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On June 7, 2026, Peter Steinberger - the creator of OpenClaw, the open-source AI agent project that became the most-starred new repo in GitHub history - posted twelve words that broke the entire AI-coding internet:

“You shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.”

Boris Cherny, the Creator & Head of Claude Code at Anthropic, said the same thing on stage a few days before:

“I don’t prompt Claude anymore. I have loops running. They’re the ones prompting Claude and figuring out what to do. My job is to write loops.”

Source: Boris Cherny: Claude Code & the Future of Engineering | Acquired Unplugged presented by WorkOS.

Two statements. Millions of views. And a wave of confusion, because almost nobody could define what a loop actually was.

This guide was written to answer this completely. And it’s not just for engineers, but also for the founders trying to ship faster with a team of two, the investors trying to evaluate which AI-native companies are building durable advantages, and the operators trying to understand why their competitors seem to be moving at a different speed.

What you’ll find inside:

• Where loops came from and how they actually work

• The full anatomy of a loop and how to build one in under ten minutes

• The 14-step roadmap from manual prompter to loop engineer

• A practical catalog of 41 pre-built loops ready to copy-paste today

• Designing loops with Claude Fable 5, Anthropic’s newest frontier AI model built from the ground up for long-horizon agentic autonomy

• Where loops fail, what they cost, and the three debts that get worse the better they work.

The leverage point has now clearly moved. Prompting was last year. This is what’s next, and it is available for everyone right now.

What Is a Loop?

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Good morning & happy Tuesday! Today, all eyes are on FinTech giant Revolut, which is about to become Europe’s first centicorn (what the $750M-$2B secondary share sale at a $115B valuation is all about, why it matters & what to expect next + bonus deep dives into the latest financials of Revolut, Monzo, and Starling, & the list of top 100 AI & tech VCs of 2026 inside), and OpenAI that finally filed the paperwork for its blockbuster IPO (why listing after Anthropic could be critical, and why all eyes should actually be on SpaceX here + a closer look at the most dangerous IPO wave since 1999 & how OpenAI turned Codex into a Wall Street analyst inside). So let’s jump straight into the fascinating stuff 🌶️

Revolut’s $115 billion secondary share sale mints Europe’s first centicorn 🇪🇺🦄

Following the money 💸 FinTech giant Revolut is about to become Europe’s first centicorn, and Klarna shareholders might want to look away.

The London-based fintech is preparing a secondary share sale at a valuation of up to $115 billion, a 53% leap from the $75 billion it commanded just in November 2025. Investor demand is already strong enough to push the deal well past its initial $750 million target. Wild, but not too surprising… 😳

On the surface, the numbers tell a clean & impressive growth story: $6 billion in revenue, $2.3 billion in pre-tax profit, and a trajectory that would make Revolut the most valuable private fintech on the planet.

But underneath the centicorn milestone sits a messier picture involving a co-founder’s sudden departure, a high-stakes U.S. bank charter application, and the uncomfortable reality that the last European fintech IPO of this magnitude ended in a bloodbath.

Let’s take a closer look at this, understand why it matters, and what to watch next.

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A check from these people doesn’t just fund a startup. It tells every other investor in the room to pay attention.

They were the early money behind Anthropic, OpenAI, SpaceX, Stripe, and Revolut - often years before the rest of the world caught on. Vinod Khosla wrote a $50 million check to OpenAI in 2019, when the company was still a nonprofit research lab. That stake alone is now worth an estimated $8 billion.

He’s not the only one on this list who saw the future early: eight of these investors backed OpenAI, eleven backed Stripe, eight backed SpaceX, and nine backed Databricks - in most cases well before the consensus formed.

So who are the 100 investors with that kind of power in 2026, and how do you actually get in front of them?

We started with the Forbes Midas List, the established benchmark for ranking the world’s top venture capitalists. Then we went further: broader coverage, deeper profiles, their most recent bets, and the one thing Forbes never hands you - direct, verified contact details.

Every profile is current, fact-checked, and built for action. You’ll find each investor’s firm, location, and the bets that built their reputation - from Gili Raanan’s early conviction in Wiz to Neil Shen’s prescient backing of ByteDance. You’ll see what they’re deploying capital into right now, so you can match your raise to the right partner. And you’ll get verified LinkedIn and X handles so you can reach out directly instead of waiting for the warm intro that may never come.

Inside, you’ll find six Sequoia Capital partners, the Founders Fund duo behind SpaceX and Anduril, three partners each from Andreessen Horowitz and Accel, the Index Ventures team that backed Revolut and Figma from London, and the DST Global and Coatue dealmakers who write the growth-stage checks that turn startups into decacorns. The list also covers Benchmark, Lightspeed Venture Partners, General Catalyst, Kleiner Perkins, Battery Ventures, Craft Ventures, and Cyberstarts, among others. If they’re writing checks that matter in AI and tech right now, they’re here.

No outdated directories. No dead links. Just the people who move markets, and a direct line to each of them.

And to make the founder journey even easier, we’re also sharing three resources that brilliantly pair with this list: The Ultimate List of Top Seed Investors for founders raising their first round, How I Built an AI Operating System to Run a Startup with Claude for those building with AI-first mindset, and Anthropic That Just Told AI Founders Exactly What to Build in 2026, which is a full founder playbook for 2026.

Your next investor is somewhere on this list. Let’s go find them:

The World's Best Venture Capital Investors In 2026

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If you’re not a subscriber, here’s what you missed this week:

1. Everything Anthropic Shipped in 2026: Every Claude Model, Agent, & Tool - And How to Actually Use Them 🤖 [29 Claude launches in 5 months. Most users missed half of them. Here’s every model, agent, and tool - with the playbook for each one]

2. The Complete Guide to Claude Dynamic Workflows 🤖 [From first principles to production: every workflow pattern, cost trap, and real-world use cases founders, builders, and investors need to master Anthropic’s most powerful Claude Code feature yet]

3. Claude Opus 4.8: The Complete Prompting Playbook for 2026 🧠 [How to get the best results from Anthropic’s most powerful model every single time, including the new Dynamic Workflows feature]

4. The Claude Finance Playbook: How CFOs Use AI to Build Models, Forecast Cash, and Close Books Faster 📊 [A practical guide to setting up Claude for real finance work, with battle-tested prompts optimized for Claude Opus 4.7]

5. Ex-OpenAI’s Aschenbrenner Bet $5.5B on AI Chips. Now He’s Betting $8.5B Against Them 💸 [Leopold Aschenbrenner’s AI hedge fund Situational Awareness went from pure AI long to 62% short semiconductors in one quarter. Here’s what changed and why the thesis still holds]

6. Robinhood’s Q1 2026: the best brokerage, the wrong price 🤷‍♂️💸 [breaking down the most important facts & figures from Robinhood’s Q1 2026 to see whether Robinhood is worth your time & money + bonus deep dive into the latest financials of its bigget competitor Coinbase, and why Robinhood’s Agentic AI play is exactly what Anthropic recently told AI founders to build in 2026]

7. OpenAI’s Codex just became a Wall Street analyst, and the moat belongs to FactSet & Moody’s 🤖📊 [what OpenAI exactly shipped and why it matters, how this hands the durable moat to companies most people aren’t even watching, and the 4 shifts we’d bet on over the next 2 years + bonus deep dives into OpenAI’s Super App strategy & how Anthropic just told AI founders exactly what to build in 2026 inside]

8. Anthropic has lapped OpenAI just in time to lead the most dangerous IPO wave since 1999 😳🔔 [why it matters & why it’s critical that the Claude maker IPOes before OpenAI & why you should in fact watch SpaceX the closes here + bonus dive into everything Anthropic shipped in 2026 so far & how to actually use it]

9. Coatue’s May 2026 Report: The $12 Trillion AI Bet and Who Ends Up on the Wrong Side 📊 [Coatue’s latest public markets report reveals the most extreme winner/loser split in tech stock history, a $12 trillion AI capex wave, and one brutal framework that predicts exactly who wins]

10. Anthropic Just Told AI Founders Exactly What to Build in 2026 🦄 [1 million conversations. 9 consumer AI domains. A full founder playbook - plus where Anthropic’s own products will and won’t compete]

As for today, here are the 3 incredible FinTech stories that are transforming the world of financial technology as we know it. This was yet another solid week in the financial technology space, so make sure to check all the above stories.

Ramp just gave accountants Free AI to get at their clients 🤖📊

The news 🗞️ FinTech giant Ramp just launched Ramp Stack, and the most telling fact here is the price. It’s free through August, and that should tell you the software was never the point.

Let’s take a closer look at this.

More on this 👉 On June 3, Ramp launched Stack, an AI system aimed not at the in-house finance teams it has served for six years but at the accounting firms that serve everyone else.

Agents Ramp calls Coworkers run the mechanical work of the monthly close across a firm’s whole book of clients: reconciliations, journal entries, transaction coding, variance analysis. Firms describe a client’s process in plain English, Stack encodes it as reusable Skills, and nothing posts to QuickBooks without human sign-off.

Design partners report closes 50% to 60% faster and reconciliations up to 9x faster, with one firm’s March close dropping to about 20 minutes. Not too shabby! On an independent benchmark of 200-plus accounting tasks graded by working accountants, Stack hit a 65.8% success rate and beat every general-purpose model tested. Nice!

Zoom out 🔎 The accounting work is real, but it isn’t where Ramp makes money. The asset is the data. Ramp already runs spend, accounts payable, and banking data for tens of thousands of businesses, so its agents work on the live numbers instead of reaching through connectors the way Pilot, Zeni, or Intuit’s own QuickBooks agents have to.

The firms are thus the channel. More than 4,500 already use Ramp, 92 of the top 100 CPA firms have clients on the platform, and firms decide which financial stack their clients adopt. Make a firm faster and more dependent on Ramp’s rails, and you pull its downstream clients onto the cards and bill-pay where Ramp’s revenue actually lives. Win-win. Thus, free through August is the giveaway, because the monetization sits downstream.

We also have to talk about the timing here, because it explains the rest.

Ramp is reportedly raising about $750M at a $40B-plus valuation, near 40 times revenue, with IPO-readiness targeted for late 2026. A multiple like that needs a bigger market than corporate-card spend, and the $150B accounting industry is one Ramp had never sold a product into. The staffing crisis also hands it a willing audience: more than 300,000 accountants have left the field and degrees sit at a 20-year low, so firms are turning away clients they can’t staff.

THE TAKEAWAY ✈️

What’s next? 🤔 Looking ahead, the collision to watch here is Intuit. QuickBooks is both the launch integration and a competitor building its own agents, which makes them partners today and rivals tomorrow. The squeeze lands hardest on pure-play AI accounting startups like Basis, fresh off a $100M raise at $1.15B, who own no source data and must knock on doors Ramp walked through years ago. The second-order effect most will miss is what Stack does to private equity’s accounting roll-up: it offers firms the centralized delivery of a consolidator without selling, which could cool a wave that has absorbed roughly $50B in six years. That said, I’d watch two things - whether free converts to paid, and whether the native-data edge survives once Xero and NetSuite arrive.

ICYMI:

Anthropic made the EU come asking for Claude Mythos 😳🇪🇺

The news 🗞️ The bloc that writes the rulebook for American tech just flew to San Francisco to ask a private company for a favor. And it’s not a small one…

Let’s unpack this.

More on this 👉 Anthropic told the European Commission over the weekend that it would let ENISA, the bloc’s cybersecurity agency, into Project Glasswing - the restricted program where a handful of organizations test Mythos before any broad release. ENISA confirms the offer but says conditions aren’t settled.

The move makes the EU the first entrant outside the US and UK, joining a club that until now meant Microsoft, Apple, JPMorgan, CrowdStrike, several US government departments, and the UK’s AI Security Institute.

Anthropic describes Mythos, previewed in April, as unusually skilled at uncovering network weaknesses and able to inflict real harm, which is why it has been rationed.

Zoom out 🔎 Here’s what most will skip: the White House has already rejected some of Anthropic’s plans to widen Glasswing on security grounds. A private company’s customer list now runs partly through Washington. Dario Amodei has been explicit, wanting allies to use Mythos to defend Ukraine and Taiwan while refusing to see it “turned on our own people.”

In other words, deploying a commercial AI product has become a quasi-diplomatic act, with the US government holding a veto over who gets in. We’ve not seen this before.

On top of that, the terms also expose the deeper shift. One unsettled condition is how much access Anthropic itself would gain to the EU’s systems while ENISA runs the tool. Using the model may mean letting the vendor inside your critical infrastructure. For a sovereignty-obsessed Brussels, that’s the real price and the real precedent: top-tier AI deployed as a two-way relationship, not a one-way sale.

THE TAKEAWAY ✈️

What’s next? 🤔 First and foremost, watch the calendar. Anthropic says safeguarded Mythos-class models reach all customers within weeks, which means this access fight is about a head start and the raw, unguarded version, not the capability itself. Frontier AI go-to-market is now splitting in two: a guard-railed product anyone can buy, and a rationed offensive-grade tier handed out by geopolitical alignment. That said, Anthropic wins the trusted-vendor slot and a government moat; ship-to-everyone economics lose, and so does the EU’s sovereignty story. Whether state-gating holds or the wide release exposes it as launch theater could go either way. The ultimate tell here will thus be how much weaker the public version turns out to be.

ICYMI: Anthropic has lapped OpenAI just in time to lead the most dangerous IPO wave since 1999 😳🔔 [why it matters & why it’s critical that the Claude maker IPOes before OpenAI & why you should in fact watch SpaceX the closes here + bonus dive into everything Anthropic shipped in 2026 so far & how to actually use it]

Stripe, Visa, Mastercard, & Coinbase could launch a stablecoin that strands Circle’s USDC 😳🪙

The BIG News 🔥 Nearly a quarter of Coinbase’s revenue flows from a single relationship: the USDC deal it struck with Circle. Which is why this week’s leak cuts so deep. Coinbase may now reportedly be helping Stripe, Visa, and Mastercard to build the stablecoin meant to replace USDC.

