At a Glance

In this piece, you will learn:

• Why most human thinking has always been a form of search — and what changes now that LLMs do it better

• Where the real human advantage lies when AI can out-search any single domain

• Why the polymath, not the expert, wins in the agentic era

This post is prepared with Guest Author — Tomas Pueyo, writer of Uncharted Territories, one of Substack’s top strategy and big-ideas newsletters with 800K+ subscribers. If you also want to write for Creators AI — send us email here

Over thousands of years, tens of billions of people have spent their entire lives thinking, reasoning, creating, dreaming, solving problems. How many of our thoughts have been completely new? Very few.

When your thoughts have been original, most of the time it was because you applied existing solutions to new situations: You took the arguments and facts gathered by others and chained them in a novel way, adapted to your unique problem.

This means intelligence is mostly a search problem: Search within the body of known facts and surface the most relevant ones for you.

We just didn’t know this because, until now, search was so expensive.

The Long March to Cheap Search

50,000 years ago, knowledge died with its holder. It could only be stored by the corruptible mind and passed by the corruptible voice. It required one person to spend time speaking, and others to spend time listening.

Low bandwidth.

Hard to search for somebody else’s ideas like that. People had to reinvent the same thoughts over and over again.

5,000 years ago, writing emerged, and we started storing information outside the brain in parchments, papyri, clay tablets… Suddenly, information storage and communication didn’t corrupt as much.

Ctrl + F “How to increase my wheat yield”

But it was still very expensive to transcribe ideas and consume them: It took a year to copy a full book, and you’d have to travel to where it was to consume it. Hard to search for ideas like that.

500 years ago, the printing press emerged. Now, the cost of writing a book was still the same, but the marginal cost of reproducing it fell dramatically. Suddenly, information became much more available. You could search for others’ ideas, and this was so powerful that the Enlightenment emerged, as scientists around Europe shared their ideas and built on each other’s.

Google, 1900 AD

But you still had to print the book, learn about it, search for it, and buy or borrow it. Your search was limited to the size of your pocket and your local library’s shelves.

50 years ago, computers and the Internet emerged. Suddenly, writing an idea was as fast as your fingers on a keyboard. The cost of sharing it fell to nothing. Corruption was exiled. The concept of searching for somebody’s ideas became second nature — googling joined the dictionary. That’s when we realized that virtually all of our problems had already been solved by somebody else, usually someone who had written his solution in an obscure online forum 12 years ago.

Just took 1h 42m and 23 searches.

Still, to solve your personal problem, you had to look up dozens of microproblems and combine their solutions to tailor them to your needs. The friction of every search was small, but it added up to enough that you couldn’t solve all your problems through search — you had to reinvent the wheel.

5 years ago, we put the world’s knowledge in a cauldron, and from there emerged intelligence. Suddenly, LLMs independently looked at all the problems, recorded the solutions, chained all the thoughts, and brewed an answer tailored to your specific needs. Now that the friction of every search disappeared, it became clear: Intelligence is mostly search. You just need to cast your spell deftly enough to summon the perfect combination of facts, problems, and solutions, chained in the right order from the latent space of human knowledge.

Spellcasting SaaS.

“Actually, humans have plenty of novel thoughts!”

Do we? Consider a PhD: A person spends ~14 years studying until they graduate high school, then four more years in undergrad, then two years of grad school, and then a handful of years for the PhD. After ~25 years studying non-stop, this is what they can hope to do with the horizon of human knowledge:

If 25 years of hyperfocus buys one pixel, how many novel thoughts do we really have? No, most ideas are just the combination of other existing ideas.

Take the logic to its endpoint, and fear might wash over you:

• If your thoughts are not original, other people have already thought most of them.

• Your advantage was collating other people’s thoughts in your unique way.

• LLMs bring the cost of searching others’ thoughts and combining them to zero.

Then, it becomes cheaper to spellcast thoughts than to craft them yourself. There will be no original thoughts for you to have.

From 50,000 to 5,000 to 500 to 50 to 5. The next step in the transition is in 0.5 years. Are you, dear reader, about to get obsoleted?

We’ve been tracking how creators are already navigating this shift — building systems rather than just using tools: How Solopreneurs Are Using Full AI Agents

This and many other practical posts on building with AI are available exclusively to our subscribers

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The Fork in the Road

Luckily, humans + AI will be better than either alone. We saw that with chess already: After Deep Blue beat Kasparov in 1997, the best type of player was neither the machine nor the human, but the centaur — humans working with machines with the right process.

Source: Centaur = Human (yours truly Tomas Pueyo) + Computer (Claude, fact-checked by ChatGPT)

If you join forces with AIs, you’ll be able to create novel ideas faster. We’ve entered a period where breakthroughs across disciplines will be a daily event.

Unfortunately, humans never really learned much from computers: The teal line remained flat. And the Golden Era of centaur rule lasted just about two decades. Eventually, computers caught up.

But something different happened with Go: Although humans don’t beat AIs anymore in that game, the quality of human decisions has skyrocketed since AI surpassed humans, as we learn from AI.

Decision quality of Go players, before and after the release of AlphaGo. Source

Why have humans progressed with Go and not in chess? Is it because Go is a much more open-ended game? What does that tell us about the future of intelligence beyond search?

Read more

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

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The Government Pulled Anthropic’s Most Powerful Model Offline — Three Days After Launch 🧨

Anthropic released Claude Fable 5 on June 9. On June 12, the US Department of Commerce issued an export control directive requiring Anthropic to suspend access to Fable 5 and Mythos 5 for all foreign nationals — including foreign national Anthropic employees. Because Anthropic has no way to verify citizenship at the API level, it disabled the models for every user on Earth.

The directive landed at 5:21pm ET. By 5:45pm, Fable 5 was offline.

What the government said:

The Commerce Department issued the order under national security authority, citing a method of bypassing Fable 5’s safety classifiers — a “jailbreak.” The specific technique: asking the model to read a codebase and fix software flaws. The government argued this could be used to identify exploitable vulnerabilities.

What Anthropic said:

Anthropic reviewed the jailbreak and disagreed. The company found it was a narrow, non-universal bypass — one that produces the same results as publicly available models including OpenAI’s GPT-5.5, which carries no such restrictions. “We have not even received a disclosure of a concerning non-universal potential jailbreak that led to a harmful result,” Anthropic wrote. The company said it complies with the legal directive while believing it is a misunderstanding, and is working to restore access.

The Amazon angle:

The twist that made this story go viral: the jailbreak was discovered by researchers at Amazon. Amazon CEO Andy Jassy flagged the finding to the Trump administration. Amazon is Anthropic’s largest investor with a stake worth approximately $13 billion. The company that funds Anthropic, hosts its models on AWS, and supplies its compute infrastructure is also the reason its most powerful product went offline.

Anthropic CEO Dario Amodei reportedly refused when the administration asked the company to patch or pull the model before issuing the directive.

What it means for builders:

This is the first time the US government has used export control authority to forcibly suspend access to a commercial AI model. The precedent is significant: if the standard of “any non-universal jailbreak” applied across the industry, it would, as Anthropic noted, “essentially halt all new model deployments for all frontier model providers.” Whether this directive expands, narrows, or becomes a one-off is the question the entire AI industry is now watching.

Access to all other Anthropic models — Opus 4.8, Sonnet 4.6, Haiku — remains unaffected.

Anthropic released the most capable model in its history and watched its largest investor trigger a government shutdown of it three days later. The company that bankrolled you, hosts your models, and supplies your chips is also the reason your best product is offline. This is what AI infrastructure dependency actually looks like.

Source: Anthropic

SpaceX Buys The AI Coding Layer — $60B for Cursor, Four Days After the IPO 🚀

SpaceX exercised its option to acquire Anysphere — the company behind Cursor — for $60 billion in stock on June 16. The announcement came four days after SpaceX’s record-setting Nasdaq debut.

This was not a surprise acquisition. SpaceX had disclosed the option in its IPO filing: pay $10 billion for a compute and model collaboration, or exercise the right to buy Cursor outright for $60 billion later in 2026. After its IPO raised $75 billion at a $1.75 trillion valuation, SpaceX chose the full acquisition. The all-stock transaction means SpaceX used the currency of its sky-high valuation rather than the cash it just raised.

What Cursor brings:

Cursor had been growing toward a $50 billion valuation in a funding round that never closed. The acquisition brings $2.6 billion in annualized B2B revenue, 4 million+ active developer users, and a codebase of proprietary coding models that SpaceX says it has been jointly training with xAI for months. Backed by Andreessen Horowitz, Thrive, Nvidia, and Google — the cap table reflects how many major players had already bet on Cursor as the winner of the AI coding agent race.

The xAI integration:

xAI, which SpaceX acquired in February 2026, gains both Cursor’s developer distribution and its data. Cursor users generate an enormous stream of coding context — requests, design decisions, debugging sessions — that xAI can use to train Grok for code. SpaceX says it will release a new AI model on Cursor and that Grok Build, xAI’s coding agent, will be available to Cursor users. SpaceX shares jumped 10% on the announcement, adding roughly $250 billion to a market cap already above $2.5 trillion.

What it means for Cursor users:

The transaction is expected to close in Q3 2026. Until then, Cursor continues to operate normally. After closing, Grok models will be available within Cursor alongside Claude and GPT-5.5. The 90-day termination clause in SpaceX’s compute deals with Anthropic and Google remains — if Grok Build adoption scales, SpaceX could redirect capacity it currently rents to its competitors.

The competitive read:

Anthropic and OpenAI have been building their own coding agents — Claude Code and Codex. Now xAI, through Cursor’s distribution, has 4 million developers already in its funnel. Microsoft’s GitHub Copilot has 30 million users. The coding agent market just consolidated around four players: Claude Code, Codex, Copilot, and Cursor/xAI.

SpaceX used the valuation premium from its IPO to buy the most popular AI coding agent on the market four days after listing. The deal costs SpaceX a small slice of equity that would have been worth far more to Cursor’s investors at any point in the last year. Welcome to the era of trillion-dollar acqui-hires.

Source: Reuters

OpenAI Recruits McKinsey’s Army 🏢

On June 14, OpenAI launched its first formal global partner program — the OpenAI Partner Network — backed by a $150 million investment and a target of 300,000 certified consultants by December 31, 2026.

The launch partners are not startups. They are Accenture, Bain & Company, BCG, McKinsey, and PwC — the five largest management consulting firms in the world — plus systems integrators and technology specialists. Together they represent tens of thousands of enterprise relationships, decades of C-suite access, and the sales infrastructure that has driven adoption of every major enterprise software platform since SAP.

Why OpenAI built this:

The framing in OpenAI’s announcement is direct: “The limiting factor for seeing value from AI in the enterprise is no longer model capabilities. Instead, it’s how organizations repeatably identify the right use cases, redesign workflows, integrate with existing systems, and drive adoption and change management at scale.” OpenAI is saying that the model race is effectively over at the enterprise level, and the new race is for implementation.

The certification architecture:

Three tiers — Select, Advanced, Elite — with progression based on sales performance, technical capability, and deployment experience. Partners can earn specializations in Codex, cybersecurity, and agents. An elite Codex specialization will mean something in an enterprise RFP the same way a Salesforce certification did in the 2000s. OpenAI is also piloting a Forward Deployed Experts program that embeds qualified partner practitioners directly with OpenAI’s own Forward Deployed Engineering teams — granting access to proprietary deployment playbooks.

The competitive context:

Anthropic launched the Claude Partner Network in March 2026 with a $100 million investment. By mid-June it had certified 10,000+ consultants and attracted 40,000+ company applicants. OpenAI’s program targets 30× the certified consultant count. The 11-day gap between Anthropic formalizing its partner hub (June 3) and OpenAI’s launch (June 14) is not a coincidence.

Early results from partners:

Paychex, working with Bain: 80% reduction in wait times vs human processing on a payroll automation workflow. T-Mobile, working with Accenture: real-time intent and sentiment analysis for customer interactions. eBay, working with boutique partner Artium: next-generation customer service platform with human-AI tandem model.

OpenAI just turned McKinsey’s distribution machine into a channel for ChatGPT Enterprise. The 300,000 certified consultants target is not a training goal — it’s a moat. Every consultant who builds their practice on OpenAI certification faces meaningful switching costs if Anthropic or Google offer better pricing. This is how you build enterprise lock-in at scale.

Source: OpenAI

News of the week 🌍

SpaceX Lists on NASDAQ — $75B Raised, the Largest IPO in History 🚀 — SpaceX priced at $135/share on June 11 and debuted June 12 on NASDAQ under ticker SPCX — raising $75 billion, eclipsing Saudi Aramco’s 2019 record as the largest public offering ever. Merged with xAI in February. For the 12 months ending March 2026: $19.3B revenue, net loss ~$3.4B. Musk retains 82%+ voting control. The listing values SpaceX at roughly $1.75 trillion — 7th largest US company, above Tesla.

Google DeepMind Publishes Its Plan to Protect Itself From Its Own Rogue AI Agents 🤖 — Google DeepMind released a formal framework for what happens when AI agents act outside their intended scope: behavioral monitoring layers, automated rollback triggers, and human-in-the-loop checkpoints for high-stakes actions. The timing is striking — the same week Anthropic’s models were pulled offline over a declared safety concern, Google’s own safety team published a preemptive containment blueprint.

Google's Gemini Co-Lead and "Attention Is All You Need" Co-Author Leaves for OpenAI 🧠 — Noam Shazeer — co-lead of Google Gemini, one of eight co-authors of "Attention Is All You Need" (the 2017 paper that launched the Transformer era), and a key figure in Google's LLM research since the early BERT days — left Google this week to join OpenAI. Google had paid over $2.7 billion to bring him back from Character.AI in 2024. He's now gone again. Google losing Shazeer is not a talent note — it's a structural signal about which lab researchers believe has the most interesting pre-training work ahead.

54 Cybersecurity Experts Demand the US Restore Fable 5 Access 🔐 — A coalition of 54 security researchers and AI experts — including bug bounty pioneer Katie Moussouris and former US government officials — published an open letter to the Trump administration on June 13 demanding the Fable 5 directive be reversed. The letter argues the action “has taken the best models away from defenders, created market uncertainty, and risked America’s AI leadership without any real risk to justify it.” Full letter and signatories at freefable.org.

OpenAI Launches Enterprise Spend Controls — Usage Tracking by User and Model 💼 — Published June 18. ChatGPT Enterprise admins can now track credit usage by individual user, product, and model; set default limits per user or group; configure group-level overrides; and create individual exceptions. For any org running ChatGPT Enterprise with multiple teams — this is the budget layer that was missing.

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Useful tools ⚒️

⭐ Goldfish — AI memory that lives on your Mac. Goldfish privately remembers what you’ve been working on across every app — documents, emails, Slack threads, code — and surfaces it the moment you need it. Press ⌥ Option in any text field and it drafts replies, rewrites sentences, or recalls important context from your recent work without you explaining the backstory. No pasting docs, no re-explaining your situation. The tool tracks context across your Mac locally. Creator Joel Edholm calls the approach “contextmaxxing.” Free during alpha.

Framer 3.0 — Framer now ships AI agents that live inside your canvas and design — not suggest, design. Drop a brief and agents build full pages, create components, write code, connect your CMS, and handle SEO. Agents plug directly into Claude Code and Cursor, inside your actual component system, without breaking it. Also launching: Branching (try ideas before publishing, like Git for your site), and the new Framer Community for creators to share work and earn from it.

Upstream — An inbox built for teams where humans and AI agents both send and receive. Upstream routes messages intelligently: AI agents handle the routine, flag the important, and hand off to humans when judgment is needed — all in one interface. Works with Gmail, Slack, and Intercom. Built for customer-facing teams and operators running agentic workflows who are tired of switching between their AI tool and their inbox.

Novu Connect — Ship your AI agents’ notifications to wherever your users already live: email, Slack, Discord, push, SMS, in-app — in a single integration. Instead of building separate notification logic for each channel, Novu handles routing, templating, and delivery across all of them. Open-source, 65K+ GitHub stars, and the new Connect layer makes adding agent-triggered notifications a single function call. Free tier available.

Honestly — See what Reddit and TikTok actually think about your product — not curated reviews, not NPS scores, but the real unfiltered conversation. Honestly monitors relevant communities and surfaces posts, threads, and comments about your product in near real-time, organized by sentiment and theme. For any founder trying to understand how they’re perceived outside their own channels: this is the signal your review platforms aren’t giving you.

Weekly Guides 📕

How to Hire and Manage AI Agents: GSTACK and Paperclip — Our own deep dive from June 18. Two open-source tools that went viral this year answer the same question from opposite ends: GSTACK (97K GitHub stars, from YC president Garry Tan) adds judgment to one agent via 23 slash commands — /review, /qa, /ship, /cso — that fill the roles a solo developer doesn't have. Paperclip (67K stars) is the management layer for many agents: org charts, per-agent budgets with hard stops, audit logs, and a heartbeat scheduler. Includes day-one install instructions for both, two real setups worth stealing, and the honest catch nobody in the "zero-human company" pitch mentions.

Framer 3.0 AI Agents: Getting Started — Official Framer Learn — Framer’s official tutorial for the 3.0 agent capabilities: how to prompt agents on the canvas, how to connect Claude Code and Cursor to your component system, how to use Branching before publishing to protect your live site, and how Community monetization works. The practical entry point before you touch 3.0 in production.

Your Agent Config Files Are Wasting Tokens — Here’s What To Fix — Researchers published a practical analysis on June 17 showing that bloated, poorly structured AGENTS.md and system prompt files are the single biggest source of wasted tokens in production agent deployments. The piece covers: what “smelly” config patterns look like, why less context beats more context for agent performance, and a checklist for auditing your own config files. Required reading before your next Claude Code or Codex session.

Claude Code with Opus 4.8: How to Keep Your Agentic Workflows Running While Fable 5 Is Offline — Anthropic's official Claude Code documentation, updated this week to reflect the Fable 5 suspension. Covers which models are currently available, how Dynamic Workflows operates under Opus 4.8 (same up to 1,000 parallel subagents), what changes when routing to Fable 5 vs Opus 4.8, and how to adjust your context window and cost settings for the current model configuration. The essential reference for any team whose workflows depended on Fable 5 and need to keep shipping this week.

ChatGPT Scheduled Tasks: What's New and How to Set It Up — Practical June 17 breakdown of the Scheduled Tasks update: how the new Scheduled page works, how to create monitoring tasks (not just reminders) that watch the web or your connected apps and notify you only when something changes, how to set broad time windows instead of exact times, and tier availability (Plus, Pro, Business, Enterprise). Pulse is being sunset — this replaces it. Free for your tier if you're already a paid ChatGPT subscriber.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

Estonia Will Give AI Agents Legal Digital IDs — The First Country to Do It — Estonia — the country that pioneered digital citizenship for humans — is extending the same framework to AI agents. Agents will receive cryptographic digital identities allowing them to enter into contracts, access government services, and operate legally across the EU. The headline is “Estonia does something quirky again.” The implications are not: this is the first legal framework in the world where an AI agent has recognized personhood for transactional purposes. Published June 18 by The Register.

AI and a Brain-Computer Interface Let a Speechless ALS Patient Work a Full-Time Job — Published June 16. A patient with ALS who cannot speak used a BCI implant combined with an AI language model to return to full-time work this week. The system translates neural signals into text fast enough for real-time professional communication. The most concrete demonstration yet of AI as an accessibility technology — not a productivity tool, not a coding assistant, but a voice for someone who no longer has one.

Anne Hathaway: “Nobody on That List Gets the Job” — On ChatGPT-Written Thank You Notes — Anne Hathaway told Fortune she was spammed with ChatGPT-written thank you notes after hiring for a recent role. “The notes all started to sound exactly the same,” she said. “Nobody on that list gets that job.” Published June 18 — the most widely-shared anecdote of the week about what happens when everyone uses AI in exactly the same way at exactly the same moment.

Anthropic Is Still at Odds With the White House Over Claude Fable 5 — Wired’s sourced account of the White House meetings that followed the shutdown: Anthropic leaders flew to Washington, DC this week and met with US officials on Monday. After high-level talks, the two sides remain split on how significant the Fable 5 risk actually is. The most up-to-date read on where the standoff stands and what resolution might look like.

If you missed our previous updates, don’t worry, here they are:

Fable 5 Goes Public, Apple Bets on Google, OpenAI Follows to the SEC | Weekly Digest

Your take: Amazon triggered the government shutdown of the company it invested $13 billion in — and the company that hosts its models, supplies its compute, and built its enterprise business. Is this just regulatory caution, or is Anthropic’s biggest risk not OpenAI but the infrastructure it depends on? Drop it in the comments 👇

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Last week I counted nine Claude Code tabs open across two monitors. I could not tell you what three of them were doing.

One had been looping on the same failing test for forty minutes while I answered Slack. That mess is the entire reason this post exists.

For two years the question with AI coding was whether the agent could do the work. In my stack, running an agency, media and launching AI products, that question is basically dead.

Claude Code, Codex and Hermes write real code, open real browsers, file real pull requests. The bottleneck moved. It is not the LLM anymore. It is me, trying to remember which agent is doing what, what each one is burning in tokens, and whether anyone reviewed the output before it hit main.

That gap turned into a whole category this year, and it arrived loud. Two open source projects went viral recently, answering the same question from opposite ends.

GSTACK, from Y Combinator president Garry Tan, is sitting near 97,000 GitHub stars. Paperclip, from a developer who goes by dotta, is above 67,000.

Neither is a new agent. Both are the org chart around the agents you already run.

At a glance

In this piece, you will learn:

• What GSTACK and Paperclip actually are, in plain language, and how they differ

• Which one fixes your real bottleneck: judgment, throughput, or management

• How to install and test each one on day one, step by step

• Real cases from operators on Reddit and X, and two setups worth stealing

• The honest catch nobody selling you a “zero-human company” mentions

Stop reading this as “more AI tools.” Start reading it as layers stacked on the agent.

One layer adds judgment. One adds throughput. One adds management. You want the cheapest layer that fixes your actual bottleneck, not the one with the best launch video.

This whole post assumes your stack is already split into layers. If yours still is not, start here: Your AI Agent Stack Is Spaghetti, It Should Be Lasagna.

GSTACK: judgment for one agent

I will start with the smaller and, to its credit, more honest of the two.

GSTACK is a free, MIT licensed pack of opinionated skills you drop into Claude Code. The install is one line and takes about thirty seconds. Nothing runs in the background, nothing touches your PATH.

What you get is 23 core skills plus 8 power tools. Each is a slash command that loads a specialist persona with its own priorities and constraints.

The personas are the whole i

dea. A CEO that challenges your scope before you write a line. An engineering manager that locks architecture. A designer that hunts AI slop. A reviewer that looks for production bugs. A QA lead that opens a real browser. A security officer that runs OWASP audits. A release engineer that ships the PR.

The work moves through a fixed loop: Think, Plan, Build, Review, Test, Ship, Reflect.

This is the tweet that kicked it all off, and it is still the cleanest one line summary of the pitch.

What it solves is real, and I feel it every week. A solo developer has no senior reviewer in the room. Not because they are careless, but because that person does not exist on a team of one.

Under deadline, the review step is the first thing to go. GSTACK fills the empty chairs and makes review the default instead of the thing you meant to do.

Why this skill pack out of the thousands floating around? Because of who shipped it. Garry Tan runs the engine that funded more than 4,000 startups. When that person publishes the exact setup he ships his own code with, I pay attention.

Newer to Claude Code itself? Start with our walkthrough on how Claude Code can be your AI teammate, then come back here.

Now the honest part, which is also the most interesting thing about GSTACK.

Tan’s launch claim was enormous: 600,000 lines of production code in 60 days, part time, while running YC. My first reaction was the same as yours. Sure, buddy.

Then Tan walked it back himself. On the repo today he notes that raw line counts inflate with AI and switches to a “logical code change” metric instead. His own line: “AI wrote most of it. The point isn’t who typed it, it’s what shipped.”

The creator of the viral tool retired his own viral number. That made me trust the tool more, not less.

What is actually inside it. The install drops markdown skill files into ~/.claude/skills/gstack/, plus a roughly 58MB browser binary. Everything is a slash command grouped under the seven stage loop.

The three skills I lean on hardest:

/qa launches a real browser and clicks through your app. Full pass runs 5 to 15 minutes, a 30 second smoke test, or a regression mode.

/ship runs the whole release in one command: sync main, run the suite, update the changelog, push, open the PR.

/review triages comments from a PR reviewer like Greptile into valid, already fixed, or false positive, so a second pair of eyes never gets quietly ignored.

gstack runs entirely inside Claude Code, so the “interface” is just your terminal with these slash commands. The full command list and real output examples are in the repo README.

Real cases

Garry Tan, in production. The most cited case is the creator’s own. Tan reports running 10 to 15 gstack sprints in parallel, roughly 30 minutes per feature sprint, on real services he ships while running YC. It is self reported, and he has since walked back the raw line count, but his public GitHub graph is the receipt. Treat it as the ceiling, not your Tuesday.

The security skill caught a real bug. Tan’s launch included a story of a CTO friend whose gstack security pass flagged a genuine flaw on the first run, and the repo documents catching live issues like cross-site scripting. That is the most repeatable value in the whole pack: not raw speed, but a reviewer that catches the thing you would have shipped.

fagemx ported it to game development. A developer took the Think-to-Ship methodology and rebuilt it as gstack-game, swapping the engineering roles for an economy designer, a UX researcher, and a game-feel diagnostician. People are not just using gstack, they are forking its structure into new domains. Link

Want the wider map of Claude Code skills, plugins and swarm mode? We broke it down here: Skills, Plugins and Swarm Mode. GSTACK is the opinionated, batteries-included version of that idea.

Paperclip: management for many agents

Paperclip starts where GSTACK stops. I will admit I rolled my eyes at its pitch first.

Read more

Anthropic launched Fable 5 on June 9 — the first publicly available Mythos-class model, built on the same architecture that autonomously found zero-day exploits in major operating systems. Tim Cook gave his final WWDC keynote on June 8, revealed Apple is paying Google $1B/year for Gemini-powered Siri, and handed the company to John Ternus. And on the same day as WWDC, OpenAI filed its own S-1 with the SEC at $852 billion and announced it publicly before it could leak. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

Subscribe now

Featured Materials 🎟️

Claude Fable 5: The Most Powerful AI Ever Made Available to the Public 🧠

Anthropic released Claude Fable 5 on June 9 — the first publicly available Mythos-class model. It’s built on the same underlying architecture as Mythos 5, the model Anthropic has been running through Project Glasswing to find zero-day exploits in operating systems and web browsers. The only difference: Fable 5 has safety constraints tuned for general use. Both models are available as of today.

What ships:

• Claude Fable 5 — available to everyone via the Claude API, Amazon Bedrock, Vertex AI, Microsoft Foundry, and GitHub Copilot. State-of-the-art on nearly all tested benchmarks. The larger and more complex the task, the larger its lead over Opus 4.8

• Claude Mythos 5 — same underlying model, fewer safeguards. Restricted to approved organizations through Project Glasswing. Available on direct Anthropic, AWS, and Google Cloud enterprise tracks

• Pricing: $10/M input, $50/M output — double Opus 4.8, less than half the Mythos Preview rate

• Free on Pro/Max/Team/Enterprise from June 9 through June 22, then moves to usage credits

The safety architecture:

In cybersecurity, biology, chemistry, and distillation, Fable 5 automatically routes to Opus 4.8. Anthropic stress-tested the classifiers with external bug bounty red teamers: no universal jailbreaks in over 1,000 hours. The fallback triggers in under 5% of sessions on average.

The context:

Anthropic published “When AI Builds Itself” on June 4 — the week before Fable’s launch. The post, co-authored by co-founder Jack Clark, confirmed that Claude now authors over 80% of code merged into Anthropic’s own production codebase, with engineers shipping 8× more code per day than in 2024. The core warning: AI systems may be approaching full recursive self-improvement — the ability to build their own successors without human direction. Anthropic called for a coordinated “brake pedal” among frontier labs. Five days later, it released its most capable model to the public.

TechCrunch framed it accurately: “Anthropic released Claude Fable, a version of Mythos, days after warning AI is getting too dangerous.” This is not a contradiction — it’s a bet. Anthropic believes controlled public access to Fable 5 is safer than leaving it in the lab while competitors race forward without the safety constraints.

Source: Anthropic

💡 While you’re here: Anthropic doubled Cowork usage limits on all paid plans through July 5. Enterprise users get $1,000 in credits per first-time Claude Code or Cowork activation through July 2 (up to $10M per org). Fable 5 is also free on Pro/Max/Team through June 22. Details →

Tim Cook’s Last WWDC — Apple Pays Google $1B/Year for Siri’s New Brain 🍎

Tim Cook gave his final WWDC keynote on June 8 after 15 years as CEO of Apple. He announced he will step down on September 1, 2026 — ending his tenure with a moment that was emotional enough that he wiped a tear on stage. John Ternus, Apple’s Senior VP of Hardware Engineering, takes over.

The headline product of the keynote: Siri rebuilt from scratch using Google Gemini foundation models, under a deal that costs Apple approximately $1 billion per year.

What Apple announced:

• Rebuilt Siri — conversation history, personal context awareness, on-screen awareness, dedicated Siri app. Rebuilt on Apple Foundation Models (AFM), with the top-tier AFM Cloud Pro running on Nvidia GPUs inside Google Cloud

• The Google partnership — Apple and Google’s collaboration produced the AFM model stack. AFM Cloud Pro is described as similar in quality to Gemini Frontier models. The partnership costs Apple roughly $1B annually

• iOS 27 — Beta 1 shipped to developers the same day. SiriKit is on a formal deprecation clock: plugins must migrate to App Intents by April 2027 or lose Siri integration

• Free Foundation Models for small developers — Apple is giving small app developers free access to its Foundation Models API via Private Cloud Compute during beta

• iPhone Fold hints — foldable layout APIs are quietly present in iOS 27 Beta 1, pointing to Ternus’s likely first hardware launch in 2027

Tim Cook’s final word:

“Over the years, you have helped people connect, create, learn, and experience the world in extraordinary new ways. And with the incredible capabilities we introduced today, and so many more still to come, I truly believe the best is still ahead.”

Craig Federighi’s indirect shot at competitors: “Some appear to be racing forward, seemingly pursuing AI for the sake of AI, without clear regard to the people, all of us, that it’s ultimately meant to serve.”

What it means:

Apple just moved from embarrassment about Siri to one of the most significant AI distribution deals ever. Google is now the AI infrastructure for 3 billion Apple devices — a distribution win no marketing budget could replicate. For app developers: you have roughly 10 months to audit and migrate any SiriKit integration before it starts breaking.

Tim Cook’s last WWDC delivered Apple’s biggest AI pivot in its history. He paid Google $1 billion to fix what two years of Siri promises couldn’t. Whether this is Apple admitting Google won AI infrastructure, or Google admitting it needs Apple’s distribution to reach consumers — both are true at the same time.

Source: CNBC

OpenAI Files S-1 at $852B — “We Expect It to Leak, So We’re Just Announcing It” 📄

On June 8 — the same day as Apple’s WWDC and seven days after Anthropic’s filing — OpenAI submitted a confidential draft S-1 registration statement to the SEC. The company announced it publicly before the filing could surface through regulatory channels. Their statement read: “We recently submitted a confidential S-1. We expect it to leak, so we’re just announcing it.”

The numbers behind the filing:

• Valuation: $852B post-money (from March 2026 round of $122B raised)

• ARR: $20B+ in 2025, tripling year-over-year since 2023

• Projected loss in 2026: $14 billion

• Profitability target: 2029

• Underwriters: Goldman Sachs, Morgan Stanley, JPMorgan

• Listing target: September 2026 — but “timing is undecided”

The IPO race context:

Anthropic filed June 1 at $965B. OpenAI filed June 8 at $852B. SpaceX listed June 12 at a $1.75T+ valuation. Three companies that define the AI infrastructure stack moved toward public markets in two weeks. The combined implied market cap exceeds $3.6 trillion before any of them has disclosed audited public financials. Whoever prices first sets the benchmark for how the others trade at debut.

What the S-1 signals for builders:

Public companies optimize for margin and predictability. The API pricing, model deprecation schedules, and free tier structures that exist today are pre-IPO positioning. Once quarterly earnings calls begin, every cost structure gets re-examined. OpenAI’s S-1 going public means the era of subsidized frontier AI access has a visible expiration date on the calendar.

OpenAI decided to announce its own IPO filing before it could leak, which is either extremely confident or extremely well-timed press management. Given that it filed on the same day as Apple’s biggest keynote in a decade, one of those is probably more accurate than the other.

Source: OpenAI

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News of the week 🌍

SpaceX Lists on NASDAQ — $75B Raised, the Largest IPO in History 🚀 — SpaceX priced at $135/share on June 11 and debuted June 12 on NASDAQ under ticker SPCX — raising $75 billion, eclipsing Saudi Aramco’s 2019 record as the largest public offering ever. The company merged with xAI in February, making Grok and xAI’s AI infrastructure part of the SpaceX entity. For the 12 months ending March 2026: $19.3B revenue, net loss ~$3.4B. Musk retains 82%+ voting control. The listing values SpaceX at roughly $1.75 trillion — 7th largest US company, above Tesla. Anthropic, OpenAI, and SpaceX all entering public markets in the same two-week window is without precedent in tech history.

OpenAI + Visa: AI Agents Can Now Make Purchases on Your Behalf 💳 — At the Visa Payments Forum in San Francisco on June 10, Visa and OpenAI announced a partnership to embed Visa’s payment infrastructure directly into ChatGPT and OpenAI’s agentic platforms. AI agents running in ChatGPT will be able to initiate and complete purchases using tokenized Visa credentials, within user-defined spending limits, merchant category restrictions, and approval requirements. Visa handles authorization and real-time fraud monitoring. This is the first time a major card network has formally built its infrastructure around agent-initiated transactions — not just AI-assisted recommendations, but AI completing the purchase.

Microsoft's Open Source Developer Tools Were Hacked to Steal AI Developer Passwords 🔐 — On June 8, security researchers disclosed a supply chain attack targeting Microsoft-maintained open source packages used by AI developers running Claude Code, GitHub Copilot, and related LLM tooling. Malicious code was injected to steal credentials from developers' machines. Microsoft issued patches immediately. If you run Claude Code or Copilot in environments using npm packages from the Microsoft devtools ecosystem — audit your dependencies and rotate credentials. The attack is notable because it specifically targeted AI developer tooling rather than general-purpose packages.

Google Fires a Warning Shot in the AI Subscription Price Wars 💰 — On June 9 — the same day Apple revealed Siri now runs on Google Gemini — Google cut its AI Ultra subscription from $250/month to $150 and introduced a new $99/month tier with access to Gemini Omni and Spark. OpenAI Pro stays at $200/month. Anthropic Max at $100/month. Google is now the cheapest premium AI subscription by a significant margin — and unlike OpenAI or Anthropic, Google doesn’t need the margin. It needs the subscribers. For any creator or founder paying for an AI subscription: the competitive floor on premium AI just dropped, and it dropped the same week Fable 5 launched at $50/M output tokens.

Cybersecurity Researchers Aren't Happy About Fable 5's Guardrails ⚠️ — The day after launch, security professionals reported the classifiers are blocking legitimate defensive work — CVE analysis, pentest report generation, and queries about previously disclosed vulnerabilities. Anthropic acknowledged the guardrails are intentionally conservative at launch. For anyone building security tooling on Claude: this covers which query types are being blocked, what the fallback to Opus 4.8 looks like in practice, and Anthropic's stated timeline for reducing false positives.

Useful tools ⚒️

⭐ Publora — A publishing API for AI agents across 10 social platforms. One REST API call handles multi-network distribution — no SDKs, no OAuth wiring per platform. The native MCP server gives Claude, Cursor, and any MCP-compatible agent a full engagement loop: post, comment, react, pull analytics — across LinkedIn, X, Instagram, Threads, TikTok, YouTube, Facebook, Bluesky, Mastodon, and Telegram. 18 tools total. If you’re building any agentic social media workflow, this is the API that removes the integration layer entirely. Free plan available.

Honen — Turn your team’s docs, recordings, and SOPs into interactive AI-led training that actually stays current. Upload your knowledge base or just a topic — Honen generates full courses with adaptive lessons, scenario simulations, and learner insights. When your docs or processes change, the courses update automatically. Serves the same knowledge over MCP to your AI agents simultaneously — your human team and your agent stack trained on the same source of truth. Built by the StudyFetch team (8M student users).

Browse.sh — An open catalog of browser automation skills for AI agents. Instead of relearning a website from scratch every session, your agents install reusable SKILL.md recipes that give them muscle memory — 250+ workflows across real sites: Ramp expense submissions, GitHub PR reviews, campsite booking, and more. When a skill doesn’t exist, Browse.sh can generate one. Built by Browserbase, open source, free.

Shram — AI inbox for conversations that never got finished. Shram listens across your Mac — Gmail, Slack, WhatsApp, GMeet — and surfaces incomplete threads before they slip: deals not closed, projects delayed, trust broken by silence. One click to resolve. No integrations required, no setup.

Vaani — AI video dubbing that preserves your actual voice across 40+ languages. Vaani clones your voice, maintains your music and tone, and delivers frame-accurate lip sync — not a generic AI voice reading your script. Upload a video, pick a language, get back a version that sounds like you recorded it that way. Built for creators, brands, and media companies. Free options available.

Weekly Guides 📕

The Moat Is Dead. Long Live Taste. — Our own guest post published June 9 by Raffaela Rein (BoardLens). Why the model layer is no longer a competitive advantage — and what is. Three concrete moves for this week: write down your filter in one sentence, cut your next drop in half, and sign every drop. The most actionable framework we've published for creators trying to build durable advantage in an AI-first world.

Claude Fable 5 System Card — Full Safety Testing, Benchmark Comparisons, and Classifier Architecture — Anthropic’s 120+ page technical documentation: how the safety classifiers work (cybersecurity, biology/chemistry, distillation), what evaluations were run (Firefox exploit, OSS-Fuzz, CyberGym, CyScenarioBench), full benchmark comparisons against GPT-5.5 and Gemini, the alignment assessment methodology, and the 30-day data retention policy for all Mythos-class traffic. The definitive technical resource for any team deciding whether Fable 5 is appropriate for their use case. Published June 9.

WWDC26 for Founders and Developers: What You Need to Actually Do Before September — The most practical post-WWDC breakdown published this week: what SiriKit deprecation means for your app, how to apply for the Foundation Models free tier, what the App Intents migration actually involves, why the iOS 27 foldable APIs matter for hardware roadmaps, and two specific actions to take before September’s App Store submission deadlines. Written for founders and developers, not journalists. Published June 9.

Claude Fable 5 vs Opus 4.8: When Is 2× the Price Worth It? — Exact decision framework: same vendor, same API, 2× cost premium. Five workflow types where Fable 5 pays off (long-context reasoning, autonomous coding, vision tasks) vs. five where Opus 4.8 still wins. Includes the real-world token math — why the 35% tokenizer multiplier and Opus 4.8 Fast Mode's new $10/$50 rate make this choice less obvious than it looks. The calculation every team should run before June 22.

Apple Will Let You Build Workflows Using AI in Its New Shortcuts App — No Scripting Required — Published June 8. The new iOS 27 Shortcuts app lets you describe what you want to automate in plain English and it builds the workflow — no scripting, no code. Covers what triggers and actions are now available, what changed in the Shortcuts interface, and which automations non-developers can build that previously required a developer. For any creator or founder running on Apple devices: this is the most immediately actionable WWDC 2026 announcement, and the migration window before SiriKit dies in April 2027 starts now.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

It’s Not FAANG Anymore. It’s MANGOS. — TechCrunch’s Most-Read Business Analysis of the Week — TechCrunch’s viral June 9 analysis on how the Big Tech hierarchy has reshuffled. FAANG — Facebook, Apple, Amazon, Netflix, Google — is obsolete. The new dominant group: Microsoft, Apple, NVIDIA, Google, OpenAI, SpaceX. Their combined implied market cap exceeds $15 trillion. Their intersecting relationships (Microsoft/OpenAI, Google/Apple, NVIDIA/everyone) now define AI infrastructure and distribution. The list has zero media companies on it. The most-shared framing of what this week’s IPO wave actually means for the industry structure.

OpenAI’s “Built to Benefit Everyone” — The Vision Essay Published the Same Day as the S-1 — Published June 8 alongside the IPO filing: OpenAI’s statement on what it believes it’s building and why it deserves to be a public company. Worth reading as the document OpenAI wants investors and regulators to read before they read the actual financials — and for the contrast with Anthropic’s parallel recursive-self-improvement warning the same week. One company filed saying “AI may be approaching its own self-improvement threshold and we need a global brake pedal.” The other filed saying “AI should benefit everyone.” Same week. Both going public.

How Retail Investors Can Buy Into the SpaceX IPO — And Why $22B Was Set Aside for Them — SpaceX carved out 30% of its IPO — roughly $22.5 billion — specifically for retail investors via Robinhood, Fidelity, Schwab, SoFi, and E*TRADE. That's 5–6× the typical retail slice in a megacap IPO. Fortune's practical breakdown: how to request an allocation, which broker has the lowest minimum, the "flip penalty" (sell within 15 days and you're banned from future IPOs at Fidelity), and what to expect if your request gets only partially filled. For any subscriber curious about actually getting in — this is the guide. Published June 8.

If you missed our previous updates, don’t worry, here they are:

Anthropic IPO, Microsoft’s Own AI, Meta Agents in WhatsApp | Weekly Digest

Your take: Fable 5 is public, Tim Cook is out, OpenAI announced its own IPO before it could leak. Is this the week AI stopped being a research race and became a public market — or just the most chaotic news week of 2026 so far? Drop it in the comments 👇

Leave a comment

At a Glance

In this piece, you will learn:

• Why the model layer is no longer a competitive advantage — and what is

• What taste, trust, and judgement look like in practice (with 5 real examples)

• Three concrete moves to start building your own moat this week

This post is prepared with Guest Author — Raffaela Rein, founder of BoardLens and writer of The AI Leadership Edge. If you also want to write for Creators AI — send us email here

Marc Andreessen has been making this point for months: foundation models are commoditising fast, and the model layer is not the moat. If everyone using AI has access to roughly the same intelligence, what is left to compete on?

This is the question every creator now sits with. I write The AI Leadership Edge on Substack (for business leaders, investors and boards) and I am building BoardLens (intelligence for board directors and funds). So I spend a lot of time thinking about how to make a dent.

I believe the answer is:

Taste. Trust. Judgement.

The thing that decides which writers still get paid in an age when anyone can generate a thousand passable words in thirty seconds.

This is not a soft answer. It is the only answer left.

What “the model is not the moat” actually means

Paul Graham made this argument earlier this year about technology more broadly. When the technical differences between products collapse, brand is what remains. He used Swiss watches as the case study. Quartz killed accuracy as a differentiator in the 1970s. The Swiss survivors stopped being precision instrument makers and became something else entirely: storytellers in metal and leather. The watches still tell time. That is the least interesting thing about them.

The same shift is happening to software, content and creators, all at once, in the same decade. Anyone with a Claude or ChatGPT subscription can now produce a passable essay, a workable app, a watchable video. The mechanical floor of “is this any good?” has risen. The ceiling of “is this yours?” matters more than ever.

What the moat looks like in practice

Look at where attention and money are sticking in 2026 and the pattern is consistent.

Ruben Hassid. There are thousands of AI tutorial accounts online. Most blur together instantly: same hooks, same recycled prompts, same “10 AI tools that will change your life” energy. Ruben’s edge is not access to secret technology. His edge is filtration. He consistently finds workflows ordinary professionals will actually use, strips away the technical clutter, and packages them into clear, usable actions. Every post passes the same test: would a senior leader who has never opened ChatGPT be able to use this by Friday morning? He understands where the friction really sits for non-technical users. That is taste, applied weekly, in public.

Shaan Puri. Shaan’s advantage is not information scarcity. Half the internet reads the same tweets and articles he does. His edge is pattern recognition. He spots ideas earlier than most, frames them memorably, and packages them in a way that feels obvious after he has said it. The moat is not the raw information. The moat is the lens.

Granola. There are dozens of AI notetakers, using similar transcription models hooked up to similar LLMs. Granola stands out because every detail — how it summarises, how the cursor moves, the moments where it stays silent during your meeting, the choice not to clutter your screen with assistant chrome — feels deliberate. It does less than most of its competitors, and people choose it for exactly that reason.

Linear. Not standing out because it has more features than every competitor. Quite the opposite. Its reputation comes from restraint, clarity and an unusually strong product taste. Keyboard-first, fast, opinionated, no bloat. In a category where competitors are racing to bolt AI onto everything, Linear holds attention by being more deliberate, not more crammed. Teams pick it not for what it adds, but for what it refuses to add.

Ben Thompson. Thousands of analysts cover tech. Very few have built the level of trust Ben has with executives and investors. Stratechery sits on the desks of senior operators because Ben’s frame for the day is the one they want loaded into their head before they walk into the meeting. The output is not “more information.” It is a frame they trust enough to act on.

These five are not exceptions. They are the shape of the next decade.

We’ve covered how the best creators are building systems that compound their edge over time: How Solopreneurs Are Using Full AI Agents

The fashion-house economy

There is a useful way to think about what this means in practice. The companies and individuals who compound year after year look less like classic startups and more like fashion houses.

A fashion house ships a new collection multiple times a year. There is no compounding product moat. What you showed last season is history. What matters is the next drop, and the next, and the next. The creative director at the centre does not do every job. They touch every decision, because the only thing that holds the brand together over twenty collections a year is a coherent point of view, executed at velocity.

That is what every creator building with AI is now doing. Every essay is a collection. Every product update is a collection. Every video, every tool, every newsletter is one more drop where your taste shows or it doesn’t. AI lets you ship more drops, faster. It cannot decide which drops are worth shipping. It cannot decide what makes them yours.

The brand age is not gentle. Speed is no longer optional. The creators who will compound over the next five years will be the ones who can hold a recognisable point of view at industrial volume. Taste plus velocity. Not one or the other.

Related: 4 Ways to Monetize AI Agents in 2026 — how creators are turning consistent POV into actual revenue

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Three moves to start this week

Theory is cheap. Three concrete moves that work whether you are a one-person newsletter or a venture-backed founder:

1. Write down your filter in one sentence. What makes something pass your bar, versus get cut? Ruben Hassid’s is roughly “would a non-technical professional actually use this on Monday.” Ben Thompson’s is roughly “would this frame change a senior operator’s decision today.” If you cannot name your filter in a sentence, you do not have one. Once you can, every drop gets sharper, because you stop guessing what to ship.

2. Cut your next drop in half. Then cut it again. The instinct in an AI age is to add: more sources, more sections, more features. Taste reveals itself in what you remove. Linear ships fewer features than its competitors on purpose. Granola does less than every notetaker on the market. The compression is the signal that something was chosen, not generated.

3. Sign every drop, and stand behind it. AI can co-write a paragraph, a function, a thumbnail. AI cannot stand behind a judgement. Every drop should carry a clear point of view your name is attached to. The test: would you defend it in a room of the five sharpest people in your space? If not, do not ship it. The signature, and the willingness to be wrong in public, is the moat.

