BitFinance with Matthew Snider

Michael Saylor’s "Never Sell" Bitcoin Strategy Just Got Edited

Matthew — Tue, 30 Jun 2026 11:51:25 GMT

Picture a shopkeeper.

She’s sure the gold in her basement will keep climbing, and she doesn’t want to wait for it. So she offers her neighbor a deal: hand her $100, and she’ll pay him $11.50 a year, forever, in cash. She takes his money and buys more gold. As long as those paper slips keep changing hands at $100, she can sell new ones and keep buying.

The wheel turns.

That’s Strategy, and the “slip” is a preferred stock called STRC (with the ironic moniker, “Stretch”).

For most of a year it held near $100 while Bitcoin did whatever Bitcoin does. I walked through the design when Saylor laid it out at Bitcoin 2026, and it was clever, a way to bridge a small, volatile asset to the deep market for steady income.

The catch is the one I flagged a few weeks back:

Gold pays nothing, while the slips demand cash every month, about $1.76 billion a year across all of them.

So the wheel only turns while three things hold: the slips trade near $100, the gold keeps rising, and the neighbor keeps lending. This month the first one broke. STRC fell to about $74, an all-time low, a quarter below the price it was built to hold, and the financing engine seized.

Today Strategy answered. The answer is worth understanding in plain terms, because the company wrapped it in a lot of language.

A reserve. Strategy set aside about $2.55 billion that can only go to the preferred holders and its lenders, roughly 17.4 months of those payments with a written 12-month floor. A few weeks ago that cushion was closer to six months. They’ve tripled it.
A preferred buyback. Up to $1 billion to buy STRC and its sister products on the open market. Buying back a $100 promise trading at $74 retires an expensive obligation at a discount. That part is sound.
A common buyback. Up to another $1 billion aimed at the common stock, MSTR.
A Bitcoin sale program. The part that changes the story. The board authorized selling BTC to fill the reserve, cover payments, and fund the buybacks, with up to $1.25 billion earmarked for the reserve alone and room for more.
BONUS: A higher dividend. The payment on STRC rises from 11.5% to 12%, a sweetener meant to coax the slips back toward $100.

Line them up and one picture forms.

Four of the five, the reserve, the dividend bump, and the two buybacks, exist to defend the slips. The fifth, selling Bitcoin, pays for the defense. The CFO said it plainly: “Bitcoin is capital.”

For a company built on hoarding Bitcoin and refusing to sell, that’s a different sentence than the one they used to repeat. 😬

You can read this two ways at once.

A company that issues when its securities are dear and buys them back when they’re cheap is acting like a capital-markets operator, and that flexibility is worth something. The same kit is also the clearest sign yet that the original design needed a backstop it wasn’t supposed to need.

Months of preferred dividend and interest coverage. The committed-cash figure is 17.4 months; the 25.9-month figure counts Bitcoin not yet sold. Source: Strategy, June 29, 2026.

How to read this, depending on who you are

In early June it was 32 coins, sold to make one payment, with Saylor framing the sale as proof he could. He finally sold.

Today it’s a named program with a dollar figure attached. The flywheel that only turned one way, raise money and buy, now turns both ways by policy. The CEO called it moving “from one-way capital issuance to active capital management,” which is fair, and also an admission that the one-way machine stopped working.

If you hold STRC, the near-term read is constructive, and the market didn’t just agree, it bought hard, sending the shares up about 12% on the day. A reserve, a higher payment, and a buyer standing under par all push the same way. Even so, STRC near $83 is still well below the $100 it’s meant to defend, and these dividends are declared by a board rather than guaranteed, so the structure still leans on Bitcoin not falling much further.
If you hold MSTR, the common stock, it’s more tangled. You now sit behind a larger, more expensive stack of promises. Part of that $2.55 billion reserve was filled by selling fresh shares, which means existing owners were diluted to protect the people standing ahead of them in line. The $1 billion buyback is a real sweetener, though it’s small against the size of the preferred stack. On top of that, the market is already skeptical: for the first time, Strategy’s combined value has slipped below the worth of the Bitcoin it holds. People are paying less for the wrapper than the contents are worth.
If you hold Bitcoin itself, which is most of us, this is the part I’d sit with. Strategy holds about 845,000 coins, near 4% of all the Bitcoin there will ever be, bought at an average around $75,700. With Bitcoin near $60,000, that pile is worth less than they paid. For most of Bitcoin’s life, its largest holders were buyers or, at worst, quiet. Strategy was the buyer everyone watched. As of today, by written policy, it’s also a programmed seller, with the selling tied to payment dates and moments of stress rather than to conviction. That’s new, and it’s the cleanest example I can point to of what I’d call the Saylor risk.

For the record, I’ll say it again:
Bitcoin doesn’t need Saylor.

It ran fine for 11 years before this company bought a coin.

What he added was a flood of money on the way up and, now, a mechanism that can turn into selling on the way down. When a single balance sheet holds 4% of the supply and has just told you it may sell into weakness to make its payments, that balance sheet becomes the largest single-company risk to the price that isn’t the market itself. Not a reason to panic. A reason to watch, closely, where their coins go.

Where this leaves us…

There’s a real case on the other side.

Several level-headed analysts read the STRC drop as a leverage cascade, forced margin selling rather than a verdict on the company. The reserve buys more than a year of time. Buying back a $100 promise at $74 is what a disciplined operator should do. The market also bought the package hard, up more than 12% intraday the day it landed.

The tailwind is real, provided it holds. A higher common stock pushes the company’s value back toward the worth of its Bitcoin, reopening share issuance on workable terms, and a firmer STRC eases the need to keep hiking the coupon or selling coins. The proviso is doing the work, though, since one strong session inside a hard month isn’t a trend.

What hasn’t changed is the shape of the thing. The business still doesn’t earn enough to cover its promises, so it covers them with capital markets, with Bitcoin’s price, and now with Bitcoin itself. A rescue kit is what you build when the product needed rescuing, and the tool with the longest reach points straight at the asset the whole story was meant to protect. I’m not betting on collapse, but I’m watching the coins, because for the first time the company has written down the conditions under which they leave.

So…are you a buyer of STRC? Or would you rather own Bitcoin?🧐

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Matthew Snider is the founder of Block3 Strategy Group, author of “Warren Buffett in a Web3 World,” and publisher of the BitFinance newsletter. He holds a Series 65 and MBA, and has been an active participant in digital asset markets since 2015. This article is for educational purposes only and should not be considered financial advice. Always consult with a qualified professional before making investment decisions.

Sources

Strategy Inc., “Digital Credit Capital Framework” related Form 8-K, June 29, 2026.
Strategy Inc. preferred dividend and interest schedule (STRC, STRK, STRF, STRD).
Nasdaq and Yahoo Finance, STRC and MSTR price history through June 2026.
Company disclosures on Bitcoin holdings (~845,000 BTC, avg. cost ~$75,700).

Stop Prompting Your AI. Start Looping It.

Matthew — Sat, 27 Jun 2026 15:48:50 GMT

There’s an gap between knowing something and being able to use it.

I passed the SIE last week, the entry-level securities exam, and this coming week I start drilling for the Series 7.

Option strategies are a heavy part of that exam, and they’re the part where reading only carries you so far. You can memorize every definition and still hesitate when it’s time to build a position under live conditions. The vocabulary comes from study, while the instinct comes from doing the thing again and again. That’s true of most skills worth having, and it’s the reason I keep looking for ways to turn study into practice.

So instead of grinding another stack of flashcards, I built something that lets me practice on real setups. It runs every morning, proposes trades, and shows me how each one would play out with profit and loss analysis.

Studying for the exam was the reason I started, though the build turned into something more useful than test prep.

This article isn’t a piece about options, and you don’t need to care about options to take something from it. The thing I built and wanted to share with y’all is called an agentic loop, and loops are quietly becoming one of the more useful ways to work.

The shape of a lot of jobs is already changing around this, and the people who learn to build loops now get to shape their own work instead of waiting to see what it turns into - so let’s dive in!

What an AI Loop Is

A loop is a system that runs on its own schedule, reads its own data, proposes something, waits for you to say yes, then acts and reports back. The shape is the whole point.

A chatbot sits idle until you prompt it, while a loop wakes up on its own, does the legwork, and hands you a decision instead of a blank box. It’s a small difference to describe and a large one to live with.

Most of the value comes from that inversion. You stop initiating the work and start reviewing it, which frees your attention for the one part that needs a human.

How Mine Runs

Here’s how I run the set up today.

I gave an agent access to a research vault holding roughly two terabytes of market data that I purchased through a data provider. It also has access to real time / recent data via Alpaca’s API.

Every morning it reads that data, builds candidate trades across a fixed list of equities, and writes me an email. The message lays out the proposed trade, the reasoning behind it, and the projected profit and loss. It places nothing on its own, and it waits.

That email is the entire control surface, which is deliberate. I read it on my phone, and if the setup holds up, I reply to approve, while a weak one gets a reply to pass.

Once I approve, the loop routes the order to Alpaca, the brokerage API I use for paper trading, and sets the limit orders, but only when the trade falls inside the parameters I defined in advance.