Circle’s recently released Q1 earnings show why the timing is brutal for Circle, we now know why Coinbase can afford the gamble, and why a token nobody has confirmed is already the most valuable threat in payments.

Let’s dive deeper into this, understand why it matters, and see what to expect next.

More on this 👉 On June 3, CoinDesk reported, citing anonymous sources and echoed by The Information, that Stripe, Visa, Mastercard, and potentially Coinbase are all in advanced stages of building a stablecoin platform to challenge Circle’s USDC and Tether’s USDT.

There is no official announcement and no confirmed token or reserve details; every company declined to comment.

The reporting still moved markets. Both COIN and CRCL dipped. And that reaction is the story - a platform nobody has confirmed already repriced the incumbent it threatens.

The build sits on real groundwork: Stripe’s $1.1 billion Bridge acquisition, Mastercard’s BVNK buy, Visa’s multi-chain settlement pilots running at a $7 billion run-rate, and Coinbase’s white-label stablecoin work.

Combined, these four reach into both traditional payments and crypto on-ramps that no current issuer touches at once. In a roughly $325 billion stablecoin market where USDT leads on raw circulation, and USDC sits as a fast-growing second, distribution is the whole game.

Circle’s own recently released earnings explain why distribution is the only game that matters for it. USDC circulation hit $77 billion in the first quarter, up 28%, with on-chain volume of $21.5 trillion. Thus, the adoption is real; the income beneath it is not durable. Importantly, some 94% of Circle’s revenue comes from interest on its reserves, a rate the Fed sets and Circle cannot touch, and revenue already fell 10% from the prior quarter on a 66 basis point rate decline. Which means Circle is fighting on two fronts at once. One is rates, where it has no lever. The other is distribution, which is exactly where the consortium aims.

ICYMI: Circle is building a monopoly in plain sight, but the toll is set by the Fed 🤷‍♂️🏦 [breaking down Circle’s latest Q1 2026 financials, understanding the real economics beneath the headline growth, & figuring out whether Circle deserves a spot in your portfolio + bonus dive into Circle’s Agentic AI bet, which is a clear escape plan]

Zoom out 🔎 Looking at the bigger picture, we must remember that USDC’s edge over Tether was never the technology; it was that Visa, Mastercard, Stripe, and Coinbase carried it. The same firms now reportedly building a competitor are the ones that gave USDC that edge. Take those defaults away while reserve income compresses underneath, and Circle competes on rails it no longer controls with a shrinking war chest, a much weaker business than the one CRCL carried at its roughly $22 billion valuation.

Coinbase is clearly the hinge, and both companies’ numbers make the stakes concrete. The USDC arrangement, which pays Coinbase most of the reserve income on USDC held on its platform, supplies 23% of Coinbase’s total revenue, one of its most profitable lines. Yet Circle’s results show the relationship is already strained: management flagged Coinbase’s growing share of USDC circulation as a drag on margin, and Circle’s structural answer, pushing USDC on-platform where it owes Coinbase nothing, grew 3.5x to $13.7 billion. Of course, the leak did not create this fight. It just armed one side of a fight already sitting in the financials.

ICYMI: Coinbase’s Q1 2026: the House always wins, until the Casino empties 🤷‍♂️🎰 [deep dive into Coinbase’s Q1 2026, breaking down the key financial facts & figures, what they mean, what’s next & whether it’s worth your time and money now + bonus deep dive into the latest financials of its close partner Circle inside]

And that is exactly why the contract renewal matters first. Coinbase’s revenue share is reportedly up for renewal around August 2026, even as it is built to auto-renew, and a credible threat to co-own a rival token hands Coinbase a stronger position than it held a week ago. Circle either pays more to keep its most important distributor or watches that distributor steer volume toward a coin it profits from directly. Both paths move value from Circle to Coinbase before a single consortium token mints.

Of course, it could still break Circle’s way. USDC’s network effects run years deep across exchanges, wallets, and settlement, and distribution is necessary but not sufficient; users need a reason to switch, which forces the consortium to give away yield or fees to pull them. On top of that, four competing owners sharing one currency is hard governance, and the Centre Consortium strained with only Circle and Coinbase inside it. More importantly, antitrust review of Visa, Mastercard, Stripe, and Coinbase pooling control of a dollar instrument could stall the project for years or force open-access terms that dull its advantage.

On, and Circle still holds cards too: a pending banking license could grant powers no four-company committee gets quickly, and its cleanest pitch becomes the neutral token not co-owned by the card networks merchants already resent on fees.

THE TAKEAWAY ✈️

What’s next? 🤔 First and foremost, we must note that the frontier everyone points to is already occupied. A consortium token would own AI-agent payments, the machine-to-machine settlement market that may one day dwarf human transactions. But we must remember it would not be entering an empty field. USDC already captures 99.8% of x402 agentic payments, and Circle has launched Agent Stack to serve that demand while routing around Coinbase entirely. But here is the asymmetry the leak’s framing hides: Coinbase’s Base chain already carries roughly 90% of on-chain agentic transactions, and that exposure is token-agnostic. If a consortium coin launches on Base, Coinbase collects on agentic volume whether the winning token is USDC or the new one. Circle only wins if USDC stays the token. Thus, Coinbase is hedged at the chain layer; Circle is betting entirely on the token layer. That asymmetry is why Coinbase can credibly menace a relationship worth 23% of its revenue, and why Circle cannot answer in kind. Then there’s the prize, which itself is apparently shrinking. Remember that every stablecoin reserve model earns the same thing, interest on Treasuries, and that income is falling for all of them at once. A 67 basis point rate move already cost Coinbase $57.5M in stablecoin revenue and took 10% off Circle’s top line in one quarter. A consortium token launched into an easing cycle thus inherits the very compression Circle is living through now. So 4 of the most powerful names in payments are now all racing to own a toll road whose toll is set by the Fed, and the toll is dropping 🤷‍♂️ Distribution decides who collects it; rates decide how much there is to collect. That said, watch three things. Whether real reserve and chain details surface or this stays a sourced rumor with no token. What the Coinbase-Circle terms look like when the renewal opens. And whether anyone, consortium or Circle, can pry agentic settlement loose from a token that already owns 99.8% of it. The product may never match the threat. But on this week’s evidence, the threat is already enough.

ICYMI:

🧠 What else I’m watching

• Monzo Mobile Launch 📶 Monzo is entering the mobile market with a SIM-only plan on the Virgin Media O2 network, offering 5G, 99% UK coverage, and unlimited calls and texts. Managed via its app, the service features rolling monthly plans at £8, £12, and £20, with a loyalty discount of 5% annually, capping at 30%. This move intensifies competition among fintechs like Revolut and Klarna, which already offer mobile plans, signaling a trend of digital banks expanding into telecom to enhance customer retention and value. ICYMI: Monzo’s 2026 financials: the £1.7 billion revenue machine that still has to prove it can escape the UK’s Orbit 🏦🇬🇧 [breaking down the unit economics that matter, the Revolut comparison investors keep getting wrong, the governance drama, IPO chances & what’s next for Monzo + bonus deep dive into Revolut’s latest financials & their foundational AI model for money inside]

• OpenPayd Nasdaq SPAC 🚀 OpenPayd has agreed to merge with Titan Acquisition Corp, valuing the fintech at $1.145 billion and raising up to $276M for its Nasdaq debut. Its platform enables businesses to move and manage money across fiat, blockchain, and stablecoin networks via a single API, serving 1,100+ customers in 180 countries and processing $240 billion annually. The IPO is said to accelerate investment in technology and compliance, positioning OpenPayd as the infrastructure for autonomous financial agents, bridging traditional and programmable finance. ICYMI: Anthropic has lapped OpenAI just in time to lead the most dangerous IPO wave since 1999 😳🔔 [why it matters & why it’s critical that the Claude maker IPOes before OpenAI & why you should in fact watch SpaceX the closes here + bonus dive into everything Anthropic shipped in 2026 so far & how to actually use it]

• MoneyGram’s MGUSD 💵 MoneyGram has launched MGUSD, a US dollar stablecoin on the Stellar blockchain, issued by Stripe-owned Bridge. Integrated into the MoneyGram app via a self-custodial wallet, MGUSD offers users a stable, dollar-denominated balance, enabling instant, low-cost cross-border transfers. Initially available in the US, the stablecoin will scale globally to MoneyGram’s 60 million customers, providing a reliable digital currency alternative in markets with inflation or financial instability. MGUSD leverages M0’s smart contracts and Fireblocks wallets, re-architecting MoneyGram’s core to support seamless stablecoin transactions. ICYMI:

💸 Following the Money

• Bulgaria-based payments platform Paypercut has raised EUR 5M in a seed round co-led by Concentric, Passion Capital, and Araya Ventures.

• TrueLayer, Europe’s leading Pay by Bank network, announced the acquisition of In3, a Dutch fintech specialising in consumer credit via bank payments.

• Hyperliquid-based prop trading platform Hypernova raises $3M in pre-seed funding.

👋 That’s it for today! Thank you for reading, and have a relaxing Sunday! And if you enjoyed this newsletter, invite your friends and colleagues to sign up:

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On May 28, 2026, Anthropic shipped arguably the most important upgrade to Claude Code since its launch.

It was not a new model, and not a faster API. It was something more fundamental: the ability for Claude to write its own orchestration program on the fly, spin up a coordinated fleet of AI agents, and hand back a single verified result - all from one prompt.

The feature is called Dynamic Workflows. And most early adopters are using it wrong.

Some burned through millions of tokens before learning the basics. Others dismissed it as a tool for large-scale code migrations and missed the fact that it works equally well for hiring decisions, investor due diligence, content pipelines, security audits, and business plan stress-testing. A handful of teams have already used it to compress months of engineering work into days - most visibly, a 750,000-line codebase rewrite completed in just 11 days 😳

Until now, understanding the full picture meant piecing together fragments across Anthropic’s docs, the original blog post by the feature’s author, community war stories on Reddit and Hacker News, expert practitioner breakdowns, and insights shared by the Claude Code team internally.

This is all of that synthesized into one guide - covering not just what Dynamic Workflows are, but why they were built, how the orchestration works under the hood, which of the six patterns to use for which task, how to avoid the token-cost traps that have blindsided entire teams, and what risks Anthropic has not put front-and-center in the docs.

Whether you write code or not, whether you are a solo founder or leading a 200-person engineering team, whether you are evaluating AI tooling for investment or trying to 10x your own output - this guide will tell you exactly what Dynamic Workflows can do for you, and exactly when they will burn you if you are not careful.

What Are Dynamic Workflows?

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Good morning & happy Thursday! Today’s issue is incredibly interesting as we’re diving deep into Stripe, Visa, Mastercard, and Coinbase, which are launching a stablecoin that could dethrone Circle’s USDC (what it’s all about, why it could be huge, why the timing is brutal for Circle & why Coinbase can afford this gamble + bonus deep dives into the latest financials of Circle & Coinbase, and the ultimate list of resources on stables inside), and fintech giant Ramp that just gave accountants Free AI to get at their clients (what Ramp Stack is all about & why it’s a brilliant play + bonus playbook on how Ramp is building AI-powered engineering teams and products & The Claude Finance Playbook inside). So let’s jump straight into the paradigm-defining stuff 🌶️

Stripe, Visa, Mastercard, & Coinbase could launch a stablecoin that strands Circle’s USDC 😳🪙

The BIG News 🔥 Nearly a quarter of Coinbase’s revenue flows from a single relationship: the USDC deal it struck with Circle . Which is why this week's leak cuts so deep. Coinbase may now reportedly be helping Stripe, Visa, and Mastercard to build the stablecoin meant to replace USDC.

Circle’s recently released Q1 earnings show why the timing is brutal for Circle, we now know why Coinbase can afford the gamble, and why a token nobody has confirmed is already the most valuable threat in payments.

Let’s dive deeper into this, understand why it matters, and see what to expect next.

Read more

Good morning & happy Wednesday! Today we’re diving deep into OpenAI’s Codex that just became a Wall Street analyst (what OpenAI exactly shipped and why it matters, how this hands the durable moat to companies most people aren’t even watching, and the 4 shifts we’d bet on over the next 2 years + bonus deep dives into OpenAI’s Super App strategy & how Anthropic just told AI founders exactly what to build in 2026 inside), and $1 trillion AI giant Anthropic, which just confidentially filed for an IPO (why it matters & why it’s critical that the Claude maker IPOes before OpenAI & why you should in fact watch SpaceX the closes here + bonus dive into everything Anthropic shipped in 2026 so far & how to actually use it). So let’s jump straight into the finnovative stuff 🌶️

OpenAI’s Codex just became a Wall Street analyst, and the moat belongs to FactSet & Moody’s 🤖📊

The BIG News 🔥 On June 2, OpenAI did something stranger than ship a new model. It aimed Codex, the tool it built to write software, at Wall Street, releasing finance plugins that turn the agent into a public equity analyst & an investment banker on demand 🤖

At first, it looks like OpenAI is finally catching Anthropic in finance. But when you think deeper, two questions almost no one is asking come up. Why is a coding tool, of all things, the product now doing banking? And while everyone argues over which lab has the better model, who’s quietly positioned to capture most of the value this unlocks? 🤔

Let’s take a closer look and answer both, and the second answer reorders who actually wins this race. We cover exactly what OpenAI shipped and why it matters, the insight buried in its own product history that explains the whole pivot, the move that hands the durable moat to companies most people aren’t even watching, and the four shifts we’d bet on over the next two years. If you allocate capital, sell into finance, or build in this space, this is crucial to understand before your competitors do.

