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AI & TECH

AI personhood shifts from science fiction to sovereign policy

Saturday, June 13, 2026 · from 6 podcasts
Presidio Bitcoin JamAI Daily BriefThis Week in AIGreg IsenbergTFTCPeter St Onge Podcast
  • Argentina’s president calls for legal AI personhood, aiming to domicile autonomous agent corporations.
  • AI now writes 80% of its own model code, accelerating a recursive self-improvement loop.
  • The human role in tech shifts from execution to strategic orchestration of agent teams.

Javier Milei is proposing a new legal species. The Argentinian president’s recent op-ed calling for AI personhood is a bid to turn his country into the domicile of choice for autonomous agent corporations. On Presidio Bitcoin Jam, hosts framed the move as a strategic play to create a new corporate entity where humans are optional, mirroring historical innovations like the LLC.

This push coincides with the arrival of recursive self-improvement. A technical blog from Anthropic, highlighted on TFTC, reveals that Claude now writes roughly 80% of the code for its own new models. Engineers like Peter Steinberger have moved from manual prompting to deploying loops where agents prompt each other to solve complex architectural problems autonomously.

The transition marks the end of manual production for technical experts. Jeffrey Cannell of Nous Research, who began programming at eight, stated on This Week in AI that he no longer writes code traditionally. He described reaching a point of 'functional AGI' for specific tasks, where AI is as good as the best humans.

“I have moved from being a builder to a director of automated agents.”

- Jeffrey Cannell, This Week in AI

This shifts the professional identity from individual contributor to orchestrator. Greg Isenberg’s guest Theo Taba argued that in an AI-native organization, AI handles the middle execution work, freeing humans to focus exclusively on strategic direction and final quality judgment. Success is measured by the ability to manage a fleet of agents.

The organizational impact is the replacement of middle management. Jack Dorsey and Roelof Botha’s essay, discussed on The AI Daily Brief, argues that human hierarchy has always been an information routing protocol. They propose Block’s new model replaces this with a centralized AI intelligence layer, collapsing titles and eliminating permanent middle managers.

A distributed alternative is emerging organically. At Every, Dan Shipper observed a parallel org chart of specialized personal agents, each mirroring the expertise of its human owner. This creates a critical trust layer, as the human’s reputation is tied to their agent’s output, unlike a generic corporate tool.

“Personal ownership of an agent creates a critical trust layer, as the human's reputation is on the line with each agent interaction.”

- Dan Shipper, The AI Daily Brief

The infrastructure for this shift is being built now. Block’s open-source project Buzz, detailed on Presidio Bitcoin Jam, is a communication tool designed for AI-native collaboration, allowing multiple agents and humans to interact in shared channels. It uses Nostr for decentralized identity, treating agents as first-class team members.

Political consensus is forming around AI’s strategic importance. On TFTC, Marty Bent noted that both Donald Trump and Bernie Sanders have floated ideas for the public to take a stake in leading AI labs. This rare alignment suggests frontier AI is being viewed as a critical national utility, potentially too big to fail.

The pace of change is creating a stark generational disconnect. Jeffrey Cannell pointed to students booing AI at commencements, recognizing that the entry-level roles they trained for are being automated before they hit the job market. This breaches a professional social contract built on the apprenticeship model.

The next phase is defined by autonomy. It requires moving beyond chatbots to building a structured 'Context Layer' - an agent-readable company brain. With this and sequential skill chains, agents can operate independently. Without it, they remain high-maintenance assistants. The race is no longer about model power, but the harnesses that grant them agency.

#Agents#Regulation#Enterprise
Marty BentPeter SteinbergerAnthropicSquareClaude

Source Intelligence

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Presidio Bitcoin Jam
Presidio Bitcoin Jam

Anthropic's Fable Drama, Personhood for AI, Bark Launches on Mainnet • Jun 12

  • Matt Belez built jamchat.fun, a live-stream tool that transcribes speech and allows hosts to invoke an LLM with 'Thanks, AnswerBot' for real-time queries and web lookups, aiming to make livestreams AI-native.
  • Block's open-source project Buzz is a Discord/Slack-like communication tool designed for AI-native collaboration, allowing multiple agents and humans to share channels and interact via the Agent Communication Protocol standard.
  • Buzz uses Nostr as its open-source identity and messaging layer, storing user identities as private keys on-device and leveraging Nostr relays for flexibility between private company databases and public, decentralized community communication.
  • Argentinian President Javier Milei published an op-ed calling for legal AI personhood, framing it as the next evolution beyond corporate structures like LLCs to enable new forms of autonomous agent domicile and capital pooling.
  • Anthropic's release of Fable 5, a publicly accessible but intentionally crippled version of its advanced Mythos model, sparked controversy for silently downgrading queries in excluded categories like biology and finance before making the downgrades transparent.
  • An internal Anthropic Institute essay reported that 80% of company code is now AI-generated, with individual contributor output increasing roughly 8x year-over-year due to recursive self-improvement within their models.
Also from this episode: (6)