Anything outside those guardrails never reaches the market. From there it tracks the position, and the next morning a fresh report tells me how every open trade is moving. None of it requires me to sit at a desk, which is the part that still surprises me. I’ve cleared a morning’s worth of decisions from my phone before finishing my coffee.

The current list runs across SPY, QQQ, NQ, and about two dozen names from the S&P 500. The terabytes of history sitting behind it are what separate a real suggestion from a guess dressed up to look like one.

2 Jobs; 1 System

The build earns its place by doing two things at once.

It drills me on trade structures every day, which is the Series 7 practice I need. At the same time, it’s a live test of a paper-trading strategy and the system underneath it. If the suggestions hold up, I’m validating something I can build on, and if they’re weak, I’m finding that out on paper rather than with real capital. The exam prep and the system test feed each other, since every structure I review for the Series 7 doubles as a data point on whether the underlying strategy deserves to survive.

That’s two problems handled by one piece of plumbing.

The longer game is repetition. By the time I add more indicators and move toward anything live, I’ll have run this same process hundreds, maybe thousands of times, with enough of a record to know it’s repeatable rather than lucky.

When the day comes to add live strategies, the question won’t be whether the process works, since I’ll have watched it work across a long record. It’s the same engine I’ve written about before, the autonomous research running on the Forge, and the loop is how that research turns into reps I can trust.

Why Loops Are the Next Phase

The reason I walked you through a trading loop has little to do with trading. Loops are becoming the way capable operations run, and most people haven’t noticed yet.

The spending tells part of the story.

Gartner projects AI agent software will draw around $206.5 billion in 2026, up 139% from the year before, the fastest-growing slice of enterprise software.

At a recent agentic finance gathering, the argument that traveled furthest was that reliability doesn’t come from a bigger model, but from the harness you build around it, the schedule and the checks and the memory, assembled first before anything clever gets layered on.

The new term of endearment on the street is “loop engineering”.

The opening sits in the gap between interest and execution. One readiness study found that only about 15% of organizations are fully prepared to run agents in production, even as nearly 60% say they’re already spending millions on them.

The bottleneck is rarely the money, and far more often the plumbing.

The people and firms that pull ahead over the next few years will be the ones who wired up a working loop with a human checkpoint while everyone else kept typing prompts into a chat window one request at a time.

So I invite you to take a moment and reflect on your own typical week.

I bet, with a little intention, you could find a task that’s repetitive, bounded, and tied to a rhythm:

the report you rebuild every Monday,
the inbox you sort before noon, or
the figures you check ahead of the same recurring decision.

There’s a loop in there waiting to exist if only you can recognize and build it.

A weekly client update can pull its own numbers, draft itself, and land in your inbox waiting for a yes, leaving you to edit rather than assemble from scratch.

Start with the checkpoint, the moment where you approve or decline, then design everything else around keeping a person standing there. The model is the cheap part, and the harness around it is the skill worth developing now, because the efficiency gap between people who can build these and people who can’t is going to keep widening.

BUFFETT FRAMEWORK QUESTION:
Would Warren Buffett hand his decisions to a machine?
He’d never give up the judgment, though he spent a career building systems that enforce a checklist and keep a person accountable for every call. A loop with a human gate is that same idea written in software, clearing away the busywork while protecting the one decision that matters.

Keep Yourself in the Loop

The pun was intentional and the caution to using these tools is real. The same autonomy that makes a loop useful is what makes a fully unattended one risky.

The Atlantic Council recently described agentic AI in markets as a contested battlespace, where autonomous systems can probe and exploit weaknesses faster than people can respond.

A loop with no one watching can be gamed, or it can compound a small error into a large one before anyone looks up. The discipline isn’t in trusting the machine to run free, but in designing the one place where you keep a hand on the wheel.

That’s the shift worth watching, the move from AI that answers when you ask to AI that works on a schedule and waits for you to decide. Mine is carrying me through the Series 7 and hardening a trading system at the same time, and the version you build will look nothing like it.

Building it is the skill that counts.

Until next time fam!

🛑 SIGN UP TO BE PART OF WHAT WE’RE BUILDING! 🛑

I’m building the early cohort for when the Discord opens and the first indicators ship. If you want a seat, this is where to claim one.
Interested? Fill out the 30-second interest form here. 👈

This Week In 2 Minutes

BlackRock’s new Bitcoin Income ETF Sells the Only Days That Matter (June 24)

BlackRock's new BITA fund hands Bitcoin holders a 15 to 25% yield by selling covered calls every month, which sounds great until you notice what a covered call caps: the up days.

Bitcoin keeps almost its entire annual return in about ten sessions, so strip those out and a +90% year flips to -25%, meaning you're trading away the rare bursts you bought Bitcoin to catch. The first generation already ran this experiment and lost, with the original covered-call ETF down near 28% while plain IBIT held its ground. You also pay more in fees and get taxed sooner for the privilege.

The income is real, but on Bitcoin you're usually funding it with your own best days.

Read the full article here.

How (and Why) I Own Quantum Tech Without Having to Pick the Winner (Jun 26)

Quantum stocks are posting numbers that look like typos, like Rigetti running almost 4,800% in a year on roughly $7 million in sales, while the entire sector booked about a billion against a projected $2.7 trillion prize.

The trap is treating quantum as one bet, buying IonQ or Rigetti and praying the physics works, when it's really a five-layer stack and the money sits in knowing which layer you're holding. One layer already has revenue and a federal deadline forcing the buying, one gets paid whoever wins the machine race, and one is the giants who can fund a decade of losses out of petty cash.

I own the whole stack through one ETF, stay bullish on the build-out and cautious on the price, and keep the lottery tickets small enough to survive the 50% drop that tends to come.

Read the full article here.

🏆 Market Winners

Micron Technology (MU): Micron surged after delivering another strong earnings beat and raising forward guidance, reinforcing the view that demand for AI memory remains exceptionally strong. While investors have become more selective around AI spending, Micron’s results suggested infrastructure demand is still healthy and enterprise customers continue expanding deployments.
(not bad YTD…)
Eli Lilly (LILY) was among the week’s strongest performers as healthcare rotated back into favor. Investors continued rewarding companies with durable earnings growth and defensive characteristics while many high-growth technology names experienced profit taking.
Bitcoin (BTC) stabilized near the $60,000 level despite weakness across many technology stocks. Its relative resilience during a volatile equity week suggests institutional buyers continue viewing Bitcoin as a strategic allocation rather than purely a speculative asset.
Large U.S. Banks: Several major U.S. banks outperformed after successfully passing the Federal Reserve’s annual stress tests. The results cleared the way for higher dividends and share repurchase programs, boosting investor confidence in the sector’s capital strength.

📉 Market Losers

1. Apple (AAPL): Apple came under pressure after announcing price increases tied to rising memory costs. Investors worried that passing higher component expenses to consumers could weigh on hardware demand during the coming quarters.
Semiconductor Stocks: The semiconductor sector experienced broad selling as investors questioned whether AI-related capital spending has become overheated. Even high-quality chip companies saw sharp declines as traders locked in gains following an extended rally.
Carnival Corporation (CCL) fell after reporting disappointing revenue trends and issuing weaker-than-expected guidance. The results renewed concerns that consumer travel demand may be moderating despite a generally resilient economy.
AI Hardware Suppliers: Several AI infrastructure and electronics suppliers sold off as investors questioned whether the massive wave of AI investment can continue at its current pace. The rotation reflected valuation concerns more than deteriorating fundamentals, but it marked one of the weakest weeks for the group in months.

👀 What to Watch This Week

U.S. Jobs Report: The June employment report will likely be the week’s biggest macro catalyst. A stronger-than-expected print could shift expectations for Federal Reserve policy and increase market volatility.
Federal Reserve Rate Expectations: Investors will continue parsing economic data for clues about whether policymakers remain on track for additional tightening or can pause later this year.
Technology Leadership: After a difficult week for semiconductors and AI stocks, traders will be watching whether buyers step back into the sector or whether leadership continues rotating toward healthcare, financials, and software.
Early Earnings Season Signals: As second-quarter earnings season approaches, investors will focus on corporate guidance more than headline results, particularly commentary around AI spending, enterprise demand, and consumer strength.

Matthew Snider is the founder of Block3 Strategy Group, author of “Warren Buffett in a Web3 World,” and publisher of the BitFinance newsletter. He holds a Series 65 and MBA, and has been an active participant in digital asset markets since 2015. This article is for educational purposes only and should not be considered financial advice. Always consult with a qualified professional before making investment decisions.

Article Sources

How (and Why) I Own Quantum Tech Without Having to Pick the Winner

Matthew — Fri, 26 Jun 2026 11:51:45 GMT

Not long ago, Rigetti, a quantum computing stock, ran up nearly 4,800% in a single year, taking its market value from around $200 million to roughly $15 billion.

Here’s the strange part. These companies barely sell anything. Rigetti booked about $7 million in revenue last year.

IonQ, the largest of the pure-plays, did about $130 million and still trades near $20 billion. The entire sector, every quantum company on the planet, booked a little over a billion dollars in sales in 2025. McKinsey thinks the technology creates up to $2.7 trillion in value by 2035.

Read those last two numbers again.

A trillion-dollar prize.
A billion in sales.

That 2,700x difference is the whole trade.

For a while it was easy to wave off as a bubble with a physics degree. Then two things happened:

In May, the government became a shareholder in nine quantum companies.
In June, the President signed two executive orders pointed straight at the technology.

The timeline stopped being a guess so that it’s no longer a matter of “IF” but “WHEN”.

Quantum isn’t one trade. It’s a stack.

Here’s the mistake I see many investors and clients make. They treat “investing in quantum” as one decision: buy IonQ or Rigetti, hold on, and hope the science works.

That’s the lottery ticket.
It’s also the smallest, riskiest layer of a much bigger structure.

Underneath the pure-plays sits a stack. One layer already has revenue and a federal deadline forcing customers to buy. One layer gets paid no matter which machine wins. One layer is the giants, who can fund a decade of losses out of petty cash. The government just took equity across it.

The money isn’t in picking the winning machine.
It’s in knowing which layer you’re buying.

That’s the reframe. Sixty seconds on what the machine does first, because the technology is what tells you which layer is real.

What the machine actually does

Picture a computer solving a maze. A normal one traces a single path, hits a dead end, backs up, and tries the next. One route at a time. A quantum computer runs every path at once, then reads off the way out.

This epic visualization brought to you by the team at 60 Minutes shows this example as roughly the real physics; not a metaphor.

The engine is interference: the machine lines up all those paths so the wrong ones cancel and the right one rings clear. A normal bit is a switch, on or off. A qubit is both at once, so a few hundred of them hold more combinations than there are atoms in the visible universe.

That’s the power; but it’s also the catch.

Those in-between states are so fragile they collapse if you look at them wrong, and keeping them stable long enough to compute is the whole engineering problem.

Quantum won’t load your email faster. It’s built for one job: finding a single right answer buried in an impossible pile of wrong ones.

A new drug molecule.
A better battery.
The cheapest route through a global supply chain.

That’s where the $2.7 trillion comes from.

Why now, and not five years ago

For years the real knock on quantum was simple. The machines made too many errors to be useful. That is beginning to change in a meaningful (and investable) way.

Google’s Willow chip crossed below-threshold error correction, the point where adding more hardware fixes more errors than it creates. IBM put a date on a fault-tolerant machine, 2029. The roadmaps started reading like project plans.

Then the checks cleared.

In May, the Commerce Department signed letters of intent to put roughly $2 billion into nine quantum companies, and took a minority equity stake in every one.

IBM got $1 billion to build Anderon, a domestic quantum-chip foundry, and matched it with a billion of its own.
D-Wave, Rigetti, Quantinuum, Infleqtion, PsiQuantum, and Atom Computing got $100 million each.

In June, two executive orders set a 2028 deadline for a national-lab machine and pulled the federal encryption-migration deadline forward to 2030 and 2031.

The 5-layer stack

Most people buy the top of this stack and ignore the rest. Here’s all five, from the speculative tip down to the foundation.

The pure-plays. The lottery tickets, and where the valuations get silly. Nobody knows which architecture wins, which is the whole point.

IonQ (IONQ), Quantinuum (QNT), trapped-ion
Rigetti (RGTI), superconducting
D-Wave (QBTS), annealing + gate-model program, both superconducting qubits
Infleqtion (INFQ), neutral-atom
Quantum Computing Inc (QUBT), photonic

IonQ trades near $20 billion on ~$130 million in sales, and these names swing 30% in a week. Smallest slice.

The security mandate. The layer most people miss, and the only one with revenue today and a law forcing the buying. The federal order makes every agency rip out today’s encryption by 2031, a procurement wave with a calendar.

SEALSQ (LAES), quantum-resistant security chips
Arqit (ARQQ), post-quantum encryption software, already landing early defense and telecom contracts
BTQ Technologies (BTQ), the same theme
Palo Alto (PANW), Cloudflare (NET), large caps shipping the new standards
SandboxAQ, the private leader, an Alphabet spinout on more than a billion raised

For the crypto crowd, the same vulnerable math secures Bitcoin, which can migrate to quantum-resistant signatures through a soft fork when it needs to.

Picks-and-shovels. Whoever wins the architecture race, they all buy the same lasers, cryogenics, control electronics, and wafers. Quantum is one market among many for these names, so you get exposure with a floor under it.

Coherent (COHR), Lumentum (LITE), lasers and photonics
FormFactor (FORM), cryogenic test
MKS Instruments (MKSI), Keysight (KEYS), control and measurement
GlobalFoundries (GFS), took $375 million to build a quantum foundry

The giants. Quantum programs funded by real businesses. You give up the lottery upside and get a quantum option bolted to a cash machine.

IBM, building the Anderon foundry, fault tolerance targeted for 2029
Alphabet (GOOGL), built the Willow chip
Honeywell (HON), owns most of Quantinuum
Nvidia (NVDA), sells CUDA-Q and NVQLink, invested in three quantum makers in 2025
Microsoft (MSFT), Azure Quantum

The index. The simplest way to own all of the above.

Defiance Quantum ETF (QTUM), 70-plus names across every layer, 0.40% fee

One ticket holds the whole stack. For most people, this is the position, and everything above is to understand what’s inside it.

The number everyone quotes is the wrong one

Here’s the discipline that matters most: The headline metric in quantum is the qubit count, and on its own it’s close to meaningless.

A machine can boast a thousand physical qubits and be further from useful work than one with fifty error-corrected, “logical” qubits. Physical qubits are raw and noisy. Logical qubits survive error correction and actually compute.

A company can announce a big number and move its stock 20% on a metric that doesn’t measure the thing you’re buying.

A qubit count is the Sharpe ratio of quantum: a precise-looking number measuring something soft underneath.

The same discipline I bring to a backtest applies here. Before you act on the number, ask what it represents. Watch logical qubits and error rates, not the press-release headline.

Managing the risks

As always, we would be prudent to discuss some of the risks and concerns here.

Sector and timeline risk sits at the bottom. The whole thesis assumes the machines scale on roughly the advertised schedule. Fault tolerance has been “a few years out” for a decade.

Idiosyncratic name risk sits in the middle. These are small, thinly covered companies. In February, short-seller Wolfpack Research accused IonQ of misleading investors, alleging a $54.6 million revenue “black hole” from lost government contracts. True or not, that’s the kind of single-name shock that doesn’t show up until it does.

Valuation and sentiment risk sits on top, and it’s the one most likely to hurt. A sector trading on roadmaps reprices violently on a single demo or delay. Priced for perfection means every miss is expensive.

Buffett Framework Question: “Am I buying a business or a roadmap?” In picks-and-shovels and the giants you’re mostly buying businesses that happen to carry a quantum option. In the pure-plays you’re buying a roadmap. That difference decides how much you commit, not whether you show up.

So how do I size around all of this? Three rules:

Size for a 50% drop. You’ll get one.
Treat it as a multi-year option, not a trade. The payoff needs machines that don’t exist yet.
Move on milestones, not press releases. Logical qubits and signed contracts, not headlines.

What I’m Watching

The qubit counts are the noise. The signal is which companies report logical, error-corrected qubits instead of raw ones, which of the nine Commerce letters convert into actual milestone payments, whether SEALSQ and Arqit start landing the federal procurement the 2031 deadline guarantees, and whether IonQ answers the Wolfpack allegations with numbers rather than press releases.

The architecture that reaches fault tolerance first resets the whole table.

I’m bullish on the technology and cautious on the price, and those sit together fine. The build-out is real, the security spending is already moving, and the odds that any single ticker is the decade’s winner are lower than the hype suggests.

Own the theme through the stack, not through one bet.

Keep the lottery tickets small. Be right about the build-out without having to be right about the company, the architecture, or the year.

You can’t see which path leads out of the maze yet.
So hold the whole thing until you can.

Until next time fam!

Subscribe now

Sources

$2 billion in Commerce equity stakes across nine companies (IBM $1B for the Anderon foundry, GlobalFoundries $375M, $100M each to D-Wave, Rigetti, Quantinuum, Infleqtion, PsiQuantum, Atom Computing, $38M to Diraq): CNBC, DataCenterDynamics, Manufacturing Dive.
The two executive orders (June 22, 2026), the 2028 national-lab machine, and the 2030/2031 encryption deadlines: NBC News, Cybersecurity Dive.
Quantinuum IPO (~$15.7B, more than 400x revenue): CNBC. IBM fault-tolerance target of 2029: Yahoo Finance.
$2.7 trillion of value by 2035, ~$1B sector revenue in 2025, and $12.6B of 2025 startup funding: McKinsey.
Rigetti’s ~4,800% twelve-month run, ~$15B valuation and ~$7M revenue: The Motley Fool, stockanalysis.com. IonQ revenue (~$130M) and valuation, plus the Wolfpack short report alleging a $54.6M revenue gap: stockanalysis.com, DataCenterDynamics.
Security and supplier names (SEALSQ, Arqit, Coherent, FormFactor, MKS, Keysight, GlobalFoundries) and Nvidia’s quantum investments: StockTitan, The Quantum Insider.
NIST post-quantum standards (FIPS 203/204/205, 2024): NIST. Willow below-threshold error correction: Google Quantum AI, Nature, December 2024.

BlackRock’s new Bitcoin Income ETF Sells the Only Days That Matter

Matthew — Wed, 24 Jun 2026 10:41:50 GMT

Strip Bitcoin’s 10 best days out of each year,
and a +90% asset turns into a -25% one.

That single line is most of what you need to know, because BlackRock just launched a fund whose design, in effect, sells those days.

Studying for the Series 7 has me living inside options theory lately, which I enjoy as a self-professed finance nerd. That’s what pulled me into the mechanics of BITA, BlackRock’s new Bitcoin Premium Income ETF, which started trading on June 16.

I've helped build and run hedge funds designed to put idle crypto to work generating income - funded by hundreds of millions in member contributions in kind - and we steered clear of the covered-call approach every time for some very important reasons.

What follows is why.

How a fund like this actually works

Bitcoin pays no dividend and throws off no cash.

A holder sitting on a large position naturally wants it to produce something, and with more than a trillion dollars of Bitcoin sitting idle, the pitch writes itself: keep your coins, collect a yield.

BITA answers that wish. It owns Bitcoin, mostly through spot and BlackRock’s own IBIT fund, and it targets a 15 to 25% annual yield while claiming to preserve about 70% of Bitcoin’s upside.

It produces that yield by selling covered calls, and the mechanics are where the trouble hides. A call option is a ticket you sell to another investor. The ticket gives them the right to buy your Bitcoin at a fixed price, called the strike, for a set window of time.

They pay you cash up front for it.
That cash is the premium, and the premium is the “income.”

Herein lies the rub.

Imagine you own a house and you sell your neighbor a ticket that lets them buy it at $500,000 anytime this year, and they hand you $5,000 today for the privilege. Great, now you have a house and $5,000 with two scenarios:

If the market stays calm, you keep the house and the cash. (income! woot!)
If a boom hits and the house is suddenly worth $700,000, your neighbor cashes the ticket, buys at $500,000, and pockets the difference. You collected $5,000 and watched $200,000 walk out the door. 😬

A covered call on Bitcoin is that trade, run every month. You collect steady premiums, and in exchange you agree to hand over the large gains whenever they arrive.

On a quiet asset, that can be a fair bargain.
On Bitcoin, it runs straight into a math problem.

The days that decide the year

Bitcoin doesn’t deliver its returns evenly. It delivers them in rare, violent bursts.

Over the past six years, a buy-and-hold returned a median of about 90% a year. Take away just the ten best trading days in each of those years, and that median flips to roughly negative 25%, with two of the six up years turning into losses.

The whole result lives in a handful of sessions, and those sessions tend to cluster around catalysts you only recognize in the rear-view mirror.

A covered call’s entire function is to cap the up days.

Run it on Bitcoin and you are short the exact days that produce the return, on the one asset most dependent on them. The “keep 70% of upside” promise sounds reasonable until you remember the upside isn’t smooth. If the slice you surrender lands on the few days that carry the year, you haven’t given up 30% of a steady climb, you’ve given up a piece of the rare bursts that are the entire reason to hold the asset.

The averaged-sounding number hides where the loss actually falls.

Selling volatility on a schedule

There’s a subtler problem in the timing.

Selling a call is selling volatility. When you sell that ticket, you’re betting the asset won’t move much, and if it moves hard, you lose more than the premium you collected. Bitcoin moves hard. The price of that bet swings too, expensive in some months and cheap in others.

BITA writes its calls on 25 to 35% of the fund every month no matter what.

BlackRock’s own head of ETFs described the income as something the fund generates “irrespective of market conditions,” which is the problem stated out loud.

Volatility is usually cheapest right after a sharp correction, once the panic drains out and the option market underprices what Bitcoin is about to do, and that’s also the moment a rebound is most likely. A calendar overwrite sells into that window anyway, collecting a thin premium precisely when it caps the most recovery.

A disciplined version of this strategy would sell only when volatility is rich and the trend is genuinely capped, maybe a quarter of the months, rather than every month on autopilot.

The first ones already failed

We don’t have to theorize, because the first generation ran the experiment. YBTC, the original Bitcoin covered-call ETF, returned about negative 28% over the past year and trailed plain IBIT in both price and total return, even through the down stretches where the overlay was supposed to cushion the fall.

The headline yields are the tell.

A fund can advertise a distribution rate north of 30% while much of that payout is return of capital, your own money handed back to you as the share price erodes underneath it. Strip that out and the real yield can sit near 3%. The paycheck looks generous until you notice part of it is your own deposit.

Then there’s the bill.

BITA charges 0.65% a year against 0.25% for holding IBIT outright, and the distributions are taxable in the year you receive them, while holding Bitcoin lets the position compound untouched until you choose to sell. You pay more in fees to earn less in total return, and you get taxed sooner on the smaller result.

Buffett Framework Question: “Would I sell my best ten days a year for a premium I might not collect?” Put that way, the answer for a long-horizon Bitcoin holder is clear. The premium is small and certain, the upside you hand over is large and concentrated, and that’s the wrong side of the asymmetry you bought Bitcoin to own.

When the trade makes sense

None of this makes covered calls useless.

On broad stock indexes and blue chips, covered-call writing is a respected, decades-old approach, and for good reason. Those returns are steadier and more predictable, the gains don’t hide in a handful of unrepeatable days, and trading the occasional capped month for steady premium is a fair exchange.

Buy-write indexes have run on that logic for years. In a flat or grinding market the overlay smooths returns, the cash is real, and an investor who needs spendable income today might reasonably accept slower long-run growth to get it.

The strategy isn’t broken, but its match with this particular asset is.
Bitcoin’s value case rests on the right tail, and a covered call is a machine for selling the right tail.

When I’ve weighed income options for members in the funds I’ve helped run, the conclusion kept landing in the same place. Income from a volatile, right-tailed asset has to be dynamic, sized and timed to volatility instead of run on a fixed monthly calendar, or it bleeds away the thing that made the asset worth holding.

For most people the cleaner path is plain spot exposure and patience, with any options overlay used as a tactical tool rather than a permanent setting.

Expect these products to multiply regardless.

People want their Bitcoin to pay them, and that wish is understandable. Issuers are happy to manufacture a yield number that markets well, even when total return suffers for it, and Goldman Sachs already has a near-identical fund lined up for July. The demand isn’t going anywhere, and the math underneath won’t change to meet it.

The income is real. The question is what you’re funding it with, and on Bitcoin the answer is usually your own best days.

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Sources:

BITA fund mechanics — 0.65% fee, monthly covered calls on 25–35% of holdings, 15–25% target yield, ~70% upside capture, June 16 Nasdaq launch

Bitcoin return concentration — +90% median annual return falling to ~−25% without each year’s ten best days (2020–2025) David Eng analysis, reported by CCN

YBTC returns and distributions — ~22% annualized before taxes / ~9% after taxes since January 2024 inception, −3.45% for 2025, return-of-capital distribution mechanics

Fee comparison and Goldman filing — IBIT 0.25%, YBTC 0.95%, BTCI 0.99%; Goldman Sachs comparable product filing. CoinMarketCap

The 100-Year-Old Receipt That Will Put Bitcoin in Your Brokerage Account

Matthew — Sat, 20 Jun 2026 12:26:01 GMT

There’s a hundred-year-old piece of Wall Street plumbing that could do something no Bitcoin ETF can. It can hand you a direct claim on real Bitcoin, hold it in the same account as your stocks, let your adviser manage it, never ask you to touch a wallet, and yet be able to redeem back for Bitcoin when you are ready.

It wasn’t built for crypto. It was invented in 1927 so Americans could own foreign companies they had no clean way to buy, and for the better part of a century it has quietly moved trillions of dollars of assets around the world. Now a team is pointing that same machinery at Bitcoin, and the result is a way to own the asset inside the accounts you already have, without the wallet, the fees, or the layers in between.

It sounds boring. That’s exactly why it might work.

The problem you already feel

If you want Bitcoin, you have plenty of options, and none of them quite gives you what you want: to hold Bitcoin the way you hold everything else, in your brokerage account, next to your stocks, where your adviser can manage it and you are not lying awake worried about a lost seed phrase.

Today, putting Bitcoin to work means picking a wrapper, and each one asks you to give something up.

A spot ETF like IBIT: simple to buy, but you own fund shares rather than Bitcoin (the fund owns your Bitcoin), and you pay a fee, as the ETF manager sells that Bitcoin behind each fund share every day you hold it.
A covered-call fund like BlackRock’s new BITA: it pays you monthly income, but caps how much of Bitcoin’s rise you actually keep.
An equity proxy like Strategy: leveraged exposure through a single stock, with dilution and corporate risk stacked on top.
Self-custody: you truly own the coins, and you also own the wallet, the keys, and every way to lose them.

Each one slips a layer between you and the asset. You end up holding a fund’s claim, a company’s stock, or a set of keys you have to guard alone. None of it is fraud. The thing you can buy easily is rarely the asset itself, more often a derivative wearing its costume.

What a depositary receipt does

Picture a coat check. You hand over your coat, the attendant gives you a small numbered ticket, and the coat goes in the back. The ticket is light, easy to carry, and easy to hand to someone else. It isn’t a photo of your coat or a rental of a similar one, but a direct claim you can turn in for the real thing whenever you want – not an IOU.

A depositary receipt is that ticket, built for a security. You can hold it, hand it to another investor on the secondary market, or turn it in and take delivery of the underlying asset.

The tool dates to 1927, when J.P. Morgan issued the first one so Americans could own shares of a foreign company without the shares ever leaving their home country. A US bank bought the real foreign stock, held it abroad, and issued a receipt that traded in New York like any other share. You bought the receipt in your normal brokerage account and owned a direct claim on the real stock sitting overseas. Today more than 6,500 institutions use depositary receipts to hold over a trillion dollars of assets this way. It’s old, well-worn plumbing.

Now point that same century-old machinery at Bitcoin.

A Bitcoin IPO without the IPO

A Bitcoin depositary receipt works the same way. A regulated custodian holds the actual Bitcoin, and you hold a receipt that is a direct claim on a set amount of it, one to one, fully backed. Bitcoin makes the model cleaner than a real coat check, because every coin is interchangeable, so you never need your exact units back, just the amount your receipt represents. The receipt carries a Ticker, CUSIP and an ISIN, the same identifiers your stocks use, and it clears through the Depository Trust Company, the pipe that settles almost every share trade in the country. It can sit in a brokerage account, get margined, and move between accounts, and the Bitcoin never touches your personal wallet.

Read that list again, because it quietly solves the whole problem. No fund fee eroding your position. No company issuing shares behind you. No covered call capping your upside. No seed phrase. You hold a claim on real Bitcoin, structured like an ordinary security on the menu.

That’s the closest thing to a true Bitcoin IPO. Not a fund that owns Bitcoin. Not a company that owns Bitcoin. A regulated, brokerage-native receipt that is Bitcoin, one to one, held for your benefit.

BUFFETT FRAMEWORK QUESTION
When you buy a Bitcoin product, what do you own? With most wrappers, the plain answer is a claim on a fund, a share of a company, or a contract on a price. With a depositary receipt, the answer is a direct, fully backed claim on a set amount of Bitcoin held in custody for you. Buffett’s whole discipline is knowing exactly what sits under your position. A receipt gives you a clean answer. A derivative gives you a paragraph.

Why this matters if you work with an adviser

This is where the receipt earns its keep, and it’s the part the ETF conversation usually skips.

Say you use a financial adviser. You want Bitcoin in the mix, but you don’t want to become your own bank, manage keys, or explain a hardware wallet to your spouse. With a depositary receipt, you don’t have to. The Bitcoin sits with a regulated custodian, your adviser holds the receipt in the same account as everything else, and it shows up on your statement like any other line item. You get the ownership and skip the operational risk.

Now flip it around. Say you are the adviser. Clients are asking for Bitcoin, and you would like to say yes, but you don’t want to touch crypto custody, take on key-management liability, or bolt an unfamiliar platform onto your practice. A receipt lets you offer access on the rails you already run. It settles through the DTC, carries a Ticker, CUSIP and an ISIN, and reconciles like any security your back office already handles. You give clients the asset without becoming a crypto company.

That two-sided fit, clean for the investor and clean for the adviser, is what turns this from a curiosity into a real on-ramp. It moves Bitcoin into ordinary advised accounts without asking anyone to change how they already work.

This is already being built

None of this is theoretical. One group well down the road is Receipts Depositary Corporation, a Houston firm started in 2022 by former Citi depositary receipt specialists. They issued the first Bitcoin depositary receipt in 2024, built on the same century-old ADR structure: cleared through the DTC, custodied at a federally chartered, OCC-regulated bank, 100% backed, with the underlying Bitcoin barred from being lent, pledged, or rehypothecated. They have since added receipts for Ethereum, Solana, and XRP, and this week they closed a $7 million oversubscribed round led by LiveOak Ventures, with OTC Markets Group, GTS, and others joining, to widen the product to more investors and asset classes. I’m pointing to them to show the category has real builders and real capital behind it, not as a recommendation.

Who it’s really for

This is the part that usually gets lost. The first Bitcoin receipts went to qualified institutional buyers, the banks and funds that have leaned on this structure for a century. That’s not the ceiling, but the proof that the plumbing works.

The whole design points the other direction, toward you. A receipt only beats a wrapper because it can sit in an ordinary advised account, owned by an ordinary investor and managed by an ordinary adviser. A Bitcoin IPO without the IPO matters only if regular people can hold it in the accounts they already have. That retail and advised version is the destination this is built for, and it’s closer than the average headline lets on.

The objective view

A better structure doesn’t win on merit alone. ETFs have distribution, marketing budgets, and a multi-year head start, and a receipt still has to earn its way onto brokerage and adviser platforms before it reaches your statement. That takes time, and none of it is guaranteed.

The appeal, though, is straightforward. A direct, fully backed claim on Bitcoin, cleared through the same pipes as your stocks, custodied by a regulated bank, with no wallet to lose and no fund skimming the position or owning that Bitcoin. For an investor who wants Bitcoin without the headaches, and for an adviser who wants to offer it without becoming a crypto custodian, that is a clean answer to a messy problem.

The tool is a century old, the use is brand new, and the gap between the two is closing faster than it looks.

I’m excited to see where we go from here! Moving on…

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This Week in 2 Minutes

Most Crypto Tokens Are Uninvestable by Design (June 16)
The setup I keep hearing is low price plus partnerships equals generational buy. It rests on two mirages and a moat that was never there.

Decentralization removes the moat on purpose, so no cash flow sits under the price and a 98% drawdown is just a number, not a floor. Partnerships prove the tech gets used, not that value reaches the token: California put 42 million car titles on Avalanche, and the fees get burned while the holder gets a headline.

Capitalism takes the open-source rails, runs them privately, and leaves the coin behind. Bitcoin and Ethereum have real network effects; most of the rest are uninvestable by design.

Read the full article here.

Coinbase Made an AI Your Fiduciary. Read the Fine Print. (June 18)
Coinbase registered an AI agent with the SEC, CFTC, and NFA as an investment adviser. It reads your full account, runs around the clock, and issues explicit buy and sell calls, then the same disclosures say its output may be wrong and the losses are yours.

A registered adviser is a fiduciary that can’t disclaim its duty of care, so claiming the standard and waiving the outcome in one product has no precedent in securities law. Two weeks earlier, Robinhood made the opposite bet: AI to make human advisers faster, with a person kept on the hook.

The commodity advice tier just went free, and the judgment tier, along with the licensed human accountable for it, got more valuable.

Read the full article here.

This Week’s Winners…🏆

Everyone who buys gas, flies, or ships things. The surprise US-Iran peace framework reopened the Strait of Hormuz and sent oil tumbling at the start of the week, which is basically a stimulus check routed through the pump. Airlines, freight, and the broad consumer all caught the updraft, and this quietly does more for household budgets than any policy headline this year.
The hawks, savers, and the dollar. Kevin Warsh held rates at his first meeting as chair and the meeting pointed to a possible hike in late 2026. Warsh spent years arguing the Fed was too soft, and he just proved he meant it, which keeps fat yields on cash and hands the dollar a credibility bid most people are sleeping on.
The old economy, briefly cool again. The Dow notched a record midweek, and Caterpillar (+3.4%) and Disney (+3.1%) led it higher Thursday. Cheap energy plus a de-risked map is rocket fuel for the unglamorous industrial and cyclical names that the AI darlings made everyone forget.

…and Losers 📉

Bondholders. The “hike on the table for late 2026” line pushed yields up as the Fed held but signaled a hawkish tilt, and anyone who loaded up on duration betting on cuts got a cold shower. The new chair has been warning about loose policy for a decade, so this is a posture, not a one-off.
Small caps. The Russell 2000 lagged and slid about 0.9% midweek while the big indexes flirted with records. Small caps run on cheap money to survive, and Warsh just told them the oxygen might get thinner.
Energy producers. The flip side of the consumer’s gift: crude cratered on the Iran deal, and the drillers and majors handed the gains right back. The same barrel that feels great at the pump is a gut-punch on an energy company’s income statement.
The “safe” blue chips. Even on Thursday’s green day, IBM fell about 4.8%, Johnson & Johnson 2.5%, and JPMorgan 2.5%. When the tape flips risk-on, the defensive and financial names are exactly what gets sold to fund the chase.

What to Watch (June 22)👀

May PCE, Thursday: Warsh’s first inflation test. The Fed’s preferred inflation gauge lands Thursday alongside the final Q1 GDP read, the first print since the hawkish hold. A hot number and the new chair looks prophetic; a soft one and the bond market finally exhales.
Micron, Wednesday: the AI cycle’s reality check. Micron has run nearly fourfold this year on AI memory demand, with revenue expected to surge year over year. If the AI trade is ever going to crack, the memory names usually flash the warning light first, so the guide matters more than the headline.
FedEx, Tuesday: the world economy’s pulse. FedEx reports Tuesday, and boxes moving is GDP you can actually count. Its outlook is a faster read on global trade and the consumer than any government statistic, so watch the guidance, not just the quarter.
Bank stress tests, Wednesday: license to pay. The Fed’s stress-test results come Wednesday and set how much capital the big banks can return through buybacks and dividends. With financials already lagging, a clean pass tends to unleash a wave of announcements that could wake the group up.

Coinbase Made an AI Your Fiduciary. Read the Fine Print.

Matthew — Thu, 18 Jun 2026 11:26:58 GMT

On Tuesday, Coinbase shipped 21 products in a single event.

Bitcoin-backed mortgages accepted by Fannie Mae.
Tokenized stocks.
Options on crypto and equities.
Pre-IPO futures on companies like SpaceX.
A merger of its U.S. and international exchanges.

Most of it is volume, the kind of release calendar built to look like momentum.

But one product launch hit me like a lightning bolt: Coinbase registered an AI agent with the SEC, the CFTC, and the NFA as an investment adviser.

It’s not a chatbot that floats ideas and tells you to do your own research, but a registered adviser that reads your full account history, runs around the clock, and tells you what to buy and what to sell.

I hold a Series 65. That license exists for a reason. Giving investment advice for compensation is supposed to carry a legal standard of care, attached to a person a regulator can examine, sanction, and hold responsible.

Coinbase just attached that standard to software. Read it one way, it’s a real shift in how retail investors get advice. Read it another, it’s a lawsuit that hasn’t been filed yet.

My read is that it’s both, and the gap between those two readings is the story.

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What It Does

Coinbase Advisor is live now for U.S. Coinbase One subscribers.

It has your full portfolio context.
It designs tax-loss harvesting strategies.
It turns breaking news into multi-asset trade recommendations.
It issues explicit buy and sell calls instead of vague suggestions.

Coinbase’s head of consumer products drew the contrast with ordinary chatbots on purpose, describing it as financial advice that will tell you what to sell and what to buy.

For a self-directed investor who was never going to open a relationship with a human adviser, this is a step up from the robo tier. The tax-loss harvesting alone carries dollar value. Around-the-clock analysis across a full account is something most retail investors have never had.

So far it sounds like a win…then you read the disclosures.

The Catch is in the Disclaimers

A Registered Investment Adviser is a fiduciary.

Under the Investment Advisers Act of 1940, any fiduciary has to put your interests ahead of its own, disclose its conflicts, and apply a duty of care to every recommendation.

That standard sits above the suitability bar brokers work under, and you can’t waive it with fine print. A human adviser who told clients “my advice might be wrong and you eat all the losses” would be answering to regulators by lunch.

Coinbase’s own disclosures say the advisor’s output “may be inaccurate or incomplete,” and that the outcomes are your responsibility. The platform claims the fiduciary standard and disclaims the result of meeting it, in the same product.

That combination has NO precedent in securities law, and NO court has tested it for an AI-driven adviser. (I’m getting the popcorn ready for the pending lawsuits.🍿)

The approval step doesn’t close the gap. The agent needs your sign-off before it trades, but that’s a confirmation click, not an independent judgment about whether the advice met a fiduciary standard in your situation.

FINRA’s 2026 oversight report named autonomous AI agents running without a human in the loop as a top investor risk, warning that a misaligned reward function could push an agent toward decisions that work against the investor. 😬

The volume changes the stakes too.

By one estimate from Citizens, an AI agent can trade 10 to 20 times more actively than a typical retail investor does on their own. A single confirmation click ends up governing a system that may touch your account far more often than you ever would.

Critics at rival firms put it plainer: the investor carries the risk and is the one on the hook when the agent gets it wrong.

This is the same problem I wrote about after the Aave disaster a few months back. The technology can perform exactly as designed and you can still be left with no one to call. Then the missing party was a custodian or an insurer. Here it’s the adviser itself, present in name and registration, absent in accountability.

Buffett Framework Question
“If this advice blows up my account tomorrow, who is legally on the hook, and can a fiduciary disclaim the outcome of its own advice?”

If the answer is a confirmation screen and a paragraph of fine print, the registration is real but the recourse is theoretical, and your position sizing should reflect the difference.

Who This is For

Strip away the headline and the answer gets clearer. This is built for the Coinbase One user who is already self-directed and doesn’t have an adviser. It’s a smarter robo, a better version of tools that customer already uses. It isn’t a substitute for the relationship a household with real complexity keeps with a human planner who coordinates taxes across accounts, handles estate questions, and talks them out of selling the bottom in a panic.

If you already pay an adviser worth paying, this doesn’t replace that person. If you don’t, it’s a real step up from trading on gut and group chats, as long as you understand that the accountability you’d assume comes with the word fiduciary is the one thing the disclaimer takes back.

Two Roads at One Fork

The timing is what makes this sharp. Two weeks ago, Robinhood faced the same decision and went the other way.

At its SYNERGY26 event, Robinhood rolled out an Advisor Network that matches its retail users with vetted human RIAs, plus Cortex for Advisors, an AI tool that writes portfolio digests, surfaces tax insights, and preps human advisers for client meetings.

The tell is what Robinhood’s own executives said: regulatory constraints still keep AI from directly managing client assets, so they built AI to make the human adviser faster and routed complex clients to a person.

Same month, same retail-first ambition, opposite bets.

Coinbase made the AI the fiduciary.
Robinhood made the AI serve the fiduciary and kept a human on the hook.

One of these firms is reading the regulatory road differently than the other, and within a year or two we’ll know which one read it right.

Coinbase and Robinhood aren’t the only two on the field.

Schwab and Citi both showed AI-powered wealth advisers at recent investor days, and eToro and Public have pushed AI trading tools to retail.

Line them all up and they sort along one axis: AI that augments a human at one end, AI that replaces one at the other. Coinbase planted its flag at the far end.

Are RIAs Cooked?

Short version: the commodity tier is, the judgment tier isn’t, and the gap between them is about to widen.

If your value to a client is rebalancing a portfolio and answering “should I buy more Bitcoin,” that work is now free inside an app, and it’s competent. An adviser charging one percent for index allocation and a quarterly check-in should feel the floor moving.
If, however, your value is judgment, planning, tax and estate complexity, and being the person who keeps a client from doing something stupid in a drawdown, you got more valuable, not less.

There’s now a confident machine telling millions of retail investors what to trade, with no one accountable for the result. The market for a human who is accountable doesn’t shrink in that world. It grows.

I walked an AI pavilion at FutureProof Miami earlier this year and left with one framing that holds up here. AI plus the adviser is a fiduciary upgrade. AI replacing the adviser is a liability. Coinbase just ran the second experiment in public, with a disclaimer attached that makes the point for me.

This is also why credentialing keeps mattering in a way the alarmist version of this story misses. A license is a person a regulator can hold responsible, a fiduciary who can’t disclaim the duty of care. Coinbase Advisor carries the registration and offloads the outcome. Those aren’t the same product, and the difference is the part that outlasts the hype cycle. The data backs the point. In CoinShares’ survey of financial advisers, 62 percent said recommending Bitcoin didn’t square with their fiduciary duty. The fiduciary question was already the hard part of this conversation, and automating the advice doesn’t soften it. It sharpens it.

The Other Half of the Stack

Coinbase didn’t only ship the advice layer. It also formalized Coinbase for Agents, which lets outside agents like ChatGPT and Claude act inside your account within limits you set, and settles their machine-to-machine payments over x402.

If that protocol sounds familiar, it should. x402 is the rail I covered in the first issue of the Agentic Finance series, the HTTP 402 revival that lets software pay software in stablecoins for fractions of a cent.

Put the two launches side by side and the shape is clear.

Coinbase Advisor is the brain that decides.
Coinbase for Agents and x402 are the hands and the wallet that act and pay.

That’s less a product than an attempt to own the whole agentic-finance stack, advice through settlement, inside one app.

The payments half is further along than most people realize. The x402 rail has already cleared more than 119 million transactions on Base and another 35 million on Solana, running near $600 million annualized as of March.

Where I Could Be Wrong

The bear case on my own take is simple.

Maybe the disclaimer holds up fine, the SEC signals it’s comfortable, and a registered AI adviser becomes the default retail tier within two years, the way robo-advisers normalized a decade ago. In that world I’m overweighting a legal tension the market just prices through.

It could cut the other way too. The first real loss that reaches a regulator or a judge could define AI fiduciary duty narrowly and fast, and freeze the category before it scales. I don’t know which happens. I know the unresolved question now sits inside a product that millions of people can use, and that’s usually how these things get settled, one painful case at a time.

This genie isn’t going back in the lamp, and it shouldn’t. Agents will keep turning up across our financial lives, each one promising to make something easier, and plenty of them will. The question that follows every one of them is the one Coinbase just stopped letting us avoid: when the easy answer is wrong, who’s still on the hook?

COIN. The pure-play on the bet that crypto rails become the connective tissue of agentic finance. Watch adoption and the disclosure language, not just the stock.
HOOD. The human-in-the-loop counter-bet. If advised assets and the Advisor Network grow fast, the conservative read on regulation was the right one.
The first AI-adviser liability case. Whenever it lands, it sets the terms for the whole category. Worth watching closer than any single product launch.
SEC guidance on AI fiduciary duty. Any signal on whether a registered AI adviser can disclaim outcomes moves the entire field at once.
x402 and Coinbase for Agents adoption. The payments layer is the leading indicator here. Watch transaction volume and which platforms wire in.

Most Crypto Tokens are Uninvestable by Design

Matthew — Tue, 16 Jun 2026 13:04:56 GMT

I keep having the same conversation: The price is low, the project has partnerships, so the person across from me has decided the token is a generational buy.

“It could go up ten times from here, so even a small position makes sense.”

I want to take that apart, because the equation rests on two mirages and a moat that was never there.

Set Bitcoin and Ethereum aside first. They captured market share and network effects that are real and hard to copy, and they’re not what this is about. This is about everything underneath them, the long list of smart-contract platforms and infrastructure tokens that trade on narrative and partnership announcements. For that group, the case for owning the token is weaker than almost anyone holding one wants to admit.

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There’s no moat in decentralization

Start with what makes any asset worth owning.

A business holds value because it can defend its profits.
Pricing power, switching costs, exclusivity, a brand, a network competitors can’t replicate.

Warren Buffett calls it a moat, and the whole job of an investor is finding one and buying it at a fair price.

Decentralization is the deliberate removal of the moat.

That’s not a criticism, it’s the entire pitch to users. The code is open source, so anyone can fork it. The network is permissionless, so no one can be charged rent or shut out. No company owns it, so no company can extract value from it. Those properties are why people find decentralization appealing, and they’re the same properties that make the token impossible to value. Capital wants control. Decentralization is the surrender of control. You can’t build a durable claim on something designed so that nobody can hold a durable claim on it.

That’s the root of everything below. No moat means no defensible value, and no defensible value means there’s nothing for the token to capture.

Buffett Framework Question: “Where is the moat?” For most crypto tokens, the answer is that the protocol was built so there wouldn’t be one. That’s a feature for the people using it and a problem for the people holding it.

Mirage #1: It’s cheap; therefore “value”!

A low price feels like safety. It usually measures the past instead.

When a stock falls, its cash flow puts a floor under it. Earnings, book value, a dividend yield that rises until buyers step in. A decentralized token has no claim on cash flow, only an emission schedule that prints more supply on a timer. Nothing sits underneath the price.

So when a token is down 98%, that’s not a bottom, it’s a number.

It can fall another 50%, another 75%, another 100% and on and on and on because there is no structural floor in crypto for assets like these.

Polkadot makes the point. It’s down about 98% from its 2021 high and printed a fresh all-time low this month, five years into its life.

The people who think they’re safe because they timed it well are often the clearest warning. Look at Polygon through four buyers.

The one who bought the 2024 peak is down about 91%.

The one who bought the exact April 2025 all-time low, the buyer everyone wishes they were, is still down about 20% a year later. Even buying the precise bottom didn’t make them whole.

The big irony is that these projects can’t really die. A company that fails goes bankrupt, and its creditors and shareholders get a claim on the cash and hard assets left behind, a real recovery value. A decentralized token has no such moment: it can’t be liquidated or wound down, so as long as a handful of holders and validators remain, it drifts lower forever, with no terminal event and nothing left to divide.

Cheap is doing none of the work people think it’s doing.

Mirage #2: It Has Partnerships!

A partnership means a company is using the technology. It does not mean value reaches the token, and the clearest evidence is that these deals have been in place for years and have done nothing for the price.

Take the one people point to:
California digitized 42 million vehicle titles on Avalanche.

The state runs it as a sovereign, permissioned chain with its own validators.

Since the Etna upgrade, a chain like that pays the Avalanche protocol a flat fee of roughly 1.33 AVAX per validator each month, fixed no matter how much activity flows through.

At today’s price that’s around nine dollars per validator a month, so the protocol’s entire take from the California DMV lands somewhere in the hundreds to about a thousand dollars a year.

Now follow that money to the token holder, because it doesn’t arrive.

Every fee on Avalanche is burned rather than paid out, so there’s no dividend and no buyback to receive. The protocol also mints new AVAX as staking rewards faster than fees burn it, which kept the token inflationary at around 3% in late 2025. The holder absorbs dilution while the network grows.

For scale, Avalanche’s single busiest fee day on record, about $521,000, came from an October 2025 liquidation cascade rather than from any enterprise use, and the whole network collected roughly 132,000 AVAX in fees for the quarter.

A state title registry doesn’t move numbers like that, and even if it did, the money gets burned, not distributed.

Set that against the comparison the bull is implicitly making. Visa earned about $40 billion last year and returned roughly $22.8 billion to its shareholders. Owning Visa is owning the toll booth, with a legal claim on the tolls. Owning AVAX while California runs titles on Avalanche is owning a token that collects a few dollars a month in validator fees and then destroys them.

The technology got adopted. The holder got a headline.

Avalanche says it plainly in its own case study. When JPMorgan’s Onyx unit, Apollo, and WisdomTree ran a tokenization trial on a permissioned Avalanche subnet, the case study notes the subnet’s economics are not dependent on any crypto token.

That’s the company confirming the gap between using the rails and owning the coin.

The pattern repeats across the names people cite:

JPMorgan. Its own ledger runs on Quorum, a private fork of Ethereum with no public token at all. The largest US bank built serious blockchain infrastructure, and there’s nothing for an investor to buy.
IBM and Hyperledger. They powered the first wave of enterprise blockchain, Maersk’s TradeLens and Walmart’s Food Trust, on permissioned, tokenless systems. TradeLens shut down in 2023.
VeChain. Built for enterprise supply chains with partners like DNV, BMW, and Walmart’s China operation. VET trades near a penny, down about 96% from its 2021 high.
Stellar. Real institutional flows, from MoneyGram settlement to a Franklin Templeton tokenized fund. XLM still sits around 75% below its 2018 high.

Adoption is a property of the network. Value accrual is a property of a claim, and these tokens don’t carry one.

The 10X argument runs backwards

The most sophisticated version of the bull case isn’t a prediction, it’s an asymmetry. The token can go up ten times, so a small bet is rational even when it usually fails.

Two things break that.

First, the catalyst people name for the 10x is the partnerships, and the partnerships have sat in place for years without moving the token. The upside thesis is disconnected from its own evidence. Whatever would drive a re-rating hasn’t shown up, and it isn’t the thing being pointed to.

Second, the downside is as open as the upside. The asymmetry people imagine is capped downside with uncapped upside. For a token with no floor, the downside is uncapped too. Down 98% can become down 99%, and down 99% can become zero.

A bet where both ends are open isn’t the asymmetry that makes a generational buy. It’s a coin flip dressed up as one.

Capitalism takes the technology and leaves the token

Now let’s tie it together.

Private companies don’t want decentralization. They want efficiency they control.

To accomplish this, they take the open-source rails and run them on permissioned instances built for their own benefit; not the network’s.

Visa, Mastercard, and JPMorgan don’t need a public token to settle faster or tokenize a fund. They need the technology, and the technology is free to take. The moment something valuable can be captured, a company captures it privately, behind its own walls, where the gains accrue to the company rather than to a floating coin. There’s no money in being decentralized, because decentralization is the one structure that guarantees the value can’t be fenced off and kept.

This is why I can be bullish on tokenization and bearish on these tokens at the same time. The shift to programmable rails is real and large. It runs on infrastructure institutions govern, and it doesn’t need the 2021 narrative tokens to get there.

One thing could change all of this thinking…

I’ll give the other side its strongest card.

IF (and this is a big “IF”) legislation like the CLARITY Act creates a clean path for a token to register as a security, that token could be required to pass real profit back to holders through a dividend or a mandated buyback.

A claim like that is exactly the moat these tokens lack, and it would change the analysis. It’s a possible future, not the present one, and most tokens won’t qualify or won’t pursue it.

Until that’s law and durable, the support under the price is the next buyer’s optimism.

Where this leaves you

The equation that starts these conversations, low price plus partnerships equals generational buy, is built on two mirages and a moat that was never there. Cheap measures the past. Partnerships measure usage, not ownership. The structure underneath all of it, decentralization, is the deliberate absence of the thing that lets an asset hold value.

Most crypto tokens are uninvestable by design; invest accordingly.

The phrase to sit with is “by design.” Nobody has to break these tokens for them to keep falling. They were built without a way to capture value, on purpose, and a 98% discount on something with no claim and no moat is not a discount.

It’s a lower price on the way to a lower one.

Securities legislation is the live risk to this view. A token that wins a clean path to pay holders gets the claim it lacks today. The same is true if a network sustains fee burns above emissions for years rather than weeks, which would start to resemble a buyback, or if one of these projects develops genuine network effects that hold up without paid incentives.

I’m watching all three, and I’ll change my mind when the data does.

Until next time…trade carefully out there friends and keep an eye out for strong, structural moats.

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Sources and notes

Drawdowns from all-time high (DOT about 98%, AVAX about 95%, ADA about 94%, POL about 91%) and Polkadot’s fresh all-time low near $0.89 on June 6, 2026: CoinGecko and CoinLore, as of mid-June 2026.
Polygon entry levels and the roughly $0.12 reference price: CoinGecko and CoinMarketCap historical data; the April 2025 all-time low near $0.15. Levels vary by feed and exact date.
Avalanche L1 validator fee of about 1.33 AVAX per validator per month, fixed regardless of transaction volume: Avalanche Builder Hub and the Etna / ACP-77 documentation (Avalanche9000, activated December 2024).
All Avalanche fees are burned; the network ran roughly 3.0% annualized inflation in Q4 2025; the busiest single fee day was about $520,715 on October 10, 2025; quarterly fees totaled about 132,016 AVAX: Messari, State of Avalanche Q4 2025 (January 2026).
The JPMorgan Onyx, Apollo, and WisdomTree tokenization proof-of-concept on a permissioned Avalanche Evergreen subnet, and the statement that the subnet’s economics are not dependent on any crypto token: Avalanche case study, “In Focus: Onyx by J.P. Morgan” (avax.network). Onyx’s own ledger is built on Consensys Quorum.
Visa’s roughly $40 billion in annual net revenue and about $22.8 billion returned to shareholders: Visa fiscal year 2025 results.
VeChain near $0.011 with an approximately billion-dollar market cap, down about 96% from its 2021 high; enterprise partners include DNV, BMW, and Walmart’s China operation: CoinGecko and Coinbase, mid-2026.
Stellar near $0.22, roughly 75% below its 2018 high; institutional use includes MoneyGram and Franklin Templeton’s tokenized money market fund: market data and company disclosures, mid-2026.
Maersk’s TradeLens, built on IBM and Hyperledger Fabric, shut down in 2023; Walmart’s Food Trust runs on the same permissioned, tokenless stack: public reporting.

The IPO Is the Exit. You're the Liquidity.

Matthew — Sun, 14 Jun 2026 15:18:47 GMT

It took less than three hours for the biggest IPO in history to teach its first lesson. SpaceX rocketed to $177 out of the gate Friday, roughly 30% above its $135 offer price, then slid steadily into the low $160s.

The people who bought the excitement lost money before lunch.

But retail buyers were not the story of Friday…it was the employees of SpaceX.

An estimated 4,000 employees became millionaires at the opening bell, including welders and cafeteria cooks who took stock instead of bigger paychecks a decade ago. Roughly 400 of them are now worth $100 million or more.

I want to sit with that for a second, because it contains the entire lesson of this article. Those employees just finished a trade they entered ten years ago at private prices. Anyone who bought the open on Friday started a trade at the exact moment thousands of informed insiders finally got permission to sell.

Warren Buffett has thought about this asymmetry longer than most of us have been investing. His conclusion:

“It’s almost a mathematical impossibility to imagine that, out of the thousands of things for sale on a given day, the most attractively priced is the one being sold by a knowledgeable seller to a less-knowledgeable buyer.”

Asked in 2019 whether he’d buy the Uber IPO, he noted that in 54 years he didn’t think Berkshire had ever bought a new issue. At Berkshire meetings he’s put it more bluntly: the sellers pick the timing, so a new issue isn’t worth five seconds of your attention.

Strong words. Let’s test them against the evidence.

The structure of the trade

Let’s think about what an initial public offering actually is for a moment.

A group of insiders who know the business better than anyone alive chooses the precise moment to sell you a piece of it.
They hire 21 banks to market it.
They time it for maximum enthusiasm.
Then the shares open for trading at whatever price the most excited buyer in America will pay that morning.

Ben Graham flagged this in The Intelligent Investor decades ago: new issues come to market under conditions favorable to the seller, which by definition makes them less favorable for the buyer. His student just said it with more color.

The modern data backs them both.

Edward Jones and FactSet studied the 30 largest IPOs of the past 20 years and found that, measured from the first traded price rather than the offer price, the average return was negative 1.4%, with the group lagging the S&P 500 through its first year.

Jay Ritter’s research at the University of Florida shows unprofitable IPOs pop 26.5% on day one and then return negative 0.5% over the next three years, trailing the market by more than 30 percentage points.

The pop is real.
It just isn’t for you.

It goes to the institutions who received allocations at the offer price, a price you were never going to get.

Two receipts from the past year

Figma priced its July 2025 IPO at $33 and closed its first day at $115.50, a 250% gain celebrated everywhere as proof the IPO market was back. Retail investors buying that open paid $85 or more. The deliberately low pricing triggered a performance clause that released insider shares after just 36 days instead of 180. Today the stock trades near $21, down more than 80% from its peak and below the original offer price. The business itself is fine, with revenue up 41% last year.

The entry price was the problem.

BitGo, the digital asset custodian, priced at $18 in January, popped about 25% on debut, and fell below its offer price by the second trading session. It now sits more than 40% below where it priced, to the point where analysts have shifted from growth stories to takeover speculation.

Two real companies.
Two functioning businesses.
Two groups of early investors who exited beautifully, and
Two groups of open-market buyers who funded the exit.

The pipeline deserves the same lens

The next twelve months bring Anthropic and OpenAI, both confidentially filed, plus expected listings from Kraken and Securitize. These are serious companies, and nothing here predicts they fail.

The Buffett model doesn’t say new public companies are bad businesses, but rather that the moment of listing is structurally the worst-informed, most-hyped, seller-optimized moment to buy them.

Consider what these filings represent. Anthropic’s last private round valued it at $965 billion. OpenAI’s at $852 billion. The venture funds, sovereign wealth funds, and employees who own those shares have watched their stakes appreciate at rates Forge Global measures at 65.7% annually for recent unicorns during their private years. The IPO is how that decade of private gains becomes spendable money. The word “offering” describes their side of the transaction, not yours.

Even Buffett’s lone modern exception proves the point. When Berkshire bought into Snowflake’s 2020 IPO, it paid the $120 offer price. Retail’s first available price that morning was roughly double that. Same day, same stock, two different trades.

The Buffett Framework Question: Would you buy a private business from its founder on the one day he chose to sell, at a price his hired marketers spent six weeks promoting, while thousands of other businesses sit on the shelf at prices set by ordinary daily auction? If not, what makes a ticker symbol different?

Where the five seconds are better spent

Here’s the part of Buffett’s model people skip. He isn’t telling you to sit in cash. He’s telling you the auction market, with its thousands of existing companies priced by daily supply and demand rather than roadshows, is where bargains actually appear, usually wearing bad headlines.

Cracker Barrel is a fresh example. Last August a logo redesign turned the company into a culture-war punching bag and the stock got left for dead. The restaurants kept serving food and generating cash the entire time. This week the company reported earnings that tripled net income year over year and beat estimates by a wide margin, and the stock jumped nearly 30% in a single day. It’s now up more than 67% this year.

The investors who got paid weren’t the ones chasing a hyped debut, but the ones willing to look at an unloved business while everyone else looked away.

To be clear, I’m not recommending Cracker Barrel, and after this week’s move much of that easy money is gone. The point is the pattern.

Opportunity in public markets tends to come from other people’s overreactions, and overreactions cluster around boring, disliked, or embarrassing situations. They almost never cluster around the most marketed security in the world on its first day of trading.

Where I could be wrong and the Watchlist…

Fairness requires the other side. Facebook fell more than 50% in its first 18 weeks and went on to return over 1,600% for patient holders. Amazon lost most of its value after 2000 and became the buy of the decade anyway. If the AI buildout compounds the way bulls project, today’s prices will look quaint, and the day-one buyers will look prescient rather than impatient. Buffett’s model manages the odds, not the certainty.

Some IPOs work.
The data just says you shouldn’t expect yours to be the one that does.

SpaceX’s first earnings call as a public company, expected in July, with a rumored early lockup window two days after it.
Nasdaq-100 inclusion around July 7 and the mechanical index buying that follows.
Anthropic and OpenAI’s public S-1 filings, where the real financials replace the leaks.
Kraken and Securitize listing timelines as the crypto IPO window stays open.
Whether Figma and BitGo find their footing, the best live experiment on post-IPO price discovery.

The biggest IPO in history will still be there in six months, with public earnings reports and a lockup expiration behind it. Buffett wouldn’t spend five seconds on it today. The auction market is open all year.

Moving right along…

This Week In 2 Minutes

“How to Go From Idea to Usable Tool With AI in a Weekend” (June 9)

This past weekend I built a free Series 7 math trainer, and I never wrote a line of code. Three tools did the work in a chain: Claude Code as the engineer, GitHub as the filing cabinet, Vercel as the launchpad. I describe what I want, it builds and ships.

The tool is the least interesting part. What matters is that the cost of building the exact thing you need has dropped close to zero, so the bottleneck isn’t money or engineers anymore. It’s whether you notice a problem and reach for the tool.

The method runs in five steps: pick a real problem you already have, gather your raw material, describe the outcome instead of the code, work in short test-and-correct loops, and be the judge of whether the result is any good. That last step is the moat. Domain expertise is what lets you catch the model when it puts a formula in wrong, which means AI replaces the tedious work sitting under your judgment, not the judgment itself.

Read the full article here.

1,000 Advisors, $28 Trillion in AUM, and Only 1 Crypto Panel (June 12)

I spent this week at Wealth Management EDGE in Boca Raton, the largest gathering of senior RIAs in the country. Last year digital assets had a main-stage slot. This year it was one panel and one booth.

The conversation didn’t shrink so much as change shape. