bitcoin and artificial intelligence

BlackRock, the largest asset manager on the planet, just published an 11-page research paper arguing that artificial intelligence may be the most underappreciated demand driver the crypto economy has ever had. It is called The Machine-Native Economy: How digital assets connect intelligence, commerce, and compute, and it comes from Head of Digital Assets Robert Mitchnick, Head of Digital Assets Research Will Su, U.S. Head of Equity ETFs Jay Jacobs, and Head of U.S. iShares Product Innovation William Helm. The language is careful, the way institutional research always is. The conclusion is not. Read plainly, BlackRock is saying that AI is machine-native intelligence, crypto is machine-native money, and the two were built for each other. For anyone who has been bullish on bitcoin and digital assets, this is the thesis, now written on BlackRock letterhead.

TLDR

BlackRock argues that AI and digital assets are converging into a single machine-native economy and that broad AI adoption is an underappreciated source of demand for crypto. The paper makes three cases. First, LLMs and blockchains share an analogous tokenization architecture, turning language and value into standardized units machines can process natively, which gives AI agents a more direct interface with on-chain assets than with fragmented legacy systems. Second, agentic commerce needs machine-native payment rails: card networks and ACH carry human onboarding, merchant fees, and settlement delays that make always-on sub-cent machine payments uneconomic, while stablecoins and protocols like Coinbase’s x402 settle around the clock with no human in the loop. Adjusted stablecoin volume topped $11 trillion in 2025, in the same range as Visa and Mastercard, growing at an 80% CAGR versus roughly 8.5% for ACH. Third, compute is becoming a trillion-dollar commodity (hyperscaler cloud revenue is projected near $1.1 trillion by 2030), and standardized, tokenized claims on compute could become a major new digital asset market. The paper also cites Bitcoin Policy Institute research in which AI models favored stablecoins for everyday payments and bitcoin for long-term value preservation, sketching an AI-native monetary architecture with bitcoin as the reserve asset.

Thoughts

Start with the most important sentence in the paper, the one tucked into the tokenization section on page four. Citing the Bitcoin Policy Institute, BlackRock describes “a potential AI-native monetary architecture in which stablecoins serve as transaction money and bitcoin as a store of value.” Think about what that means. When you ask the machines themselves which money they would choose, they reach for dollars on-chain to spend and bitcoin to save. That is exactly the division of labor bitcoiners have described for a decade: stablecoins as the checking account, bitcoin as the treasury. Every stablecoin transaction an agent makes is denominated in a currency its issuer can inflate. An agent optimizing for long-horizon value, with no nostalgia, no home bias, and no attachment to any particular central bank, has one obvious answer for savings: a fixed supply of 21 million, final settlement, no counterparty, and no one who can change the rules. BlackRock rightly notes these are simulated model responses, not observed behavior. But simulations are where agent behavior starts, and the direction is not ambiguous.

The payment rails argument is where the bull case becomes mechanical rather than philosophical. BlackRock lists the problems with legacy rails bluntly: account setup that needs a human, merchant fees that make tiny transactions uneconomic, settlement that takes a business day or longer, and scalability limits as machine volume grows. An AI agent cannot walk into a bank branch. It cannot pass a credit check. It will want to pay a fraction of a cent for an API call, thousands of times an hour, at 3 a.m. on a Sunday. The only rails that do this natively are blockchains. x402, which revives the long-dormant HTTP 402 “Payment Required” status code, lets an agent pay for a resource inside the web request itself. It is worth remembering that the bitcoin world got here first: Lightning Labs’ L402 protocol paired the same 402 status code with Lightning payments years ago. The idea that the internet finally gets a native payment layer, and that the layer is crypto, is no longer a cypherpunk dream. It is in a BlackRock paper, next to Stripe, Visa, Google, and OpenAI protocols.

