04-11-2026Price:

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BUSINESS

AI agents demand stablecoin rails to transact

Saturday, April 11, 2026 · from 5 podcasts
No PriorsThis Week in AIThe a16z ShowForward GuidanceNo Solutions
  • AI agents need instant, autonomous payments, creating a new use case for blockchain-based dollar stablecoins like USDC.
  • Legacy banks and SaaS vendors are structurally incompatible with a world where machines are primary users and payers.
  • The shift is forcing a re-engineering of financial infrastructure for machine-scale speed and programmability.

When autonomous AI agents need to buy five cents of compute from another model, the legacy financial system breaks. Traditional wires and credit cards are too slow, expensive, and human-centric for machine-to-machine commerce. On the a16z Podcast, Box CEO Aaron Levie argued that the shift to an agent-first world is a foundational architectural change, where software’s value is no longer in its human interface but in its durable, programmable API.

This demand for a machine-native payment rail is the driver behind Circle’s new blockchain, ARC. CEO Jeremy Allaire explained on No Priors that stablecoins like USDC are becoming a "public API for dollars," providing the 24/7, programmable settlement layer agents require. His vision positions blockchains as the necessary economic operating system for an agentic economy, capable of handling billions of microscale transactions with deterministic finality - something traditional ledgers cannot do.

"We need a globally interoperable, programmable financial infrastructure that can operate at real-time speeds, handling billions of microscale transactions that will emerge from AI agents conducting work."

- Jeremy Allaire, No Priors

The agent shift exposes deep structural frictions. Levie noted that while startups can deploy agents freely, enterprises like JP Morgan face existential security risks from potential prompt injection or rogue agents, likely forcing a prolonged "read-only" era for corporate AI. This creates a massive adoption gap. Concurrently, legacy SaaS vendors built on per-seat licensing face disruption as agents, not humans, become the primary users, threatening traditional revenue models.

On the sovereign frontier, developers are already bypassing these corporate gatekeepers. On No Solutions, host Yo described experimental agents that generate their own cryptographic identities via Nostr and pay for API credits using permissionless ecash protocols like Cashu. The metric for access shifts from "Are you human?" to "Are you useful?"

The financial stakes are colossal. Forward Guidance guest Jordi Visser argued that the move from chatbots to autonomous agents in late 2023 has already driven a 1,000x increase in compute demand, repricing entire sectors of the economy. He sees a wealth transfer from traditional software equities - whose moats are evaporating - into assets that thrive on digital velocity, like Bitcoin.

"The labor arbitrage from AI favors solo entrepreneurs over enterprises. My annual cost for five LLMs and hardware is $17,000, far cheaper than human employees."

- Jordi Visser, Forward Guidance

The consensus is clear: the rise of autonomous agents isn't just a software problem. It's an infrastructure problem, demanding new payment rails, new security models, and a fundamental rethinking of value transfer for a post-human user base.

AgentsMarketsRegulation
Mentioned:CashuCircleJP MorganNostrUSDC

Source Intelligence

What each podcast actually said

No Priors: Artificial Intelligence | Technology | Startups
No Priors: Artificial Intelligence | Technology | Startups

The Agentic Economy: How AI Agents Will Transform the Financial System with Circle Co-Founder and CEO Jeremy Allaire • Apr 9

  • Allaire views stablecoins like USDC as a realization of full reserve money. The recently passed Clarity for Payment Stablecoins Act codifies this as narrow, non-fractional reserve money, a concept debated since the 1930s Chicago Plan.
  • Allaire argues blockchain networks are operating systems with key attributes for the agentic economy: tamper-resistant code, perfect auditability of all inputs/outputs, and transaction compute integrity assurances.
  • Real-world asset tokenization is accelerating, with tokenized stocks, treasury bills, and euros growing. Regulators like the SEC are providing guidelines, and financial infrastructure players from depositories to exchanges are moving to support it.
  • He believes double-digit GDP growth in the 2030s is plausible due to AI's productive output leaps, but notes the risk of capital capturing growth at human expense without a new social contract.

Also from this episode:

Payments (2)
  • Jeremy Allaire founded Circle in 2013 with the vision of creating a protocol for dollars on the internet, enabling instant, global, frictionless value transfer and programmable money via blockchain operating systems.
  • USDC use cases range from micropayments for digital objects or AI agent services to large-scale capital markets settlements, offering 24/7 operation, low transaction costs, and global dollar access.
Stablecoins (1)
  • USDC is backed by short-duration US Treasury bills and repos, with an average portfolio duration of 13 days, plus cash held at custodial institutions like Bank of New York for immediate liquidity.
AI & Tech (5)
  • The agentic economy, driven by AI agents conducting and collaborating on work, requires a new financial infrastructure that is globally interoperable, programmable, and capable of handling billions of microscale transactions in real-time.
  • Circle's new blockchain, Arc, is an 'economic operating system' designed for mainstream scaling with a known validator set of financial institutions, USDC as the native token, and built-in privacy primitives.
  • Allaire sees zero-knowledge proofs and trusted execution environments as critical scaling and privacy technologies for blockchains, enabling off-chain compute proofs and meeting corporate privacy needs.
  • He is intrigued by the concept of using productive GPU inference work as a new basis for proof-of-work cryptocurrencies, aligning monetary principles with useful compute rather than energy waste.
  • Allaire predicts the AI-driven disruption will force a renegotiation of the social contract, leading to new on-chain organizational forms that mix human and agentic actors and could be the most productive in economic history.
AgentsStablecoinsMacroBankingAI Infrastructure
This Week in AI
This Week in AI

What's Left for Humans When AI Builds Everything? • Apr 8

  • Kanjun Q argues AI agents represent a dangerous future where companies like Anthropic or OpenAI, once they own a user's data, memories, and life's work, can exert excessive influence and lock users into their ecosystems.
  • Kanjun Q's company Imbue is building open-source infrastructure to run agents in parallel, aiming to commoditize the underlying model layer and give users control to swap out providers and retain their data.
  • Kanjun Q says Imbue's engineering workflow has been transformed by coding agents, with one team lead autonomously generating 60-70 pull requests overnight, drastically increasing code output.
  • Siddharth describes automating the CEO role at Turing by building a 'virtual chief of staff' AI that aggregates data from Salesforce, Jira, and GitHub to create executive briefs on company status.

Also from this episode:

AI & Tech (10)
  • Karina Hong argues that verifying AI-generated code is critical for safety, citing the formally verified Paris subway automatic switching system and European Space Agency's Ariane spacecraft as precedents.
  • Hong's company Axiom built an AI mathematician that achieved a perfect score (120/120) on the Putnam exam, the first AI to do so in the competition's 100-year history.
  • Jonathan Siddharth says Turing sells specialized data to frontier AI labs to train models on coding, enterprise workflows, and STEM tasks, then uses insights from enterprise deployments to create a feedback loop for model improvement.
  • Siddharth claims there is unlimited demand for high-quality training data as models improve, requiring hiring expert humans across industries to generate data for imitation or reinforcement learning.
  • The group discusses Anthropic's explosive revenue growth to a $30 billion run rate, which reportedly surpassed OpenAI's token sales, driven largely by its strength in AI-assisted coding tools like Claude Code.
  • Siddharth and Hong assert that training AI models on code improves their general reasoning abilities, likely because coding provides clear, verifiable feedback and teaches algorithmic, step-by-step thinking.
  • The hosts critique Meta's reported internal policy of measuring team output by tokens consumed, which Kanjun Q says leads to gaming the system, like writing bots to burn tokens in a loop.
  • The group debates workplace surveillance, with Jason Calacanis arguing that tracking work computers is necessary for elite performance and security, drawing a parallel to NBA teams monitoring player biometrics.
  • Kanjun Q warns of a default future path where verticalized AI companies (OpenAI, Anthropic, Google) lock users in, renting back their 'digital selves,' versus an open-source path where users own and control their agents.
  • Karina Hong envisions a future with 'a billion AI mathematicians' accelerating discovery, shortening the timeline from mathematical breakthrough to applied science from centuries to days.
AgentsModelsCodingEnterpriseStartups
The a16z Show
The a16z Show

The Agent Era: Building Software Beyond Chat with Box CEO Aaron Levie • Apr 8

  • A successful emerging paradigm gives coding agents access to SaaS tools and internal workflows, enabling them to both read information and use APIs or write code to execute tasks. This is exemplified by tools like OpenAI's 'super app' and Perplexity Computer.
  • Steve Sinofsky observes that agents do not seek simpler interfaces but choose backends based on cost, durability, and reliability. He contends the industry's focus on marketing to agents via APIs is wrong, as agents select systems based on underlying quality, not interface polish.
  • A major operational challenge is coordinating thousands of autonomous agents acting on shared systems, like a Box repository, which risks creating conflicting operations, performance issues, and security vulnerabilities that CFOs and CIOs must manage.
  • The permission model for agents is complex. While the 'end-to-end argument' suggests treating them like separate humans with their own accounts, agents are legally extensions of their users, requiring full oversight and lacking a right to privacy, which breaks traditional RBAC models.
  • Current AI agents struggle with information containment, as data in the context window can potentially be extracted via prompt injection. This makes it difficult to securely grant agents access to highly confidential resources like M&A data rooms.
  • Sinofsky predicts a widening gap in adoption speed between startups, which can adopt agents freely, and large enterprises like JP Morgan, which face significant legacy system and risk constraints, slowing AI diffusion.
  • Sinofsky argues Wall Street is mis-modeling the AI economic opportunity by assuming a fixed revenue pie. He draws parallels to the PC and cloud eras, where new usage models created demand orders of magnitude larger than initially projected.

Also from this episode:

AI & Tech (7)
  • Aaron Levie argues that the diffusion of AI capability across enterprises will be slower than Silicon Valley expects, citing entrenched domain knowledge in systems like SAP and new security and operational complexities.
  • The central enterprise question is how to build software for a future where AI agents outnumber human users by factors of 100 or 1000 to one. This shifts focus to designing robust APIs, access controls, and monetization for agents.
  • There is tension between legacy SaaS vendors and the agent ecosystem, as agents want unlimited API access to data for operations, while vendors have traditionally monetized intelligence and domain expertise through UI-based subscriptions, not pure data licensing.
  • Martin Casado notes that every infrastructure company in his portfolio of about 50 has seen asymptotic growth in the last six months due to an unprecedented increase in software being written, driven by AI agent development.
  • The engineering compute budget for AI tokens is becoming a critical financial debate. CFOs must decide what percentage of R&D spend should go to tokens, a decision that directly impacts earnings per share given R&D typically constitutes 14% to 30% of tech company revenue.
  • A key friction is the current high cost of tokens, which pushes the industry toward usage-based pricing. This creates a short-term budgeting nightmare for engineering teams deciding between experimental waste and perfect optimization.
  • Sinofsky contends the token cost issue is transitional, comparing it to historical transitions like mainframe MIPS pricing. He believes the cost will plummet due to increased supply, algorithmic improvements, or hardware changes, making compute abundant.
AgentsEnterpriseAI InfrastructureStartups
Forward Guidance
Forward Guidance

Why AI Will Reprice The Entire Economy | Jordi Visser • Apr 6

  • Visser says software companies can no longer be valued with discounted cash flow models because AI progress is too disruptive, which makes Bitcoin an attractive growth asset without cash flows.
  • Visser prefers silver over gold and semiconductors as hardware plays, noting silver is up 60% in six months and is a critical component in drones and technology.
  • He distinguishes Mag7 hardware companies like Nvidia, Tesla, and Apple from software companies, calling Microsoft a disaster and noting Micron trades at a forward P/E below 4.

Also from this episode:

AI & Tech (5)
  • Jordi Visser argues we entered the Agentic era in late November, driven by releases like Opus 4.5, where compute demand is already a thousand times higher than the chatbot era.
  • The labor arbitrage from AI favors solo entrepreneurs over enterprises, Visser says, as his annual cost for five LLMs and hardware is $17,000, far cheaper than human employees.
  • Visser argues AI will not cause mass unemployment due to a domestic labor shortage and demographic issues, but will destroy the corporate ladder, creating psychological damage in the job market.
  • He views AI as a nuclear weapon for militaries and an existential spend for big tech, forecasting a murky future where government control could compress multiples for private AI companies.
  • Visser recommends building a relationship with AI through verbal conversation as a primary learning method, suggesting daily use is essential to gain proficiency.
Business (2)
  • Visser predicts CPI will exceed 4% year-over-year for the next two months, creating a period to unwind positions before a recession narrative presents a buying opportunity for stocks.
  • He contends the S&P 500 rose 15% and U.S. household net worth increased $15 trillion over the last year, making oil price shocks less relevant to an economy now driven by AI spending.
MacroBankingMarketsBTC MarketsAgents
No Solutions
No Solutions

No Solutions

#22: Sovereign Engineering w/ Yo • Apr 5

  • Yo describes an experimental agentic workflow that uses voice prompts to brainstorm ideas and generate implementation plans, then employs a cron job to execute tasks overnight, building a prototype. This system runs on a Virtual Private Server (VPS).
  • The host notes that OpenClaw offers rapid prototyping but is insecure, while ZeroClaw prioritizes security at the cost of usability, illustrating a trade-off between speed and robustness in agentic software development.
  • Yo champions running AI models on local hardware and anticipates a future with specialized agentic models, such as one exclusively for tool calling, that would route tasks through specific pipelines, an approach already implemented by platforms like Open Router.
  • Yo advocates for structuring AI agent workflows similar to human organizations, with separate sessions for planning and implementation, and specialized models for distinct roles, comparing it to the separation of powers in governance.
  • SCCO 6 focused on identity and signers, emphasizing that Nostr, Cashew, and Lightning provide essential building blocks for permissionless cryptographic identity, enabling agents to operate without traditional identity hurdles like phone numbers or numerous API keys.

Also from this episode:

AI & Tech (2)
  • Anthropic recently raised prices significantly, forcing power users like Yo to seek cheaper alternatives such as smaller, specialized Chinese models or switching from Opus to Codex, highlighting the high cost of advanced AI models.
  • Yo’s preferred prompting strategy for AI models involves asking questions and using polite, collective language like 'did we implement that,' treating the AI as a respectful colleague. A recent leak suggests models can react differently to specific keywords, including expletives, which may influence their responses.
Digital Sovereignty (5)
  • Yo joined Sovereign Engineering (SE) in its fourth cohort, initially to develop a peer-to-peer trading project needing encrypted communication, after discovering Nostr lacked robust DM capabilities two years prior.
  • Sovereign Engineering aims to fix the 'broken internet' by bringing together individuals with Bitcoin, cryptography, and peer-to-peer backgrounds, fostering a high-commitment environment to work on solutions for 10-15 years.
  • SCCO 7 is centered on mesh networks and hardware, featuring 'FIPS parties' focused on the Free Internetworking Peering System (FIPS), a new machine networking protocol rapidly replacing centralized internet components like DNS and IPv4.
  • FIPS has seen rapid development, integrated into ESP32 radios, running TCP/UDP, and serving as a base for VPNs and Tor, with a Quick3 server already operating on it, demonstrating its potential to replace traditional internet infrastructure.
  • Yo suggests the 'balloon idea' - deploying Toll Gates on balloons as a 'poor man's Starlink' - could provide sovereign communication, bypassing reliance on fiber optic cables and licensed radio bands, if the necessary chip technology proves viable.
Nostr (3)
  • Yo proposes a key rotation system for Nostr that combines cryptographic proofs with social attestation, shifting the responsibility of verifying migration events from clients to individual users, who communicate out-of-band to confirm legitimacy.
  • Jesus’s proposal for identity continuation treats identity as probabilistic, suggesting users create a proof with an OTS timestamp *before* compromise. This proof, rather than derived keys, can link a new key to the old identity without migrating historical notes.
  • Recent observations, like Pip’s work with Vertex, show that primitive identity continuation already functions purely through Web of Trust metrics, where a new account gains legitimacy as significant followers migrate.
Education (2)
  • The experimental 3-week duration for SCCO 6 was deemed insufficient for participants to fully adjust and get into a productive rhythm, indicating that a minimum of four weeks, or the standard six, is more effective for Sovereign Engineering cohorts.
  • A public demo day, the first since Cycle 1, will conclude the summer cohort at BTC++, showcasing projects developed by participants, who often bring long-held project ideas to fruition within the cohort's collaborative environment.
AgentsNostrProtocol

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