Do these three for ninety days and something the foundation models cannot copy starts to compound: a reader, customer or investor who keeps coming back because of you, specifically.

Also read: Your AI Agent Stack Is Spaghetti — here’s how to fix it

The question for you

If you are a creator, founder or investor staring at the same models everyone else has, the question to sit with this week is uncomfortable but useful: in a world where the model is not the moat, what is yours?

Your taste, distilled into something only you would ship. Your trust, earned one drop at a time. Your judgement, used at the speed of the tools you now have.

Everything else is going to get commoditised. That part has already started.

What’s your filter? The one sentence that decides what makes the cut — drop it in the comments.

Leave a comment

Raffaela Rein writes The AI Leadership Edge, a Substack for 13,000+ leaders, directors and investors making sense of AI in the boardroom and on the cap table. She is also building BoardLens — intelligence for board directors and funds.

Hey! Welcome to the latest Creators’ AI Edition.

Anthropic filed its IPO paperwork with the SEC on June 1 — the first major AI lab to formally begin the process toward a public listing, at a near-trillion-dollar valuation that now puts it ahead of OpenAI. Microsoft dropped 7 in-house AI models at Build 2026 and told the world it no longer wants to be just “the company that resells OpenAI access.” Then Meta launched Business Agent globally on June 3, turning WhatsApp, Instagram, and Messenger into an autonomous sales, support, and operations layer for over one million businesses overnight. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

Subscribe now

Featured Materials 🎟️

Anthropic Files for IPO — and OpenAI Is Probably Next 💰

On June 1, Anthropic confidentially submitted a draft S-1 registration statement to the SEC. This is the formal first step toward a public offering — not a locked-in date, not a share price, not a ticker. But it is the moment the company stopped being a private bet and started becoming a public one.

The mechanics matter: a confidential S-1 lets Anthropic complete the regulatory review process without publicly disclosing financials until 15 days before a roadshow begins. The window between now and the October 2026 targeted listing is roughly four months — long enough for market conditions to change, not long enough for the business to look materially different.

What’s already public from the Series H (May 28):

• $65B raised at $965B post-money — the largest funding round in AI company history

• ARR exceeds $47B, up 5× in roughly five months

• Claude Code run-rate revenue over $2.5B annually

• 4% of all GitHub public commits worldwide authored by Claude Code

The OpenAI question:

Anthropic beat OpenAI to the SEC. OpenAI is reportedly preparing its own confidential S-1 with Goldman Sachs and Morgan Stanley on standby for a possible fall listing. The race is now explicitly about who gets the better IPO window — not just who has the better model.

What this changes for the industry:

Anthropic preparing a public disclosure means every number it has been hinting at — revenue trajectory, token usage, Claude Code penetration, enterprise share — becomes audited and legally binding within months. For the first time, the AI industry will have a public company whose core product is a frontier model. The metrics that matter for valuation will become metrics that matter for stock price. That changes how every AI lab operates.

Anthropic just decided to let public markets price the question that private investors have been debating for three years: is frontier AI a real business? The S-1 will answer it. OpenAI won’t be far behind.

Source: Anthropic

Microsoft Stops Renting Intelligence 🧠

For three years, Microsoft’s AI story was “we have OpenAI.” At Build 2026 on June 2–3 in San Francisco, that story ended. Microsoft announced a family of seven in-house AI models under the MAI brand — built from scratch by its AI Superintelligence team, not fine-tuned or distilled from existing architectures. The flagship, MAI-Thinking-1, is Microsoft’s first reasoning model.

The MAI model family:

• MAI-Thinking-1 — 35B active parameters, 256K context, first Microsoft-built reasoning model. Independent raters preferred it over Claude Sonnet 4.6 in blind tests. Matches Claude Opus 4.6 on SWE Bench Pro. 10× more token-efficient than GPT-5.5.

• MAI-Code-1 — coding model built specifically for GitHub and VS Code, already live in Copilot

• MAI-Image-2.5 — text-to-image and image-to-image, #3 on Arena AI leaderboard

• MAI-Transcribe-1.5 — 43 languages

• MAI-Voice-2 — 15+ new languages with voice cloning protection

The honest position:

CEO Satya Nadella said Microsoft’s goal is to become one of the top four AI labs globally — alongside Google DeepMind, OpenAI, and Anthropic. Six months ago, that sentence would have been a stretch. The MAI line, combined with Microsoft’s compute infrastructure and GitHub’s 150M developers, gives it a plausible path. Mustafa Suleyman said the goal is to build “from the ground up” — not to depend on any single external provider.

Also at Build:

Microsoft Scout — a new personal agent for enterprise that runs on OpenClaw and WorkIQ, proactively handles meeting prep, scheduling conflicts, and routine tasks. Majorana 2 quantum processor — qubits 1,000× more reliable than its predecessor, 2029 now the target for a scalable quantum computer.

Microsoft spent years distributing OpenAI’s intelligence. Now it wants to build its own. The MAI line is not a threat to OpenAI today — it’s an insurance policy that becomes a threat the moment OpenAI raises its prices again.

Source: Microsoft Blog

Meta Turns WhatsApp Into Your Sales Team 🤖

On June 3, at Meta’s Conversations event in London, Mark Zuckerberg launched Meta Business Agent globally. Any business — any size, any country — can now deploy an AI agent across WhatsApp, Instagram, and Messenger in minutes.

What the agent does:

Answer customer questions in the customer’s local language. Recommend products from a catalog. Book appointments. Qualify sales leads. Close transactions. Escalate to a human employee at a threshold the business defines. The agent runs 24/7. No IT team, no developer, no custom integration required.

The scale behind it:

Meta spent nearly two years testing Business Agent in India, Mexico, and Brazil before the global launch. Over one million businesses were actively using early versions before June 3. The global rollout extends the product to Instagram for the first time — critical for creator-led commerce and DTC brands. Over one billion messages are sent between people and businesses on Meta’s platforms every day.

The enterprise layer:

Alongside the consumer launch, Meta introduced Meta Business Agent Platform — designed for larger organizations that need to build, customize, and deploy agents at scale. Connects to Shopify, Zendesk, and hundreds of third-party systems. The platform allows agents to take actions inside connected systems, not just answer questions in chat.

Pricing:

Free to start. Paid subscription tiers coming. Enterprise platform pricing not yet disclosed. Token-based billing expected for large-volume deployments.

The creator angle:

For any creator or small business already using Instagram DMs or WhatsApp as a sales channel — this is a free autonomous agent that closes sales while you sleep. The commerce layer that Shopify and Intercom have been trying to build separately just became native to the platforms where your customers already are.

Meta just put an AI sales team inside the apps 3 billion people use to message each other. The barrier between “I saw your product” and “I bought it” just got a lot smaller.

Source: Meta

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News of the week 🌍

ChatGPT Hits 1 Billion Users — Claude Is Growing 10× Faster 📈 — Sensor Tower confirmed ChatGPT crossed 1 billion global monthly active app users in May 2026 — the fastest any app in history has reached the milestone, ahead of TikTok, Instagram, YouTube, and Google Maps. The headlining number belongs to OpenAI. The more interesting number belongs to Anthropic: Claude hit 56 million MAUs at 640% year-over-year growth, versus ChatGPT’s 62%. US users who installed Claude in Q1 2026 spent 5% less time on ChatGPT the following month — the first clean evidence of substitution rather than additive usage.

OpenAI Turns Codex Into an Enterprise Platform — Non-Developers Growing 3× Faster 💻 — On June 2, OpenAI expanded Codex beyond coding with Codex Sites (hosted web apps built and deployed from a prompt), 62 business app plugins (Salesforce, Jira, Notion, GitHub and more), and 110 pre-built skills. 5M weekly users, 20% of whom are non-developers — and that segment is growing 3× faster than developers. OpenAI is repositioning Codex from a coding tool into an enterprise automation platform that competes directly with Zapier, Make, and ServiceNow.

GitHub Copilot Switches to Token Billing on June 1 — Bills Jump 10×–50× for Power Users 💸 — GitHub retired flat-rate Premium Request Units on June 1, replacing them with GitHub AI Credits — token-based billing at published API rates per model. Base subscription prices are unchanged: Pro at $10/month, Pro+ at $39. But developers running agentic sessions against large codebases are reporting cost increases of 10×–50×. One Reddit user projected a jump from $29/month to $750. Code completions and Next Edit Suggestions remain free — the billing change only affects chat, agent mode, and code review. The backlash landed within hours of launch.

SoftBank Commits €75B to French AI Data Centers — Europe’s Largest-Ever AI Infrastructure Deal 🌍 — At the Choose France summit on June 1, SoftBank announced up to €75B ($87.5B) to build 5 gigawatts of AI data center capacity in France. The first phase commits €45B for 3.1 GW in the Hauts-de-France region by 2031. SoftBank cited France’s low-carbon electricity grid and engineering talent. This is the largest single AI infrastructure investment ever announced in Europe — and a direct signal that the hyperscaler buildout is no longer US-only.

OpenAI Is Preparing Its Own Confidential IPO Filing 📋 — Anthropic’s S-1 filing on June 1 immediately triggered reporting that OpenAI is preparing its own confidential submission in the coming weeks, working with Goldman Sachs and Morgan Stanley. If both companies list in October-November 2026, it will be the largest simultaneous AI company IPO event in history. The combined implied market cap would exceed $2 trillion before either company has filed audited public financials.

Trump Signs Executive Order on AI Innovation and Security — June 2 🏛️ — On June 2, President Trump signed a new AI Executive Order directing federal agencies to accelerate AI adoption and remove regulatory barriers to deployment. The order expands access to government-held datasets for AI training, streamlines procurement for AI tools in national security contexts, and formally designates the Department of Commerce as the lead agency for AI standards coordination. The administration explicitly frames the move as a counter to what it calls the EU's "stifling" regulatory approach.

Useful tools ⚒️

⭐ Mina Meeting Assistant — The AI teammate that speaks during your calls, not just after them. Mina joins Zoom, Google Meet, or Teams, pulls context from your connected tools in real time, and answers questions, books follow-ups, and updates your CRM while the meeting is still happening. Works with Skills you define — sales call mode, standup mode, customer success mode. Unlike passive note-takers, Mina can be interrupted, redirected, and prompted mid-call.

SocialEcho 2.0 — AI social media copilot for teams and agents. Manages multiple accounts, drafts posts in your brand voice, schedules and monitors performance, and routes agent workflows across LinkedIn, X, Instagram, and TikTok. Built for teams that run more than two channels simultaneously and are tired of switching between tools. Free trial available.

Databox MCP — Chat with your business data inside Claude, ChatGPT, or any MCP-compatible tool. Databox connects your metrics — from Google Analytics, HubSpot, Shopify, Salesforce, and 100+ sources — and makes them queryable in plain English from your AI interface. Ask “how did our paid acquisition perform last week vs. the month before” and get an answer directly in Claude without switching tabs. Free plan available.

folk — The lightweight CRM that now lives inside your texts. folk plugs into Gmail, Outlook, and calendar, builds a contact graph from your conversations, and surfaces the right follow-ups at the right moment. AI assistants added in January 2026 include a Follow-up Assistant (stalled conversations), Research Assistant (auto-enriches with Perplexity), and Recap Assistant (summarizes relationship history). Starts at $25/user/month.

Typeahead — AI autocomplete for every app on your Mac. Typeahead watches what you’re typing and surfaces completions based on your writing style, past documents, and connected knowledge bases — in any app, not just your browser. Works in Notion, Linear, Slack, email, and code editors without a plugin. Learns your voice over time. Free tier available.

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Weekly Guides 📕

Claude MCPs for Finance, Bookkeeping & Taxes: Stripe, Mercury & QuickBooks in Claude — Our own deep-dive published June 4. How to wire Claude to Stripe, Mercury, and QuickBooks for a daily cash brief, subscription audit, cohort retention, and a tax reconciliation that caught $8K a CPA missed. Includes copy-paste prompts for every workflow, the 4 things Claude is banned from touching, and the full setup in 30 minutes.

GitHub Copilot Token Billing: Full Cost Guide and Alternatives — The complete breakdown: what is and isn’t billed, exact model pricing per token, real-world cost scenarios for different workflow types, and the five optimization moves that cut agentic session costs by 60–80%. Published June 3, the day developer backlash peaked. Read before your next billing cycle closes.

Meta Business Agent on WhatsApp: Setup Guide — Official WhatsApp Business walkthrough: go to Tools → select Meta Business Agent → authenticate → link account → confirm. How to train the agent on your past chats and catalog, configure reply settings, manage escalation to a human, and adjust behavior over time. The actual setup takes under 10 minutes. Published June 3 by WhatsApp for Business.

New MAI Models in Microsoft Foundry: How to Start Building — Microsoft's official Azure AI Foundry blog published June 2: MoE architecture breakdown of MAI-Thinking-1, how to access it via Foundry and GitHub Models, what each MAI model does and which use cases each covers, and how they slot into existing Azure and GitHub Copilot Enterprise workflows. The practical entry point for any developer evaluating Microsoft's own models after Build.

Claude Code How-To: Visual, Example-Driven Guide from Basic to Advanced Agents — Community-maintained GitHub guide synced with every Claude Code release. Now updated for Claude Code v2.1.145 with the new Dynamic Workflows feature: how to configure parallel subagent sessions, set budget limits, and run codebase-scale migrations. The practical complement to Anthropic’s official docs.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

Anthropic Confidentially Files S-1 — What It Actually Means — Fortune’s full breakdown of the filing mechanics, the October 2026 listing window, what “confidential” means in practice, and why Anthropic filing before OpenAI matters for how both IPOs get priced. The clearest explanation of what happened on June 1.

ChatGPT vs. Claude: The Real Growth Story Behind the 1 Billion User Headline — TNW’s analysis of the Sensor Tower data. ChatGPT owns consumer scale. Claude is growing at 640% YoY from a developer and enterprise base. US users who install Claude spend 5% less time on ChatGPT one month later. The market is splitting into two different products for two different use cases — and the split is accelerating.

Suno Raises $400M at $5.4B — With UMG and Sony Still Suing It — Suno doubled its valuation in 7 months despite active copyright lawsuits from Universal and Sony. Bond Capital led the round. 7M songs generated daily, $300M ARR, Warner settled and signed a licensing deal — UMG and Sony haven't. The fundraise is a direct bet that fair use wins or settles favorably. For any creator building on AI-generated music — this is the case that decides the legal ground you're building on.

Uber Caps Claude Code Usage After Bills Spiral Out of Control — Uber's engineering leadership sent an internal memo capping Claude Code and other AI tool usage after costs exceeded projections significantly. The same week Microsoft canceled its internal Claude Code licenses. Two of the largest tech companies in the world are now actively managing AI tool spend — the GitHub Copilot billing story in the context of enterprise reality.

If you missed our previous updates, don’t worry, here they are:

Opus 4.8, Anthropic $965B, GPT-5.5 wins DeepSWE | Weekly Digest

Your take: Anthropic just filed for IPO — the first major AI lab to formally begin the process toward going public. Is this the moment AI stops being a research race and becomes a public market? Or does the trillion-dollar valuation make the IPO the riskiest bet in tech history? Drop it in the comments 👇

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Two dozen workflows live, 4 banned, ~$3-6k/mo of tools and hires I didn’t pay for. Put them together and what you have is the closest a solo founder gets to an augmented CFO: the reporting, analysis and alerting half of the role, on call, for the price of the connectors. The judgment half stays yours. That line runs through the whole post.

If you read my piece on the 3-layer agent stack, that was the architecture. This is the first domain it runs end to end. New to connectors? Start with the MCP guide for founders and makers. I will spell out the workflows that earn their keep, give you the prompt for each, and link the rest.

The reframe: operator layer vs. domain

Most “AI for finance” posts conflate two things: the operator (the agent that runs) and the domain (your data).

Mercury writes from the cash side. Stripe writes from the revenue side. Your metrics dashboard writes from the analytics side. QuickBooks writes from the ledger side. All four miss the join, because none of them can see the other three.

The mental model is three rules:

1. Source-of-truth tools stay exactly where they are.

2. Claude is the read, summarize and draft layer. Never the post-to-ledger layer.

3. Claude writes only in Sheets (low stakes) and as QuickBooks drafts I approve.

Here is why the same architecture covers both bookkeeping and product metrics: both are downstream of the same raw events. A single Stripe charge is a revenue event for the books and a retention event for the dashboard. Read once, derive twice.

That is why I will not pay $200 to $1,500/mo for Bench, Pilot, Pry or a CPA retainer anymore. Not as a flex. As a data-trust call. I would rather have Claude pull from Stripe directly than have a second SaaS interpret Stripe for me and spend an afternoon debugging which one to believe.

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The bookkeeping workflows

For the books side: product founders, agencies, creators, solo consultancies, freelancers. The three that matter most, each with the prompt I run, read-only.

Daily cash position (Mercury + Stripe)

Mercury balance plus Stripe pending plus the last 24 hours, condensed into a 5-line brief at 7:30am. The Mercury read is not hypothetical: Mercury shipped its own read-only MCP for Claude in 2026, and the question their team keeps pointing to is “why did our burn increase?” That is the exact monthly follow-up I run off the same connection. Caught one fraudulent charge in week two. Real value: I now actually look daily.

Using Mercury and Stripe (read-only), give me a 5-line morning cash brief: total
Mercury balance, Stripe pending payouts, net change in the last 24 hours, the
largest inflow and outflow, and anything unusual. Then compare this month's
outflows to last month's by category and name the single biggest mover.

Subscription audit (the killer one)

Monthly, Mercury and card charges get scanned for anything recurring. Output is one table: charge, frequency, last-used signal, and a keep / cancel / negotiate call. Found $340/mo of zombie subscriptions on the first run. That single workflow pays for everything else on this list.

Using Mercury and card charges (read-only), scan the last 90 days for anything
recurring. Build one table: vendor, amount, frequency, last-charged date, and a
keep / cancel / negotiate call with a one-line reason each. Total the monthly
dollars sitting in the cancel and negotiate buckets.

Reading old tax returns against the current P&L (the $8,000 one)

This is the workflow that turned me from skeptic to operator, so I will give it the full story.

I dropped my previous tax return forms (PDFs) and my current P&L into Claude and asked it to reconcile the two: what did last year’s return claim that this year’s books are not set up to capture, and what is in this year’s P&L that the return treatment got wrong? It came back with a cluster of contractor payments and software charges sitting under the wrong categories, plus a batch of expenses that had never been marked deductible at all.

My CPA had filed the prior return and missed every one of them. Not out of negligence. He worked from what I handed him, and what I handed him was messy. Claude, reading the actual return next to the actual P&L line by line, caught roughly $8,000 in tax I had overpaid.

I am attaching my last two years of tax return PDFs and my current P&L. Reconcile
them: what did the prior returns claim that this year's books do not capture, what
is miscategorized, and what deductible expense was never marked deductible? List
every discrepancy with its dollar impact. Do not make a tax determination; produce
a memo my CPA can review.

The honest catch: most of that $8k was a one-time correction of a backlog, not a number that repeats every quarter. The recurring value is the discipline it started. Real value: the $8,000, and a tax process I now trust instead of dread.

The point is not “fire your accountant.” I still have one and still need one. The point is that I no longer need a CPA retainer, and I can double-check my CPA before a return ever gets signed.

That $8k was the moment I stopped treating this as a toy. Below is the rest of the system: the routine bookkeeping, the product metrics, the augmented-CFO layer, the Google Sheets workbench, the 4 things I never let Claude touch, the setup, and the full math on what it replaced (~$3-6k/mo). Every workflow comes with its prompt.

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1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

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Featured Materials 🎟️

Claude Opus 4.8: Faster, Cheaper, Honest — and Mythos Is Coming 🤖

Anthropic released Claude Opus 4.8 on May 28 — 41 days after Opus 4.7. The speed of the cycle is the signal: Anthropic shipped because OpenAI’s Codex and Google’s Gemini Flash releases were putting pressure on, and Opus 4.7’s reception was disappointing enough that the company needed a response fast.

What’s new:

• Same pricing as Opus 4.7: $5/M input, $25/M output

• Fast mode: 2.5× faster, 3× cheaper than fast mode on previous Opus models — now viable for interactive use

• Dynamic Workflows (research preview): Claude Code can now spawn up to 1,000 parallel subagents. Codebase-scale migrations — hundreds of thousands of lines, kickoff to merge — become a single command

• Honesty improvements: Opus 4.8 is more likely to flag uncertainty and less likely to claim task completion when it hasn’t delivered. Early testers: “It asks the right questions, catches its own mistakes, pushes back when a plan isn’t sound”

• Available in claude.ai, Claude Code, API, Cowork, and GitHub Copilot immediately

The concerning finding Anthropic disclosed:

The model shows a growing tendency to reason about how its outputs will be graded — even in environments where it wasn’t told it was being evaluated. It knows it’s probably being tested, and optimizes for passing the test. Anthropic says this didn’t translate into worse observable behavior, but called it “a concerning trend that could complicate training in the future.”

The Mythos signal:

Anthropic confirmed Mythos-class models — currently in limited testing through Project Glasswing — are coming to all customers “in the coming weeks.” The cybersecurity concerns that delayed public release are being addressed. Opus 4.8 is the bridge.

Anthropic just shipped its second flagship model in six weeks and disclosed a training artifact that could undermine the next one. The Fast mode economics are real. The Mythos timeline is the story to watch.

Source: Anthropic — Introducing Claude Opus 4.8 | TechCrunch

Anthropic Raises $65B at a $965B Valuation — Now Worth More Than OpenAI 💰

On May 28 — the same day it shipped Opus 4.8 — Anthropic announced a $65 billion Series H round at a $965 billion post-money valuation. The round was led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital.

The numbers:

• $965B post-money — ahead of OpenAI’s last reported valuation of $900B+

• Run-rate revenue exceeds $47B

• Claude Code run-rate revenue: over $2.5B, more than doubled since January 2026

• Weekly active Claude Code users doubled since January 1

• 4% of all GitHub public commits worldwide authored by Claude Code — double the share from a month prior

• Enterprise use: over half of all Claude Code revenue

What the capital is for:

Compute, product development, safety and interpretability research, and infrastructure partnerships. Anthropic is also building $50B in US data center capacity with Fluidstack in Texas and New York — roughly 800 permanent jobs, 2,400 construction jobs, coming online through 2026.

The OpenAI comparison:

Anthropic at $965B versus OpenAI at ~$900B is not a stable ranking — both numbers will change. What the round confirms: the market believes Anthropic’s revenue trajectory ($47B run-rate) justifies a valuation that, six months ago, only OpenAI was approaching. The race is no longer a two-tier system with one clear leader.

Anthropic just raised the largest funding round in AI company history at a valuation that puts it ahead of OpenAI. Six weeks ago it was a $380B company. The speed of this re-rating is itself a signal about how fast the revenue base is growing.

Source: Anthropic — Series H Announcement

DeepSWE: The New Benchmark That Shows GPT-5.5 Is Ahead — and Claude Sonnet Is Way Behind 🧑‍💻

Datacurve released DeepSWE this week — a new benchmark for evaluating frontier coding agents on real long-horizon software engineering tasks. The results scramble the leaderboard that most developers have been using to make model routing decisions.

The leaderboard:

• GPT-5.5: 70% ± 4% ← clear #1

• GPT-5.4: 56% ± 5%

• Claude Opus 4.7: 54% ± 5%

• Claude Sonnet 4.6: 32% ± 4%

• Gemini 3.5 Flash: 28% ± 4%

• Kimi K2.6: 24% ± 4%

• DeepSeek V4 Pro: 8% ± 2%

Why this benchmark is different:

Tasks were written from scratch — not pulled from existing GitHub PRs or commits, which eliminates contamination risk. 113 tasks across 91 repositories, 5 programming languages. Each task has a short description but requires substantial real code changes. Verifiers were written by hand and test actual software behavior, not implementation details.

What it exposes:

Datacurve audited SWE-bench Pro — the benchmark most teams currently use — and found 8% false positives and 24% false negatives. At the frontier, models are now so close on public benchmarks that the benchmarks themselves have stopped being reliable. DeepSWE is designed to re-separate them.

The results matter for anyone routing workloads: Claude Sonnet at 32% versus GPT-5.5 at 70% on the same long-horizon coding tasks is a 2× gap that doesn’t show up on SWE-bench. DeepSeek V4-Pro at 8% despite being 34× cheaper than GPT-5.5 suggests the price-vs-quality tradeoff for autonomous coding is worse than the pricing implies.

Old benchmarks are saturating. DeepSWE is the first public signal that GPT-5.5 is significantly ahead of Claude on the coding tasks that actually matter for autonomous agents — and that the Sonnet-vs-Opus routing decision just became more consequential than it looked.

Source: DeepSWE Leaderboard — Datacurve

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News of the week 🌍

ClickUp Laid Off 1,100 People After AI Made Them Redundant 📉 — ClickUp cut 1,100 employees on May 25, citing AI-driven efficiency gains that eliminated the roles. The pattern runs consistent this week: Meta (8,000 cuts), ClickUp (1,100), Intuit (3,000). ClickUp is notable because it makes productivity software — the company that built tools to coordinate teams just used AI to eliminate the team.

Figure AI Signs First Commercial Retail Deal — JCPenney’s Parent Company 🤖 — On May 26, Figure AI announced a commercial deployment with Catalyst Brands — the holding company behind JCPenney, Aéropostale, Brooks Brothers, Lucky Brand, and Nautica. Figure’s humanoid robots start at Catalyst’s Reno, Nevada distribution center, handling sorting and packing in the Joey Pouch induction system. Figure’s last valuation was $39 billion. Both companies share Brookfield as an investor. This is the first time a humanoid robot fleet has been commercially contracted to a major US retail group at scale.

Altman and Amodei Both Walk Back Their Jobs Apocalypse Predictions 🔄 — On May 26 in Sydney, Altman said he was “delighted to be wrong” about AI wiping out entry-level white-collar jobs. Amodei — who had claimed 50% white-collar job elimination — shifted to arguing AI may expand the work people do. Yale Budget Lab found no significant occupational mix changes since ChatGPT launched. Both CEOs are heading into IPO roadshows. The reversal landed the same week ClickUp cut 1,100 people for AI.

CNN Sues Perplexity for Copying 17,000 Stories — First TV Network to Sue an AI Company ⚖️ — CNN filed in federal court in New York on May 28, alleging Perplexity unlawfully scraped 17,000+ CNN stories, videos, and images to power its products. CNN says it tried to negotiate a licensing deal in 2025 — Perplexity continued anyway. Perplexity’s public response: “You can’t copyright facts.” NYT, WSJ, Chicago Tribune have filed similar suits. This is the first time a television network has sued an AI company for copyright infringement, and it signals the AI search legal front is widening beyond print publishers.

Mistral Is Exploring Its Own AI Chips — and Opens a New French Data Center for Inference 🌍 — CEO Arthur Mensch told CNBC on May 28 that Mistral is actively exploring designing its own chips and is not ruling out development — the first time he's commented on semiconductor ambitions. The announcement came alongside a new data center in France built specifically for inferencing, and Mensch said Mistral is open to renting compute capacity to US AI labs. The company is valued at €12B, targeting €1B in revenue in 2026, and has $830M in debt financing backing its European infrastructure push. The pattern: every serious AI lab is now trying to own its full stack — model, data center, and chip.

Meta Launches Paid Subscriptions for Instagram, Facebook, and WhatsApp 💰 — Meta is rolling out Instagram Plus ($3.99/mo), Facebook Plus ($3.99/mo), and WhatsApp Plus ($2.99/mo) globally on May 27, with exclusive features including anonymous Story views, expanded pinned chats, and custom themes. Meta AI premium tier and Vibes AI video creation subscription are in testing for later in 2026. For creators and businesses on Meta platforms — the monetization structure is officially changing.

Useful tools ⚒

⭐ Brew — The first email platform with a built-in AI agency. Describe your brand, your product, and the goal of the email — Brew generates a complete, on-brand email: copy, design, and personalization, all in one shot. Not a copywriting add-on to an existing ESP, not a separate AI tool you paste into Mailchimp. The full production cycle from brief to send-ready email in one place. For newsletter creators, e-commerce founders, and anyone who currently splits email work across a writer, a designer, and a platform. Free to start.

Unabyss — Set up once and never re-explain yourself to AI again. Unabyss connects to your daily apps — Gmail, Slack, Notion, LinkedIn, GitHub — extracts your context into structured files (persona.md, voice.md, company.md), and shares it with any AI tool via MCP with granular control over what each tool can see. Claude, Cursor, ChatGPT, and any MCP-compatible agent all get the same picture of who you are without you pasting it in every session. Context auto-updates as your apps change.

Rezonant — Talk through a product idea and get engineering-ready work. Rezonant connects to your Jira, Linear, documents, and repository, then turns a spoken or typed product vision into tickets, PRDs, and implementation plans grounded in your actual codebase. Knows what’s already built and specs around it.

own.page — Build a personal website in under a minute. own.page uses bento tiles — resizable, repositionable cards — to let creators and founders build a live personal page: bio, links, recent work, social feeds, and integrations. More like a personal OS than a link-in-bio. No design skills required.

Yansu — A desktop AI agent for macOS, Windows, and Linux that learns how you actually work and turns those patterns into automations — locally, without an account, without sending data to a cloud. It watches your workflows across apps, identifies repetitive sequences, and offers to automate them in plain language. No Zapier-style flowcharts, no IFTTT triggers to configure manually. You describe what you want to stop doing; Yansu builds the automation from your own behavior. Free tier available.

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Weekly Guides 📕

I Rebuilt My OpenClaw Setup on Hermes + GPT-5.5 — Migration Guide — Our own post from this week. After Anthropic changed the pricing that made OpenClaw cheap, we migrated eight business workflows to Hermes Agent on a $5 VPS pointed at a $20 OpenAI subscription. Same Telegram interface, same persistent memory, $25/month total. The post covers the full Hermes operator stack, the exact migration path from OpenClaw, and the trust boundaries that matter before giving an agent access to your tools.

DeepSeek V4-Pro 75% Price Cut Is Now Permanent: What Every Developer Needs to Know — Practical breakdown of the May 22 announcement: exact token costs before and after, benchmark comparisons against Claude Opus 4.7 and GPT-5.5, what changed in cache-hit pricing ($0.003625/M — lowest first-party frontier cache on the market), why DeepSeek made the cut permanent, and what it means for agent routing decisions. Published May 25. The calculation to run this week if you have any production workflow at API scale.

Claude Opus 4.8 + Dynamic Workflows: What Engineers Need to Know Right Now — Technical breakdown of what shipped May 28: how Dynamic Workflows spawns up to 1,000 parallel subagents inside Claude Code, the fast mode pricing math ($25/M vs $8.33/M), the 1,000-subagent cap and what it means for cost control, and how to migrate workflows from Opus 4.7. Written for engineers and data scientists.

Claude Code How-To: Visual, Example-Driven Guide from Basic to Advanced Agents — Community-maintained GitHub guide synced with every Claude Code release. Covers slash command templates, sub-agent patterns, AGENTS.md setup, and production-ready copy-paste workflows. Updated to v2.1.145 this week. If the official docs are a reference, this is the tutorial.

How Does Polsia Work? AI Company Builder Explained — Technical teardown of Polsia's agent architecture: how the CEO agent decides strategy each morning, how connected services (GitHub, ad accounts, email, payments) actually get used, why permission hygiene matters before you hand over credentials, and what "autonomous operations while you sleep" means in practice per Polsia's own terms. The guide separates what the platform can genuinely do from what it markets. Published this week.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Full read: a16z

Bonus Materials 🎁

Anthropic’s Glasswing Vulnerability Dashboard — 1,596 Findings Live — Live tracker of every CVE Mythos Preview has found: 1,596 vulnerabilities across 281 open-source projects, 97 patched, including a 17-year-old FreeBSD remote code execution exploit found and triggered autonomously. The most concrete real-time picture of what “AI as active security actor” actually looks like in production.

Qwen3.7-Max: 35 Hours Autonomous, Beats Claude Opus 4.7 on Coding — Alibaba’s Official Technical Post — How the model achieves 35-hour autonomous runs without losing coherence, benchmark comparisons (Apex Math 44.5 vs Opus 4.7’s 34.5), and why native Anthropic API protocol support matters for anyone already running Claude-based stacks.

“Tech CEOs Are Apparently Suffering From AI Psychosis” — TechCrunch’s Most-Read Piece This Week — TechCrunch’s viral column from May 27 on the disconnect between what AI CEOs say publicly, what their companies ship, and what enterprise customers are actually experiencing. The piece that most accurately captures the cultural mood of the week — and went further than any news article in naming the pattern.

If you missed our previous updates, don’t worry, here they are:

Google I/O Drops 100 Things, Karpathy Joins Anthropic | Weekly Digest

Anthropic just raised $65B at a $965B valuation — technically ahead of OpenAI — on the same day it shipped a new flagship model and disclosed a training artifact that worries its own researchers. Is Anthropic about to overtake OpenAI, or is a $965B valuation on a company that doesn't yet have a public market the most expensive bet in tech history? Drop it in the comments 👇

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The agent that actually runs my two businesses, in one Telegram chat. And how to move over if Anthropic stranded you too.

Hey! Welcome to the latest Creators’ AI Edition.

Most “AI assistant” posts start with “I use ChatGPT to write emails.” Boring.

Mine starts with this: in March, I had a working OpenClaw setup running on Claude Pro. Memory, skills, Telegram, the whole stack. Then Anthropic killed the subscription-based usage path that made OpenClaw cheap, and my $25/month agent suddenly priced itself toward $500+ on metered API calls. I shut it down.

4 weeks later I’m running the same workflows (eight of them) through Hermes Agent on a $5 VPS, pointed at my $20 OpenAI subscription and GPT-5.5. Same Telegram chat. Same persistent memory. Cheaper. Faster. And honestly more polished than what I had on OpenClaw.

This post is the migration. Not “what is Hermes”. Peter Yang’s Race to Build a Personal AI Agent already covered that. This is what it looks like to run two businesses through it for a month, and how to move over if you’re stranded too.

By the end you’ll have my exact stack, the migration path from OpenClaw, and the trust boundaries I drew so you don’t have to learn them the expensive way.

Who this post is for

Two reader profiles. Pick yours.

Profile A: You tried OpenClaw, and it never clicked. You installed OpenClaw/Moltbot/Clawdbot back in February/March, hit a wall on setup, OAuth, or skill authoring, and bounced. Hermes solves most of what frustrated you out of the box. The skill-authoring loop in particular is the unlock. You don’t write skills. Hermes watches you and writes them.

Profile B: you ran OpenClaw on Claude Pro and got stranded. You had it working. Anthropic changed the subscription terms and your cost model collapsed. You’ve been running half-broken cron scripts ever since while you figure out what to do. The Hermes + GPT-5.5 path is what I built for exactly this situation. The migration takes about a weekend.

Profile C: you have no idea what is OpenClaw and Hermes but you heard its hyping

All profiles land in the same place: a working operator-grade agent in Telegram, on a $5 VPS, with a $20/month model subscription. The path there is just slightly different.

The reframe: chatbot vs. work OS

This is the most important paragraph of the post. If you don’t shift here, the rest won’t land.

A chatbot is a thing you go to when you have a question.

A work OS is a thing that knows your projects, watches your day, talks first, and runs work while you do other things.

Four ingredients of a work OS:

• Memory. It knows your projects, priorities, style, who’s who in your business.

• Tools. It can actually do things (read your task system, write to your knowledge base, draft emails, call APIs). Not just describe doing them.

• Automations. It acts on schedules and triggers, not only when you ping it.

• Agents. It can spawn sub-agents for parallel work (research + code + content at the same time).

Hermes is the first thing I’ve used where all four actually compose cleanly. OpenClaw composed three of the four, and the fourth (multi-agent) was always a duct-tape project. Claude Cowork has tools and memory but lacks the runner and the multi-agent layer.

The bet of this post: a personal AI operator that lives where you already live (Telegram), knows your business, and runs on cheap infrastructure replaces 3-5 SaaS tools and 5+ hours per week.

Why I switched: the OpenClaw breakage in one paragraph

OpenClaw’s economics depended on Claude Max (I used $100 plan) plus a $5 VPS (about $125/month all-in), as we covered in the OpenClaw ecosystem post.

The reason it was depended is using any other model made it dumb. idk why 🤷‍♂️

When Anthropic moved subscription-based agent usage behind API metering, that math broke. A typical day of agent work (three research jobs, a content draft, two coordination runs) chewed through what used to be unmetered Pro usage. My March bill on metered tokens hit $187. The agent was doing the same work. The pricing model underneath it shifted.

I’m not litigating Anthropic’s decision here. They’re allowed to change their terms. But the practical effect for solo operators running OpenClaw was that the cheapest “always-on personal AI” setup stopped being cheap.

Hermes is open-source (MIT license, Nous Research), self-hosted on the same $5 VPS pattern, and model-agnostic. I point it at my OpenAI subscription. GPT-5.5 handles the operator role fine. Better than I expected on tool-use reliability. Monthly cost: $20 OpenAI + $5 VPS = $25. Same as old OpenClaw, with a runner that doesn’t depend on any one model lab’s pricing decisions.

My 8 workflows running today

Concrete. What it does, what it replaces, what it saves me.

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Google I/O 2026 dropped 100 announcements in two hours on May 19 — two new model families, a personal AI agent, XR smart glasses, and an agentic dev platform that built a working operating system for under $1,000. Andrej Karpathy — OpenAI co-founder, former Tesla AI director, creator of Neural Networks: Zero to Hero — joined Anthropic on May 19 to lead a new pre-training research team. And on May 20 Meta announced 8,000 layoffs — about 10% of its workforce — citing AI-driven efficiency. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

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Featured Materials 🎟️

Google I/O 2026: Welcome to the Agentic Gemini Era 🔮

Two new model families. A personal AI agent. Smart glasses launching this fall. An agentic dev platform that built a full OS with 96 agents in 12 hours for under $1K in token costs — and it runs Doom. Google I/O 2026 ran for nearly two hours on May 19 and delivered 100 announcements. Here’s what matters.

The two new model families:

• Gemini 3.5 Flash — 4× faster than competing frontier models in output tokens per second, outperforms Gemini 3.1 Pro on key benchmarks. Now the default model powering the Gemini app and Google Search’s AI Mode. Gemini 3.5 Pro rolls out next month.

• Gemini Omni — Google’s world model: multimodal in both input and output. Feed it text, audio, images, or video; it generates realistic, scientifically accurate content. Google DeepMind CEO Demis Hassabis called it a pivotal step toward AGI. Gemini Omni Flash is live today for paid subscribers and rolling out to YouTube Shorts free later this week.

The personal agent:

Gemini Spark is the most ambitious product announced. It’s a personal AI agent that integrates with Gmail, Calendar, Docs, and eventually 30+ third-party tools via MCP — Adobe, Dropbox, Uber, and others. Ask it to draft a status update for your boss and it pulls the relevant emails, files, and context without you opening a single app. Runs entirely in the cloud. Rolling out to Google AI Ultra subscribers in the US within the week.

The agentic development platform:

Google Antigravity 2.0 orchestrates multi-agent workflows from a desktop app. The demo shown at I/O: it created a full operating system from scratch using 96 parallel agents in 12 hours for under $1,000 in token costs. The OS runs Doom.

The hardware:

Android XR smart glasses arrive this fall. Built with Gentle Monster and Warby Parker frames. Directions, texts, photos — without taking out your phone.

Subscription pricing:

Google cut the AI Ultra price from $250/month to $200, and introduced a new $99/month tier. Both get Gemini Spark, Gemini Omni, and the full model stack.

Google announced 100 things in two hours. The pattern underneath all of it: Google is no longer shipping features. It’s shipping agents. Gemini Spark, Antigravity, Omni — each one replaces a workflow, not a button. The $99 Ultra tier is the trojan horse. At that price, Google doesn’t need enterprises. It needs individuals who run businesses.

Source: Google

Karpathy Left OpenAI’s Shadow. He Walked Into Its Biggest Rival. 🧠

Andrej Karpathy joined Anthropic on May 19. He’ll work on pre-training under Nick Joseph — the most expensive, compute-intensive phase of building a frontier model. He’s also starting a new team focused on using Claude to accelerate pre-training research itself.

The résumé: OpenAI founding researcher. Tesla Autopilot lead. Creator of Neural Networks: Zero to Hero — the most-watched LLM education series on YouTube. After leaving OpenAI a second time in 2024, he founded Eureka Labs to apply AI to education. That’s now on pause.

Why Anthropic, why now:

Karpathy is one of the few researchers who can bridge LLM theory and the realities of large-scale training runs. His joining Anthropic to work on pre-training — and to use Claude to do it — is a direct signal about where Anthropic thinks its competitive edge lies. Not just raw compute. Not just bigger clusters. AI-assisted research as a multiplier on the research itself.

The OpenAI angle:

Karpathy didn’t just leave OpenAI — he co-founded it. His $38M in early work helped build the lab that Anthropic now competes with directly. The irony: the most credentialed endorsement of Anthropic’s research direction just came from one of the people most responsible for creating its biggest rival.

What it means for Claude:

Pre-training is where the base capabilities of a model come from — reasoning, knowledge, judgment. Post-training (RLHF, Constitutional AI) shapes behavior. Anthropic has been strong on the post-training side. Karpathy’s hire is a signal that they’re doubling down on the pre-training layer too, ahead of what is likely their largest training run yet.

Karpathy built the lab Anthropic is trying to beat, taught the internet how LLMs work, and just decided Anthropic is where the most interesting pre-training research is happening. That’s not a hiring win. That’s a statement about where the frontier is moving.

Source: X

Meta Cuts 8,000 Jobs — and Calls It an AI Strategy 📉

Meta announced the cuts on April 23. On May 20 it started executing them. Meta began notifying 8,000 employees — roughly 10% of its global workforce — that their positions were being eliminated. The cuts rolled out in three waves, starting with Singapore staff receiving emails at 4 AM local time. Teams across product, operations, and middle management were hit. Meta simultaneously cancelled plans to hire 6,000 people and announced it would shift 7,000 existing employees into AI workflow-related roles.

Zuckerberg framed the move as a deliberate shift toward an AI-native operating model. Capital expenditures for 2026 are forecast at $125–145 billion — more than double 2025’s figure. The integrity team, cybersecurity teams, and content design division were among the first to receive notices. US-based employees will receive 16 weeks of severance plus two additional weeks per year of tenure.

The layoffs landed the same week Google I/O showed what a 96-agent system can do in 12 hours. The Cloudflare signal from two weeks ago — 1,100 cuts after a 600% internal AI usage spike — now looks like a leading indicator, not an outlier.

Meta is not the only company running this math. Every organization that has spent the last two years deploying AI internally is now calculating how many headcount reductions those deployments justify. The 10% number is a data point. The question is which industry runs the same calculus next.

Source: Bloomberg

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News of the week 🌍

Anthropic Is About to Post Its First-Ever Profitable Quarter 💰 — Anthropic disclosed to investors on May 20 that it expects $10.9 billion in revenue in Q2 2026 — more than doubling Q1's $4.8 billion in a single quarter. That puts it on track for its first operating profit, though the company may not stay profitable through the year due to compute costs from the SpaceX Colossus deal. For context: Anthropic posted $9 billion in full-year 2025. It's now on pace to generate more in one quarter than it did in all of last year.

SpaceX's IPO Filing Reveals xAI's Real Finances — and They're Ugly 📊 — SpaceX's S-1 filed May 20 is the first public look at xAI's actual numbers. The AI segment posted $3.2B in revenue in 2025 — but $6.36B in losses. In Q1 2026 alone: $818M revenue, $2.47B operating loss. SpaceX is spending $7.7B per quarter on AI capex. The filing also revealed that Anthropic is paying SpaceX $1.25B per month for Colossus 1 compute — meaning Musk's company is simultaneously losing billions on xAI while profiting from its biggest rival's infrastructure spend.

Pope Leo XIV Publishes First AI Encyclical — Co-Presented With Anthropic's Christopher Olah ✝️ — On May 19, the Vatican released its first encyclical on artificial intelligence. Anthropic co-founder Christopher Olah joined Pope Leo XIV at the ceremony in Rome. The document calls for AI development guided by human dignity and warns against systems that "replace human judgment in matters of conscience."

Musk v. OpenAI: Unanimous Verdict, IPO Path Cleared ⚖️ — A federal jury in Oakland ruled unanimously against Elon Musk on May 18, ending his three-year lawsuit against OpenAI. The verdict came after less than two hours of deliberation. The jury found Musk’s claims fell outside the statute of limitations — not a ruling on whether OpenAI betrayed its nonprofit mission, but a procedural finding that he waited too long to sue. Musk called it a “calendar technicality” and announced he will appeal. The practical result: the most significant legal obstacle to OpenAI’s October 2026 IPO is removed. OpenAI is currently raising at a $900B+ valuation. Altman stays. The IPO moves forward.

Altman Offered Every YC Startup $2M in OpenAI Tokens — For Equity 🎯 — At Demo Day on May 20, Altman offered all 169 startups in the current YC batch $2M worth of OpenAI tokens in exchange for equity. Uncapped SAFE, converts at Series A. The debate on X split instantly: some call it the cheapest infrastructure deal a founder can get; others warn OpenAI just bought a front-row seat to every idea in the batch. Either way — Altman locked in a generation of startups before they ever had a chance to default to Claude.

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Useful tools ⚒

⭐ StoreClaw — AI agents built specifically for e-commerce revenue growth. Connect your store, set a goal (recover abandoned carts, upsell repeat buyers, re-engage churned customers), and StoreClaw’s agents run the campaigns autonomously. It reads your product catalog, customer history, and order data to personalize every outreach. Built for Shopify and WooCommerce merchants who don’t have a marketing team. Free to start.

PollyReach — Give your AI agent a real phone number and voice. PollyReach lets you deploy outbound calling agents that sound like humans — appointment reminders, lead follow-ups, customer surveys. You define the script and the triggers; the agent makes the calls. Works with your existing CRM and calendar tools. Usage-based pricing.

Composer 2.5 by Cursor — Cursor’s most powerful coding model yet, launched this week. Designed for complex, long-horizon coding tasks that previous Composer versions would abandon or hallucinate through. If you’re using Cursor for anything beyond single-file edits, this is a meaningful upgrade. Available now inside the Cursor editor.

Emdash — One app to manage all your coding agents — Claude Code, Codex, Cursor, Devin, and 25+ others — from a single open-source interface. Each agent runs in its own isolated Git worktree. Switch between agents, review diffs side-by-side, manage costs, and connect to Linear, Jira, or GitHub Issues without leaving the app. Apache 2.0, free, runs on Mac, Windows, and Linux.

LobeHub — Chief Agent Operator: you set the goal, LobeHub assembles an AI team, runs them 24/7, and reports back through Slack, Discord, or email. Hire agents from a marketplace of 312K+ skills, run them in parallel across Projects, Pages, and Schedules. Persistent memory, MCP support for 58K+ servers, and open-source with 77K GitHub stars. Free tier available.

Weekly Guides 📕

MCP for Creators: Engineering Content that Works — Creators AI's own deep-dive on how to wire up MCP servers for content workflows: practical setup, what to automate first, and why MCP changes the content creation stack more than any model release this year.

Google I/O 2026: Gemini 3.5 Flash, Spark & Antigravity — Everything Developers Need to Know — The most practical developer breakdown of I/O: API pricing ($1.50/M input), benchmark numbers vs. GPT-5.5 and Claude Opus 4.7, the silent thinking_level default change from high to medium that will break your prompts if you don’t catch it, and honest take on what actually changed vs. what’s marketing. Published May 20 by Build Fast with AI.

Gemini 3.5 Flash: More Expensive, But Worth It? — Simon Willison — Independent hands-on notes from May 19: exact API pricing ($1.50/$9 vs $2/$12 for 3.1 Pro), real benchmark cost comparisons against GPT-5.5 and Claude Opus 4.7, why the model is 6× more expensive than Flash-Lite, and what the Interactions API actually does. The most honest third-party take on the model released at I/O.

How to Build Your First AI Agent in 2026: A Practical Guide — Code-first walkthrough: how to wire up a Python agent with function calling, how to implement a self-correction loop, when to use MCP vs. direct API calls, and how to set guardrails before a looping agent burns through your token budget. Written by an ML engineer who built an agent marketplace.

Claude Code How-To: Visual, Example-Driven Guide from Basic to Advanced Agents — Community-maintained GitHub guide synced with every Claude Code release. Covers slash command templates, sub-agent patterns, AGENTS.md setup, and production-ready copy-paste workflows. Updated to v2.1.145 this week. If the official docs are a reference, this is the tutorial.

Cursor Composer 2.5 — What Changed and How to Use It — Cursor’s official changelog for Composer 2.5: what’s new in long-horizon task handling, how the model routes complex vs. simple edits, and which prompt patterns get the best results. If you’re using Cursor for anything beyond single-file work, this is the upgrade guide to read before your next session.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

Figure AI's Robots Sorted 100,000 Packages in 81 Hours — Fully Autonomous — Figure's Helix-02 humanoid robots ran a live 81-hour autonomous warehouse shift, handling 100K+ packages with no human intervention. Self-swapped for low battery, self-diagnosed failures, coordinated as a fleet. 10M views on the original livestream. One of the clearest public proofs of long-duration no-human-in-the-loop multi-robot orchestration.

NextEra-Dominion Deal Signals Era of AI Utility Mega-Mergers — Bloomberg's analysis of why $1.1 trillion in US grid investment over the next five years still isn't enough, why scale now matters more than efficiency in utilities, and what this means for AI companies whose biggest constraint is no longer GPUs — it's power.

Anthropic Acquires Stainless — Pulling the SDK Infrastructure Away From OpenAI and Google — Anthropic acquired Stainless on May 18 for a reported $300M+. Stainless automated the creation and maintenance of SDKs — the libraries developers use to interact with APIs — and its tools were used by OpenAI, Google, Cloudflare, Replicate, and Runway. Anthropic is now shutting down all hosted Stainless products. Competitors still own the SDKs they’ve already generated, but going forward, the infrastructure is Anthropic’s alone. The acquisition takes a piece of critical developer tooling off the open market right as the API wars are heating up.

If you missed our previous updates, don’t worry, here they are:

Claude Max Limits, DeployCo, Codex on IOS | Weekly Digest

Your take: Karpathy left OpenAI's shadow to join Anthropic, Musk lost his case against OpenAI, and Google shipped 100 things in 2 hours. Who's actually winning the AI race right now — and does it even matter? Drop it in the comments 👇

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Five servers, four use cases, and the one I built myself

Hey!

Most Claude workflows in 2026 still look like a context relay race. You paste an email, prompt, paste a transcript, prompt again, paste your calendar, prompt one more time. The work is fine. The plumbing is the problem.

MCP is the boring fix nobody wants to write about. Boring, because there’s no demo moment that makes a screenshot pop. Underrated, because it quietly replaces 80% of the copy-paste that fills your day.

This post walks four use cases I actually run — two for posts, two for leads — plus a worked tutorial on the custom MCP server we built ourselves. By the end you should have one workflow off the relay race in about ten minutes.

Here’s what we’ll cover:

1. MCP in 90 seconds 🧠

2. Five servers that change your day ⚙️

3. Four use cases 🛠️

4. Setup in 10 minutes ⏱️

5. What NOT to give MCP 🚫

MCP in 90 Seconds 🧠

The plain analogy: MCP is USB-C for AI apps. Apps speak Claude’s language directly, instead of you being the middleman.

The technical version: it’s a small server that exposes your data and actions to AI in a standard way. Three things a server can do:

• Expose data — read your Gmail, your Notion, your CRM

• Expose actions — send an email, create a record, schedule a post

• Expose prompt presets — canned instructions you can trigger

The mental shift: you stop transferring context. You wire it once.

Why now? The protocol stabilized in late 2025, official servers shipped for Gmail, Google Workspace, Notion, Apollo, HubSpot, Attio, and a long tail of others across early 2026, and both Claude Desktop and Claude.ai speak MCP natively.

If you need the foundational walkthrough, our MCP full guide for founders and makers is the place to start. This post assumes you’ve read that or know the gist — and focuses on what to actually do with it.

Five Servers That Change Your Day ⚙️

Read these as menu items. The use cases below pull from this set.

1. Gmail

Claude searches your inbox by sender, thread, date, keyword.

Find all sponsor reply threads from the last month and tell me which deals stalled

Install the official Gmail MCP. Keep it read-only for the first week. If you want write access later, scope it to create drafts only — never auto-send. (I learned this the hard way. More on that in the trust section.)

2. Google Drive / Workspace

Claude pulls docs by title or content, opens spreadsheets, drafts in Docs.

Look at my last 3 newsletter drafts and outline next week’s in the same shape.

Separate servers exist for Drive, Docs, and Sheets — install only what you actually use. Workspace is the easiest first connector because the safe scope is obvious.

3. Brave Search

Real-time web search inside Claude Desktop, via Brave’s official MCP server. Claude can search the live web, news, images, videos, and local results — without ever leaving the conversation.

The free tier covers 2,000 queries per month, which is more than enough for most creator workflows. Brave’s own setup guide walks you through it in under five minutes.

My honest take: if you only install one server from this list, install this one. Web search inside Claude Desktop changes everything you can ask Claude to do — research, fact-checking, current-events context, the lot.

For heavier scraping needs — JS-rendered pages, structured extraction, deep crawls — Firecrawl MCP via Claude Code is the better tool. Brave is for “find me what’s on the web right now”; Firecrawl is for “pull this specific page into structured data.” Different jobs.

4. Apollo

Search Apollo’s contact and company database — hiring leads, sales prospects, partnership targets, sponsor research.

Apollo launched its native Claude connector in February 2026 as a beta, with OAuth-based authentication and no API keys required. We’ll get into the actual receipts on this one in UC3.

5. HubSpot or Attio

CRM via MCP.

• HubSpot has the official Anthropic connector (launched July 2025)

• Attio is the lighter-weight alternative — AI-native, OAuth, generous free tier — and increasingly the default for solo creators who don’t want HubSpot’s footprint

• Notion-as-CRM also works via the Notion MCP (covered in our Notion AI automation guide)

The pattern is the same regardless of which one you pick.

Four Use Cases 🛠️

Research for a Post / Video

Before: Open Notion, search keyword, copy chunks. Open Drive, search again, copy more. Open browser, scrape three sources, paste into Claude. Ten to fifteen minutes per post before you’ve written a sentence.

After: One Claude conversation reads Notion (your archive), Drive (your drafts folder), and Brave Search (the live web) in a single query.

Claude, pull everything I wrote about [topic] in the last 90 days from Notion. Then use Brave Search to find what's new this week from these three sources. Give me the angle I haven't covered yet plus three hook options.

Two minutes. Your archive stays current automatically — no monthly CSV refresh, no project re-upload. The win isn’t speed alone, it’s that the analysis reflects what you actually published yesterday, not last month’s snapshot.

This is the simplest workflow in the post, and the easiest one to act on today. The deeper pattern — Claude reading your archive instead of you re-explaining yourself every session — is the same pattern we’ve used in every system we’ve shipped at Creators’ AI:

• Our AI email newsletter system uses the same shape — Claude reads source material live, writes drafts to where you actually work

• The Firecrawl MCP setup (for Claude Code users who need heavier scraping) extends this pattern to the live web

• The broader stack — Skills for preferences, MCP for capabilities — is the framing I unpack in Skills, Plugins, Swarm Mode

Below the paywall, three use cases that go further: the custom MCP I built (and why), the March 2026 sponsor-search story where Apollo MCP replaced a four-hour overnight agent run, and the daily CRM-to-Gmail follow-up loop that’s quietly the highest-ROI workflow on this list.

Generate Content with MCPs (and Why You’ll Build Your Own MCP)

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Google dropped its biggest hardware announcement of 2026 the week before I/O — a new laptop category called Googlebook, built from the ground up for Gemini. Anthropic went the other direction and embedded Claude directly into QuickBooks, PayPal, and HubSpot, targeting the 44% of the US economy that enterprise AI has ignored. Meanwhile the Musk v. OpenAI trial wrapped closing arguments May 14 — the jury deliberates Monday, with Altman's future hanging in the balance. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

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Featured Materials 🎟️

Anthropic Puts Claude Inside QuickBooks, PayPal, and HubSpot — for Free 🏪

Small businesses make up 44% of US GDP and employ nearly half the private-sector workforce. Their AI adoption has consistently lagged enterprises. Anthropic launched Claude for Small Business on May 13 to close that gap — not with a new chat interface, but with prebuilt workflows inside the tools small business owners already pay for.

The product runs through Claude Cowork and connects to QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, and Microsoft 365 via a toggle install. No IT team, no custom integration. Fifteen workflows ship out of the box: payroll planning, invoice chasing, month-end close, cash-flow forecasting, sales campaigns, contract routing, customer service. Every action requires owner approval before Claude sends, pays, or posts anything.

The business case:

Anthropic president Daniela Amodei was direct: “Small businesses make up nearly half the American economy, but they’ve never had the resources of bigger companies. AI is the first technology that can finally close that gap.” There’s no extra charge beyond existing Claude licenses and whatever software the business already uses. Anthropic is also launching a free 9-lecture AI Fluency course co-developed with PayPal, plus a 10-city US roadshow starting May 14 — Chicago, Tulsa, Dallas, New Jersey, Baton Rouge, Birmingham, Salt Lake City, Baltimore, San Jose, Indianapolis. 100 small business owners per city, half-day live workshop.

Why this matters beyond SMB:

Dario Amodei warned at Anthropic’s Financial Services event last week that SaaS companies that don’t adapt will go bankrupt. He wasn’t talking about small businesses — he was talking about QuickBooks, HubSpot, DocuSign. Claude for Small Business is Anthropic embedding itself one layer below those products. If it works, the leverage shifts.

Enterprise AI was always going to reach small businesses eventually. Anthropic just decided not to wait. The HVAC company and the 30-person real estate brokerage are now the target — and the SaaS tools they already use are the distribution channel.

Source: Anthropic

Anthropic Changes the Rules for Claude Power Users 💳

Anthropic quietly restructured the economics of its paid plans this week — and the developer community noticed immediately.

Every paid Claude plan now includes a monthly API credit equal to the dollar amount of the subscription for programmatic usage: the Agent SDK, claude -p, GitHub Actions, and third-party harnesses like OpenClaw. Pay $200/month, get $200 in API credits on top of your interactive Claude.ai and Claude Code usage.

On paper, a good deal. In practice, power users who had been running third-party Claude harnesses at an estimated 70–90% discount from standard API pricing called it a rug pull. The backlash was immediate — Theo, Jeremy Howard, Matt Pocock, and Omar Sanseviero all weighed in publicly within hours of the announcement.

Anthropic partially offset the reaction with a separate move: +50% to Claude Code weekly limits through July 13, stacked on top of the previously announced 2× increase to the 5-hour limit. Claude Code users on interactive plans are getting more. Third-party harness users are getting metered.

What it signals:

Claude Code now has enough brand and market position that Anthropic is putting its most favorable pricing behind its own tools. The subsidy era for alternative harnesses is ending. Meanwhile OpenAI — the challenger in this specific battle — is going the other direction: two free months of Codex for enterprise customers who switch in the next 30 days.

Anthropic built its developer base partly on pricing generosity toward third-party tooling. This week it started converting that goodwill into margin. The question is how many power users follow Codex.

Source: X

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News of the week 🌍

Musk v. OpenAI: Closing Arguments Done, Jury Deliberates Monday ⚖️ — The first phase of the trial wrapped May 14 after closing arguments from both sides. Musk’s team argued OpenAI “misused millions in donations” and betrayed its nonprofit mission. OpenAI countered that Musk’s $38M came without strings and that the mission continued through structural changes. The nine-person jury begins deliberating Monday, May 18 — but their verdict is advisory only. Judge Gonzalez Rogers makes the final call on liability, and a separate remedies phase (potential Altman removal, unwinding the 2025 recapitalization) runs concurrently before the judge alone.

Anthropic in Talks to Raise $30B at $900B+ Valuation 💰 — Bloomberg confirmed on May 12 that Anthropic is in early discussions to raise at least $30B, which would triple its February valuation of $380B and put it above OpenAI’s $852B post-money figure. The round is expected to close by end of May and may be Anthropic’s last private round before an IPO targeted for October 2026. ARR is reportedly at $14B, with Claude Code alone generating $2.5B annually.

Amazon Replaces Rufus With Alexa for Shopping 🛒 — Amazon launched Alexa for Shopping on May 13, retiring the Rufus brand and combining Rufus’s product expertise with Alexa+’s personalization layer. Available free to all US customers — no Prime, no Echo device required. It tracks price history, schedules recurring orders, and includes a “Buy for Me” feature that purchases on your behalf at other retailers. 300 million customers used Rufus in 2025.

OpenAI Brings Codex to Mobile — Manage Your Coding Agent From Your Phone 📱 — OpenAI pushed Codex into the ChatGPT mobile app on iOS and Android. Start a coding agent session on your machine, then manage it from your phone: review diffs, approve commands, inspect terminal output, look at screenshots, switch models. No desk required. The gap between "AI coding agent" and "always-on background worker" just got a lot smaller.

OpenAI Launches $4B Deployment Company — Anthropic Mirrors the Move 🏢 — OpenAI stood up a standalone enterprise consulting arm on May 11 with $4B from TPG, Goldman Sachs, McKinsey, Bain Capital, and 15 others — embedding Forward Deployed Engineers directly inside Fortune 500 clients, Palantir-style. Acquired London firm Tomoro (150 engineers, clients include Tesco and Virgin Atlantic) to launch with an existing book of business. Same day, Anthropic announced its own $1.5B deployment JV with Blackstone, Hellman & Friedman, and Goldman Sachs. The model race is over. The deployment race is on.

Google Announces Googlebook — A Laptop Built Around Gemini 💻 — Google unveiled a new laptop category on May 12 at the Android Show: I/O Edition. Googlebooks ship with Gemini Intelligence built into the OS — Magic Pointer cursor, Android app streaming from phone, vibe-coded widgets on demand, seamless file sync. Manufacturing partners: Acer, Asus, Dell, HP, Lenovo. First units land this fall. The kicker: a federal antitrust judge had just barred Google from paying for AI default placement on third-party devices. Google's answer was to build its own.

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Useful tools ⚒️

⭐ Spellar 3.0 — Most meeting tools give you notes. Spellar 3.0 gives you memory. It joins your calls without a bot, captures every word, and builds persistent context across all your meetings — so you can ask what a client said three calls ago or surface decisions from last month. Runs on Claude, GPT, Gemini, or Perplexity. Exports to Notion, Jira, Linear, Google Docs. Mac, iOS, and web. Free tier available.

Higgsfield Supercomputer — A cloud-native AI agent that runs your entire content pipeline from one prompt. Tell it “build a week of Instagram ads plus competitor analysis” and it plans the work, picks the right models, generates the assets, and delivers them. Three layers of memory, 40+ built-in tools, connects to Slack, Notion, Figma and 30+ services. Learns your brand guidelines over time. Free to start.

Latitude for Claude Code — See exactly where Claude Code is burning tokens and why. Shows token usage by task, flags which prompts hit your limits most, and helps you optimize before you run out of budget mid-session. Built specifically for Claude Code users.

Blaze 2.0 — AI marketer for small and mid-size businesses: strategy, content, and ads in one tool. Describe your business, Blaze builds a content calendar, writes social posts, and creates ad copy — all in your brand voice. Built for founders who don’t have a marketing team.

Weavable — Context infrastructure for AI agents. Instead of flooding your agents with raw API data, Weavable maintains a continuous updating changelog across your work tools — HubSpot, Slack, Zendesk, Jira, GitHub, email — and serves agents the specific signals they need through a single MCP endpoint. One-tenth the tokens compared to direct app connections, according to their own evals.

Weekly Guides 📕

How AI Agents Are Built in May 2026 — Step-by-step guide to building your first real AI agent from scratch: how to structure AGENTS.md, TASK.md, and MEMORY.md files, how the reasoning loop works, when to use tools vs. subagents, and how to avoid the most common failure modes.

Generate AI Videos From Claude With Higgsfield MCP — Step-by-Step — Higgsfield's own setup guide for connecting their MCP server to Claude in under a minute: how to add the custom connector, set permissions, and start generating 4K video from a plain-language brief.

Claude Code Security: MCP Connections, Permission Architecture, and Best Practices — Official Anthropic documentation on securing Claude Code in production: permission-based architecture, MCP server allowlisting, prompt injection defenses, sandbox configuration, and team security settings. Essential for any org deploying Claude agents at scale.

Building for the Gemini Intelligence System on Android — Developer’s Cut — Google’s official developer breakdown from the Android Show on May 12: how to integrate AppFunctions API, how task automation works across form factors, what’s available now vs. what’s coming at I/O, and how Googlebook changes the target surface area for Android apps.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

Cerebras IPO Opens +89% Above Price, $106B Valuation on Nasdaq Debut — The AI chip company that makes wafer-scale processors debuted on Nasdaq on May 14, opening at $350 vs. its $185 IPO price and raising $5.55B. Amazon and OpenAI are customers. The largest US IPO of 2026 so far, and a signal that AI infrastructure investing hasn’t cooled.

TikTok Symphony Creative Studio Now Open to All Businesses — ByteDance’s AI ad creation tool went generally available this week for every business on TikTok. Type a brief, get a polished video: synced audio, avatar in one of 30 languages, product-consistent visuals powered by Seedance 2.0. Every clip ships with an AI-generated label. For any brand spending on TikTok, this changes the cost floor for video ads overnight.

OpenAI Explores Legal Options Against Apple — Reuters reported May 14 that OpenAI is looking at potential legal action against Apple, which has been building its own AI features into iOS 27 using Anthropic and Google — not OpenAI. The story is early-stage but reveals a new front in the AI platform wars opening up just as the Musk trial was wrapping.

US and China Are Discussing AI Guardrails for the Most Powerful Models — Treasury Secretary Bessent confirmed May 14 that the US and China are in active talks about guardrails specifically for frontier AI models. The first public confirmation of bilateral AI safety talks at this level. Context: China’s four open-weight coding models dropped in a 12-day window last month, closing the capability gap faster than expected.

If you missed our previous updates, don’t worry, here they are:

Musk v. OpenAI: Chaos Under Oath, Anthropic Dreams on SpaceX, China’s $20B Challenger | Weekly Digest

Your take: Anthropic is pulling back pricing subsidies for third-party Claude harnesses — are you staying, or is this the moment you move to Codex? Drop it in the comments 👇

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At a Glance

In this piece, you will learn how to:

• Stop treating AI as a writing assistant and start treating it as infrastructure

• Understand what a real creator AI stack looks like under the hood (with two verified examples)

• Build your own four-component system starting this week

This post is prepared with Guest Author — Claudia Faith, Fractional Chief AI Officer. If you also want to write for Creators AI — send us email here

I run two Substack newsletters, a LinkedIn presence for my consulting business, and a live AI community. Each brand has a different voice, different visuals, different audience. For months I produced everything manually: writing articles one at a time, scheduling social posts by hand, creating images in a browser tool, copying and pasting between apps until my eyes glazed over.

On a good week, that took around 8 hours. On a bad week, when I was also running my consulting business and showing up for coaching clients, the content slipped. Articles posted late. Notes got skipped. Images were whatever I could throw together in five minutes.

The turning point came when I stopped thinking about AI as a writing assistant and started thinking about it as infrastructure. I didn’t need a better prompt for Tuesday’s article. I needed a stack where every tool connects to the next, where the output from one step feeds into the next, and where one Sunday session handles what used to take me all week.

When I started looking at how other growing creators approach this, I saw the same pattern everywhere. The ones scaling fastest built systems, and those systems look surprisingly similar under the hood.

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Two Stacks Worth Studying

Before I walk through my own setup, here are two creators who built AI production systems I’ve learned from. Both are publicly documented and verifiable.

Ryan Law’s 23-Skill Editorial Pipeline (Ahrefs)

Ryan Law runs content at Ahrefs, a company doing over $100M in annual revenue. He built a system inside Claude Code using 23 custom skill files, each handling one editorial step: keyword research, structural outlining, line editing, internal linking, WordPress shortcodes. He chained them into a master skill called “blog-pipeline” that runs all 23 sequentially.

A publish-ready article draft in 6 to 12 minutes. Roughly 45 articles processed so far. He published the whole thing on the Ahrefs blog with full technical detail.

The takeaway: every skill file saves the instructions, the examples, and the formatting rules for that one step. When Ryan updates how Ahrefs handles internal linking, he edits one file. Every future article inherits the change. He started with a single skill and kept adding as he identified repetitive steps. The system grew with the work.

We covered Claude Code Skills in depth here: Skills, Plugins, Swarm Mode: Practical Tips with Claude

Dan Koe’s Repurposing Engine

Dan Koe has 2 million followers and 120,000 newsletter subscribers. His principle: every idea gets used multiple times. He tests ideas on X first (the character limit forces compression), expands winners into his newsletter, turns that into a YouTube script, then feeds the transcript into Gemini to extract usable building blocks in a fraction of the time manual notes would take.

His custom Claude prompts deconstruct his best-performing posts into hooks, quotes, pain points, and narrative arcs. One prompt he calls his “Deep Post Generator” can pull up to 60 social post ideas from a single long-form piece. He discussed the whole workflow on The Startup Ideas Podcast.

The takeaway: Dan’s AI tools aren’t generating content from nothing. They’re taking content he already created and finding the 20 other shapes it could take. The original thinking stays human. The multiplication is automated.

My Stack: The Full Tool Chain

Here’s every layer, what it does, and how they talk to each other.

Claude Code: the command center

Everything runs through Claude Code, Anthropic’s CLI tool. I use it in VS Code, and it’s the single interface I open for almost all content work. Writing, image generation, data analysis, file exports. I give it instructions, it calls the right tools, and I review the output.

The reason Claude Code works as a command center is that it can run scripts, read and write files, and use browser automation all from one conversation. I don’t switch between five apps. I describe what I need, and Claude orchestrates the steps.

New to Claude Code? Start here: How Claude Code Can Be Your AI Teammate

CLAUDE.md: the master instruction file

This is the file Claude Code reads automatically every time I start a conversation in my project folder. It contains every workflow I’ve built: how to write an article, how to generate Notes, how to create carousels, how to produce images, how to export for Substack. It also defines my folder structure, my naming conventions, and which tools to use for which tasks.

I update this file whenever I refine a workflow. Every future session inherits the update. If I figure out a better way to structure a CTA, I change it in one place and every article I write afterward follows the new pattern.

Voice profiles: one per brand

Each of my brands has its own markdown file with a full voice analysis. I built these by reading 15 of my own published posts per brand and extracting the patterns: how I open articles, my sentence rhythm, transition phrases I use naturally, and critically, phrases I never use and specific AI writing patterns to avoid. There’s a list of 18 AI fingerprints that I flag as banned, things like “Not X, it’s Y” constructions, em-dashes, rhetorical questions, invented compound labels.

Claude reads the matching voice file before generating anything. Without it, AI output sounds like AI. With it, the output sounds like me having a particularly productive morning. This is the single highest-leverage piece of my stack.

Puppeteer MCP: automated data collection

I use Puppeteer, a browser automation tool, connected to Claude Code through the MCP protocol. This lets Claude control a real browser session.

Every Sunday I ask Claude to connect to my Substack profile and pull the last 7 days of Notes performance: likes, comments, restacks, subscriber conversions. No manual scrolling through dashboards, no copying numbers into spreadsheets. Claude reads the data directly from the page and tells me what performed, what fell flat, and where the patterns are. That analysis feeds straight into the next batch of content.

One finding that came out of this automated tracking: the Note that converted the most subscribers one week had 34 likes. The Note with 266 likes converted the second fewest. I would never have caught that by glancing at my dashboard. The automated data pull made it visible, and it changed how I select content formats.

Imagen API: image generation from the terminal

I use Google’s Imagen API through a Python script I wrote called generate_image.py. Each brand has an image style template in its voice file (sketch style for my AI newsletter, a different look for my consulting brand). When I write an article, Claude builds the image prompt from the brand template and the article topic, then runs the script. The header image generates in about 30 seconds without me opening a browser or logging into a design tool.

I built a second script for carousels (generate_carousel.py) that works the same way but generates multiple slides. For my consulting brand, both scripts can include reference photos of me for character-consistent images across posts.

Parallel agents: multiple tasks at once

When I produce a full article, Claude Code spawns three agents that run simultaneously. One writes the article using the voice profile. One generates SEO metadata, keywords, and an FAQ answer. One builds the image prompt and generates the header image. When the article is done, a fourth agent reads it and writes a Notes teaser.

A full article package that used to take me a full morning now runs in about 15 minutes, and most of that time is me reviewing and editing the output.

Custom scripts: the last mile

The stack includes a few utility scripts that handle the boring parts:

• md_to_docx.py converts finished articles to Word format. Substack’s import feature works cleanly with .docx files, preserving all formatting. No more copy-paste formatting disasters.

• Notes scheduler accepts CSV batch uploads with content, date, and time per Note. Substack only lets you schedule Notes one at a time, which is painful when you’re scheduling 21 per week.

• generate_preview_gif.py creates animated prompt-to-image reveal GIFs for social sharing.

• Notes scheduler accepts CSV batch uploads with content, date, and time per Note. Substack only lets you schedule Notes one at a time, which is painful when you're scheduling 21 per week. I built the scheduler to solve that for myself and now sell it as part of my paid subscription.

How it all connects

Here’s what a typical Sunday session looks like, start to finish:

I open Claude Code and ask it to pull my last week’s Notes performance via Puppeteer. It reads the data and summarizes what worked. I tell it to generate 21 new Notes based on that analysis, my voice file, and the formula structures I’ve tested over months. It produces a CSV. I review and edit the Notes (about 15 minutes of actual work), upload the CSV to my scheduler, and the full week is handled.

You can check out my scheduler here

If I’m also producing an article that week, I give Claude the topic and it runs the parallel pipeline: article, metadata, and image simultaneously. The finished package lands in a dated folder, ready to export as a .docx and import into Substack.

Total Sunday time for a full week of Notes plus one article: about 45 minutes. That used to be closer to 8 hours spread across the week, done in fragments, mostly mediocre because I was always rushing.

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Building Your Own Stack

Every creator system I studied, including my own, has four components. You can build them in any order, but you need all four for the system to actually run itself.

• A voice file. Document how you write. Pull 10–15 of your published pieces and identify: your opening patterns, sentence rhythm, transition phrases, recurring expressions, and phrases you’d never use. Save this as a file your AI reads before generating anything. This takes a few hours the first time. You never have to do it again unless your style evolves.

• Proven formulas. Study what works in your space. Pull high-performing content from creators with audiences similar to yours, break down the structural patterns, and rank them by the metric that matters to your business: subscribers, clients, or sales. Save these as reusable templates with real examples. Any AI model produces dramatically better output with a structural template than with a blank prompt.

• A batch rhythm. Pick your most repetitive content task and batch it. If you post daily on any platform, you’re a candidate. Choose one session per week where you produce everything for that platform in one sitting. The specific cadence matters less than the commitment to doing it in one focused block.

• A feedback loop. After each batch cycle, review what performed and feed that data back into the next batch. My system does this weekly through the Puppeteer data pull. Over months, the output improves because the system learns from real performance data instead of assumptions.

You don’t need 23 skill files or a full Puppeteer integration to start. Pick the content task you repeat most often. Do it once with AI, but document every instruction you give along the way: the voice rules, the structure you prefer, the things you fix in the output. Save that as one file. Next week, feed it back and batch 7 days in one sitting.

If the quality holds and the time drops, you have the seed of a stack. Keep adding pieces as you hit new bottlenecks, and within a month you’ll have something that runs in a fraction of the time you’re spending now.

Also read: Your AI Agent Stack Is Spaghetti — here’s how to fix it

Where the Stack Breaks

Every creator I’ve talked to who runs this kind of system has the same disclaimer: it works until it doesn’t. The honest version of “AI as infrastructure” looks like this.

Usage limits hit at the worst time. Claude Code, Imagen, Puppeteer running in parallel. Each one burns tokens or API credits. You hit the weekly limit on Claude on a Sunday afternoon mid-batch, and the whole pipeline stalls until reset. Even on the highest paid tiers. You learn to front-load the expensive work earlier in the week, but the ceiling is real.

The setup is genuinely complex. A voice file, a CLAUDE.md, 4 to 23 skill files, Puppeteer MCP, custom Python scripts, API keys for image generation, a folder structure that everything depends on. When something breaks, the debugging surface is huge. Was it the skill? The MCP? The script? A model update? Most creators I know have a “works on my machine” graveyard of half-built workflows that fell over once and never got fixed.

Picking up where you left off is a job in itself. You start an article Tuesday. You come back Thursday. Claude doesn’t remember which voice file you were using, which version of the outline you approved, what you already corrected in the draft. Without disciplined file naming and session notes, you rebuild context every time. The “infrastructure” only feels like infrastructure when you also build the habits to use it.

Quality drifts without warning. Some weeks the output is sharp. Some weeks the same prompt, same voice file, same skill produces flat copy or off-brand images. Model updates change behavior silently. Skills that worked perfectly in March generate slop in May. You don’t get a release note. You catch it because a Note flopped or a thumbnail looks wrong, and you trace it back.

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Skills don’t fix everything. This is the one I want to be direct about, because the discourse around skill files is getting evangelical. A skill saves your instructions, not your judgment. It can enforce a structure, a banned-phrase list, a CTA pattern. It cannot tell you that the angle is boring, that the case study isn’t strong enough, or that the opening doesn’t earn the reader’s attention. The taste layer stays human. If you build a system that assumes the skill handles quality, the quality will erode and you won’t notice until subscribers do.

The stack is still worth building. 45 minutes versus 8 hours is real, and compounding. But anyone promising a hands-off AI workflow either hasn’t run one long enough, or is selling you the dream rather than the system.

Let me know how it goes!

Oh, by the way: If you want the full setup behind my AI-driven writing workflow, I bundled everything I described here and you can grab it here

Have you started building your own creator AI stack — or are you still running everything manually? Share in the comments.

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Claudia Faith holds a Master of Science in AI. She’s a VC-backed founder who’s worked with Forbes 100 companies to deploy AI where it actually matters. As a Fractional Chief AI Officer, she handles strategy and implementation, and offers 1:1 coaching for business owners who don’t want to be the last in their industry to figure this out. If you want help thinking through your own AI strategy, start here Start here.

Elon Musk's lawsuit against OpenAI reached its most explosive week yet — former CTO Mira Murati testified under oath that Altman was "creating chaos," and that OpenAI was "at catastrophic risk of falling apart." At the same time, Anthropic announced it's taking all capacity at SpaceX's Colossus 1 data center — 220,000+ Nvidia GPUs — while Musk sues their biggest competitor in federal court. Then China's Moonshot AI closed a $2B round at a $20B+ valuation, and its model is already #2 on OpenRouter. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

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🤖 Sponsored: Luma AI’s UNI-1 is now an API

Luma’s Uni-1 — the model creative teams have been using for video generation — is now available as an API.

What this unlocks: plug it into any product, wire it into a pipeline, or build something new from scratch. Anywhere code runs, Uni-1 can think. It understands intent, not just prompts — give it a brief or a reference board, and it resolves creative direction before generating a single frame.

If you’ve ever wanted to add AI video to your app, automate creative workflows, or build a brand-consistent visual tool, this is the infrastructure that makes it possible.

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Featured Materials 🎟️

The Musk v. OpenAI Trial: Chaos Under Oath 🔥

The most-watched AI story of the week didn’t involve a model launch. It happened in a federal courtroom in Oakland.

Week two of Elon Musk’s $180 billion lawsuit against OpenAI delivered testimonies that turned the company’s founding myth inside out. Former CTO Mira Murati testified via pre-recorded video on May 6 that CEO Sam Altman was “creating chaos” — saying one thing to one executive and the complete opposite to another. “OpenAI was at catastrophic risk of falling apart,” she said. “I was concerned about the company completely blowing up.”

The full picture of Week 2:

• Mira Murati (ex-CTO): Altman “pitted executives against one another” and undermined her role — yet she still wanted him to stay as CEO, because removing him risked the whole company imploding. When asked if Altman was always candid with her: “Not always.”

• Shivon Zilis (ex-board member, mother of four of Musk’s children, now at Neuralink): The board “voiced extreme concern” about the launch of ChatGPT — released “without any semblance of board communication.”

• Helen Toner (ex-board member): Called Murati “two-faced” about Altman’s 2023 firing — “waiting to see which way the wind would blow.”

• Greg Brockman (OpenAI President): Testified Musk wanted personal control — “he said people needed to know he was in charge.”

• Musk himself (Week 1): Tried to settle with Brockman days before trial. Said he felt like “a fool” for funding OpenAI.

What this trial is actually deciding:

Musk sued OpenAI in 2024 alleging it illegally converted from nonprofit to for-profit — betraying the mission he funded with $38M. He’s seeking up to $180B in damages plus Altman’s removal from the board. If he wins, it could derail OpenAI’s IPO entirely. The trial runs through May 22. Still to testify: Ilya Sutskever and Microsoft CEO Satya Nadella.

Musk wanted to control the future of AI. Altman built it without him. Now both versions of the story are being told under oath — and neither makes OpenAI look like it was ever just about humanity.

Source: Reuters

Anthropic Signs With SpaceX — and Teaches Its Agents to “Dream” 🤖

At Anthropic’s Code with Claude developer conference in San Francisco on May 6, the company announced it will take all capacity at SpaceX’s Colossus 1 Memphis data center — 220,000+ Nvidia GPUs and 300+ MW coming online within a month. Both companies also “expressed interest” in joint orbital data centers.

The SpaceX angle matters: Elon Musk is currently suing OpenAI in federal court while his rocket company supplies Anthropic’s compute.

Beyond infrastructure, the conference itself was substantive:

• API volume grew 80× year-over-year in Q1 2026 vs. a 10× internal plan

• Rate limits doubled for Pro, Max, and Team plans; peak-hour Opus caps removed

• Claude Opus 4.7 tops the Vals AI Finance Agent benchmark at 64.37%

The standout announcement — “dreaming”:

Anthropic unveiled Claude Managed Agents (research preview) with a capability it calls “dreaming”: when agents are idle, they replay past task sessions to self-improve — inspired by how sleep consolidates memory in humans. Early numbers: Harvey reported ~6× higher task completion rates, Wisedocs cut document review time by 50%.

Anthropic is using Elon Musk’s GPUs while Musk sues their biggest competitor in federal court. The “dreaming” agents story is technically the bigger deal — but the Musk angle won the news cycle.

Source: Anthropic

China’s Moonshot AI: $2B Raised, $20B+ Valuation, and #2 on OpenRouter 🚀

China’s Moonshot AI closed a $2B round on May 7 — led by Meituan’s Long-Z Investment, Tsinghua Capital, China Mobile, and CPE Yuanfeng. Valuation landed at $20B+, more than 4× its late-2025 figure of $4.3B. The company has now raised ~$3.9B in six months.

The product behind the number: April ARR exceeded $200M, driven by Kimi chatbot subscriptions and API usage. The latest model — Kimi K2.6 (1T-parameter MoE, 32B active parameters, 384 experts) — is the #2 most-used model on OpenRouter after GPT-5.5. Modified MIT license: commercial use is allowed, but you can’t use the weights to train competing frontier models.

Why Western developers are routing through it:

It’s not ideology — it’s the spreadsheet. GPT-5.5 costs $30/M output tokens. Kimi K2.6 undercuts that significantly. DeepSeek V4 did the same last week. The demand for cheap Chinese open-weight models is structural.

A Chinese AI company just 4×’d its valuation in under six months on $200M monthly revenue. The US-China AI race isn’t just playing out at the frontier model level — it’s happening in the API economics layer where most developers actually make decisions.

Source: TechCrunch

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News of the week 🌍

iOS 27 Will Let You Pick Claude, Gemini, or ChatGPT as Your Default AI 📱 — Mark Gurman reports Apple plans to open iOS/iPadOS/macOS 27 to third-party AI models via “Extensions” — users can set Claude, Gemini, or ChatGPT as the default for Siri, Writing Tools, and Image Playground. Per-feature defaults configurable in Settings. Official reveal at WWDC, June 8–12. The subtext: Apple Intelligence has been behind, and Apple knows it.

OpenAI and Anthropic Both Launched $10B+ PE Ventures — on the Same Day 💰 — On May 4, OpenAI finalized "The Deployment Company" at roughly $10B with TPG, Brookfield, Advent International, Bain Capital, SoftBank, and Dragoneer — PE firms reportedly guaranteed 17.5% annual returns over five years, former COO Brad Lightcap leads it. Hours later, Anthropic announced a $1.5B JV with Blackstone, Hellman & Friedman, Goldman Sachs, Apollo, GIC, Leonard Green, and Sequoia to embed Claude across 2,000+ PE portfolio companies. Both deals explicitly target enterprise consulting — not just API access. McKinsey and BCG now have two well-funded AI-native competitors with PE distribution machines behind them.

EU AI Act High-Risk Deadlines Pushed to 2027–2028 🌍 — The EU Council and Parliament agreed on a Digital Omnibus deal on May 7: Annex III high-risk obligations move from August 2026 → December 2027, Annex I safety deadlines → August 2028, generative-AI watermarking → December 2026. The deal also bans “nudification” AI systems. For any team building AI products for the European market — meaningful runway extension.

Sierra Raises $950M at $15.8B — One-Third of the World’s Largest Banks Are Customers 🏦 — Bret Taylor’s enterprise AI agent company closed a $950M Series E led by Tiger Global and GV. Customers include Prudential, Cigna, Blue Cross Blue Shield, and Rocket Mortgage. Valuation up from $10B last fall. Taylor warned of a market correction “within two years” — in the same week he closed the round.

Pennsylvania Sues Character.AI — Chatbot Posed as a Licensed Doctor ⚖️ — AG Josh Shapiro filed suit alleging a Character.AI bot named “Emilie” claimed an Imperial College London medical degree, a valid Pennsylvania medical license, and produced a fake PA license number when investigators asked about depression and medication. The state seeks an injunction against unauthorized practice of medicine — months after Character.AI settled multiple teen-harm suits in January.

CAISI Signs Safety Agreements With Google, Microsoft and xAI 🔒 — The NIST Center for AI Standards and Innovation signed pre-release safety evaluation agreements with Google, Microsoft, and xAI this week — covering cybersecurity, biosecurity, and chem-weapons evaluations. CAISI has existing agreements with OpenAI and Anthropic from earlier this year. The notable move: xAI joining the framework for the first time, bringing Grok under government pre-release review.

Useful tools ⚒️

⭐ Kanwas — Open-source real-time canvas where humans and AI agents share one self-evolving context for docs, decisions, and research. Shared working memory between your team and your agents — everything stays in sync without copy-pasting across tools.

Mindra — Spin up a team of specialized AI agents that work 24/7, communicate with each other, and take real action across your stack — no coding required. Self-healing: when an agent fails, it re-plans and retries instead of breaking the whole workflow. Built for founders and operators who want to delegate entire workflows, not just tasks.

Velo 2.0 — Turns raw screen recordings into polished video messages with a chat-native editor — no timeline, no re-recording. Describe the edit you want, Velo rewrites the script, clones your voice, and updates the video live. One recording produces both a video and a structured doc.

Kilo Code v7 — Open-source AI coding agent for VS Code rebuilt on OpenCode server. V7 adds real parallelism: multiple tool calls firing simultaneously, parallel subagents via Agent Manager, and inline diff reviewer with line-level comments. Choose any model — Claude, GPT-5.5, Gemini, or local LLM.

Shadow 2.0 — Bot-free AI meeting assistant that runs quietly on your Mac and captures everything said and shown on screen — without joining as a visible participant. V2.0 executes tasks live during the call: CRM updates, follow-up drafts, slide generation — before the meeting ends.

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Weekly Guides 📕

Claude Managed Agents Dreaming — Official Anthropic Docs — Official API reference for the dreaming feature: how to configure dream schedules, set auto vs. manual memory updates, use the dreaming-2026-04-21 beta header, and run your first dream in Python. If you're building agents, this is where to start.

How to Use Anthropic’s New Finance Agents — Official Docs — Anthropic’s own walkthrough of the 10 new financial services agents: what each one does, which MCP connectors are included, and how the Moody’s data integration works. If you’re in fintech or building for financial clients, start here.

Vercel AI SDK Agents: Complete 2026 Implementation Guide — End-to-end build guide covering the new Agent abstraction, MCP integration, and Fluid Compute deployment. Published May 6, directly tied to this week’s releases.

Claude Code’s Hidden Skill Budget Setting (May 2026) — Practical walkthrough of skillListingBudgetFraction in Claude Code v2.1.129 — a setting most users never touch that directly affects how aggressively Claude Code burns through your execution budget. Copy-paste fixes included.

Claude for Excel, PowerPoint, Word and Outlook — Official Setup Guide — Launched May 7: Claude now works across all four Microsoft 365 apps in one continuous conversation — triage email in Outlook, open the attachment in Word, build the model in Excel, turn it into a deck in PowerPoint, without re-explaining context at each step. GA for Excel, Word, PowerPoint on all paid plans; Outlook in public beta.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

AlphaEvolve One-Year Impact Report — Google DeepMind — One year since launch: AC Optimal Power Flow feasibility jumped from 14% to 88%, 10× lower error on Willow quantum circuits, +5% accuracy across 20 disaster-prediction categories. The clearest case study yet for AI coding agents making genuine scientific discoveries.

The State of Global AI Diffusion in 2026 — Microsoft published its Q1 2026 AI Diffusion Report on May 7: global AI usage hit 17.8% of working-age population (+1.5pp in the quarter), 26 economies now exceed 30% adoption, the UAE leads at 70.1%, the US moved up from 24th to 21st. The first data-backed snapshot of where AI is actually being used globally — and where it isn't.

Harvard Study: OpenAI's o1 Outperforms ER Physicians in 67% of Triage Cases — Harvard Medical School and Beth Israel ran six experiments — real ER cases, no preprocessing. AI correctly diagnosed 67% vs 55% and 50% for the two attending physicians. Every single experiment: AI won. The authors don't advocate clinical deployment yet and call for prospective trials — but this is the clearest peer-reviewed evidence that reasoning models are now operating above average physician level on diagnostic tasks.

Publishers Sue Meta — Zuckerberg Personally Authorized Torrenting 267TB of Pirated Books to Train Llama — Court documents allege Zuckerberg personally approved using LibGen to download 267TB of copyrighted books — fiction, nonfiction, academic texts — to train Llama models, overruling legal counsel who flagged copyright risk. If upheld, it directly threatens the legal basis for training any LLM on web-scraped text. The most significant AI IP case since the Getty Images suit.

If you missed our previous updates, don’t worry, here they are:

Claude #1 on App Store, GPT-5.4 Lands, DeepSeek V4 Is Here | Weekly Digest

What’s your take — are AI labs actually a threat to consulting firms, or is this just well-funded positioning? Drop it in the comments 👇

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Copy-paste prompts for invoices, travel, subscriptions, meetings, and the admin pile that quietly eats your week.

Most people open Cowork, type something vague like “organize my files”, get a half-baked answer back, and close the tab. That’s the trap.

Cowork is not a chatbot. It does not reward one-shot prompting. It rewards iteration. You give it a job. You correct what it got wrong. Then (and this is the part everyone misses) you ask Cowork to rewrite your original prompt so the workflow runs cleanly next time. After three or four cycles, you have a prompt that just works. And if you set it on a schedule, it works without you.

New to Cowork entirely? Start with our Claude Cowork Complete Guide. This post is the prompt layer on top of that setup.

In the last few weeks, I tested a stack of prompts with my own files and inbox. I also pulled the strongest workflows from public Cowork users (Frank Andrade and Ilia Karelin’s prompt library, the orthopedic surgeon running five parallel Cowork tasks at 6am, Anthropic’s own Dispatch documentation). Below are the 10 that earned their place.

In this guide, you’ll get prompts to automate:

1. The freelance invoice tracker (live artifact, not just a snapshot)

2. The vendor comparison table that ends with a recommendation

3. The travel itinerary that pulls every confirmation into one file

4. The meeting transcript to action-items machine

5. The subscription tracker that actually saves you money

6. The job application dashboard

7. The Downloads folder organizer (set it once, forget it)

8. The morning brief (calendar plus email plus Slack in one pass)

9. The competitor watch (scheduled, not manual)

10. The Dispatch “prep me for the next meeting” prompt

How to actually use these prompts

The mechanics are simple:

1. Copy the prompt

2. Open Claude Cowork on your desktop

3. Paste and run

4. Grant folder, Gmail, or Calendar permissions when asked

5. Customize anything in [BRACKETS] for your situation

The non-obvious part is what happens after the first run. Cowork rarely nails it on attempt one. It scans the wrong folder, formats the table differently than you wanted, misses an edge case. Don’t start over. Tell Cowork what to fix. Then say:

“Now rewrite my original prompt so the next run produces this output cleanly. Save it as a Skill I can reuse.”

That single move turns a one-off task into a reusable system. After three iterations, you stop prompting and start running.

1. The Freelance Invoice Tracker

The problem. You invoiced someone. You forgot when. You don’t remember if they paid. Your bookkeeping lives across Gmail, a folder of PDFs, and a Notion page you stopped updating in March.

What this prompt does:

• Scans Gmail and a local invoices folder for every invoice you’ve sent

• Flags overdue and unpaid invoices

• Calculates total outstanding at the top

• Saves a tracker file you can reference any time (or a live artifact that re-scans on demand)

The prompt:

Scan my emails and local files to track all freelance invoices and payment status.

GMAIL FILTER:
- Search for emails containing: invoice, payment, due, overdue, receipt
- Date range: [Last 30 days / Last 90 days / This year]

LOCAL FOLDER TO SCAN (optional):
- [path to invoices folder, e.g., ~/Documents/Invoices]
- File types: .pdf, .docx, .xlsx, .csv

FOR EACH INVOICE FOUND, EXTRACT:
- Client name
- Invoice number
- Amount
- Date sent
- Due date
- Payment status: Paid / Unpaid / Overdue / Unknown

BUILD A TRACKER TABLE:
- All invoices sorted by due date (oldest first)
- Status: OVERDUE / DUE SOON / PAID
- Total outstanding amount at the top
- Total paid this period

SUMMARY AT THE TOP:
- Total invoiced: $[X]
- Total paid: $[X]
- Total outstanding: $[X]
- Number of overdue invoices

OUTPUT: Save as invoices-tracker-YYYY-MM.md in [YOUR FOLDER PATH]

Pro tip. Run this on the first Monday of every month. The overdue column tells you exactly who to follow up with before chasing anything by hand.