Then look at the numbers in the middle of the paper, because they are staggering. Adjusted stablecoin transaction volume exceeded $11 trillion in 2025, putting it in the same broad range as Visa and Mastercard. Circulating stablecoin supply is north of $300 billion. From 2020 to 2025, adjusted stablecoin volume compounded at 80% a year, against roughly 8.5% for ACH. And this happened before agentic commerce showed up in any meaningful volume. Humans alone, with clunky wallets and regulatory fog, built a payment network that rivals the card giants in five years. Now add the GENIUS Act in the U.S., MiCA in Europe, and licensing regimes in Hong Kong and Singapore. Then add millions, eventually billions, of autonomous agents that pay far more often than any human ever will. BlackRock also makes the second-order point clearly: settlement on permissionless networks drives demand for blockspace and validator services, a direct transmission channel to native cryptoassets. The stablecoin boom is not a rival to crypto. It is fuel for the chains underneath it.

The compute section in the back half is the part most coverage will skip, and it is the most important long-term idea in the paper. BlackRock argues that compute is becoming a distinct, investable commodity: cumulative AI capex above $5 trillion through 2030, hyperscaler cloud revenue near $1.1 trillion by 2030, and inference on track to be the largest AI workload. Commodities get financial markets, and BlackRock expects standardized, tokenized compute contracts that can be “represented, transferred, pledged as collateral, and settled through programmable infrastructure.” Here is the bitcoin angle the paper leaves implicit: AI and bitcoin run on the same scarce input, energy. AI turns electricity into intelligence. Bitcoin proof of work turns electricity into the hardest money ever created. Bitcoin miners are already among the best-positioned owners of powered land and grid interconnects on earth, and many have been signing AI hosting deals. The machine economy’s two most important commodities, compute and sound money, are both energy commodities, and bitcoin is the only monetary asset whose issuance is anchored in physical energy expenditure.

Finally, weigh the conclusion and the signal in the byline. BlackRock ends by saying digital assets “could become increasingly integral to AI’s economic infrastructure,” across stablecoins, tokenized real-world assets, and “native cryptoassets that support blockchain settlement.” It points to Stripe’s August 2026 agreement to acquire OpenRouter as evidence that compute procurement, usage billing, and programmable settlement are merging. And two of the four authors run BlackRock’s ETF and iShares product businesses. This is not an academic exercise. It is the firm that runs the iShares Bitcoin Trust telling its clients the demand story for crypto is about to get a second engine. The first engine was institutions discovering bitcoin as a macro asset. The second is the machines. The paper admits that agentic payments are nascent and that compute-market liquidity is thin. That is the bullish part. You do not get a paper like this when the trade is crowded. You get it when the smartest money in the room can see the curve but the market has not priced it yet.

Key Takeaways

BlackRock calls AI “the defining technology theme of this era” and digital assets a concurrent theme with major implications for financial infrastructure, and says the two are now converging.
The paper’s central framing: AI is machine-native intelligence and digital assets are machine-native money.
BlackRock says broad AI adoption “may represent an underappreciated source of demand, utility, and application growth across the digital asset economy.”
Agentic AI, systems that plan and execute multistep tasks with limited human intervention, pushes AI from generating content to taking real-world action, including purchases and financial transactions.
LLMs split text into tokens, map them to numeric IDs, and embed them as vectors. Blockchains represent value and ownership claims as standardized tokens recorded on a ledger. Different functions, same idea: convert real-world inputs into machine-native formats.
Because both systems use structured, machine-readable data, AI agents can interface with blockchain data more directly than with fragmented legacy databases.
Tokenizing more asset classes reduces bespoke integrations and lets agents orchestrate complex multi-asset workflows: checking balances and rules, executing authorized transactions, and verifying settlement.
Compliance (AML, KYC, and the new “know-your-agent” or KYA checks) generally happens off-chain, with verified results passed on-chain to determine eligibility.
Bitcoin Policy Institute research found AI models in controlled simulations generally favored stablecoins for everyday payments and bitcoin for long-term value preservation.
BlackRock frames that result as a potential AI-native monetary architecture: stablecoins as transaction money, bitcoin as the store of value.
Crypto rails are “particularly well suited” to high-frequency, sub-cent, around-the-clock machine-to-machine transactions like API calls, on-demand data, and consumption-based compute.
Legacy rails struggle with agents because of human-dependent onboarding, merchant fees that kill micropayments, slow settlement and dispute finality, and scaling limits.
Modified traditional rails will still matter for business-to-machine and consumer-to-machine commerce, where agents deal with human-run businesses.
Agentic payments sit on foundational standards: Anthropic’s Model Context Protocol (MCP, November 2024) for tool and data access, and Google’s Agent2Agent (A2A, April 2025) for agent coordination.
Coinbase’s x402 uses the HTTP 402 “Payment Required” status code to let machines pay inside web requests. It is blockchain-agnostic, with USDC as an early primary use case.
x402 offers 24/7, near-real-time, verifiable settlement, which reduces counterparty exposure for providers and lets them release data or services the moment payment confirms.
More x402 usage on permissionless networks could increase demand for blockspace and validator services, a transmission channel to native cryptoassets.
Other protocols in the stack include Stripe and Tempo’s Machine Payments Protocol (MPP), Stripe and OpenAI’s Agentic Commerce Protocol (ACP), Google’s AP2 with cryptographic mandates, and Visa’s Trusted Agent Protocol (TAP).
BlackRock’s example workflow: a user asks an agent to book a trip under $2,500, the agent delegates to a travel sub-agent via A2A, the sub-agent pays for fare data via x402 settled on-chain, and the primary agent books through ACP.
Stablecoins are likely to lead transactional use because price stability gives agents a reliable unit of account.
Stablecoins are the largest category of tokenized real-world assets, with more than $300 billion in circulation as of September 2026.
Adjusted stablecoin transaction volume exceeded $11 trillion in 2025, in the same broad range as Visa and Mastercard’s annual payment volumes.
Stablecoin volume grew at an 80% CAGR from 2020 to 2025, versus about 8.5% for ACH, which still moved $93 trillion in 2025.
Regulatory clarity, including the GENIUS Act, MiCA, Hong Kong’s licensing regime, and Singapore’s framework, should support continued stablecoin growth.
Stablecoin growth spills over to the chains that settle them. On networks like Ethereum, native assets such as ETH pay for consensus, validators, and fees, so more activity can mean more value capture.
Purpose-built stablecoin chains like Circle’s Arc, where USDC is the native gas asset, offer a complementary model.
Some estimates put cumulative AI capital spending above $5 trillion between 2025 and 2030, and BlackRock says ongoing operating spend deserves equal attention.
Consensus estimates for AWS, Microsoft Intelligent Cloud, and Google Cloud imply about $1.1 trillion in combined revenue by 2030, a 29% CAGR from 2025.
Compute is becoming a distinct, large, investable economic resource that could support a new class of digital assets.
Inference is expected to be the largest AI workload by 2030, and its user base is far larger and more fragmented than the concentrated training market.
Challenges remain, including chip-generation differences, regional energy costs, and settlement standards, but BlackRock calls them “important but ultimately resolvable.”
BlackRock expects standardized products, including exchange-traded compute futures, and tokenized compute claims that can be transferred, pledged as collateral, and settled on programmable rails.
Agents could shop real-time compute marketplaces on price, latency, location, and hardware, then pay per use, per model token, or per job via x402.
Stripe’s August 2026 agreement to acquire OpenRouter, which routes workloads across more than 400 models from over 80 providers, signals that compute procurement, billing, and programmable settlement are converging.
BlackRock’s conclusion: as agents grow more capable, digital assets could become integral to AI’s economic infrastructure across stablecoins, tokenized RWAs, and native cryptoassets.

Detailed Summary

Two Technology Waves Become One

BlackRock opens by naming AI as the defining technology of the era and digital assets as a parallel wave with deep implications for financial infrastructure. For years the two ran on separate tracks. The paper argues that they are now merging because AI is gaining the ability to act on economic networks, not just talk about them. Agentic AI plans and executes multistep tasks, calls external tools, and increasingly makes purchases and initiates financial transactions. Once software can spend money, the question of which money it spends and which rails it uses becomes central, and BlackRock’s answer is that blockchains provide the programmable infrastructure that connects intelligence to economic activity.

Tokens All the Way Down

The first pillar is architectural. An LLM tokenizes text into words or sub-words, maps them to numeric IDs, and converts them to embeddings the model can compute on in parallel. A blockchain tokenizes value: cash, a money market fund interest, a security, or another claim becomes a standardized token recorded on a distributed ledger. Transactions are machine-readable data governed by rules. The network verifies authorization, smart contracts apply asset-specific conditions, and once finalized the transfer becomes part of the canonical ledger. BlackRock’s Figure 1 sets these two pipelines side by side, “AI is changing the world” becoming vectors on one side and a $100 money market fund interest becoming a finalized on-chain record on the other. Because both speak structured, machine-readable formats, agents can plug into on-chain assets more directly than into siloed legacy systems, and broader tokenization across asset classes reduces the bespoke integration work that slows automation today.

Bitcoin as the AI-Native Store of Value

The paper cites the Bitcoin Policy Institute study “Which money do AI agents prefer?”, in which model outputs across controlled simulations generally chose stablecoins for everyday payments and bitcoin for long-term value preservation. BlackRock is careful to say these are simulated responses rather than observed agent behavior, but it takes the result seriously enough to describe a potential AI-native monetary architecture: stablecoins as transaction money, bitcoin as the store of value. For bitcoin holders, this is the key passage. It places bitcoin at the base of the machine economy’s balance sheet rather than at the edge of it.

Why Agents Need New Payment Rails

BlackRock argues that capable agents “increasingly demand payment and asset infrastructure designed natively for machine-speed commerce.” Crypto rails fit high-frequency, sub-cent, 24/7 machine-to-machine payments for API calls, data, and compute. Legacy systems are poorly matched: account setup and credentialing assume a human, merchant fees make very small payments uneconomic, ACH settles in a business day or so, card disputes keep finality open for longer, and volume scaling is uncertain. Modified traditional rails will still serve agents dealing with human businesses and consumers, but the high-velocity machine layer points on-chain.

The Agentic Protocol Stack: MCP, A2A, x402, ACP, and More

The paper maps an emerging stack. At the base are Anthropic’s Model Context Protocol, which standardizes how AI applications reach external tools and data, and Google’s Agent2Agent, which lets agents coordinate across platforms. On top sit payment protocols. Coinbase’s x402 uses the HTTP 402 status code to let agents pay inside a web request with near-real-time verifiable settlement, reducing provider counterparty risk. It is chain-agnostic and led by USDC for now, and on permissionless networks its growth could drive demand for blockspace and validator services. Alongside it are Stripe and Tempo’s Machine Payments Protocol, Stripe and OpenAI’s Agentic Commerce Protocol for programmatic checkout on merchants’ existing rails, Google’s AP2 with cryptographic authorization mandates and audit trails, and Visa’s Trusted Agent Protocol for verifying trusted agents. BlackRock’s Figure 2 walks through a trip booking in which a primary agent, a travel sub-agent, x402 data purchases, and ACP checkout combine to return an itinerary and receipts to the user.

Stablecoins Already Rival the Card Networks

Stablecoins are expected to lead agent transactions because a stable unit of account makes pricing predictable. They are the largest tokenized RWA category at more than $300 billion in circulation. Adjusted stablecoin volume exceeded $11 trillion in 2025, in the same broad range as Visa and Mastercard (BlackRock’s Figure 3 notes the measures are not directly comparable), and grew at an 80% CAGR from 2020 to 2025, while ACH, still far larger at $93 trillion, grew about 8.5%. The GENIUS Act, MiCA, and Asian licensing regimes add tailwinds. Crucially, the benefits flow to the settlement networks. Stablecoins are issued across multiple chains, from general-purpose networks like Ethereum, where ETH pays for consensus and fees, to purpose-built chains like Circle’s Arc, where USDC itself is the gas asset. More payment activity means more demand for blockspace and potential value capture, subject to each network’s fee and staking design.