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Good morning & happy Tuesday! Today we’re diving deep into Robinhood’s latest financials to see why it’s the best brokerage at the wrong price (breaking down the most important facts & figures from Robinhood’s Q1 2026 to see whether Robinhood is worth your time & money + bonus deep dive into the latest financials of its bigget competitor Coinbase, and why Robinhood’s Agentic AI play is exactly what Anthropic recently told AI founders to build in 2026), and Anthropic, which just made the EU come asking for Claude Mythos (what it tells us, what it matters & what’s next for AI deployment + bonus deep dive into Everything Anthropic Shipped in 2026 & How to Actually Use It). So let’s jump straight into the interesting stuff 🌶️

Robinhood’s Q1 2026: the best brokerage, the wrong price 🤷‍♂️💸

Earnings time 🤑 Robinhood is the best-run brokerage of its generation, and the first quarter proved that quite well.

Platform assets reached $307B, up 39% year over year. Trailing-twelve-month net deposits hit $67.8B, and they keep flowing straight through every market drawdown. Gold subscribers climbed 36% to a record 4.3M. Revenue crossed a billion dollars in a single quarter, on what Morningstar calls the richest unit economics of any broker its size. Let that sink in.

And yet the most important number in the quarter wasn’t the 15% revenue growth or the 39% jump in platform assets. It was 92% - the share of revenue that is cyclical trading flow or rate-sensitive spread, the part that thins the moment crypto cools and the Fed starts cutting. You didn’t have to wait to watch it happen. Sequentially, revenue fell 17%, and net income fell 43%, the instant both did 🤕

The tension between those two stories, a franchise that compounds through anything and a P&L that buckles on a dime, is exactly what makes Robinhood arguably the most interesting risk/reward profile in fintech right now. The market pays north of 40 times earnings, the multiple of a secular compounder, for a business that just behaved like the high-beta cyclical it has always been.

Let’s dig in deeper into Robinhood’s Q1 2026, unpack the figures that actually matter, and see whether Robinhood is worth your time & money in the years to come.

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Anthropic shipped a major Claude release roughly every two weeks since January 1, 2026. 29 distinct product updates in a matter of five months 😳

New models, new agents, new desktop apps, new certification programs, multibillion-dollar funding rounds, and a restricted-access AI that cybersecurity firms say rewrites the threat landscape.

Between January and May, Claude went from a chatbot with a text box to an execution platform that writes code across parallel repos, runs autonomously for days on a single /goal command, manages payroll through QuickBooks, and identifies zero-day vulnerabilities hidden for over a decade. Anthropic’s run-rate revenue went from $14 billion to $47 billion in the same period 🤯

That said, the gap between what Claude can do today and what most people are asking it to do is the single largest source of wasted capability in enterprise software.

If you’ve ever been wondering whether you’re getting the most out of Claude right now, the answer is almost certainly NO.

That’s why below is every launch in chronological order, with benchmarks, business context, and a practical “how to get the most out of it” section for each product & feature.

Read it end to end or skip to the product you care about the most. Either way, you’ll leave knowing exactly what changed and what to do differently.

The Scale of the Blitz: 2026 by the Numbers

Before the timeline, let’s look at the context that makes every launch make sense.

The revenue trajectory alone tells an insane story: $1 billion ARR in December 2024, $9 billion by end of 2025, $14 billion in February 2026, $30 billion by April, $47 billion by late May.

No company in the history of B2B software has scaled this fast. Anthropic is basically converting safety-lab credibility into execution-platform dominance at a pace that collapses entire product cycles into weeks.

The Four Modes: What Claude Actually Is Now

Most people still think of Claude as a chatbot. That framing is three-quarters wrong. Claude now operates in four distinct modes, and the mode you choose determines what’s possible.

The one-line rule: Chat for quick questions. Cowork for real work on your files. Code for development. Projects for recurring work with consistent context.

January 2026: The Year Begins With a Structural Shift

January 12 - Anthropic Labs + Claude Cowork Research Preview

On January 11, Anthropic launched Anthropic Labs, an experimental product arm co-led by Instagram co-founder Mike Krieger, built to move fast on consumer product ideas that wouldn’t fit the core research roadmap. The next day, Claude Cowork shipped as a research preview.

Cowork changed the narrative. Described internally as “Claude Code for the rest of your work,” it lets any knowledge worker give Claude a folder on their computer and assign it multi-step tasks. Claude reads, creates, and edits files autonomously, running in a sandboxed virtual machine. No terminal required. No coding background needed.

Launch use cases: converting receipt screenshots into expense spreadsheets, reorganizing downloads folders, drafting reports from scattered notes across a folder.

→ How to get the most out of Cowork: Start with a sandboxed test folder, not your main drive. Give Claude a deliverable (”Create a Q2 summary report from every PDF in this folder, output as .docx”) rather than a list of steps - it plans better when given outcomes. Use the /schedule command to set up recurring tasks that run automatically. Build a SKILL.md file in your working folder with your preferred output formats, brand voice, and standard templates - Cowork reads it every time.

January 26 - Interactive Apps (Connectors) Launch

Claude users gained the ability to call up interactive, logged-in apps inside the chat interface. Launch lineup: Slack, Canva, Figma, Box, and Clay, with Salesforce listed as coming soon - all built on MCP. Available to Pro, Max, Team, and Enterprise subscribers.

This was the architectural moment when Claude became a hub - not a tool running alongside your stack, but a layer that reaches into it and acts on your behalf.

→ How to get the most out of Connectors: Use the Clay connector for prospect research directly in Claude: “Pull the last three funding rounds for every company in my Clay table and summarize the key investor signals.” Use the Slack connector for async workflows: “Check all messages in #product-feedback from this week and draft a categorized summary for the engineering team.”

February 2026: Ten Updates in 28 Days

February was the month Anthropic stopped announcing products and started shipping infrastructure.

February 4 - The Ad-Free Commitment (and the Super Bowl Shot at OpenAI)

Anthropic declared Claude would remain ad-free while OpenAI announced it was testing ads in the ChatGPT free tier. The company ran a Super Bowl campaign with the tagline “Ads are coming to AI. But not to Claude.”

The business logic, per the announcement: “Our business model is clear: we earn revenue through enterprise agreements and paid subscriptions, which we reinvest to enhance Claude for our users.” This was both a genuine product decision and a competitive positioning move - signaling to enterprises that their conversations with Claude will never be mined for advertising targeting.

February 5 - Claude Opus 4.6: The Model That Redefined “Capable”

Claude Opus 4.6 launched with four headline capabilities:

• A 1 million token context window - entire codebases, lengthy contracts, or months of documentation held in working memory simultaneously

• Agent Teams (research preview) - multiple agents splitting larger tasks in parallel, each owning its piece

• 78.3% on MRCR v2 at 1M tokens - highest score among frontier models at that context length

• 14.5-hour task completion window - the longest continuous work window of any frontier model at launch

• Native PowerPoint integration as a side panel - Opus 4.6 could operate inside PowerPoint, not just output files to be imported

• Pricing: $5/$25 per million input/output tokens; 128K max output tokens

→ How to get the most out of Opus 4.6 / Agent Teams: Agent Teams performs best with well-defined task boundaries - describe each sub-task’s scope and expected output clearly upfront. Use the 1M context window for tasks that previously required chunking (long contract review, full codebase analysis, multi-document synthesis) rather than as a substitute for good prompt structure.

February 17 - Claude Sonnet 4.6: The Default Just Upgraded

Claude Sonnet 4.6 launched twelve days after Opus 4.6 and became the immediate default model for all Free and Pro users.

Key benchmark results:

• 60.4% on ARC-AGI-2 - skills specific to human intelligence

• 79.6% on SWE-Bench Verified - software engineering tasks

• 89.9% on GPQA Diamond - graduate-level scientific reasoning

• 30–50% faster than Sonnet 4.5; users preferred it over Sonnet 4.5 in 70% of head-to-heads

• 1M token context window in beta

• Pricing: $3/$15 per million input/output tokens

The most telling data point isn’t a benchmark: users chose Sonnet 4.6 over Opus 4.5 in 59% of head-to-head comparisons. The mid-tier model had crossed the quality threshold for the majority of office work, and anyone still defaulting to Opus for routine tasks was burning unnecessary tokens.

→ How to get the most out of Sonnet 4.6: Use Sonnet 4.6 as your default for writing, analysis, and Q&A. Reserve Opus for tasks that explicitly require extended reasoning, large context processing, or multi-agent coordination. The practical workflow many power users have landed on: plan and design with Opus, execute with Sonnet.

February: Cowork Gets Scheduled Tasks, 12 New Connectors, and Department-Specific Plugins

Alongside the model releases, February brought a wave of Cowork expansions:

• Scheduled Tasks - recurring workflows that run automatically on a schedule (use /schedule in any task)

• 12 new MCP connectors - expanded ecosystem for connecting external services

• 10 department-specific plugins - investment banking, HR, private equity, wealth management, design

• Private Plugin Marketplaces for enterprise deployments

• Direct PowerPoint and Excel export - spreadsheet operations and presentations as first-class Cowork outputs

• Cross-app context sharing between Excel and PowerPoint

March 2026: The Platform Matures

March 2 - Memory: Free for Everyone

Claude’s memory feature expanded to the free tier. All users now get context-aware conversations that build on previous sessions. What shipped alongside the expansion:

• A memory import tool - transfer chat history and preferences from ChatGPT, Gemini, or any AI via a pre-written extraction prompt

• Project-level memory controls - keep personal and work contexts separate

Every Claude conversation now has a longer tail. Free users stopped experiencing Claude as a stateless tool and started experiencing it as an ongoing working relationship.

→ How to get the most out of Memory: Be deliberate about what you tell Claude to remember. Open a dedicated session and say: “Remember: I’m a [role], I write for [audience], my preferred output format is [format], I work in [industry]. Always apply these defaults.” Use project-level memory isolation to prevent work context from bleeding into personal conversations. Periodically review and prune memories via the memory management panel so outdated context doesn’t degrade output quality.

March 3 - Voice Mode Rolls Out in Claude Code

Voice mode launched in Claude Code, rolling out initially to ~5% of users. How it works: type /voice to toggle, then hold spacebar to speak. The transcript appears inline at the cursor position - you can type part of a prompt, voice the middle, and continue typing without losing context. Voice transcription tokens don’t count toward rate limits.

→ How to get the most out of Voice in Claude Code: Use voice for the parts of coding where typing slows you down: describing system architecture (”Start a new session. Build a FastAPI backend for a multi-tenant SaaS app with JWT auth and a PostgreSQL ORM. Scaffold the full folder structure first.”), thinking through logic, or debugging out loud while Claude responds. Pair with push-to-talk to maintain control over when Claude hears input.

March 11 - Inline Visualizations: Claude Draws What It Knows

Claude gained the ability to generate interactive charts, diagrams, and visualizations inline within conversations, automatically or on request.

Key distinctions from Artifacts:

• Inline - appear within the conversation, not in a permanent side panel

• Built in HTML and SVG, not image generation

• Dynamic - interactive elements (clickable periodic table, adjustable compound interest curves)

• Data-aware - can incorporate live web data (weather, prices) when search is enabled

Available on all plan types, including free. Excel and PowerPoint integrations with reusable Skills for charts and spreadsheets also shipped the same day.

→ How to get the most out of Visualizations: Ask Claude to explain quantitative concepts and add “show me this as an interactive chart” - the output usually includes adjustable parameters. For data analysis, paste raw data and ask for a visualization first before asking for interpretation - the visual often surfaces patterns that change which questions you ask. Enable web search for live-data visualizations (real-time pricing charts, weather patterns).

March 11 - Claude Partner Network + $100M Commitment

Anthropic launched the Claude Partner Network with an initial $100 million commitment. Partners get training, technical support, joint market development funding, and access to the Claude Certified Architect (Foundations) certification - a technical exam for solution architects building production applications with Claude. The exam covers five domains: agentic architecture (27%), MCP tool design, Claude Code configuration, prompt engineering, and context management.

→ How to get the most out of the Certification: The Claude Certified Architect exam is available through Anthropic’s Skilljar platform. Five official preparation courses are published through Anthropic Academy and Coursera. Agentic architecture is the highest-weighted domain - prioritize it.

March 13 - 1M Context Window Goes GA at Standard Pricing

Requests over 200K tokens no longer carry a premium. A 900K-token request costs the same per-token rate as a 9K-token request. Also: 600 images or PDF pages per request - up 6× from 100.

One documented case showed that raising context from 200K to 500K actually reduced total token usage because the model spent less time re-reading earlier information, a counterintuitive efficiency gain that only surfaces at scale.

→ How to get the most out of 1M Context: Full codebase analysis, end-to-end contract review, and entire book-length document summarization are now single-session tasks. For the highest-value use: load entire project folders, not just individual files. Cross-file dependency tracking, global refactoring, and full-document synthesis that chunked approaches can’t match all become possible in a single pass.

April 2026: The Agentic Shift Goes Enterprise

April 7 - Claude Mythos Preview + Project Glasswing

The most consequential and most restricted launch of the year: Claude Mythos Preview.

Anthropic’s internal assessment: Mythos is too powerful for public release due to its cybersecurity capabilities. Its extended-context reasoning, recursive self-correction, and agentic scaffolding create a new threat category. During testing, Mythos identified thousands of zero-day vulnerabilities - many of them one to two decades old - across open-source and proprietary software.

Rather than release it publicly, Anthropic launched Project Glasswing, deploying Mythos to 12 founding cybersecurity partners (40 total with access) to find and fix vulnerabilities before the model reaches the open market. Founding partners: Amazon, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks. Anthropic pledged up to $100 million in Claude credits to the initiative.

