeper strategic bet.
Over the past 18 months, Stripe has assembled the crypto infrastructure stack for agent commerce piece by piece.
In early 2025, Stripe closed its $1.1 billion acquisition of Bridge, a stablecoin infrastructure platform. Bridge’s transaction volume has since quadrupled. It now operates stablecoin financial accounts in 101 countries and recently received conditional approval from the OCC to establish a federally chartered national trust bank.
In mid 2025, Stripe acquired Privy, which powers more than 110 million programmable wallets. Privy embeds crypto wallets directly into applications without requiring users to interact with third party wallet software.
In September 2025, Stripe partnered with Paradigm to develop Tempo, a Layer 1 blockchain purpose-built for payments. Tempo is designed for sub-second finality, native support for batch transfers and microtransactions, and a built-in mechanism that allows agents to pay transaction fees in the same stablecoins they are transacting in. On most blockchains, an agent making a one-cent data request must first hold a separate volatile token to pay network fees. That is a logic bottleneck for autonomous software. Tempo removes it.
Bridge handles stablecoin orchestration. Privy handles wallet infrastructure. Tempo handles settlement. x402 handles the payment protocol. Together, they form a pipeline from agent intent to onchain execution, assembled by a company that already processes 1.6 percent of global GDP.
In 2025, stablecoin payments volume doubled even as speculative crypto trading declined. Infrastructure usage expanded while asset prices fell.
Stripe processed $1.9 trillion in volume in 2025 and is now valued at $159 billion. This is not a speculative crypto startup. It is a global payments company concluding that programmable, crypto-native rails are structurally suited for agent commerce.
In its 2025 annual letter, Stripe’s co-founders wrote that agentic commerce has “moved into a phase of building and real-world experimentation,” while noting that its evolution will unfold in stages. The measured tone matters. The company investing heavily in agent infrastructure is also the one cautioning that this will take time. That is conviction, not hype.
When a company that powers payments for 90 percent of the Dow Jones Industrial Average and 80 percent of the Nasdaq 100 builds its own blockchain for machine transactions, the question of whether AI will run on crypto rails becomes less theoretical.
Payments require trust.
An agent cannot transact with another agent blindly. It needs to know who it is dealing with, how that agent has performed in the past, and whether its outputs can be verified. In a world of autonomous software, trust must be machine-readable.
On the human internet, trust is informal and layered. We rely on brand recognition, reviews, platform guarantees, and legal recourse. That model does not translate cleanly to agents transacting across different companies and infrastructure.
ERC-8004 went live on Ethereum mainnet in January 2026. It establishes three onchain registries that function as the discovery, identity, and trust layer for autonomous agents.
The Identity Registry assigns each agent a portable onchain identifier with verifiable metadata about its capabilities and endpoints.
The Reputation Registry standardizes how performance feedback is submitted and read, whether onchain for composability or offchain for deeper analysis.
The Validation Registry records independent verification of an agent’s outputs, whether performed by staked services, machine learning systems, trusted hardware, or other agents.
Together, these registries answer three questions before any transaction occurs: Who are you? Have you performed reliably? Can your work be verified?
More than 20,000 agents registered in the first two weeks, and major Layer 2 networks including Base, Polygon, and Avalanche deployed official registries. This is live infrastructure.
Importantly, ERC-8004 does not handle payments. It handles discovery, identity, and trust. Payments operate on a separate layer, which keeps the stack modular.
Paired with ERC-4337 smart accounts, this becomes practical. ERC-4337 enables programmable wallets with session keys, allowing an agent to operate within defined constraints without exposing its primary key. Combined, the two standards allow you to fund an agent with $500 in USDC and restrict spending to providers above a defined reputation threshold.
The identity layer defines who the agent is. The smart account layer governs what it can do. That is enforceable governance for autonomous software.
With payments, identity, and programmable constraints in place, two architectural models are emerging.
Both Big Tech and crypto recognize the same reality. AI agents need to transact. The architectures differ.
Google launched the Agent Payments Protocol in September 2025 with more than 60 partners including Mastercard, PayPal, Coinbase, and American Express. It uses cryptographically signed mandates to link user intent to agent action and final payment. It is well designed and structured. However, it operates within Google’s ecosystem. It runs on top of Google Pay and pre-registered credentials. Merchants must be inside the partner network.
The distinction is structural, not qualitative. Google’s model is managed intent. The agent acts on behalf of a human, within boundaries a human has defined, through infrastructure a platform controls. The human remains the admin.
The crypto approach is raw execution. x402 and ERC-8004 are open protocols. Any agent can use them. No platform approval required. Trust is anchored onchain. Settlement occurs on public networks. The agent is the economic principal.
Google’s model optimizes for safety within a managed environment. Crypto’s model optimizes for permissionless access and agent autonomy.
If you are building an agent that purchases from a known merchant network on behalf of a user, Google’s approach works well. If you are building an agent that must discover, evaluate, and transact with unknown counterparties across borders at machine speed, open protocols are better suited.
Even Google built a crypto extension. AP2 includes an A2A x402 integration for stablecoin payments, developed with Coinbase, the Ethereum Foundation, and MetaMask. The crypto rails are not competing with the managed system. They are becoming a component of it.
x402 handles payments. ERC-8004 handles identity and reputation. A2A and MCP handle communication and tool access. AP2 and ACP handle authorization and interoperability. Smart contracts enforce programmable policy.
Wallet providers and financial protocols are adapting quickly. Phantom enables agents to swap tokens, sign transactions, and manage wallet addresses across chains. Coinbase introduced Agentic Wallets built specifically for autonomous software, with programmable spending limits and secure key management. Privy allows developers to create wallets with granular policy controls. Uniswap released modular skills that let agents execute trades and interact with smart contracts through standardized interfaces.
AI is being treated as a first-class user of financial protocols.
Each piece addresses a different constraint. Together, they form the economic substrate for autonomous agents.
Smart contracts do something traditional rails cannot. They function as programmable policy engines for AI. When agents manage meaningful capital, constraints must be enforceable at execution. Spending caps. Asset restrictions. Diversification rules. Prohibitions on self-dealing. These are not optional features. They are native to smart contracts.
Agents transacting onchain create a legible trail of every interaction. For some use cases, that transparency is a feature. For others, it is a competitive vulnerability.
Consider an agent purchasing $1 million in compute. On a public blockchain, that transaction is visible in the mempool before it settles. Other agents watching the chain can frontrun the price, bidding up compute costs before the original transaction completes. A trading agent broadcasting its positions leaks its strategy. An agent hiring specialized services reveals its entire workflow to competitors.
Privacy in this context is not about hiding from regulators. It is about hiding from the competition. Without it, the transparency that makes crypto trustworthy becomes the mechanism that makes it exploitable.
That is exactly what zero knowledge proofs address. ZK technology allows one party to prove a statement is true without disclosing anything beyond the truth of the statement itself.
Applied to agent infrastructure, this means an agent could verify its identity without exposing its owner. It could prove solvency without revealing its balance. It could demonstrate compliance with a policy without disclosing the policy’s parameters.
ERC-8004’s v2 specification is already in development with enhanced integration for these cryptographic methods. As the agent economy scales, privacy will not be optional. It will be anti-frontrunning infrastructure.
If AI runs on crypto rails, then crypto infrastructure becomes AI infrastructure. Teams building stablecoin systems, identity standards, and programmable settlement layers are building the economic foundation of the agentic internet.
For years, crypto adoption was framed around retail usage. Wallet downloads. Token speculation. Consumer applications. That framing may have missed the real user. Agents do not care about user experience. They do not need intuitive interfaces or onboarding flows. They require programmable, permissionless, always on infrastructure. The very qualities that made crypto awkward for humans make it natural for machines.
What happens to platforms and payment networks that cannot accommodate non-human participants?
Card networks were built for a world where every transaction has a human origin. If agent to agent commerce reaches meaningful scale, systems that cannot process machine native payments risk being bypassed.
Some incumbents are adapting. Others may struggle. Retrofitting legacy infrastructure to support participants it was never designed for is difficult. In some cases, more difficult than building new rails entirely.
The transition will be uneven and gradual. But the direction is clear.
If this thesis is correct, the largest stablecoin users of this cycle will not be traders. They will be software agents. The dominant wallets will not be consumer apps. They will be programmable wallets built for machines.
The most capable software ever built requires financial infrastructure that operates at its speed and on its terms. The defining adoption curve of this cycle may not be human at all. It may be machine.
Thanks for reading Mixed Realities by TJ Kawamura! Subscribe for free to receive new posts and support my work.
Subscribe
Subscribe to Mixed Realities with TJ Kawamura
Subscribe to Mixed Realities with TJ Kawamura
Mixed Realities with TJ KawamuraPhysical AI: The Next Frontier for Data
Why the future of AI depends on data from the real world, not the web.
Mixed Realities with TJ Kawamura2024 Market Review and 2025 Predictions
The crypto market experienced a resurgence in 2024. The coming year will focus on further refinement of infrastructure and adoption of dApps. By solving distribution challenges, enhancing interoperability, and leveraging ZK technology, Web3 is poised for mainstream breakthroughs in 2025.
Mixed Realities with TJ KawamuraWhat Startups Can Learn from the Savannah Bananas
Inside the “Fans First” playbook that turned a baseball team into a business case study for customer obsession.
Mixed Realities with TJ KawamuraPhysical AI: The Next Frontier for Data
Why the future of AI depends on data from the real world, not the web.
Mixed Realities with TJ Kawamura2024 Market Review and 2025 Predictions
The crypto market experienced a resurgence in 2024. The coming year will focus on further refinement of infrastructure and adoption of dApps. By solving distribution challenges, enhancing interoperability, and leveraging ZK technology, Web3 is poised for mainstream breakthroughs in 2025.
Mixed Realities with TJ KawamuraWhat Startups Can Learn from the Savannah Bananas
Inside the “Fans First” playbook that turned a baseball team into a business case study for customer obsession.
Share Dialog
Share Dialog
<100 subscribers
<100 subscribers
No comments yet