Compute Becomes a Tradable Commodity

AI needs vast amounts of compute and energy. Investors have focused on capex (estimates above $5 trillion from 2025 to 2030), but BlackRock stresses the operating spend that flows through the cloud compute market. Hyperscaler consensus implies about $1.1 trillion in cloud revenue by 2030. McKinsey projections in the paper’s Figure 4 show inference growing into the largest AI workload, with its share of data center power demand rising sharply. Large resource markets historically develop trading, financing, and hedging infrastructure, and GPU-backed financings are early evidence that compute is on the same path. BlackRock acknowledges real design problems (chip-generation productivity, regional energy costs, cash versus physical settlement) but points to commodity precedents like basis markets and contracts for difference. It expects exchange-traded compute futures and tokenized compute claims that can be transferred, pledged, and settled programmatically, which could broaden institutional participation and open a new market for digital assets.

Agents That Buy Their Own Compute

The paper’s Figure 5 imagines an agent running an extended analysis that continuously estimates its own compute needs, queries real-time marketplaces across GPU, specialized, and edge providers for price, latency, and reliability, and provisions capacity just in time, paying per use, per token, or per job over x402. BlackRock cites Stripe’s August 2026 agreement to acquire OpenRouter, which routes workloads across more than 400 models from over 80 providers, as an early strategic signal. Given Stripe’s work in payments, stablecoins, billing, and agentic commerce, the deal points toward agents that autonomously source and pay for compute over blockchains and programmable rails.

BlackRock’s Conclusion

BlackRock closes by saying AI and blockchain-based digital assets are converging as machines take a larger role in economic activity. Tokenization gives agents a direct interface to programmable assets, stablecoins and x402 handle high-frequency always-on payments, and liquid compute markets could let agents source, finance, and pay for the resources they run on. The ecosystem is early, with agentic payment activity and compute-market liquidity still limited. But as agents become more capable, the firm expects digital assets to become increasingly integral to AI’s economic infrastructure, expanding utility across stablecoins, tokenized RWAs, and native cryptoassets.

Notable Quotes

“At the core of this convergence, AI and digital assets both arise from a common foundation: AI represents machine-native intelligence, while digital assets represent machine-native money.”
BlackRock, Executive Summary, the thesis of the whole paper in one sentence

“This paper examines this growing relationship and explains why broad AI adoption may represent an underappreciated source of demand, utility, and application growth across the digital asset economy.”
BlackRock, Executive Summary, on why the market is mispricing the AI and crypto link

“These findings reflect simulated model responses rather than observed agent behavior, but point to a potential AI-native monetary architecture in which stablecoins serve as transaction money and bitcoin as a store of value.”
BlackRock, on Bitcoin Policy Institute research into which money AI models prefer

“Crypto-native blockchain rails are particularly well suited to high-frequency, sub-cent, machine-to-machine (M2M) transactions that take place around-the-clock, including API calls, on-demand data, and consumption-based compute.”
BlackRock, on why agentic commerce points on-chain

“Where settlement occurs on permissionless networks, greater usage could increase demand for blockspace and validator services, creating a potential transmission channel to native cryptoassets.”
BlackRock, on how x402 payment volume could flow through to crypto assets

“Adjusted stablecoin volume remained well below the $93 trillion transferred over ACH in 2025; from 2020 to 2025, however, it grew at an 80% CAGR, compared with approximately 8.5% for ACH.”
BlackRock, on the growth gap between stablecoins and legacy payment rails

“As this market expands, compute is becoming a distinct, large, and increasingly investable economic resource that could support a new class of digital assets.”
BlackRock, on the emerging market for tokenized compute

“In our view, this could support a future in which agents autonomously source and pay for compute over blockchains and other programmable payment rails.”
BlackRock, on Stripe’s agreement to acquire OpenRouter

Read the full BlackRock paper, The Machine-Native Economy: How digital assets connect intelligence, commerce, and compute, here.

Tag: bitcoin and artificial intelligence

BlackRock’s Machine-Native Economy Paper Is the Most Bullish Institutional Case Yet for Bitcoin and Crypto: AI Agents, Stablecoins, x402, and the Coming Market for Tokenized Compute