The publicly available Opus models are intentionally less capable in cyber domains than Mythos - Anthropic has explicitly said Opus 4.7 is “broadly less capable” than the preview. Mythos-class capabilities are expected to reach all customers once Glasswing safeguards mature.

April 8-9 - Cowork Goes GA + Claude Managed Agents Launch

On April 9, Claude Cowork became generally available for all paid subscribers on macOS and Windows. Enterprise capabilities added at GA: role-based access controls, group spend limits, usage analytics, Zoom MCP connector, and per-tool connector controls.

Simultaneously, Claude Managed Agents launched in public beta. The pitch: composable APIs for building and deploying cloud-hosted agents at scale. Managed Agents handle infrastructure, state management, permissioning, and model upgrade compatibility so teams reach production in days, not months. Early users in production: Notion, Asana, and Sentry.

→ How to get the most out of Managed Agents: Managed Agents are purpose-built for long-running workflows with audit requirements, think regulatory compliance checks, multi-day research tasks, and orchestrated financial workflows. Use them when a Cowork session would need to survive machine restarts or require audit logs.

April 14 - Claude Code Desktop Redesigned for Parallel Agents

The Claude Code desktop app received a full redesign around a new mental model: you are not waiting for one task - you are routing work across several repos simultaneously.

Key changes:

• Session sidebar - all active and recent sessions in one panel, auto-archived when a PR merges

• Side chat (⌘+;) - branch a conversation without interrupting a running session

• Integrated terminal - run tests or builds without leaving the app

• In-app file editor and rebuilt diff viewer

• Extended preview panel - HTML, PDF, local server previews

• Drag-and-drop layout - arrange terminal, preview, diff viewer, and chat freely

• Routines (research preview) - scheduled automations running on Claude Code’s cloud infrastructure even when your laptop is off

→ How to get the most out of Parallel Sessions: The redesign enables true parallel work: spin up a session for a feature branch, another for a bug fix, a third for test generation - review and tweak each without context-switching. Use Routines for cron-style workflows: scheduled test runs, nightly code reviews, automated dependency updates.

April 16 - Claude Opus 4.7: The New Production Flagship

Claude Opus 4.7 launched as Anthropic’s most capable publicly available model at the time:

• 64.3% on SWE-bench Pro - new high for production-realistic coding

• 72.5% on SWE-bench Verified and 43.2% on Terminal-bench

• 90.9% accuracy on BigLaw Bench for Harvey at high effort - cross-domain professional reasoning

• 13% improvement over Opus 4.6 on Anthropic’s internal 93-task coding benchmark, including four tasks neither Opus 4.6 nor Sonnet 4.6 could solve

• 3× vision resolution - dense diagrams, screenshots, visual data

• Multi-agent coordination improvements: more sophisticated task decomposition

• New xhigh effort level - maximum compute allocation

• New .docx redlining and .pptx editing for document workflows

→ How to get the most out of Opus 4.7: The xhigh effort level is your highest-quality lever - use it for tasks where accuracy matters more than speed. Activate it for legal document review, complex multi-file refactoring, and financial analysis. For vision: pass in full-resolution screenshots (not compressed) to take advantage of the 3x resolution improvement.

April 17 - Claude Design: The Anthropic Labs Consumer Play

Claude Design launched from Anthropic Labs as a research preview for Pro, Max, Team, and Enterprise subscribers. Built for founders and product managers without design background, it generates visual assets from plain-English descriptions.

Output formats: PDFs, shareable URLs, PPTX files, direct Canva export. Teams can apply design systems by pointing Claude at a codebase and design files for visual consistency. Powered by Opus 4.7.

The market reaction was immediate. Three days earlier, Anthropic CPO Mike Krieger had quietly resigned from Figma’s board. On launch day, Figma stock fell 7.28%, dropping from $20.32 to $18.84. Investors read the sequence clearly: Anthropic wasn’t building around Figma - it was building against it.

→ How to get the most out of Claude Design: Give Claude a design reference (”match the visual language of [brand]”) alongside your content requirements - it produces far more on-brand output than open-ended prompts. For consistency across a project, upload your brand guidelines as a file before starting. Use the Canva export for assets that need further editing; use the URL output for quick stakeholder presentations.

May 2026: The Enterprise Play Accelerates

May 1 - Skills Marketplace: Public Launch

The Anthropic Skills Marketplace opened publicly with ~600 skills at launch (roughly 90 first-party, the rest community-contributed). Skills are packaged Markdown-and-script capabilities that extend Claude without requiring custom code. Install with claude skills install [name]; callable across Claude Code, Claude.ai, and Cowork.

Internal pre-launch usage showed a 47% reduction in tokens-per-task on document workflows once Skills replaced inline prompts. Paid skills include a 15% revenue share to Anthropic.

→ How to get the most out of the Skills Marketplace: Browse first-party skills before building custom prompts - Anthropic’s internal skills are optimized against real usage patterns and will often outperform a prompt you write from scratch. For recurring workflows, create a project-specific skill by saving your best prompt as a .md file in ~/.claude/skills/[name]/ and it becomes a callable skill instantly.

May 5 - 10 Financial Services Agents + Microsoft 365 Integration + Moody’s MCP

Anthropic launched ten financial services agent templates targeting banks, insurers, asset managers, and fintechs:

• Research and Client Coverage: Pitch builder, Meeting preparer, Earnings reviewer, Model builder, Market researcher.

• Finance and Operations: Valuation reviewer, General ledger reconciler, Month-end closer, Statement auditor, KYC screener.

Each template combines Skills, Connectors, and subagents for sub-tasks. Deployable as plugins for desktop use or Managed Agents for long-running cloud sessions with audit logs.

New data connectors: Dun & Bradstreet, Fiscal AI, Financial Modeling Prep, Guidepoint, IBISWorld, SS&C IntraLinks, Third Bridge, Verisk, and Moody’s MCP app (proprietary credit ratings covering 600M+ companies).

Microsoft 365 integration expanded: Claude now works natively in Excel, PowerPoint, and Word via add-ins, with Outlook coming soon.

Claude Opus 4.7 leads Vals AI’s Finance Agent benchmark at 64.37%.

→ How to get the most out of the Finance Agents: The KYC screener and earnings reviewer agents are plug-and-play once you connect your data connectors. The highest-ROI workflow for finance teams: use the Month-End Closer agent connected to your accounting system for automated reconciliation, then have the Statement Auditor flag anomalies before human review.

May 6-7 - Code with Claude: The Developer Conference Goes Global

Anthropic held its second annual Code with Claude developer conference in San Francisco on May 6, with a second day added for indie developers and early-stage founders after demand overwhelmed the initial ticket lottery. The conference then traveled to London (May 19), with Tokyo schedule next (June 10).

The conference doubled as a product launch vehicle. Announcements made at or around Code with Claude: new Managed Agents capabilities (dreaming, outcomes, and multiagent orchestration), higher Claude Code and Opus API rate limits, and several of the May launches that followed in the days after.

On the Managed Agents side, three new capabilities shipped: Dreaming lets agents run test scenarios against past data to learn before executing live. Outcomes lets you define a measurable success condition (similar to /goal for Code, but for cloud-hosted agents). Multiagent orchestration coordinates multiple specialized agents on a single workflow. Self-hosted sandboxes and MCP tunnels followed on May 19, letting enterprise teams run agent execution environments on their own infrastructure while keeping the orchestration layer on Anthropic’s.

May 11 - Claude Platform on AWS: General Availability

Anthropic launched its native Claude Platform on AWS, the first cloud provider to offer direct access to the full Claude Platform experience through existing AWS accounts. The service gives developers Claude’s first-party APIs, the Claude Console, Managed Agents, web search, code execution, and MCP connectors - all managed through AWS IAM credentials, billed through AWS Marketplace, and audited through CloudTrail.

The distinction from Amazon Bedrock: Bedrock keeps AWS as the data processor within AWS’s security boundary. Claude Platform on AWS is Anthropic-operated, with data processed outside the AWS boundary, but gives teams access to every native Claude API feature and beta on the same day it launches. Over 100,000 organizations already use Claude models on AWS Bedrock; the Platform launch adds a more direct integration path for teams that want the full feature set without managing a separate Anthropic account.

May 12 - Claude Code /goal: Set It and Walk Away

Claude Code v2.1.139 introduced the /goal command - the feature early adopters are calling “the single most underrated AI feature of 2026.” The concept: instead of prompting Claude turn by turn, you define a completion condition and Claude works autonomously across multiple turns until that condition is met. Sometimes for hours. Sometimes for days.

How it works: type /goal "all tests in test/auth pass and lint is clean" and Claude starts iterating - writing code, running tests, debugging failures, trying new approaches - without checking in with you. After each turn, a lightweight supervisor model (Haiku) evaluates the completion condition. When the condition is met, the goal clears. Available in interactive mode, the -p flag, and Remote Control.

The same release shipped Agent View (claude agents), a unified dashboard for monitoring all active Claude Code sessions from a single screen. Together, /goal and Agent View moved Claude Code from conversational tool to orchestration platform - developers can now define objectives across multiple parallel sessions, then check back when they’re done.

→ How to get the most out of /goal: Write completion conditions that are specific and verifiable: “all TypeScript errors resolved and the app compiles” rather than “fix the code.” Conditions support up to 4,000 characters, so be precise about scope. Pair /goal with parallel sessions for maximum throughput: set a migration goal in one session, a test-writing goal in another, and a documentation goal in a third. Be aware that parallel agents consume rate limits per-session - five agents uses quota 5x faster.

May 13 - Claude for Small Business: The SMB Play

Claude for Small Business launched as a Cowork plugin that puts Claude inside the tools small businesses already run on: QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, and Microsoft 365. No extra charge beyond an existing Claude subscription.

The package ships with 15 pre-built agentic workflows spanning payroll planning, month-end close, overdue invoice chasing, marketing campaigns (from HubSpot analysis to Canva assets), contract review, lead triage, employee onboarding, and a “Monday morning business pulse” covering cash position, pipeline, and commitments. Toggle it on, connect your apps, pick a workflow. Claude builds the plan and asks for approval before anything sends, posts, or pays.

→ How to get the most out of Claude for Small Business: Start with the three workflows closest to your biggest time sinks - for most small businesses, that’s month-end close, invoice chasing, and Monday morning pulse. Connect QuickBooks and your email first; these unlock the highest-value automations. Review every output before it executes (Claude asks for approval by default). For solopreneurs: the marketing campaign workflow that chains HubSpot analysis → content generation → Canva asset creation is the closest thing to having a junior marketing hire.

May 14–19 - Partnership Blitz: PwC, KPMG, and the Big Four

PwC alliance expansion (May 14):

PwC is deploying Claude to build agentic tools for clients across engineering teams and embedding AI across dealmaking processes. 30,000 US employees are now certified in Claude Code, expanding to 364,000 globally. A joint Center of Excellence has been established, and Claude is already live in ChatPwC across finance, supply chain, and deal engagements.

KPMG global alliance (May 19) - the first Big Four firm to embed Anthropic’s frontier AI into its core global client delivery platform:

KPMG launched its Digital Gateway Powered by Claude, integrating Cowork and Managed Agents into its Microsoft Azure-based platform. All 276,000 KPMG professionals globally get access to the full Claude suite. KPMG becomes Anthropic’s preferred consulting partner in Private Equity, with focus areas spanning tax, advisory, deal execution, and cybersecurity (including restricted Mythos capabilities).

These are not integration partnerships. They are delivery model redesigns - both firms are rebuilding how professional services work around Claude’s agentic layer.

May 18 - Anthropic Acquires Stainless: Locking In Developer Infrastructure

Anthropic acquired Stainless, the SDK generator behind every official Anthropic SDK and widely used by OpenAI, Google, Cloudflare, Replicate, and Runway.

Founded in 2022 by former Stripe engineer Alex Rattray, Stainless converts API specifications into production-ready SDKs across TypeScript, Python, Go, Java, and Kotlin. It also generates CLI tools and MCP servers - the connectors that link agents to external APIs and business systems.

Post-acquisition: Stainless will wind down all hosted products for external customers. The team moves to Anthropic to work exclusively on Claude Platform developer experience. What Anthropic bought is thus control over the “last mile” of agent development - the layer that determines how easily Claude connects to external software - while simultaneously removing that infrastructure from competitors’ reach.

May 28 - Claude Opus 4.8 + Series H at $965 Billion

The biggest double-announcement in the 2026 blitz.

Claude Opus 4.8 is now Anthropic’s most capable publicly available model.

New capabilities shipping with Opus 4.8: Dynamic Workflows (Enterprise, Team, Max - research preview) let Claude Code plan and run hundreds of parallel subagents in a single session, enabling codebase-scale migrations across hundreds of thousands of lines of code. Effort Control lets you select effort level (Low, Medium, High, Max) in Claude.ai and Cowork, with High as the default. Fast Mode (research preview, Claude API) delivers up to 2.5× higher output tokens per second at 3× lower cost; set speed: "fast" in your API call. Mid-conversation System Messages let the Messages API accept role: "system" messages mid-conversation, enabling instruction updates without restating the full prompt - preserving prompt cache hits and reducing cost in agentic loops.

→ How to get the most out of Opus 4.8: The single biggest lever is Effort Control. When the model reasons shallowly on a hard task, raise the effort level rather than rewriting your prompt. Avoid max effort for routine tasks - it burns tokens and adds latency without proportional quality gains. Use high (default) for complex reasoning; medium for structured generation tasks; low for quick reformatting or Q&A. The new honesty improvements mean when Opus 4.8 adds a caveat, take it seriously - it’s more calibrated than before. State the full scope of any rule you set (”this applies to all file types, not just Python”) because the model will interpret scope literally rather than inferring it.

The Series H: $65 billion at a $965 billion post-money valuation. Co-led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital, with Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ, and XN as co-leads.

What’s next: Anthropic is working on models with Opus 4.8 capabilities at lower cost, a new model class “more intelligent than Opus 4.8,” and Mythos-class access expected to reach all customers “in the coming weeks” as Glasswing data matures.

Three Threads Running Through All of It

Thirty-five launches across five months is a lot of surface area. Individual products matter, but the pattern underneath them matters more. Step back from the timeline and three strategic threads emerge - each one visible only when you see the full sequence at once.

1. From chatbot to execution platform. Every major launch - Cowork, Agent Teams, Managed Agents, Dynamic Workflows, the Financial Services Agents - is infrastructure for Claude doing work autonomously. The product category isn’t “AI assistant” anymore. It’s AI labor. The question Anthropic is answering has shifted from “How can Claude help you think?” to “What can Claude do while you’re not watching?”

2. Free tier as acquisition engine. Memory, visualizations, connectors, and Skills have all been extended to free users. Each expansion shrinks the gap between “trying Claude” and “rebuilding workflows around Claude,” routing users toward paid plans as they hit rate limits or need desktop capabilities. The funnel is the product.

3. Safety as competitive moat. The ad-free commitment, the restricted Mythos Preview, Project Glasswing, and the alignment improvements in each model release aren’t just ethical positioning - they’re market strategy. Anthropic is making itself the AI lab that enterprises and governments trust with their most sensitive workflows. Every Glasswing partner is a future Managed Agents customer. Every alignment benchmark is a line item on a procurement evaluation. In a market where every frontier lab can claim performance, trust is the differentiator that compounds.

And that’s not even it! In case you missed it, check out these resources as well:

And that’s a wrap.

In 2023, I started sharing resources to help you become a more successful entrepreneur, investor, or business leader. I hope you will find these new resources valuable too.

Keep dreaming & keep building.

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11. How to Build an AI Agent from Scratch (With Working Code) 🤖 [The design framework, working code, and hard-won lessons that take you from “I want to build an AI Agent” to a working one - in under 60 minutes]

12. Claude Cowork Commands, Scheduled Tasks & Automation Workflows: The Operator’s Playbook 🤖 [Every real slash command, the exact prompts that run deal flow and investor updates on autopilot, and the failure modes that will cost you hours if you don’t know them first]

As for today, here are the 2 fascinating FinTech stories that are changing the world of financial technology as we know it. This was yet another wild week in the financial technology space, so make sure to check all the above stories.

Your next broker is a chatbot 🤑🤖

The news 🗞️ The gap between “AI can analyze markets” and “AI can execute trades” just closed, and it happened inside a chatbot.

Let’s take a look.

More on this 👉 Liquid Co-Invest, built by ex-Two Sigma quant Franklyn Wang, ships as a ChatGPT App Store plugin and Claude connector that turns a conversation into a full trading workflow.

Fund an account, pull institutional-grade analysis (on-chain flows, funding rates, liquidation maps, macro context with sourcing), build a multi-asset portfolio, and execute live trades.

→ Crypto perps with leverage, US equities, FX, commodities, Polymarket positions, and pre-IPO secondaries in names like OpenAI, Anthropic, and SpaceX.

→ Over 500 markets, available in all 50 states, setup in about a minute.

→ Non-custodial, so funds stay in the user’s wallet, and the AI can’t move money without an explicit tap on a confirmation card showing exact position details.

Zoom out 🔎 Looking at the bigger picture, the smart observation here isn’t about the interface. It’s about where the moat sits.

Liquid doesn’t own the AI. OpenAI and Anthropic do, and every model upgrade makes Liquid’s product better at zero marginal cost. What Liquid owns is the hard, unglamorous infrastructure underneath: deep venue integrations across Hyperliquid, Lighter, and Ostium, regulatory positioning that sidesteps custody headaches, and access to private-market secondaries that most retail investors can’t touch. And that’s exactly what Anthropic itself recently told AI founders to build:

Most importantly, they’re not competing with the likes of Robinhood, Coinbase, etc. They’re making their interface feel like a fax machine 😬

The incumbents won’t sit still, of course.

Robinhood already has the regulatory licenses, 24 million funded accounts, and a mobile-native DNA that rewired retail trading once before. Coinbase sits on the deepest institutional crypto rails in the US and has been quietly building agent-friendly APIs.

Both will almost certainly ship their own LLM connectors or acquire someone who has. Their real advantage is custody: they already hold the assets. But that’s also their constraint. Building conversational execution on top of legacy brokerage architecture is a different engineering problem than building it natively, and incumbents tend to bolt on what insurgents build in.

The race therefore now is whether Robinhood and Coinbase can move fast enough to own the agentic layer themselves, or whether they get relegated to dumb pipes that infrastructure players like Liquid route through.

THE TAKEAWAY ✈️

What’s next? 🤔 First and foremost, we must note that the risk profile is real here, though. Leverage in the hands of someone who has just learned to prompt is a rough combination. Herding effects from thousands of users feeding similar queries into the same models could amplify volatility rather than dampen it. And regulators haven’t even begun to wrestle with liability when an AI’s suggestion leads to a blowup… Looking ahead, I’d watch two things. First, whether traditional brokers scramble to build their own LLM connectors or try to acquire the infrastructure layer. Second, the pre-IPO access angle. If conversational AI makes illiquid private secondaries feel as tradable as spot crypto, that reprices how late-stage companies think about liquidity timelines entirely. The first-order story is a better trading UI. The second-order story is a new distribution channel for capital markets, and that’s the one worth tracking. Either way, this is a perfect glimpse into the future of agentic finance.

ICYMI:

Catena Labs wants a bank charter for AI Agents, and the OCC said “We’ll Listen” 🏦🤖

The news 🗞️ The first serious regulatory test for agentic finance is now on the clock.

Catena Labs, founded by Circle co-founder Patrick Neville, emerged from stealth last week with a $30M Series A from Acrew Capital and a16z crypto, bringing total funding to $48M.

But the money isn’t the story here.

The story here is that the OCC accepted Catena’s filing for a national trust bank charter - a de novo institution designed from scratch to let AI agents custody assets, settle payments, and manage money under federal supervision.

Let’s take a quick look to see what it’s all about, why it matters, and most importantly - what’s next.

More on this 👉 Every major player in payments is now racing to build agent infrastructure.

→ Stripe launched its Agentic Commerce Protocol with OpenAI, then joined Google’s UCP.

→ Coinbase shipped AgentKit and x402.

→ Google partnered with Coinbase and AmEx on agent payments.

All of them are API layers and protocols that ride on existing banking relationships. Catena is the only one trying to become the bank. That’s a slower path, but if the charter clears, it’s a moat that no SDK can replicate.

Zoom out 🔎 The entity, Catena Trust Bank, N.A., would be a limited-purpose uninsured trust company headquartered in New York: no branches, no deposits, no lending. Pure custody, conversion, and settlement for fiat, stablecoins, and digital assets under the GENIUS Act framework.

Think Anchorage Digital’s charter playbook, rebuilt for non-human clients. Agents get verifiable identities, deterministic spending policies, kill switches, and full audit trails. Operators keep a control plane. The bet is that enterprises won’t hand agents real budget authority without a regulated fiduciary on the other side.

THE TAKEAWAY ✈️

What’s next? 🤔 First and foremost, we must note that the 30-day public comment window closes around late June 2026. If you want to gauge how seriously Washington takes autonomous AI as an economic actor, watch this docket. Of course, charter approvals typically take 12-18 months, and approval is far from certain. But acceptance of the filing alone tells you something: regulators are engaging with the premise that AI agents need their own financial identity framework, not a retrofit of human-centric KYC. Looking ahead, the second-order question nobody’s asking yet is this: if Catena gets chartered, who’s liable when an agent makes a bad trade? The charter creates a fiduciary wrapper, but the case law doesn’t exist. The first dispute will matter more than the first transaction. Either way, agentic finance is the future 🤖

ICYMI: Google Universal Cart and the Universal Commerce Protocol are Google’s bid to become the operating system of e-commerce ⚙️🛍️ [what Google Universal Cart is all about, why it matters & how it can transform e-commerce forever + bonus deep dives into Google’s UCP, how it just won Agentic Commerce & why AmEx skipped the protocol race altogether inside]

🧠 What else I’m watching

• AI Banking Companion 📱 CommBank is testing a conversational AI interface, CommBank Companion, within its app to help customers manage spending, savings, and financial goals. The tool analyzes live data to provide insights on budgets, subscriptions, cash flow, and business projections. Currently in employee and select business banking trials, it aims to shift banking from customer navigation to proactive, relevant information delivery. ICYMI: ChatGPT’s new personal finance feature is OpenAI’s bid to kill the fintech app economy 🤖📲 [what it’s all about, which fintechs should worry the most, how it connects to OpenAI’s Super App Play & what’s next + bonus deep dives into Anthropic & how it’s becoming Wall Street’s OS, their SME OS play, and what AI startup founders should build in 2026]

• TD AI Mortgage Boost 🏠 TD Bank’s new agentic AI model, developed at its Layer 6 R&D center, automates mortgage and home equity loan pre-adjudication, slashing processing time from 15 hours to under 3 minutes. The AI classifies documents, extracts data, validates income, and flags discrepancies before generating underwriter summaries - improving both accuracy and speed. This marks a foundational step in TD’s AI strategy, aiming to generate $1 billion in annual value by combining human expertise with AI efficiency. ICYMI:

• BNP-Mistral AI Defense 🛡️ BNP Paribas has renewed its three-year partnership with Mistral AI to bolster cyber defenses against vulnerabilities exposed by tools like Anthropic’s Mythos. With Mythos inaccessible to European banks, BNP is collaborating with Mistral to develop a similar cyber-focused AI model, addressing the speed and scale of emerging threats. The partnership also aims to simplify internal knowledge access, deploy an AI employee assistant, and create on-premises AI tools for sensitive processes like KYC. BNP emphasizes a multi-model approach, prioritizing performance, data sensitivity, and geographic alignment to drive operational and regulatory impact. ICYMI:

💸 Following the Money

• Scapia, an Indian outfit that combines co-branded credit cards with travel booking, has raised $63M in a funding round led by General Catalyst.

• Relay, a business banking and money management service for SMEs, has secured $50M in growth investment from General Catalyst.

• MoonPay announced its fourth acquisition of the year with Decent, as it launches MoonPay Trade.

👋 That’s it for today! Thank you for reading, and have a relaxing Sunday! And if you enjoyed this newsletter, invite your friends and colleagues to sign up:

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On May 28, Anthropic announced a $65 billion Series H round at a whopping $965 billion post-money valuation, making it the most valuable private (AI) company on Earth. The same day, it released Claude Opus 4.8.

That timing is definitely intentional here. Opus 4.8 is the model Anthropic is betting its near-trillion-dollar story on.

Anthropic describes it as an upgrade on Opus 4.7 “with improvements across benchmarks” and “a more effective collaborator,” available at the same price ($5/$25 per million input/output tokens). The framing is clear as Opus 4.8 builds on 4.7’s foundation rather than breaking from it.

The prompting instincts you developed with 4.7 still apply. But there are meaningful new capabilities, and one genuinely new paradigm, that change what’s achievable if you know how to use them.

The biggest one is undoubtedly Dynamic Workflows in Claude Code: the ability for Claude to write its own orchestration scripts, spin up parallel subagents, and scale test-time compute trivially. Anthropic engineers have been using this as a daily driver for months. The rest of the world found out yesterday.

To make this as easy and seamless as possible, we’re also sharing Opus 4.8 Prompt Optimizer, a custom Claude skill that rewrites any prompt using the framework below automatically. Step-by-step setup instructions are at the end.

If you’ve read the Claude Opus 4.7 Prompting Playbook, the good news is that your existing prompts still work. But the new capabilities reward a specific set of upgrades, particularly around the effort stack, honesty directives, and Dynamic Workflows. This guide covers all of it.

Below is everything you need to get the best output this model can produce, every single time.

Step 0 - Choose the Right Model

Before writing a single word of your prompt, choose the right model. Anthropic’s current lineup includes Claude Opus 4.8, Claude Sonnet 4.6, and Claude Haiku 4.5, each optimized for different workloads.

→ Claude Opus 4.8 is now the flagship. It scores 69.2% on SWE-bench Pro, 88.6% on SWE-bench Verified, and 1890 Elo on GDPval-AA (121 Elo points ahead of GPT-5.5 on economically valuable professional tasks). It excels at long-horizon autonomous tasks, complex reasoning, and knowledge work. It costs more and runs slower via the API ($5/MTok input, $25/MTok output), but when the task demands genuine thinking - strategic analysis, multi-step research, complex autonomous execution - nothing else in the generally available lineup comes close.

→ Claude Sonnet 4.6 is still the balanced workhorse: strong reasoning at faster speed and lower cost. For most everyday tasks, Sonnet covers roughly 80% of use cases well.

→ Claude Haiku 4.5 remains the speed specialist: fastest, cheapest, ideal for high-volume, straightforward tasks like classification, extraction, and summarization.

💡 The rule of thumb: Start with Sonnet. Move to Opus when you need genuine cognitive depth, or when you’re running a Dynamic Workflow that demands long-horizon coherence. Drop to Haiku when speed matters more than intelligence.

The Framework

Anthropic’s 31-page+ documentation describes a hierarchy of techniques ordered by impact. Most people jump straight to advanced techniques and skip the fundamentals. That’s backwards.

Here’s the framework in the order that matters, updated for the specific behaviors of Opus 4.8.

1. Set the Effort Level (Most Important)

This is the single most important variable in your prompt, and most people never set it at all.

The effort parameter controls how much intelligence the model applies to a task. Like Opus 4.7, Opus 4.8 respects effort levels strictly, especially at the low end. The system remains: low, medium, high (default), xhigh, and max.

One change from 4.7: Opus 4.8 defaults to high effort (4.7 defaulted to xhigh), which Anthropic says spends similar tokens on coding tasks while performing better. Adaptive thinking is off by default and must be explicitly enabled.

The Effort Level System for Opus 4.8:

In the API:

client.messages.create(
    model="claude-opus-4-8",
    max_tokens=64000,
    thinking={"type": "adaptive"},
    output_config={"effort": "xhigh"},
    messages=[{"role": "user", "content": "..."}],
)

When running at max or xhigh, set your max_tokens to at least 64k - the model needs room to think, reason, and execute across tool calls and subagents.

Key Opus 4.8 update from the System Card: At minimum effort, Opus 4.8 matches the peak performance of Opus 4.7 at maximum effort on SWE-bench Pro. The floor has risen. But the ceiling has expanded too - xhigh and max now unlock capabilities (particularly in long-horizon agentic work) that Opus 4.7 couldn’t reach at any effort level.

For Dynamic Workflows specifically, enable ultracode mode: this uses workflows aggressively without requiring you to ask for them every time.

💡 Practical rule: If you see shallow reasoning on a complex task, raise the effort level before changing your prompt. That’s almost always the correct fix.

2. Be Specific - More Than You Think

The single highest-leverage prompting move is specificity. Opus 4.8, like 4.7 before it, is extremely literal. Vague prompts get scoped, not generalized. If you want an instruction applied across all sections, not just the first, you have to say so. If you want exceptional output, you have to request it explicitly.

Anthropic’s own recommendation still holds: think of your prompt as instructions to a brilliant but literal new hire on their first day. They’ll do exactly what you say, so say exactly what you mean.

❌ Weak prompt:

Write about market positioning.

✅ Strong prompt:

Analyze the 3 most effective market positioning strategies for B2B fintech companies targeting mid-market in a crowded category. For each strategy, explain what's driving its effectiveness, provide one specific company example, and assess whether it's likely to strengthen or weaken over the next 18 months. Apply this framework to all three strategies, not just the first.

The difference isn’t more words. It’s more specificity, explicit scope, and a format directive at the end.

3. Use XML Tags for Structure

This is Claude’s structural superpower, and still almost nobody uses it correctly.

Claude was specifically trained to recognize XML tags as structural markers. When your prompt has multiple components - context, instructions, data, constraints, output format - XML tags prevent Claude from mixing them up. Here’s what that looks like for a strategic decision:

<context>
You are helping me evaluate a potential Series A investment.
The company is a vertical fintech targeting logistics operators,
currently at $2.4M ARR growing 15% MoM.
</context>

<instructions>
Analyze the three key risks that most commonly derail vertical fintech companies
at this stage.
For each risk, explain the warning signs and what a founder should be doing
to mitigate them.
Apply this analysis to all three risks, not just the most obvious one.
</instructions>

<constraints>
- Be direct. Give me your honest assessment, not a balanced "it depends."
- Use specific examples from real companies where possible.
- Flag any assumptions you're making.
- Maximum 600 words.
</constraints>

Claude sees the tags and immediately understands that <context> is background (not a task to respond to), <instructions> is the actual task, and <constraints> are the guardrails.

Tag names are flexible - there’s no magic set of “correct” tags. Use whatever makes semantic sense: <background>, <rules>, <examples>, <output_format>. Consistency across your prompts matters more than the specific names you choose.

4. Show Claude What Good Looks Like

If one technique consistently separates good outputs from great ones, it’s this: show Claude what good looks like.

Instead of describing the tone, format, or style you want in abstract terms, provide two to three concrete examples. Claude will pattern-match against these far more reliably than it will follow descriptive instructions alone. Wrap examples in <example> tags (multiple examples in <examples> tags) so Claude distinguishes them from instructions.

<examples>
  <example>
    Input: "We need to cut 20% of the engineering budget"
    Output: "Reducing engineering spend by 20% requires prioritization
    across three areas: contractor headcount, infrastructure costs, and
    tooling licenses. Here's a phased approach that preserves our two
    highest-impact product initiatives..."
  </example>
</examples>

Now analyze this situation using the same approach:
"We need to extend our runway by 6 months without reducing headcount"

Anthropic recommends 3–5 examples for best results. You can also ask Claude to evaluate your examples for relevance and diversity, or to generate additional ones based on your initial set.

5. Prompt for Visible Reasoning (Chain-of-Thought)

For complex problems requiring analysis, multi-step reasoning, or strategic judgment, telling Claude to work through its reasoning before producing a final answer dramatically improves accuracy.

The simplest version: add “Think through this step by step before giving your final answer” to your prompt.

The more structured version uses tags to separate reasoning from output:

<instructions>
Evaluate whether we should expand into the Southeast Asian market this year.
Before giving your recommendation, work through the analysis inside
<analysis> tags.
Consider: market size and growth trajectory, regulatory requirements by
country, competitive landscape, our current operational capacity,
and capital requirements vs. expected payback period.
Then provide your final recommendation with a clear resource allocation
suggestion.
</instructions>

Forcing visible reasoning prevents Claude from pattern-matching to the most likely answer and back-filling justification after the fact.

Adaptive thinking in Opus 4.8: Claude dynamically decides when and how to think based on the effort setting and task complexity. At high and xhigh effort, deep reasoning is largely automatic for demanding tasks. Extended thinking with a fixed budget_tokens is no longer supported - adaptive thinking is the only thinking-on mode, and Anthropic’s evaluations show it reliably outperforms the old fixed-budget approach.

To guide reasoning explicitly, add this to your system prompt:

✅ “After receiving results, carefully reflect on their quality and determine optimal next steps before proceeding. Use your thinking to plan and iterate based on this new information, then take the best next action.”

6. Load Rich Context

Claude can only work with what you give it. The more relevant context you include, the more tailored and accurate the output becomes.

Upload documents. Paste data. Provide company background. Share your goals. Explain your audience. Don’t make Claude guess what you already know.

<background>
Our company builds financial infrastructure for neobanks in emerging markets.
We're Series B, $12M ARR, primarily serving West Africa and Southeast Asia.
Our main competitors are Banking-as-a-Service players like Railsbank, Synapse,
and local incumbents.
We differentiate on compliance coverage and local payment rail integrations.
</background>

<data>
[Paste your Q1 metrics, customer feedback, churn data, or whatever's relevant]
</data>

<task>
Based on this context, identify our three biggest growth opportunities
for the next quarter.
</task>

For long documents (20k+ tokens), put the documents at the top of your prompt, above your instructions and query. Queries at the end can improve response quality by up to 30% on complex, multi-document inputs.

7. Specify the Output Format

Don’t leave the structure of Claude’s response to chance. If you want a table, ask for a table. If you want a specific word count, state it. If you want an executive brief with defined sections, describe each section.

<output_format>
Respond with:
1. A one-paragraph executive summary (3-4 sentences max)
2. A comparison table with columns: Factor | Current State | Target State | Gap
3. A "Recommended Actions" section with 3 specific next steps, ranked by impact
</output_format>

Explicit format specs eliminate the most common frustration people have with AI: getting a 2,000-word essay when you wanted a concise brief, or getting bullet points when you needed flowing analysis.

8. Define Constraints - Especially “Don’ts”

Telling Claude what not to do is just as important as telling it what to do. Without constraints, Claude defaults to its training patterns, which can mean hedge-heavy output that sounds like a committee wrote it.

<constraints>
- Do NOT open with "In today's rapidly evolving landscape" or any variant
- Skip the preamble. Start with the most important insight.
- No bullet points — write in prose paragraphs
- If you're uncertain about a claim, flag it explicitly rather than hedging
  everything
- Maximum 500 words
- Be direct. I want your honest assessment, not a balanced "it depends."
</constraints>

One note specific to Opus 4.8 from the System Card: The model has a tendency toward over-elaborate refusals on certain categories of requests. If you’re getting unnecessary hedging or qualification on legitimate business tasks, add a clear statement of legitimate purpose to your prompt - e.g., “This analysis is for an internal investment committee memo. Please provide a direct, unqualified assessment.”

9. Control Verbosity

Opus 4.8 calibrates response length to how complex it judges the task to be - short on simple lookups, much longer on open-ended analysis. If your use case requires specific verbosity, tune it explicitly.

To decrease verbosity:

“Provide concise, focused responses. Skip non-essential context and keep examples minimal.”

To increase depth:

“This is for board-level review. Go deep on every dimension - don’t compress the analysis.”

Key insight from Anthropic itself: positive examples showing appropriate concision work better than negative instructions like “don’t be verbose.” Show Claude a response at the length and depth you want, and it will match that pattern far more reliably than it will follow abstract length instructions.

10. Use Dynamic Workflows (The New Paradigm)

This is new to the Opus 4.8 era, and it’s the most powerful capability yet.

What it is: Dynamic Workflows in Claude Code allow Claude to write its own orchestration scripts that spin up tens to hundreds of parallel subagents, instead of making tool calls one at a time. The control flow is code, which means Claude won’t drift or forget halfway through thousands of files. The harness is encoded.

How it works: Mention the word “workflow” in any Claude Code prompt, and Claude will spin one up automatically. For aggressive workflow use without prompting, enable effort ultracode in your settings.

What it unlocks:

• Massively parallel execution: A five-agent team can handle BrowseComp tasks using just 20% of the latency of a single agent with a 10M-token limit, while scoring higher. The Orchestrator with Blocking Subagents configuration reached the highest scores overall.

• Adversarial verification: Route critical work through adversarial judges that each attack the output from a different lens. You can go as far as a tournament-style bracket, scoring competing plans against each other.

• Scalable test-time compute: Workflows let you scale test-time compute trivially. There has never been an easier way to do this in Claude Code.

• Reusable automation: Because workflows are scripts under the hood, you can save them, commit them to your repo, share them with your team, and turn them into skills.

The flagship real-world example: Jarred Sumner (creator of Bun) used Dynamic Workflows to rewrite the entire Bun runtime from Zig to Rust, roughly 750,000 lines of code, eleven days from first commit to merge, 99.8% of the existing test suite passing. The workflow mapped Rust lifetimes for every struct field in the Zig codebase, then wrote every .rs file as a behavior-identical port of its .zig counterpart, hundreds of agents working in parallel with two reviewers on each file. A fix loop then drove the build and test suite until both ran clean.

When to reach for a workflow:

→ The task is too big for a normal session (codebase-wide bug hunt, large refactor, multi-source research).

→ You need higher confidence that something is right (critical features, financial models, security reviews).

→ You want to parallelize work across many files or domains simultaneously.

🔥 Cost warning: Workflows can get expensive due to all the parallel agents. Get a feel for token usage on smaller tasks before unleashing them on larger ones. You can disable them with config or enterprise settings if needed.

Best practices for Dynamic Workflows:

1. Trigger intentionally. Say “workflow” explicitly for important tasks. For routine work, keep workflows off to manage costs.

2. Enable ultracode for coding sprints. When you’re in a focused engineering session, effort ultracode removes the friction of triggering workflows manually.

3. Use adversarial judges for critical outputs. Don’t just generate - verify. Route the output through a judge agent with explicit attack criteria.

4. Save your best workflows as skills. If a workflow solves a recurring problem well, commit it. This is institutional knowledge that compounds.

5. Set a token budget before large tasks. Ask Claude to estimate token usage before running a large workflow, then decide if the cost is justified.

6. Use blocking subagents for accuracy-critical tasks. Async subagents reduce latency but the Orchestrator with Blocking Subagents configuration scores highest when accuracy matters more than speed.

7. Let Claude choose the architecture. Don’t over-specify the workflow structure. Claude knows when to parallelize, when to serialize, and when to use a tournament bracket.

8. Test on a sample first. On large codebases or research tasks, run the workflow on a representative 10% before committing to the full task.

Putting It All Together

Here’s what a properly structured Claude Opus 4.8 prompt looks like when you combine all the techniques covered above:

<context>
I'm the CEO of a B2B fintech startup ($8M ARR, 45 employees).
We're deciding whether to raise a Series B now or extend runway and raise
in 18 months.
Current runway: 14 months. Revenue growth: 12% MoM. CAC payback: 8 months.
</context>

<instructions>
Analyze both timing options. Before giving your recommendation, work through
the trade-offs in <analysis> tags, considering:
- Current market conditions for fintech Series B rounds
- Our specific metrics relative to typical Series B benchmarks
- The risk/reward of raising now vs. in 18 months at potentially better metrics
- What we should use the 18 months to optimize if we extend
Then provide a clear recommendation with a specific action plan.
Apply your analysis to both options equally — don't weight one by default.
</instructions>

<constraints>
- Be direct. Give me your honest read, not a balanced "it depends."
- Use specific benchmarks from comparable fintech Series B raises where possible.
- Flag any assumptions you're making about market conditions.
- Keep the total response under 700 words.
</constraints>

<output_format>
1. Analysis (in <analysis> tags)
2. Recommendation (2-3 sentences, clear and direct)
3. 90-day action plan (5 specific actions, whether we raise now or extend)
</output_format>

This prompt is clear, structured, specific, and constrained. It tells Claude exactly what to do, how to think about it, what to avoid, and how to format the response. The output from this kind of prompt is categorically different from “Should I raise a Series B now or wait?”

10 Mega Prompts Engineered for Claude Opus 4.8

As always, understanding the principles is one thing. Having them already engineered into prompts you can use today is another.

Below are 10 Mega Prompts, each structured using every technique above and calibrated for Claude Opus 4.8’s specific behaviors. Each covers a high-leverage task for founders, operators, and investors.

How to use these prompts:

• Fill in every [PLACEHOLDER] before submitting. Blank placeholders produce generic output.

• Enable web search for prompts that reference real-time data: Research (#1), Competitor Analysis (#8), Stock/Deal Analysis (#9), and Reverse-Engineering (#10). Without it, Claude will rely on training data and flag confidence gaps.

• Use xhigh or high effort for all 10 prompts. For #9 and #10, start at xhigh or max.

• Mix and match: you can add chain-of-thought <analysis> tags, output constraints, or anti-patterns from one prompt into any other.

• Alternatively, you can use Opus 4.8 Prompt Optimizer skill that rewrites any prompt using this framework automatically. Step-by-step setup instructions are at the end.

Prompt #1 - Deep Research Brief

Produces an executive brief with sourced findings, expert perspectives, and contrarian views.

Read more

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Small businesses have always had the ideas. They’ve rarely had the capacity.

And yet they carry the economy. There are 36 million small businesses in the U.S. alone - 99.9% of all firms - contributing 43.5% of GDP and employing 46.5% of the private-sector workforce. Globally, SMEs represent roughly 90% of all businesses and generate up to 70% of both employment and GDP across OECD countries. They are not a footnote to the economy. They are the economy.

Yet for decades, the tools that could change how they operate - real automation, intelligent data analysis, multi-system orchestration - were priced and engineered for enterprises with dedicated IT teams and six-figure software budgets. The owner who handles sales, operations, invoicing, marketing, HR, and customer service simultaneously has never had access to the intelligent support that enterprise teams take for granted.

That gap is exactly what Claude for Small Business is built to close.

On May 13, 2026, Anthropic launched it as a plugin for Claude Cowork that puts Claude inside the tools your business already runs on: QuickBooks, PayPal, HubSpot, Canva, DocuSign, Slack, Google Workspace, and Microsoft 365.

One toggle. No developer. No API keys. No third-party integration platform required.

What this means in practice: Claude chases your overdue invoices, drafts tone-matched follow-ups based on each client’s payment history, and queues them in Gmail for your approval - all from a single command. It runs a Monday morning business pulse that pulls your cash position, pipeline, and calendar into a one-page brief. It builds a hiring packet - job post, interview guide, offer letter - and routes it through DocuSign. It plans your payroll, flags cash shortfalls, creates Canva assets in your brand colours, and reviews incoming contracts for liability traps.

It does all of this without sending, posting, or paying a single thing without your explicit approval.

The economics are honestly wild: at $20/month for Claude Pro, even a conservative estimate of five recovered hours per week at $50/hour owner-time valuation returns $250 per week against a $20 subscription. The ROI is not marginal at all, but it depends on the setup time you invest upfront.

This guide was built for this exact reason and covers every single step:

→ What Claude for Small Business actually is

→ How to set it up correctly

→ How to build the CLAUDE.md business brain that makes everything smarter

→ The most powerful workflows with copy-paste prompts,

→ A deep dive into every integration, and

→ The real limitations and risks you need to understand before letting Claude anywhere near your finances.

Let’s dive in.

The Architecture: Three Layers You Need to Understand

Understanding the structure prevents most of the confusion around what Claude for Small Business can actually do.

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Good morning & happy Wednesday! Today we’re diving deep into another British digital banking gem Starling Bank, which just published its 2026 annual report (unpacking the most important financial facts & figures, how it stacks against Monzo & Revolut, & why Starling’s balance sheet is a fortress + bonus deep dives into the latest financials of Monzo & Revolut inside), and why your next broker and investment advisor could be Claude AI or ChatGPT (what Liquid Co-Invest is all about, why it’s interesting & what it tells us about the future of agentic finance + bonus dive into how this perfectly fits the AI products Anthropic itself told founders to build in 2026 & the full Claude Playbook for Finance inside). So let’s jump straight into the good stuff 🌶️

Starling Bank’s 2026 financials: 2.5x Monzo’s profit on half the revenue, and a £70M SaaS pipeline that changes the IPO math 📈🧠

Following the money 💸 All three biggest UK neobanks finally published their annual results, with two of them within days of each other. The headlines wrote themselves: Revolut posted £1.7 billion in pre-tax profit on £4.5 billion in revenue. Monzo crossed £1.7 billion in revenue and added 3 million customers. Starling’s revenue fell 5.6% 🤕

Starling clearly lost the headline war. But it may have won everything else.

On £887M in revenue, Starling produced £217M in statutory profit, a 24.5% PBT margin. Monzo, on nearly double the revenue, managed £87M, a margin of roughly 5%. Revolut is in a different category entirely: a global fee-income platform serving now over 70 million customers across 39 countries, barely lending at all. Starling is thus the only one of the three that looks like an actual bank, lends like an actual bank, and earns like the best of them 🏦

But the number that should change how you think about Starling isn’t in the P&L. It’s buried in the platform business.

Engine, Starling’s SaaS banking-as-a-service arm, generated just £10.9M in revenue last year. Rounding error. Except it now has £70M in committed annual recurring revenue, four live clients across five countries, and a single partnership (Tangerine, a Scotiabank subsidiary) that alone will migrate over 2 million accounts onto the platform over the next decade. If Engine scales to £100M in ARR, a 10x revenue multiple on that segment alone could add £1B or more to Starling’s IPO valuation, nearly matching its entire equity book value today. Not too shabby!

That makes Starling two businesses stitched together inside one balance sheet: a high-margin UK bank with a £525M capital surplus, and a nascent fintech infrastructure company with global reach and zero balance-sheet consumption. The market will eventually have to price both. It hasn’t done so yet.

So let’s dive deeper here and unpack the full financial picture: the Monzo and Revolut comparisons in detail, why Starling’s balance sheet is a fortress (and why Monzo’s lending book tells a very different credit story), how Engine’s pipeline and committed ARR stack up against the valuation math, what the FCA restrictions still in place mean for the growth re-acceleration thesis, and the specific entry price at which the risk/reward turns decisively in Starling’s favour ahead of a likely 2027–2028 IPO.

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Good morning & happy Tuesday! Today we’re diving deep into British neobanking gem Monzo, which just published its 2026 annual report (breaking down the unit economics that matter, the Revolut comparison investors keep getting wrong, the governance drama, IPO chances & what’s next for Monzo + bonus deep dive into Revolut’s latest financials & their foundational AI model for money inside), and Catena Labs that wants a bank charter for AI Agents (what they are trying to do, why it’s worth paying attention & how Catena stacks agents other players in agentic finance space + bonus deep dive into Google’s bid to become the OS for all e-commerce, Circle’s Agent Stack play, and a full guide on how to build an AI Agent from sractch inside). So let’s jump straight into the interesting stuff 🌶️

Monzo’s 2026 financials: the £1.7 billion revenue machine that still has to prove it can escape the UK’s Orbit 🏦🇬🇧

Following the money 💸 Monzo Bank makes more money per customer than Revolut. That sentence would have been unthinkable two years ago. But recently dropped 2026 annual report confirmed it: Monzo now generates £167 in revenue per active personal customer, while Revolut generates £66. The gap is the difference between a primary bank 🏦 and a multi-currency spending card 💳, and it’s the number that will anchor every IPO conversation Monzo has this year.

The headline figures are definitely strong.

• Revenue surged 39% year-on-year to £1.71B.

• Gross profit broke £1B for the first time.

• Statutory pre-tax profit hit £87.3M (up 44%), or £172.6M adjusted for an FCA fine and restructuring charges. Three consecutive years in the black.

• A customer base of 15.2M, with one in five UK adults now banking with Monzo.

• Deposits up 55% to £25.7B. Four revenue streams each clearing £300M, up from one a year ago.

By every growth metric, this is a neobank that has crossed into proper banking territory.

Yet, the investment case isn’t about what Monzo has done. It’s about what happens next, and that’s where the picture gets complicated.

The board pushed out its previous CEO partly over frustration with the company’s UK concentration. His replacement, Diana Layfield, exited the US within weeks of taking charge and is betting the company’s next phase on European markets where Revolut already has a decade-long head start and millions of entrenched customers. Monzo’s statutory profit margin is 5.1%. Revolut’s is 38%. The CET1 ratio fell from 56% to 33% in a single year. And Layfield has told the FT she’s “not in a hurry” to IPO, which is either confidence or a warning that the timeline is longer than the market expects.

So let’s dive deeper and break down the unit economics that matter, the Revolut comparison investors keep getting wrong, the governance drama behind the CEO transition, and where the valuation range of £6-10 billion starts to look either like a bargain or a stretch. The answer depends on a single question: can Monzo replicate in Dublin and Barcelona what it built organically in London?

The Numbers That Matter

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Thanks to AI, finance teams are now shipping board decks, closing books, and building models under the same pressure as always - except now with fewer people. Claude AI can cut a 13-week cash forecast from four hours to 40 minutes, but only if you set it up right. A bad prompt still produces a confident, wrong DCF. A good one produces an auditable first draft that your controller can actually use.

This guide gives you the exact setup: a 60-minute configuration walkthrough, seven copy-paste prompts (DCF, LBO, three-statement model, 13-week cash forecast, unit economics, credit analysis, and monthly close), and the specific failure modes that will burn you if you don’t know about them. Every prompt uses XML-structured instructions optimized for Opus 4.7’s literal instruction-following - the formatting pattern that consistently produces the tightest financial outputs.

Whether you’re a CFO running a $30M SaaS company, an associate building an IC deck, or an FP&A lead trying to close in five days instead of ten, the prompts below are ready to drop into your next session.

And once you’ve built these workflows, the Cowork Automation Playbook and the /goal Guide for AI Agents show you how to put them on autopilot - scheduled deal scoring, autonomous research runs, morning briefings that generate themselves.

Why Claude Opus 4.7 for Finance

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If you’re not a subscriber, here’s what you missed this week:

1. SpaceX IPO: Why the Biggest Listing in History Might Be Uninvestable at $1.75 Trillion 🪐 [The S-1 reveals a $7 billion Starlink profit machine subsidizing $30B in annual AI cash burn, while Elon Musk holds 85% of the vote with no sunset, no independent board, and no path to remove him]

2. Ex-OpenAI’s Aschenbrenner Bet $5.5B on AI Chips. Now He’s Betting $8.5B Against Them 💸 [Leopold Aschenbrenner’s AI hedge fund Situational Awareness went from pure AI long to 62% short semiconductors in one quarter. Here’s what changed and why the thesis still holds]

3. Coatue’s May 2026 Report: The $12 Trillion AI Bet and Who Ends Up on the Wrong Side 📊 [Coatue’s latest public markets report reveals the most extreme winner/loser split in tech stock history, a $12 trillion AI capex wave, and one brutal framework that predicts exactly who wins]

4. Anthropic Just Told AI Founders Exactly What to Build in 2026 🦄 [1 million conversations. 9 consumer AI domains. A full founder playbook - plus where Anthropic’s own products will and won’t compete]

5. ChatGPT’s new personal finance feature is OpenAI’s bid to kill the fintech app economy 🤖📲 [what it’s all about, which fintechs should worry the most, how it connects to OpenAI’s Super App Play & what’s next + bonus deep dives into Anthropic & how it’s becoming Wall Street’s OS, their SME OS play, and what AI startup founders should build in 2026]

6. Google Universal Cart and the Universal Commerce Protocol are Google’s bid to become the operating system of e-commerce ⚙️🛍️ [what Google Universal Cart is all about, why it matters & how it can transform e-commerce forever + bonus deep dives into Google’s UCP, how it just won Agentic Commerce & why AmEx skipped the protocol race altogether inside]

7. The Complete Claude /goal Guide for AI Agents 🤖 [What Claude /goal actually is, why most invocations fail, and how to write production-grade goal conditions for financial research, backtesting, and live market monitoring]

8. How to Build an AI Agent from Scratch (With Working Code) 🤖 [The design framework, working code, and hard-won lessons that take you from “I want to build an AI Agent” to a working one - in under 60 minutes]

9. Claude Cowork Commands, Scheduled Tasks & Automation Workflows: The Operator’s Playbook 🤖 [Every real slash command, the exact prompts that run deal flow and investor updates on autopilot, and the failure modes that will cost you hours if you don’t know them first]

10. The System for Never Hitting Claude’s Limits 🤖 [Most users burn the majority of their token allocation on architecture mistakes, not actual work. Here’s the operational framework that fixes it]

As for today, here are the 2 incredible FinTech stories that are transforming the world of financial technology as we know it. This was yet another insane week in the financial technology space, so make sure to check all the above stories.

Revolut is rebuilding its revenue mix before anyone sees the S-1 🤑🏦

Following the money 💸 FinTech giant and Europe’s most valuable private company Revolut, doesn’t have a banking problem. It has a revenue-quality problem, and two moves in the past week show it knows exactly how to fix it.

Let’s take a look.

More on this 👉 The London fintech is launching a private banking arm targeting clients with £500,000 or more in deposits, with services rolling out later this year. The FCA just granted its trading subsidiary permission to offer leveraged products, managed portfolios, and investment advisory.

Meanwhile, a leaked internal memo from CEO Nik Storonsky on May 15 designated Revolut Business as the company’s top strategic priority, backing it with dedicated growth teams, a £1,000-per-head employee referral bonus for new business accounts, and a commitment to bundle business banking into every new market starting 2027. Not too shabby!

Zoom out 🔎 Read these two developments together. Revolut posted £4.5 billion in revenue last year and £1.7 billion in pre-tax profit. Impressive numbers, but 76% of revenue comes from fees on cards, FX, subscriptions, and crypto trading. Public markets will discount that mix. It’s volatile, competition-exposed, and hard to model forward. Private banking (AUM-based fees, advisory, lending spreads) and sticky B2B relationships (£940 estimated ARR per business customer, 800,000 accounts and climbing) are the antidote.

The private banking play targets a specific gap. Coutts now requires roughly £3 million minimums. Revolut already has millions of UK users in the £500,000-to-£3 million range who are barely monetized. Hiring ex-HSBC private bankers to pair with a digital-first platform is the right hybrid model for mass-affluent clients who want service without the theater.

We can note that wealth already generated £663 million last year. Even modest early traction, say 5,000 clients at £1.5 million average AUM, adds £5-8 billion in managed assets and tens of millions in high-margin recurring revenue.

THE TAKEAWAY ✈️

What’s next? 🤔 Looking ahead, there are several things to watch. First and foremost, the IPO isn’t coming before 2028, and the $200 billion valuation target demands a story that looks more like JPMorgan than Robinhood. Thus, every quarter between now and then (if/when possible), track two numbers: AUM growth in the new wealth segment and B2B’s share of gross profit (already approaching 20%). If both inflect upward, Revolut gets its multiple re-rating before it ever files. If the white-glove service falls flat or credit underwriting stumbles in new markets, the narrative unravels fast. All in all, we can definitely agree that the strategy is correct here. Execution is the only remaining question, and it’s not a small one. But so far, Revolut hasn’t had any problems executing…

ICYMI: Inside Revolut’s PRAGMA: The Foundation Model Trained on 40 Billion Banking Events 🧠 [Architecture, performance benchmarks vs. Stripe, Mastercard, and Visa, regulatory risks, and why PRAGMA may be the most consequential AI bet in consumer finance]

Revolut is the most profitable FinTech on Earth, and it hasn’t even started lending yet 🤯📈 [unpacking the fee-income fortress that makes Revolut structurally different from every competitor, the lending optionality coiled inside that balance sheet, the regulatory dominoes now falling in sequence, and whether $75B is expensive or whether the market is about to find out it was way too cheap + more bonus reads on Revolut, how it’s leveraging AI & bonus deep dives into the latest financials of Nubank, SoFi, Robinhood, and Coinbase inside]

OpenAI is paying for YC’s next batch in tokens it prints itself 🫣💸

The news 🗞️ OpenAI co-founder & CEO Sam Altman just found a way to turn inference compute into venture capital.

Let’s unpack this.

More on this 👉 OpenAI is now offering every startup in YC’s Spring 2026 batch - roughly 169 companies - $2 million in API credits through an uncapped SAFE. That’s ~$338 million at retail prices, paid in a currency OpenAI manufactures at a fraction of face value as marginal inference costs keep falling.

In return, OpenAI gets equity that converts at the next priced round, plus something worth more than any cap table line: deep visibility into what the sharpest AI-native founders are actually building, at cohort scale, in real time.

And that’s hard to put a price tag on.

Zoom out 🔎 Of course, the playbook isn’t new. Yuri Milner’s 2011 blanket bet on YC through SV Angel was the template - $150K cash per company, broad exposure, no picking. Altman, who was at YC when that deal went down, is running the same play with a critical twist: he’s paying in his own product. Every “dollar” of credits consumed flows back as usage data, training signal, and switching costs. The founders get transformative compute runway. OpenAI gets lock-in dressed up as patronage 😎

Altman’s framing - “tokenmaxxing” over headcount - tells you exactly where he wants these companies to land architecturally: inference-heavy, deeply coupled to OpenAI’s stack, expensive to migrate. For teams already planning to burn through trillions of tokens, the dilution math on an uncapped SAFE looks tolerable.

For those building thin orchestration layers on top of foundation models, taking this deal is handing your playbook to the one company best positioned to replicate it inside ChatGPT.

THE TAKEAWAY ✈️

What’s next? 🤔 Looking ahead, here’s what you should watch. First, competitive response: if Anthropic or Google counter with credits-for-equity programs (or credits with no equity attached), the subsidy war gets interesting fast. Second, post-credit behavior: when the $2M runs dry, do these startups stay on OpenAI or scatter to multi-model architectures? That answer will tell you whether this was customer acquisition or ecosystem capture. Third, product overlap: track OpenAI’s feature launches over the next 18 months against what gains traction in this batch. If the pattern rhymes with Amazon Marketplace or Apple’s App Store history, founders will notice, and the next cohort won’t sign as easily.

ICYMI: ChatGPT’s new personal finance feature is OpenAI’s bid to kill the fintech app economy 🤖📲 [what it’s all about, which fintechs should worry the most, how it connects to OpenAI’s Super App Play & what’s next + bonus deep dives into Anthropic & how it’s becoming Wall Street’s OS, their SME OS play, and what AI startup founders should build in 2026]

🧠 What else I’m watching

• More Bank AI Layoffs 🏦 Standard Chartered plans to cut 7,800 back-office jobs - 15% of its 52,000 support roles - over four years, targeting Chennai, Bengaluru, Kuala Lumpur, and Warsaw. CEO Bill Winters frames the move as replacing lower-value tasks with AI-driven automation, not cost-cutting, while some staff will be reskilled. The bank is investing in AI to streamline operations, positioning the technology as a key enabler for efficiency and transformation in its workforce strategy. Yet another reminder to productize yourself 😉 ICYMI:

• Fintech AI Skills Drive 📈 Zopa and ClearScore launched Jobs2030, a coalition upskilling 100,000 fintech and banking professionals in AI by 2030, with 22 members already on board. The free initiative, built around technology, content, training, hiring, and advocacy, offers five courses and 12 modules by year-end, tailored to compliance, engineering, operations, and product roles. Training on Zenarate integrates with LLMs like Gemini and ChatGPT, combining AI coaching, simulations, and real-world scenarios. Backed by Innovate Finance, The City of London Corporation, and venture firms, the program aims to deliver practical AI skills and foster better customer outcomes through industry-led expertise. ICYMI:

• AI Streamlines Vendor Risks 🤖 Deutsche Bank’s TPRM AI accelerates third-party vendor risk assessments, cutting document review time from 30 minutes to under two minutes for up to five files. Developed in-house, the system uses sequential AI agents to retrieve control questions, draft responses, and propose outcomes with 90% accuracy, followed by human validation. This cloud-based solution scales with demand, reducing onboarding timelines and costs while maintaining regulatory compliance. ICYMI:

💸 Following the Money

• Mercury, a provider of banking services for entrepreneurs and the tech industry, has raised $200M in a Series D at a $5.2B valuation.

• Stablecoin-powered digital banking and investment platform Fasset has raised $51M in Series B funding.

• National Australia Bank has acquired account-to-account payments platform Banked. Financial terms were not disclosed.

👋 That’s it for today! Thank you for reading, and have a relaxing Sunday! And if you enjoyed this newsletter, invite your friends and colleagues to sign up:

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SpaceX dropped a 200,000-word S-1 on Wednesday evening. Buried inside is the financial blueprint for the largest IPO ever attempted - roughly three times the size of Saudi Aramco’s record-setting 2019 offering - at a valuation that would make SpaceX worth more than every publicly traded company on Earth except a handful of tech giants. The asking price: a whopping $1.75 trillion 🤯

The numbers tell a story of two companies forced into one balance sheet.

→ Starlink, the satellite broadband business, generated $7.2 billion in EBITDA last year on 63% margins, doubled its subscriber base to over 10 million, and operates 75% of all maneuverable satellites in orbit. It is, by almost any measure, the most valuable connectivity asset built in a generation 🛰️

→ Then there is xAI - Musk’s AI division, merged into SpaceX earlier this year - which burned $6.4 billion in operating losses and is spending at a $30 billion annualized capex rate with Grok trailing every frontier AI competitor. The AI segment alone is responsible for dragging a profitable company into an annual net loss of $4.9 billion. Outch 🤖

And then there is the governance. Musk will hold 85.1% of voting power after the IPO. He serves as CEO, CTO, and Chairman. His compensation is tied to milestones that include a Mars colony of one million inhabitants. He cannot be fired. Three of the largest pension funds in America - CalPERS, the NYC Comptroller, and NYS Comptroller, representing $1 trillion in combined assets - have already publicly demanded structural reforms. SpaceX rejected every one.

What’s interesting is that the S-1 also discloses a $45 billion computing deal with Anthropic that is cancelable on 90 days’ notice, a $60 billion option to acquire AI startup Cursor with a $10 billion breakup fee, $131 million in Cybertruck purchases from Tesla at retail price, and a $20 billion bridge loan that matures in 16 months.

The prospectus also runs to 37 pages of risk factors 😬 Oh, and the total addressable market SpaceX claims? $28.5 trillion, a figure that includes asteroid mining and Mars colonization 🪐

The key question now is not whether SpaceX has built something extraordinary. It clearly has. The question is whether, at 94x revenue, 266x EBITDA, and with a governance structure that gives public shareholders no voice and no recourse, the SpaceX IPO is worth your time and money, and whether its price leaves any margin of safety at all.

Our analysis of the full S-1 says it does not.

Valuation & Deal Structure: The Largest IPO in History

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Good morning & happy Thursday! Today’s issue is especially interesting as we’re diving deep into Google, which is now going all in to become the operating system for the whole e-commerce (what Google Universal Cart is all about, why it matters & how it can transform e-commerce forever + bonus deep dives into Google’s UCP, how it just won Agentic Commerce & why AmEx skipped the protocol race altogether inside), OpenAI that just found a way to turn inference compute into venture capital (what their deal with Y Combinator is all about, what are the risks & challenges here + bonus deep dive into OpenAI, which is now trying to kill the FinTech app economy & how Anthropic told AI founders exactly what to build in 2026 inside). So let’s jump straight into the finnovative stuff 🌶️

Google Universal Cart and the Universal Commerce Protocol are Google’s bid to become the operating system of e-commerce ⚙️🛍️

The BIG News 🔥 Arguably one of the most consequential announcements at I/O 2026 wasn’t another AI model. It was a shopping cart.

Google is no longer content to sit at the top of the funnel. With Universal Cart and the Universal Commerce Protocol (UCP), it’s clearly making a play to own the entire commerce stack: the intent layer, the reasoning layer, and now the protocol layer that connects every merchant to every AI agent.

The pattern is unmistakable if you’ve watched Google before. Define the open standard. Get the ecosystem to adopt it. Then win on the implementation no one else can match.

Let’s dive deeper into this, break down how the product works, what UCP actually standardizes, who benefits, who gets squeezed, and the specific second-order effects investors and operators should be tracking over the next 12 to 24 months.

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Three weeks ago, I published a deep dive on Leopold Aschenbrenner and Situational Awareness LP, the $225M-to-$5.5B AGI-infrastructure hedge fund that quietly became the most intellectually coherent portfolio in public markets. The thesis, in one line, is this: buy the bottlenecks of the AGI buildout, short what AI eats.

Power, compute, memory, optics, fabs on the long side. Legacy IT services on the short side. Simple, sequenced, almost monastic.

In fact, it’s the same split Coatue’s May 2026 report frames as sellers of scarcity vs buyers of scarcity, and the two theses validate each other almost line for line.

Then, earlier this week, the latest 13F dropped. And the book broke its own template.

Gross notional jumped from $5.52 billion to $13.68 billion, a 148% expansion in 90 days. The book went from 29 to 37 disclosed positions across 29 distinct tickers (eight names are now held on both sides simultaneously).

But the headline isn’t the size. It’s the shape.

For the first time in the fund’s history, the disclosed book is net short: 38.1% long, 61.9% short. The short side is a wall of single-name puts on every Tier-1 name in the semiconductor stack: NVDA, AVGO, AMD, MU, TSM, ASML, INTC. Plus ORCL puts. Plus, SMH ETF puts back in size as the largest position in the book at 14.94%.

An important caveat before we go further: 13F filings disclose put and call notional but do not specify whether the fund is long or short those options. The near-universal read of this filing, and the one that fits the fund’s prior pattern of using puts as directional shorts, is that these are bought puts expressing a bearish view. That interpretation is what follows. But directional intent cannot be confirmed from the filing alone.

YTD return on the disclosed positions: +39.45%. SPY: +8.40%. That’s 31 percentage points of alpha in just 4.5 months 🤯

You can watch the whole book live on the Situational Awareness tracker, direction-corrected for P&L, bucket exposure, and the full overlay structure. It updates with every filing.

→ So does this break the thesis? No.

→ Does it change everything about how to express it? Yes.

And anyone running an AI-infrastructure book, building in the space, or trying to figure out what to do with the semis they bought in 2024 needs to update their priors immediately.

Let’s walk through it all.

What Actually Changed in Q1 2026

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Good morning & happy Tuesday! Today we’re diving deep into OpenAI, whose new personal finance features inside ChatGPT is a bid to kill the FinTech app economy (what it’s all about, which fintechs should worry the most, how it connects to OpenAI’s Super App Play & what’s next + bonus deep dives into Anthropic & how it’s becoming Wall Street’s OS, their SME OS play, and what AI startup founders should build in 2026), and fintech giant Revolut that’s actively rebuilding its revenue mix before the IPO (two interesting moves from Europe’s most valuable startup, what they tell us & what’s next + bonus deep dives into Revolut’s latest 2025 financials & foundational AI model PRAGMA inside). Let’s jump straight into the incredible stuff 🌶️

ChatGPT’s new personal finance feature is OpenAI’s bid to kill the fintech app economy 🤖📲

The news 🗞️ The fastest way to destroy a software category is to make it a feature inside something everyone already uses.

OpenAI just connected ChatGPT to 12,000 banks and brokerages. Chase, Fidelity, Schwab, Robinhood , AmEx - all feeding real transaction data into a conversational AI layer powered by GPT-5.5. For the hundreds of millions of users already asking ChatGPT money questions, the answer now comes grounded in their actual spending, investments, and liabilities.

For every fintech startup that built an AI budgeting app on top of Plaid over the past three years, the threat is now existential. And for the broader AI industry, this is the clearest signal yet of where the Super App race is heading: whoever owns your financial context owns the most defensible consumer relationship in tech.

Let’s take a closer look at this, break down who this kills, who survives, what Anthropic’s opposite bet tells us about where AI finance is actually going, and the second-order regulatory and competitive dynamics most are missing.

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