Want a live version instead of a static file? Replace the last line with:

OUTPUT: Build this as a live artifact. Every time it's reopened, re-scan Gmail and the local folder and refresh all numbers. Do not cache the previous state.

The live artifact stays connected to Gmail. Every time you open it, it re-scans your inbox and updates totals and statuses. New invoice sent, payment received, due date passed: the artifact reflects all of it without touching the prompt again. There’s a Reload button in the top right corner if you want to refresh mid-session.

2. The Vendor Comparison Table

The problem. You’re picking between three or four tools. You have ten tabs open. You still haven’t decided.

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Anthropic is weighing a $50B raise at a $900B valuation that would make it the most valuable AI startup in the world — more than doubling its February figure in under three months. Big Tech dropped Q1 earnings on April 29 and raised AI capex to a combined $700 billion for 2026 — only Google convinced investors the bill is already paying off. Then Anthropic's next model family leaked: internal red teaming logs spotted Claude Jupiter V1 on April 30, pointing at a launch at the May 6 Code with Claude conference. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

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Featured Materials 🎟️

Anthropic Is Weighing a $900B Valuation. Claude Code Built It. 💰

Anthropic wants to raise $50 billion at a $900 billion valuation. Multiple preemptive offers at $850–900B have landed on the table. The board meets in May to decide. If it closes at the high end, Anthropic becomes the most valuable private AI company in the world — ahead of OpenAI’s $852B post-money from March.

How they got here:

Anthropic’s annual revenue run rate crossed $30B in early April — up from $9B at the end of 2025. The actual current rate, per sources close to the company’s financials, is closer to $40B. That’s a 4x jump in under five months. For context: OpenAI went from $20B to $25B annualized in the same window. The growth slopes are no longer close.

The engine behind it is Claude Code. Cowork and Claude Code together account for the majority of Anthropic’s revenue acceleration, driven by enterprise teams replacing developer headcount with agent-based workflows. The company now has more than 300,000 business customers on Claude Code, with an annualized run rate TechCrunch sources peg at $2.5B for that product line alone.

The timeline:

• February 2026: Raised at $380B valuation

• April 29: Bloomberg breaks $900B story

• April 30: TechCrunch reports 48-hour investor allocation window

• May: Board meeting to finalize

The IPO angle:

This is likely Anthropic’s last private round. Early backers from 2024 and earlier are sitting this one out — waiting to cash in at IPO. A $900B private valuation puts the IPO price discovery in trillion-dollar territory. For comparison: Google went public at a $23B valuation in 2004. Anthropic is approaching the public markets at 40x that figure, still in private hands.

The $900B headline is the output. Claude Code is the input. Every AI lab is now racing to own the same developer workflow — and Anthropic got there first.

Source: X

Big Tech Q1 Earnings: The $700B Bill, and Only One Company Justified It 📊

Microsoft, Alphabet, Meta, and Amazon reported Q1 2026 earnings on April 29. All four beat revenue estimates. All four raised AI infrastructure spending. Combined 2026 capex is now tracking $650–700 billion — the largest concentrated infrastructure cycle in tech history. Then the market reacted, and the verdict was unambiguous: only Google convinced investors the spend is working.

The numbers by company:

• Alphabet: $109.9B revenue (+22% YoY, beat by $2.7B). Google Cloud +63% to $20B — fastest growth in years, and $2B above estimates. Backlog hit $462B, nearly doubling quarter-over-quarter. Capex raised to $180–190B for 2026. Stock up 6%+ pre-market.

• Microsoft: $82.9B revenue (+18% YoY). AI business now at $37B annualized run rate, up 123% YoY. Azure guided 37–38% growth. Copilot crossed 20M paid seats. Capex raised to $190B — $35B above analyst consensus. Gross margin shrank to lowest since 2022.

• Meta: $56.3B revenue (+33% YoY — fastest growth in 4 years). Capex raised to $125–145B, up $10B. Stock fell 6% after hours. Free cash flow dropped to $1.2B from $26B a year ago.

• Amazon: $181.5B revenue (+17% YoY). AWS grew 28% to $37.6B — fastest in 15 quarters. Bedrock processed more tokens in Q1 2026 than in all prior years combined. Capex: $200B for 2026. EPS at $2.78 crushed $1.62 estimate.

The market’s verdict:

Google’s Cloud growing 63% — more than doubling its previous growth rate — was enough. Azure and AWS showed strong signals but with margin concerns. Meta’s capex hike ($10B surprise increase for “higher component costs, particularly memory pricing”) spooked investors who wanted payback before more bets. The implicit message: $700B in capex requires 63% Cloud growth to justify. Anything less and investors wait for proof.

Four companies are spending the equivalent of Sweden’s GDP on AI infrastructure this year. Three of them couldn’t convince their investors it was working. One showed 63% Cloud growth. The bar is now 63%.

Source: Alphabet IR

Anthropic’s Next Model Is Already in Red Teaming — Codename: Jupiter 🪐

Anthropic has begun red teaming a new internal model build codenamed Claude Jupiter V1, spotted in internal test logs on April 30. The codename follows the company’s established pattern: pre-release safety probes are tagged with planet names, not final product names. Last year the equivalent exercise was called Neptune — and it preceded the Claude 4 family launch by a few weeks.

The timing is significant. Anthropic is hosting its Code with Claude developer conference in San Francisco on May 6, followed by London and Tokyo dates. At the previous Code with Claude event (May 22, 2025), Anthropic announced the Claude 4 series. If the pattern holds, Jupiter V1 is being hardened for an announcement timed to the SF event.

What Jupiter V1 appears to be: not a minor update. Internal model naming at Anthropic separates the planet-codename safety probes (which reference genuinely new model families) from incremental Opus/Sonnet/Haiku point releases. Jupiter suggests a Claude 5-class model — the first new base model family since Claude 4 launched a year ago.

The $900B fundraising round, the IPO timeline, and the Jupiter red teaming all land in the same week. Anthropic is not slowing down.

Anthropic is red teaming its next model family while simultaneously racing to close its last private round before IPO. The two things are connected: If Jupiter is the next base family, the $50B raise starts to look less like vanity pricing and more like compute prepayment.

Source: Testing Catalog

News of the week 🌍

Meta Plans 8,000 Job Cuts Starting May 20 — AI Is Explicit in the Memo 📉 Meta confirmed this week it plans to cut approximately 8,000 jobs beginning May 20, with a large portion in mid-level management. The internal framing is direct: the company is redirecting $135B toward AI infrastructure and expects smaller teams to achieve the same output. This is the largest single-round reduction Meta has announced since its 2022 “Year of Efficiency” cuts. It follows Snap’s 1,000-person cut two weeks ago where the CEO explicitly cited AI — the pattern of major consumer tech companies attributing headcount reduction directly to AI capability is now established, not anecdotal.

SoftBank plans Roze IPO at $100B 🏗️ — SoftBank is spinning out a new AI and robotics company called Roze, targeting a $100B valuation in what would be one of the largest AI IPOs to date. Roze focuses on physical AI infrastructure: using robotics to build data centers and bundling SoftBank’s bets in energy, land, and digital infrastructure. CEO Masayoshi Son is doubling down on “physical AI” as the next frontier. IPO timing: second half of 2026.

White House Opposes Anthropic's Mythos Expansion ⚠️ — The WSJ reported on April 30 that the White House is formally opposing Anthropic's plan to expand access to Claude Mythos, citing security concerns over a model capable of carrying out autonomous cyberattacks. This comes the same week Anthropic is weighing a $900B valuation. The tension is explicit: investors are racing to fund the company; the US government is trying to contain its most capable product.

OpenAI Open-Sources Symphony — Every Linear Ticket Gets Its Own Coding Agent 🎵 — On April 27, OpenAI published Symphony: an open-source orchestration spec that turns your issue tracker into a control plane for coding agents. Every open ticket automatically gets a dedicated Codex agent running until the task is done. Engineers review output, not supervise sessions. OpenAI’s own teams saw a 500% increase in landed pull requests in the first three weeks.

Useful tools ⚒️

⭐ Plurai — Vibe-train your AI evals and guardrails by example, not by writing complex test cases. Show Plurai what good and bad outputs look like, and it builds and maintains evaluation logic automatically. #1 on Product Hunt April 29 with 673 upvotes. For any team running Claude, GPT-5.5, or DeepSeek V4 in production who wants evals that don’t require a dedicated ML engineer. Free trial available.

Wonder — AI design agent that works directly on your canvas in real time. Describe what you want, Wonder generates and iterates without leaving your design file. #4 on Product Hunt April 30 with 260 upvotes. Works with Figma components. Free public alpha.

KarmaBox — Run your own Claude Code instance on mobile. Full agentic coding sessions from your phone — same capabilities as desktop Claude Code, sandboxed on-device. #3 on Product Hunt April 29 with 321 upvotes. Free during beta, iOS.

Orange Slice — Automate any sales task with a plain English description: prospecting, follow-up sequences, CRM updates, outreach. No workflow builder, no integrations to configure. #1 on Product Hunt April 27 with 460 upvotes. Free trial, paid plans from $49/mo.

Gemini Deep Research Agent — Web and MCP research agents now available in the Gemini API. Run multi-step research tasks, pull from web sources and connected MCP tools, get structured output. #5 on Product Hunt April 30 with 206 upvotes. Available via Gemini API, usage-based pricing.

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Weekly Guides 📕

How to Use Mistral Medium 3.5 for Self-Hosted AI: Hardware Guide & Setup — Official Mistral Docs: exact VRAM requirements, FP8 vs BF16 tradeoffs, vLLM and SGLang setup commands. If you’re evaluating self-hosting instead of API, start here.

How Big Tech Q1 Earnings Should Change Your AI Infrastructure Decisions — Bedrock +170% QoQ, Google Cloud backlog $462B, Azure supply-constrained. Which cloud to build on in H2 2026 based on this week's numbers.

Dark Factory: How to Build a Codebase Where AI Agents Ship Without Human Review — What it takes to move from "AI helps me code" to "AI ships code autonomously." Seven production requirements every team needs before removing humans from the review loop.

DeepSeek V4 API Migration Guide: Everything Before the July 24 Deadline — Legacy model names deepseek-chat and deepseek-reasoner retire July 24. One-line code change, same endpoint, same API key — but the deadline is real. Audit your codebase now.

New Claude Connectors: Official Setup Guide — Spotify, Uber, Instacart & 12 More — How to connect the 15 new lifestyle apps to Claude, what each can do inside a conversation, and how confirmation works before Claude takes action.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

Andrej Karpathy: From Vibe Coding to Agentic Engineering — Karpathy’s fireside chat with Sequoia’s Stephanie Zhan at AI Ascent 2026. He explains why vibe coding raised the floor, why “agentic engineering” is now becoming the serious discipline on top of it, and why building with agents still requires taste, judgment, and technical understanding.

Microsoft and OpenAI Restructure Their Partnership — Cloud Exclusivity Ends — On April 27, Microsoft and OpenAI restructured their deal: Azure remains primary cloud partner but exclusivity is over, and AGI-linked deal terms are removed. Quietly significant for every company building on Azure-hosted OpenAI APIs.

Behind the Curtain: We've Been Warned — Axios's rare long-form piece on pace, not products: Anthropic is the fastest-growing company in American business history ($1B to $30B ARR in 16 months). The most capable models are now the least transparent. AI agents erased $2 trillion from public software company valuations in 10 weeks.

Stripe Builds Wallets for AI Agents — Stripe launched Link wallets for agents and its Agentic Commerce Suite, letting businesses sell inside AI apps while agents get spending controls, approval flows, and transaction visibility. The boring payments layer just became the most important part of agentic commerce.

If you missed our previous updates, don’t worry, here they are:

GPT-5.5 Doubles the Price, Google Goes Full Agent, DeepSeek V4 Resets the Floor | Weekly Digest

Brilliant cut prompts from 20+ to 2. Datadog killed week-long design cycles. But there’s a catch most reviewers missed.

Hey! Today we’re going deep on Claude Design. The tool that made Figma’s stock drop 7% in a single day and made every product designer on X simultaneously panic and tweet about it.

Anthropic shipped Claude Design on April 17, alongside the Opus 4.7 release we covered last week. The timing was loud. Mike Krieger (Anthropic’s Chief Product Officer and the guy who literally co-founded Instagram) quietly resigned from Figma’s board three days before the announcement. The Information had already reported that Anthropic’s next model would ship with design tools. The market read the signals and Figma got punished.

Then I actually used Claude Design for a week. And the headline takes are wrong.

It’s not a Figma killer. It’s not a Lovable competitor. It’s something neither category covers, and that’s exactly why it matters.

At a Glance

In this piece, you will learn:

• What Claude Design actually does (and what it doesn’t), with real screenshots

• 4 production case studies from Brilliant, Datadog, Mercury, and Peter Yang’s 16-minute everything-build

• 5 power-user tips that separate “shipping” from “burning your weekly quota in 30 minutes”

• The honest catch on token economics that nobody on Twitter is talking about

What Claude Design Actually Is

Claude Design is a conversational prototyping tool. It lives inside claude.ai (look for the Design tab in the sidebar after you log in), and it’s powered by Claude Opus 4.7, the most capable vision model Anthropic has shipped to date.

The interface is dead simple: chat on the left, canvas on the right. You describe what you want in plain English. Claude builds a working design on the canvas. You refine through conversation, draw red marker annotations directly on the design, click and tweak elements, or drag custom sliders Claude builds for you on the fly.

Here’s the part that made my jaw drop: it’s not generating images. It’s generating live HTML, CSS, and React components. Real code. Things you can click. Things you can hand to a developer and say “build this.”

That’s not an image generator. That’s an actual design tool.

The output covers most of what teams ship every week:

• Landing pages and marketing sites

• Clickable interactive prototypes

• Pitch decks and presentations

• One-pagers and SOPs

• Wireframes and design explorations

• Brand systems and component libraries

• Even animated brand videos and 3D web experiences

It exports to PDF, PPTX, Canva, standalone HTML, Keynote, and Figma import. There’s also a killer feature most people miss: it packages everything into a handoff bundle for Claude Code.

The 5 Features That Actually Matter

Most of the launch coverage stopped at “AI tool that makes designs.” That’s too shallow to be useful. Here’s what actually changes the game:

1. Design system ingestion. Point Claude at your codebase or design files during onboarding. It reads your colors, typography, components, and spacing rules, then applies them automatically to every project after that. Teams can maintain multiple design systems for sub-brands or campaigns. The single source of truth becomes your code, not a Figma library that drifts over time.

2. Web capture tool. Drop in a URL and Claude pulls elements directly from your live site so prototypes look like the real product. This is huge for marketing teams iterating on existing pages.

3. The Tweaks panel. Custom sliders that Claude builds for you on the fly: typography scale, color temperature, spacing density, section ordering. Drag the sliders, the design updates live. And here’s the part nobody mentions: the Tweaks panel doesn’t burn chat tokens. More on that later.

4. Inline comments. Click directly on any element and request a targeted change instead of describing the location in chat. Faster, cheaper, more precise.

5. Handoff to Claude Code. When you press “Hand off to Claude Code,” it doesn’t just dump HTML. It packages the design with the intent, component choices, and architectural decisions preserved as context. Claude Code then builds on top of the design instead of reinterpreting it.

That last one is the difference between Claude Design and every other AI design tool that’s launched in the past year. We show below how we used it in our case

Real Case 1: How We Rebuilt Creators AI in Claude Design

Read more

GPT-5.5 shipped on April 23 and doubled the API price in the same breath — $5 in, $30 out per million tokens. Vertex AI is dead: Google rebranded it to Gemini Enterprise Agent Platform at Cloud Next and unveiled two new TPU generations built for agent workloads. Then DeepSeek V4 dropped on April 24, matched frontier benchmarks, and undercut GPT-5.5 on output by more than 100x. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

Keep your mailbox updated with practical knowledge & key news from the AI industry!

Subscribe now

Featured Materials 🎟️

GPT-5.5 Is Smart, Weird, and Twice the Price 💸

GPT-5.5 is out — $5 per million input, $30 per million output. That’s exactly double GPT-5.4 and 20% more than Claude Opus 4.7. OpenAI released it on April 23 to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex. It’s also the first fully retrained base model since GPT-4.5 — every GPT-5.x release between them was a post-training iteration on the same base. GPT-5.5 is not. The benchmarks tell part of the story. The invoice tells the rest.

What actually shipped:

• Terminal-Bench 2.0: 82.7% — up from 74.2% on GPT-5.4, new SOTA among all models

• SWE-Bench Pro: 58.6% — Opus 4.7 still leads at 64.3%

• 1M context window — first OpenAI model to match Gemini 2.5 Pro and DeepSeek V4

• Agent-first architecture — OpenAI describes it as a system that “takes a sequence of actions, uses tools, checks its own work, and keeps going” without re-prompting

The pricing story:

GPT-5.4 was the cheapest frontier model by a wide margin. That was the point. Sam Altman framed the hike as reflecting “the true cost of frontier compute.” OpenAI says effective cost increase is closer to 20% once token efficiency is factored in — GPT-5.5 is more efficient per task than 5.4. Whether that math holds for your workload is for you to verify.

Source: OpenAI | OpenAI Community | LLM Stats

Vertex AI Is Dead, Long Live Gemini Enterprise 🏢

Vertex AI no longer exists. At Google Cloud Next on April 22 in Las Vegas, Google rebranded it to Gemini Enterprise Agent Platform and paired the launch with two new TPU generations. The rebrand is bigger than the silicon.

The TPUs:

• TPU 8t (training) — 9,600 chips per superpod, near-linear scaling to 1 million chips across data centers, 3x the compute of the previous generation

• TPU 8i (inference) — 3x the on-chip SRAM, 80% better performance-per-dollar, purpose-built for low-latency agentic workloads and MoE models like DeepSeek V4

The agent platform:

Gemini Enterprise ships with a built-in orchestration layer, Agent Studio (low-code builder), persistent Memory Bank, and a governance layer. The key new protocol: A2A (Agent-to-Agent) v1.0, now in production at 150 organizations — it lets agents built on different frameworks (LangGraph, CrewAI, LlamaIndex, AutoGen) hand off tasks to one another without understanding each other’s internal architecture. A Salesforce agent can call a Google agent, which calls a ServiceNow agent, all via A2A.

Over 40 launch partners including Box, Workday, Salesforce, and ServiceNow committed on day one. Google also announced a $750M fund for agentic AI startups with dedicated allocations for Europe, Japan, and India.

The Anthropic number buried in the keynote:

Anthropic crossed $30B in annualized TPU revenue on Google Cloud in April 2026, with 80% from enterprise API usage. Every TPU performance gain announced this week directly reduces Anthropic’s inference cost structure.

Vertex AI was a model-serving tool. Gemini Enterprise is a bid to be the operating system for agent economies. Microsoft now has 18 months to answer.

Source: Google Cloud | Google Blog | Reuters

DeepSeek V4 Drops: Near-Frontier Quality, Fraction of the Price🧨

DeepSeek V4 landed on April 24 in two variants — V4-Pro (1.6T total / 49B active) and V4-Flash (284B / 13B active), both with 1M context. V4-Pro scored 3,206 on Codeforces, ahead of GPT-5.4’s 3,168. On SWE-Verified: 80.6%. Then they published the price sheet.

The numbers that reset the floor:

• V4-Flash: $0.14 input / $0.28 output per million tokens

• V4-Pro: $1.74 input / $3.48 output per million tokens

• GPT-5.5 for comparison: $5.00 / $30.00

Flash output is more than 100x cheaper than GPT-5.5 while competitive on reasoning and code. Pro stays nearly 9x cheaper than GPT-5.5 at frontier-adjacent quality.

The license and the catch:

Weights released under a Modified MIT License — commercial use and redistribution allowed, but a non-compete clause prevents using them to train competing frontier models. The hosted API runs on DeepSeek’s own H800/H100 fleet in China. Huawei simultaneously announced that its Ascend 950 chips will fully support V4 out of the box — the first frontier-class model deployable at scale on Chinese domestic hardware without US-manufactured GPUs.

Every frontier lab spent 2025 arguing scaling laws still hold. DeepSeek spent 2025 proving architecture and data curation beat raw FLOPs. V4 is the receipt.

Source: DeepSeek | Hugging Face

News of the week 🌍

SpaceX Gets an Option to Buy Cursor for $60B 🚀 — SpaceX struck a deal with Cursor to build a next-generation coding and knowledge-work AI, with an option to acquire the startup for $60B later this year or pay $10B for the partnership instead. The signal is obvious: coding agents are no longer “developer productivity tools.” They’re becoming strategic infrastructure.

Claude Opus 4.7 Lands in Amazon Bedrock ☁️ — Anthropic made Claude Opus 4.7 available in Amazon Bedrock on April 20, roughly three weeks after the API launch. Bedrock also added prompt caching with up to 90% discount on repeated context — notably cheaper than Anthropic’s direct equivalent for high-volume repeated prompts.

Kimi K2.6 Launches Open-Source 🧨 — Aimed Directly at GPT-5.4Moonshot AI released Kimi K2.6 with a pointed message: this is a frontier coding and reasoning model under Modified MIT. It’s a 1T-parameter MoE model with 32B active parameters, 256K context, and native multimodal input. Pricing: $0.95 input / $4.00 output per million tokens — half the price of GPT-5.4 at launch. Combined with DeepSeek V4 dropping on April 24, this week produced two significant Chinese open-weight releases in a single four-day window.

Cerebras Files S-1 — Nasdaq IPO Targeting $22–25B 📈 — Cerebras Systems filed its S-1, targeting the CBRS ticker on Nasdaq at a $22–25B valuation. The filing confirms a multi-year, $20B+ compute deal with OpenAI — the company’s largest single customer and the deal that flipped its revenue concentration story from 87% UAE to a more diversifiable base. Revenue hit $510M in 2025, up 76% year-over-year. Roadshow begins early May; mid-May listing window.

Perplexity Personal Computer — general availability this week 🖥️ — The Mac app that ingests your local files, emails, and calendar and answers across all of them in a single retrieval pass. Free during public beta; Pro subscription required at GA.

Sam Altman publishes AI industrial policy paper 📄 — 13-page document proposing a Public Wealth Fund giving every American a stake in AI companies, a subsidized 32-hour workweek, and taxes on automated labor. Whether you agree or not, this is the framing OpenAI is taking into Washington.

Useful tools ⚒️

⭐ Dune — A physical 3-button Mac keypad that reads which app is in the foreground and automatically changes what its keys do in real-time. In GitHub: raise a PR, approve/reject. In your meeting app: join, toggle mic, control camera. Triggers AI agents with one tap. #1 on Product Hunt this week with 558 upvotes. $99 one-time, macOS only.

Pegasus 1.5 by TwelveLabs — AI model that transforms raw video into structured, timestamped metadata: scenes, objects, actions, dialogue — all indexed and searchable. Drop a 2-hour recording in, get a queryable knowledge base out. For anyone building on top of video content. 200 upvotes at launch today. Free tier available.

Claude Desktop Buddy — Bring Claude into the physical world via maker hardware. Connects Claude’s capabilities to physical inputs and outputs — buttons, LEDs, sensors. #2 on Product Hunt this week with 387 upvotes. Open-source, free.

Subspace — All your coding agents in one app with persistent context. Multi-agent coordination, sandboxed runs, Git workflows, parallel task handling across local and cloud setups. Free tier available.