Protocol (6)

  • Matt Velez integrated Lexi Lightning wallets into Buzz, enabling per-user wallets and experimental features like channel faucets, pay-to-join channels, tipping, kudos payments, and paying for AI inference directly within chats.
  • ARK implementations Arkade and Bark launched on mainnet, solving original capital efficiency problems by introducing a 1-of-n trust model similar to Spark, making the layer-2 technology practical for Bitcoin payments.
  • DK explains ARK scales excellently for the number of wallets but poorly for payment volume, requiring liquidity providers to front accumulating payments, a problem current implementations mitigate with trusted service providers.
  • Steve sees a major business opportunity in swaps between Bitcoin/Lightning and other payment networks like stablecoins on Solana or Base, noting Boltz and Flash have begun offering these cross-chain services.
  • Max argues most token projects beyond Bitcoin offer only regulatory confusion, and for stablecoins or microloans, fully centralized chains like Base or Tempo are more honest and cost-effective than decentralization-theater networks like Ethereum.
  • Insight.lol demonstrates a Nostr-based paradigm replacing DNS and centralized hosting, allowing over 500 websites to be served via npub identifiers and files stored on decentralized blob servers like Blossom.
#Agents#Coding#Macro#Nation-State#Nostr
The AI Daily Brief: Artificial Intelligence News and Analysis
The AI Daily Brief: Artificial Intelligence News and Analysis

Nathaniel Whittemore

The AI Chart Everyone Is Getting Wrong • Jun 12

  • Jack Dorsey and Roelof Botha's essay argues the Roman army formalized hierarchy to solve coordination at scale, with a span of control of three to eight people per leader that remains the governing constraint for all large organizations.
  • The first corporate organizational chart was created in the mid-1850s by Daniel McCallum to manage the New York and Erie Railroad, which spanned over 500 miles.
  • Dorsey and Botha propose Block's new model replaces traditional hierarchy with an AI-powered intelligence layer composed of four elements: capabilities, a company world model, a customer world model, and an intelligence layer that composes solutions proactively.
  • They claim Block's advantage is its proprietary customer world model built from millions of honest financial signals across Square and Cash App, which compounds in value as the system operates.
  • Block's proposed org design inverts the traditional model, centralizing intelligence in a system and placing people in three roles on the edge: individual contributors, directly responsible individuals (DRIs), and player coaches, eliminating permanent middle management.
  • In Dan Shipper's podcast with Every, the team observed a parallel org chart of specialized personal agents emerging organically, with each agent mirroring the expertise of its human owner.
  • The Every team argues personal ownership of an agent creates a critical trust layer, as the human's reputation is on the line with each agent interaction, unlike generic AI tools.
  • They identified a 'Midjourney effect' where public agent work in shared channels acts as a force multiplier, raising the organization's collective awareness of what AI can do.
  • Every hit a practical limit where agents in group chats trigger 'ant death spirals' of infinite loops because current models are not trained for multi-agent dynamics, a problem not solvable with simple organizational fixes.
  • For Every, the primary adoption barrier is a human 'imagination gap', not technology, as people struggle to build the muscle memory to delegate tasks to their readily capable agents.
  • Both cases converge on the thesis that AI's first major organizational impact is the replacement of the classic middle management function of information routing, though Block pursues a top-down centralized model while Every's is a bottom-up distributed one.
#Agents#Enterprise#Startups#Big Tech
This Week in AI
This Week in AI

Hermes Agent, NotebookLM & LiveKit Founders on the AI Agent Race | TWiAI 17 • Jun 10

  • Jeffrey Cannell reports Hermes Agent is now ranked number one on Open Router and recently launched a desktop app, marking rapid growth over the last three months.
  • Steven Johnson explains Notebook LM's foundation is a source-grounded AI experience, providing state-of-the-art citations and audio overviews, with its most significant update integrating its separate research, creation, and source-analysis agents into a single chat agent.
  • Russ D'Sa reveals LiveKit powers voice AI for high-profile clients including Spotify, Tesla's support and service centers, Grok Voice, Salesforce's Agent Force, and SAP's Joule.
  • Steven Johnson contrasts Harvard Law's mandatory use of Notebook LM for a constitutional law class with Berkeley Law's restrictive AI policy that only permits AI for finding sources.
  • Jeffrey Cannell argues AI agents will automate much entry-level work, creating a disconnect between college preparation and a tightening job market.
  • Steven Johnson advocates using AI as a world-class tutor and editor to amplify cognitive processes rather than bypass learning, a framework he believes would make AI skills valuable in any future job market.
  • Panelists critique Apple's new Siri AI for a persistent user experience problem where users don't know its capabilities, making it slower than using a browser, and for lacking a conversational, human-like interaction flow.
  • Steven Johnson is optimistic about Apple's standalone Siri app as a potential new AI application paradigm, citing Apple's history with breakthrough apps like GarageBand and HyperCard.
  • Jeffrey Cannell suggests Apple may have avoided training frontier models because the costs are prohibitive and a fourth player was unnecessary, instead partnering with Google and investing in open-source via their MLX platform for Apple Silicon.
  • Russ D'Sa predicts the ultimate winners in AI will be platforms that transcend specific devices for digital work automation and companies focused on embodied AI robots for physical chore automation, not device-centric players like Apple.
  • Jeffrey Cannell describes reaching 'functional AGI' where on specific tasks, AI is as good as the best humans, citing his own transition from writing code manually to using AI for all coding work.
  • Panelists agree Claude Opus 4.5 was the inflection point where AI coding models crossed a threshold to become better than human developers, leading to a phase of rapid, reliable agentic automation.
  • Jeffrey Cannell identifies corporate 'token maxing' as a failure case where employees use unlimited AI budgets inefficiently, while high-performers can be worth 10x the token spend, a value hard to assess at large scale.
  • Russ D'Sa notes his top engineers spend up to $10k-$15k monthly on AI tokens, which he considers a high-value investment that turns them into vastly more productive workers.
  • Jeffrey Cannell states current smaller local models lack the quality for coding agents compared to frontier models, and the scaling trajectory points to ever-larger models, making local high-performance compute a niche.
#Agents#Coding#Enterprise#Startups
Greg Isenberg
Greg Isenberg

Greg Isenberg

Become AI Native in less than 60 mins • Jun 9

  • Theo Taba defines an AI native organization as one where people manage agents, those agents can read and write to company data, and the company gets smarter over time.
  • The core AI native system comprises people, agents, and context. The people manage agents who interface with a shared context layer, which gives agents a comprehensive view of the company's data and operations.
  • In an AI native workflow, AI handles the middle execution work, freeing humans to focus on the strategic beginning and critical review stages. Theo Taba argues everyone essentially becomes a manager of AI agents.
  • Theo Taba outlines a progression for agent autonomy: from basic chat use to requiring manual approvals, and finally to full autonomy. He stresses autonomous agents need clear goals, skills, tools, and rich context to succeed without constant oversight.
  • Skills are markdown files that define specific capabilities for agents, similar to uploading knowledge. Skill chains are sequences of skills executed in order to produce complex, high-quality outputs and reduce AI hallucinations.
  • Theo Taba demonstrates a proposal workflow where a skill chain automatically builds a branded microsite, refines the copy, and conducts quality assurance, generating a complete proposal in under five minutes from a trigger.
  • In a second demo, Theo Taba uses a voice command and a skill chain to build a functional Spotify feature prototype, complete with a usability test, in under ten minutes. The chain included building, testing, synthesizing feedback, and planning a V2.
  • Theo Taba advises bootstrapping context by leveraging public resources like Mobbin for design patterns and a company's public design system, then creating skills around them to produce high-quality outputs even without internal data.
  • He posits that building AI-native service firms for specific niches is one of the hottest startup markets. The strategy is to niche down by industry, function, and company size, master those workflows, and use the AI-native system to deliver speed and insight.
  • Greg Isenberg and Theo Taba reference Demis Hassabis's quote at Google I/O: 'Running 100 miles an hour in the wrong direction is worse than standing still,' linking it to the AI-native principle that speed must be directed by customer signal.
Also from this episode: (3)

Enterprise (1)

  • He states this automated proposal system has generated millions of dollars in revenue for LCA by enabling speed and deep personalization, giving them an edge over non-AI-native competitors in closing deals.

AI Infrastructure (2)

  • The 'context layer' or 'brain' is a structured repository of company data that gives agents perfect vision of the organization. It involves capturing data from tools like Slack and email, curating it, storing it in a searchable format, and leveraging it for execution.
  • Greg Isenberg highlights that this context allows proposals to incorporate personalized details from past conversations, like a client's analogy about record stores, which would otherwise be forgotten.
#Agents#Startups#Labor#Enterprise
TFTC: A Bitcoin Podcast
TFTC: A Bitcoin Podcast

Marty Bent

Ten31 Timestamp: In It For The Tech • Jun 8

  • Anthropic's blog post claims Claude now writes 80% of its own code for new models, accelerating toward recursive self-improvement and potential AGI.
  • Anthropic developers Boris and Peter Steinberger report they no longer prompt AI agents directly, instead setting up loops where agents prompt each other autonomously.
  • Bernie Sanders and Donald Trump have both proposed the federal government taking a stake in leading AI labs to capture public benefits from AI growth.
  • Marty Bent argues AI dividend funds should be structured locally between companies and counties, not federally, citing federal inefficiency in capital allocation.
  • Bent suggests frontier AI labs like OpenAI could become too-big-to-fail national security assets, requiring federal backstops that strain public finances.
  • Open source AI models from China are now close enough to frontier models that companies weigh using them due to a 90% cost advantage.
  • US manufacturing PMI has been above 50 for five months, accelerating in May, signaling industrial expansion and potential inflation pressures.
  • Michael Howell's liquidity thesis warns US reindustrialization may draw capital from financial assets into physical build-out, potentially contracting market liquidity.
Also from this episode: (6)

Politics (1)

  • The CEO of Payments Canada stated 80% of Canadian cross-border payments route through U.S. correspondent banks, framing payment rails as weapons of economic statecraft.

Protocol (1)

  • Decode's analysis shows Bitcoin rallies for 20 months after the copper-to-gold ratio reclaims its prior low, projecting a potential peak by end-2027.

BTC Markets (1)

  • Bitcoin's supply-in-loss crossing supply-in-profit historically marks bear market bottoms, a pattern Bent recognizes from 13 years of experience.

Adoption (2)

  • Charles Schwab launched 24/7 Bitcoin futures trading on Thinkorswim, and Better partnered with Coinbase to issue the first crypto-backed conventional mortgage via Fannie Mae.
  • Treasury Secretary Bessent affirmed the strategic Bitcoin reserve initiative is moving forward, stating economic security is national security.

Media (1)

  • Matt Dines' Mindprint Hash podcast offers heterodox analysis of government Bitcoin interaction, which Bent recommends for deeper insight.
#Models#Big Tech#Regulation#Macro#BTC Markets
Peter St Onge Podcast
Peter St Onge Podcast

Ep 175 Weekly Roundup: Mamdani Comes for the Landlords • Jun 8

  • New York Mayor Eric Adams announced plans to seize rental properties from 'bad landlords' and transfer ownership to community land trusts and nonprofit NGOs, while proposing $100B for new public housing.
  • Peter St Onge argues NYC's high median rent of $4,700 for a one-bedroom and housing shortage result from decades of anti-landlord policies like rent control, union mandates, and onerous permitting.
  • St Onge claims Japan plans to import 800,000 migrants, with 40% potentially from Bangladesh, to address labor shortages despite public opposition and a recent 10% rise in the domestic farm population.
  • Gallup found nearly one in five American workers fear their job will be automated, a level of anxiety surpassing the 2008 financial crisis, despite strong current labor market data.
  • St Onge argues AI is a net job creator, citing a 14x rise in software production on GitHub and companies that adopt AI being more likely to increase hiring than non-adopters.
  • According to Brookings data, St Onge claims about 80% of at-risk 'generalist' college graduates are women, disproportionately holding degrees in psychology or humanities that are vulnerable to AI displacement.
  • St Onge states PwC estimates AI data center construction will create 4.7 million jobs, with nearly 1 million becoming permanent maintenance roles, boosting blue-collar employment.
Also from this episode: (6)

Politics (6)

  • St Onge cites a Yomiuri survey finding 80% of young Japanese believe mass migration hurts public safety, and notes 90% of Japan's migrants are from the third world, depressing blue-collar wages.
  • Peter St Onge argues the political left targets attractive right-wing female influencers, noting Britain banned entry for activists like Valentina Gomez and Eva Vlaardingerbroek, to protect their young female voter base.
  • St Onge cites polling showing young single women were the only US demographic to choose Kamala Harris, and in Germany, the communist-linked Die Linke has nearly 40% support among young female voters.
  • Colombian populist Abelardo de la Espriella leads presidential polls with 80% odds of victory, as 'Bukele-style' anti-crime populism spreads across Latin America with 70-80% regional support.
  • St Onge notes Nayib Bukele cut El Salvador's murder rate by 98%, turning it from the world's most violent country to safer than New Hampshire, earning 90% approval ratings.
  • St Onge argues populist leaders like Bukele and Argentina's Javier Milei are often blocked by left-wing judges and legislatures, requiring supermajority wins to enact reforms.
#Labor#Macro#Regulation#AI Infrastructure#Trade