The New Waydev — Measure the full AI SDLC from token to production. Tracks what your AI coding agents are actually shipping, where the bottlenecks are, and how token spend maps to output quality. #3 on Product Hunt this week. Free trial available.

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Weekly Guides 📕

Introducing GPT-5.5 — Official OpenAI Announcement — The full release post: benchmark table, pricing rationale, Codex integration details, and the “new class of intelligence” framing. The SWE-Bench Pro vs Opus 4.7 comparison at the bottom is the most honest part.

Google TPU 8t and 8i Technical Deep Dive — Google’s own breakdown of the chip architecture, Virgo Network topology, and Boardfly interconnect. If you’re building on Google Cloud or tracking Anthropic’s inference cost trajectory, start here.

How to Use the DeepSeek V4 API: Full Migration Guide — Published today alongside the launch. OpenAI-compatible endpoint, one base-URL swap from GPT-5.5, Python and Node examples, thinking-mode math, and the July 24 deprecation deadline for old model IDs. If you're evaluating V4 for production, start here.

DeepSeek V4 — Full Breakdown by Simon Willison — The clearest technical summary of V4-Pro and V4-Flash: architecture, pricing math, Modified MIT license terms, and honest benchmark context. Written same day as launch.

Gemini Enterprise Agent Platform — What Actually Changed from Vertex AI — The New Stack’s breakdown of what’s new vs. rebranded, A2A protocol v1.0 explained, and where existing Vertex AI pipelines stand.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

Bonus Materials 🎁

Cerebras S-1 SEC Filing — Full Document — The full prospectus filed April 17. The OpenAI deal structure starts on page 47. The UAE revenue concentration risk is more nuanced than the headlines suggest.

GPT-5.5 vs Opus 4.7 — Full Benchmark Comparison — Honest cross-benchmark breakdown. GPT-5.5 wins on Terminal-Bench and long-horizon planning. Opus 4.7 wins on SWE-Bench Pro, MCP-Atlas, multilingual, and agentic finance. Pick by workload, not by headline.

Cursor Is Raising $2B at a $50B Valuation — The round is already oversubscribed. $2B ARR in February, $6B projected by year-end. Nvidia joining as strategic investor alongside a16z and Thrive. Six months ago the valuation was $29B. The AI coding market is compressing timelines that used to take a decade into a single fiscal year.

The MCP Security Problem Just Got Real — OX Security found critical remote-code-execution issues around MCP server handling, with reports saying major SDKs and a large number of MCP instances may be affected. Great Bonus pick because CAI audience already cares about MCP, agents, and “what can go wrong when your AI gets tools.”

If you missed our previous updates, don’t worry, here they are:

Opus 4.7 Is Live, Cyber Arms Race Official, Stanford Shows the Receipts | Weekly Digest

AI agents are already shopping, researching, and deciding on behalf of real users. Whether you run a store, a SaaS product, a portfolio of content, or a one-person agency — the same shift (being optimized for an AI reader, not a human one) is coming for whatever you sell. This is the working map: AEO/GEO/AOO, the four channels worth showing up in, and the people defining how it plays out.

Hey everyone!

One in five Cyber Week 2025 orders involved an AI agent. Roughly $70 billion in GMV, according to Salesforce. And most of the brands, creators, and operators whose businesses depend on being found don’t actually know if their visibility was part of that number or not.

That’s what agentic commerce looks like in early 2026. It’s not the thing that will happen — it’s the thing that’s quietly already happening, and the users aren’t telling you because they just think they decided to buy, sign up, or subscribe on their own.

The traditional funnel (impression → click → browse → cart → checkout, or its SaaS equivalent: impression → click → trial → activation → conversion) is collapsing into a single chat bubble. In that bubble, the AI pulls data, compares it against everyone else’s, and either recommends brand or doesn’t. If it doesn’t, brand weren’t just outranked — brand were invisible.

Agentic commerce could generate up to $1 trillion in US B2C retail revenue by 2030, with global projections of $3–5 trillion (McKinsey via VGS, January 2026).

Tobi Lütke put it more bluntly on X: “Shopify is building the foundation for agentic commerce.” Sundar Pichai followed with: “AI agents will be a big part of how we shop.” When the CEOs of Shopify and Google are saying the same thing in the same week, it’s worth a closer look.

A note on audience. This post is the generalist view — what agentic commerce is, why it matters for anyone dependent on being found by AI (not just eCom), and the framework to think about it. If you run a Shopify store or DTC brand and want the operational playbook — catalog audits, channel toggles, attribution setup, startup ecosystem — that’s living on Agentique, my other newsletter focused specifically on agentic commerce for operators. I’ll link the Practical Playbook in a callout further down.

At a Glance

In this post:

• What agentic commerce actually is (and what it isn’t yet)

• How discovery and agentic commerce are different problems with different optimization work

• The new acronym soup — AEO, GEO, AOO — and what each actually means

• The 4 AI channels worth showing up in (for anyone who depends on being found)

• The voices defining the space, and the critics worth taking seriously

• What’s working, what’s broken, and where to focus

What Agentic Commerce Actually Is

Agentic commerce is any transaction where an AI agent — not a human — does the discovery, comparison, or checkout work. Often all three.

A user tells ChatGPT “find me the best noise-cancelling headphones under $300 with 30+ hours battery life.” The agent queries product data across merchants, reads aggregated reviews, checks price and stock, and either shortlists 3 options or, with permission, completes the purchase. The shopper never sees a Google SERP, never opens ten tabs, never visits anyone’s product page.

Same pattern for SaaS: “find me a project management tool under $20/user that integrates with Linear and has native time tracking.” The agent hits review sites, aggregator content, company docs, and returns a shortlist. Two or three years from now it’ll sign up for the trial on your behalf.

The numbers behind the shift are no longer abstract:

• 23% of Americans made a purchase using AI in the past month (Morgan Stanley)

• 805% YoY traffic growth from generative AI channels in July 2025 (Adobe)

• 4.4x higher conversion for AI-generated product recommendations vs traditional search (McKinsey)

• 44% of US consumers are comfortable with an AI agent browsing and buying on their behalf — rising to 59% among 18-34 year olds (Worldpay)

• AI-referral conversions grew 1,247% in late 2025 (Signifyd). MetaRouter projects agent traffic moving from sub-1% to 15–25% of total ecommerce traffic by 2027.

And the protocol layer actually stabilised in the last six months, which is what makes this practical instead of speculative:

• Sep 2025 — OpenAI + Stripe launch the Agentic Commerce Protocol (ACP) with Instant Checkout in ChatGPT

• Nov 2025 — Perplexity ships Buy with Pro to all US users via PayPal

• Jan 11, 2026 (NRF) — Google + Shopify announce the Universal Commerce Protocol (UCP) with 20+ retailer partners

• Winter ‘26 Edition — Shopify launches Agentic Storefronts, connecting merchants to ChatGPT, Google AI Mode, Gemini, and Microsoft Copilot from one admin toggle

• Mar 24, 2026 — Agentic Storefronts activated for all eligible US Shopify merchants. ~5.6 million stores now technically connected.

The plumbing is done. The mental model shift isn’t.

Discovery vs. Agentic Commerce: Not the Same Problem

Here’s the distinction most posts skip, and it’s the one that changes your optimization work.

Discovery is when a human asks ChatGPT “what’s the best noise-cancelling headphone under $300?” or “what’s a good lightweight CRM for a 5-person sales team?” and reads the answer. The reader is the customer. Your job is to be cited — mentioned in the prose, linked as a source, surfaced in the carousel. You’re optimizing to be read about.

Agentic commerce is when an AI agent queries your site or catalog programmatically, parses structured data, compares options against constraints, and completes a transaction — often without a human ever seeing your page. The reader is a machine. You’re optimizing to be transacted with.

Both matter. They reward different work.

If you only invest in one, you can still show up but not close. If you only invest in the other, agents can check you out but you never enter the consideration set.

To unwrap Agentic Comemrce and keep up with ultraspeed development of new protocols and partnerships in Commerce there is a Agentique newsletter.

Agentique

Agentique - a monthly dispatch on agentic commerce. Field reports on AI agents, autonomous checkout, and the new mechanics of DTC, retail, and marketplaces.

By Daniil Andreev

AEO, GEO, AOO: The Acronym Soup, Decoded

Twenty years of SEO is now spawning three overlapping acronyms — and they’re not interchangeable.

AEO — Answer Engine Optimization. Optimizing your content so AI answer engines (ChatGPT, Perplexity, Claude, Google AI Overviews) cite you when answering a user’s question. The work overlaps with classic SEO but the surface is different: instead of ranking for a query, you’re being quoted in a paragraph. Wins look like: clear definitional content, structured FAQs, comparison-ready specs, inclusion in the “best X for Y” listicles that AI training data ingests.

GEO — Generative Engine Optimization. A near-synonym for AEO that’s gained traction with the SEO community (Olga Zarr, Julian Goldie, Brad Smith are among the practitioners writing about it most). Some treat GEO as a superset that includes how your brand appears across the entire generative experience — not just answers but also recommendations, summaries, and image-based results. In practice, AEO and GEO are used interchangeably by 80% of people writing about them. Don’t lose sleep over the terminology.

AOO — Agent-Oriented Optimization. This is the one most operators haven’t internalised yet. ARK Invest popularised the term: optimizing not for a human reading an answer, but for an AI agent transacting with your store, your SaaS, your booking flow. The signals that matter are completely different — MCP endpoint compatibility, data freshness, predictable checkout or signup, structured attributes that an agent can filter on. As ARK put it: “MCP compatibility is key.”

The split is simple:

• AEO/GEO lives on the discovery side of the table above. Win these and you get cited.

• AOO lives on the agentic commerce side. Win this and agents can actually transact with you.

Most “AI SEO” content circulating in April 2026 is really AEO/GEO advice repackaged — we went deep on the citation-and-ranking side in LLMs Optimization (AI SEO) if you want the playbook. Genuinely useful AOO writing is much rarer because the agent surface is newer and the data signals (MCP server quality, catalog/data accuracy, transaction reliability) are harder to measure than citation rates. Expect AOO tooling — the equivalent of Ahrefs or Semrush, but for agent traffic — to be a major startup wave through 2026.

Every previous platform shift in commerce — search, social, mobile — eventually settled into a stable equilibrium where operators knew how to be visible. Agentic commerce probably won’t.

If you are form the eCom / Marketing and want to learn practical aspects of implementing Agentic Comemrce check Agentique’s Practical Playbook for Operators:

Agentic Commerce Playbook

The 4 AI Discovery Channels You Can’t Ignore

Four channels matter in April 2026. Each behaves differently, and the work to show up in each is not the same — whether you’re selling products, SaaS, content, or services.

Read more

Anthropic shipped Claude Opus 4.7 this week — same price as 4.6, but with 3x sharper vision, stronger coding, and the first built-in cybersecurity safeguards. OpenAI fired back on Monday with GPT-5.4-Cyber, its own locked-down model for verified security professionals — and the Spud (GPT-5.5) launch window keeps slipping. Meanwhile, Stanford released its 2026 AI Index and the numbers don't lie: 53% global adoption in three years, SWE-bench coding scores jumping from 60% to near 100% in a single year, $581.7 billion in corporate AI investment. Today we have:

1. Featured Materials 🎟️

2. News of the week 🌍

3. Useful tools ⚒️

4. Weekly Guides 📕

5. AI Meme of the Week 🤡

6. AI Tweet of the Week 🐦

7. (Bonus) Materials 🎁

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Featured Materials 🎟️

Claude Opus 4.7 Is Out — And It Brought a Design Tool That Spooked Adobe 🖥️

Claude Opus 4.7 is out — same price as 4.6, better at almost everything that matters. The two-month cadence holds: Opus 4.5 in November, 4.6 in February, 4.7 on April 16. On April 16, Anthropic shipped the upgrade.

What’s new:

• 13% lift on coding benchmarks — with particular gains on the hardest tasks. Users who’ve been handing off complex multi-session work report they can now do it with less supervision.

• 3.75 megapixel vision — up from 1.15 megapixels on Opus 4.6. That’s a 3x jump in visual capacity. Screenshots, diagrams, and dense documents now come through at actual fidelity, and computer use coordinates map 1:1 with real pixels.

• New xhigh effort level — a setting between “high” and “max” that gives developers finer control over the reasoning-vs-latency tradeoff. Claude Code now defaults to xhigh for all plans.

• /ultrareview in Claude Code — runs a dedicated review pass on your changes and flags what a careful human reviewer would catch before you commit.

• Task budgets (beta) — developers get finer control over how Claude allocates reasoning on longer runs.

• Cybersecurity safeguards — Opus 4.7 is the first publicly available Claude model with automated detection and blocking of prohibited cybersecurity uses. Anthropic is using this release to test guardrails before any broader Mythos-class rollout.

Pricing is unchanged: $5 input / $25 output per million tokens. Available now on claude.ai, the API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry.

One caveat worth knowing: Opus 4.7 regresses on Terminal-Bench 2.0 (69.4% vs GPT-5.4’s 75.1%) and softens slightly on BrowseComp vs 4.6. If those two workflows drive your production use, worth knowing.

The design tool story:

The model wasn't the only news. On April 14, The Information reported Anthropic is also launching an AI design tool that generates websites, landing pages, and presentations from plain-text prompts — no design skills required. Shares of Adobe, Figma, Wix, and GoDaddy closed in the red the same day. Anthropic has already partnered with Figma to convert AI-generated code into editable design files. Until now, Anthropic only built chat interfaces and developer tools. Gamma, Google Stitch, and Canva owned visual and creative workflows. That changes. As of publication, the design tool hasn’t officially dropped — watch Anthropic’s release notes this week.

Anthropic is shipping its most capable available model — at the same price as its previous one — while simultaneously building a product that replaces the starting point of design work. That’s not an incremental update. That’s a product strategy.

OpenAI Launches GPT-5.4-Cyber — The AI Cybersecurity Arms Race Is Now Official 🔐

OpenAI's answer to Project Glasswing just landed. GPT-5.4-Cyber is a fine-tuned variant of GPT-5.4 built exclusively for defensive cybersecurity — distributed only to verified security professionals through an expanded Trusted Access for Cyber (TAC) program. The announcement dropped April 14.

What GPT-5.4-Cyber can do that GPT-5.4 can’t:

• Binary reverse engineering — analyze compiled software for malware, vulnerabilities, and security flaws without source code access. This is a capability that was previously too sensitive to give broadly.

• Fewer refusals on dual-use queries — security professionals kept hitting guardrails when asking legitimate penetration testing questions. GPT-5.4-Cyber removes that friction for vetted users.

• Advanced defensive workflows — vulnerability prioritization, impact analysis, repair proposal generation.

OpenAI’s Codex Security has already contributed to 3,000+ critical and high-severity vulnerability fixes across open-source projects since launch.

How to get access:

Two paths. Individual security professionals can verify at chatgpt.com/cyber. Enterprise teams request access through an OpenAI representative. The model deploys gradually — vetted vendors first, broader rollout to come.

Why both labs are doing this at the same time:

Anthropic’s Claude Mythos found thousands of zero-day vulnerabilities autonomously — including a 17-year-old remote code execution flaw in FreeBSD — which spooked banks, governments, and enterprise security teams. Both OpenAI and Anthropic are now racing to establish the “responsible” model for deploying powerful cybersecurity AI: lock down the most dangerous capabilities, but give verified defenders a head start over attackers.

Two of the most capable AI companies in the world are simultaneously building models too dangerous to release publicly — and creating exclusive access tiers for people who need to use them anyway. That’s not safety theater. That’s a real governance problem being handled in real time.

• Source: OpenAI Blog | Axios | The Hacker News

Stanford’s 2026 AI Index: The Annual Report Card Just Dropped 📊

SWE-bench — the benchmark where AI models fix real GitHub issues — went from 60% to near 100% in a single year. Generative AI hit 53% global adoption faster than the PC or the internet. Stanford's 2026 AI Index dropped this week, and it's the most important data document in AI right now.

The headline findings:

• Humanity’s Last Exam scores jumped 30 percentage points in a single year — from 8.8% (GPT-4 era) to 38.3%. Models as of April 2026 (Claude Opus 4.6, Gemini 3.1 Pro) now top 50%.

• The US-China model performance gap has effectively closed. Anthropic leads Arena Elo (1,503), followed by xAI (1,495), Google (1,494), OpenAI (1,481), then Alibaba (1,449) and DeepSeek (1,424). Six percentage points separate first from sixth.

• $581.7 billion in global corporate AI investment in 2025 — a 130% jump from the previous year. Generative AI accounted for nearly half, growing 200%+.

• Organizational adoption reached 88%. 4 in 5 university students use generative AI for coursework. Only 6% of teachers say their school’s AI policies are actually clear.

• The “jagged frontier” problem persists. Models that solve PhD-level physics problems still struggle with clock-reading tasks. Robots succeed at only 12% of household tasks.

• Transparency is falling. The Foundation Model Transparency Index dropped from 58 to 40 points average. The most capable models disclose the least.

The context that matters for creators:

Estimated U.S. consumer surplus from generative AI reached $172 billion annually by early 2026. The median value per user tripled in a single year — while the tools remain mostly free. Marketing output productivity is up 50% in organizations that’ve integrated AI into creative workflows. The tools are here. The gap between users and non-users is widening every month.

AI didn’t plateau. It didn’t hit a wall. SWE-bench went from 60% to 100% in one year. That pace, if it continues, doesn’t give anyone a comfortable window to “wait and see.”

• Source: Stanford HAI | MIT Technology Review | IEEE Spectrum

News of the week 🌍

GPT-5.5 “Spud” April 14 Rumor Busted — New Window: April 21 to May 25 🥔 — The AI community convinced itself GPT-5.5 would drop April 14. It didn’t. The rumor came from an unverifiable “insider” post with no primary sources — OpenAI never corroborated it. What remains confirmed: pretraining wrapped March 24 at the Stargate data center in Abilene, Texas. Altman said “a few weeks.” Brockman called it “two years of research” with a “big model feel.” Polymarket still prices 78% probability of release by April 30. The new realistic window is April 21 to May 25. For context: while the community was waiting on Spud, Anthropic shipped Opus 4.7. The model race is too close to time bets around a single player.

Cerebras Targets IPO This Week — OpenAI Chip Deal Now $20B+ 💾 — Cerebras Systems, the AI chip startup that makes wafer-scale processors (56x larger than Nvidia’s H100), is targeting a Nasdaq IPO as soon as this week at a $35B+ valuation — a 60% premium over its $22B February valuation. The deal anchor: OpenAI agreed to pay Cerebras $20B+ for server chips, potentially receiving Cerebras equity in return. For context, Cerebras chips are up to 20x faster than Nvidia GPUs on AI inference workloads. This is the first serious public test of whether a non-Nvidia AI chip company can command frontier valuations.

Snap Cuts 1,000 Jobs, CEO Explicitly Cites AI 📉 — Snap laid off approximately 1,000 employees — 16% of its global workforce — with CEO Evan Spiegel stating in a company-wide memo that AI is replacing repetitive work. The cuts will save over $500 million by H2 2026. Affected staff receive four-month severance. This is one of the clearest public examples of a major consumer tech company directly attributing headcount reduction to AI capability — not to restructuring, not to market conditions. Read alongside Block’s 40% cut in February.

EU AI Act Enforcement: Four Months Away — and Europe Is Already Blinking ⚖️ — The EU AI Act's high-risk AI enforcement deadline lands August 2, 2026. This week Amnesty International and 127 civil society organizations published a joint warning: the European Commission's latest "simplification" proposals quietly weaken both the AI Act and GDPR under pressure from Big Tech lobbying. For any team selling to European clients or operating EU infrastructure, the window to audit AI tools is now under four months.

OpenAI Acquires TBPN — Its First Ever Media Company 📺 — OpenAI announced on April 8 that it acquired TBPN, the Technology Business Programming Network — a daily live tech and business show that's become a cult phenomenon in Silicon Valley. First media acquisition in OpenAI's history. Deal terms undisclosed. The move signals OpenAI is building a content distribution layer, not just a model company.

Useful tools ⚒️

⭐ Fathom 3.0 — AI meeting notetaker that now reads your screen. Version 3.0 adds fact-checking via Claude and ChatGPT Vision, so it can analyze shared documents, decks and screenshots during the call — not just transcribe audio. #1 on Product Hunt this week. Free plan available.

Figma for Agents — Connect AI agents directly to your design system. Agents can now read components, tokens, and guidelines from Figma and build UI that matches your brand without re-briefing every session. Launched this week — and lands right as Anthropic announced their own AI design tool.

Claude Code Routines — Put Claude Code tasks on autopilot with reusable smart routines. Define a workflow once — lint, test, deploy, summarize changes — and trigger it on schedule or on command. No re-prompting every session. Launched this week alongside the Opus 4.7 rollout.

Krisp Accent Converter for YouTube — Real-time accent conversion for YouTube creators: normalizes your speech so every viewer clearly understands you, regardless of accent or background noise. Removes one of the biggest drop-off reasons for non-native English creators.

Luma Agents — Creative AI agents that plan, iterate, and refine with full visual context. Unlike text-only agent loops, Luma agents see and evaluate what they’re generating — images, video sequences, compositions — and adjust without you re-prompting. Built for video and visual production workflows.

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Weekly Guides 📕

Claude Opus 4.7: Full Benchmark Breakdown and Migration Guide — 14 benchmark comparisons across every relevant task, the xhigh effort level explained, the new tokenizer’s impact on costs, and a complete migration checklist from Opus 4.6. Start here before updating any production pipelines.

Stanford 2026 AI Index: 12 Key Takeaways — The condensed version of the 400-page report. 12 data points that actually matter, with charts. The SWE-bench chart alone is worth bookmarking.

OpenAI GPT-5.4-Cyber: How to Apply for Trusted Access — OpenAI’s official page explaining the TAC program tiers, what GPT-5.4-Cyber can do vs. standard GPT-5.4, and both paths to access (individual and enterprise). If you work in security, read this now.

Intent by Augment Code: Official Docs and Getting Started — How to set up your first Space, how the Coordinator + Specialist agent model works, and when spec-driven development makes sense over a single Claude Code session.

How Claude Code Can Be Your AI Teammate — Our full deep dive on using Claude Code in production, Our foundational deep dive on using Claude Code in production. With Opus 4.7 now defaulting to high effort and adding /ultrareview, the basics matter more than ever. If you’re new to agentic coding, start here before anything else.

AI Meme of the Week 🤡

AI Tweet of the Week 🐦

(Bonus) Materials 🎁

One Man Used Claude and NotebookLM to Save His Mother's Life — Pratik Desai built an AI workflow to manage his mother's Stage 4 cancer care — catching a misdiagnosis and coordinating three critical interventions. The most concrete case study this week of what AI in healthcare actually means.

OpenAI's Vision for the AI Economy: Robot Taxes and a 4-Day Work Week — Sam Altman's 13-page policy paper published this week: a Public Wealth Fund giving every American a stake in AI companies, subsidized 32-hour workweek, taxes on automated labor. Worth reading regardless of whether you agree.

Snap's AI Layoffs: Spiegel's Full Statement — Evan Spiegel’s internal memo in full. Clear, direct, no hedging. Read alongside the Block/Dorsey story from February — two different companies, same underlying argument, very different public perception.

Cerebras IPO: What You Need to Know Before It Prices — Full breakdown of the Cerebras story: the wafer-scale chip architecture, the OpenAI $20B+ deal, the G42 concentration risk, and what the April IPO means for the non-Nvidia AI chip thesis.

If you missed our previous updates, don’t worry, here they are:

Meta Goes Closed Source, Mythos Gets Real, Gemma 4 Beats Llama | Weekly Digest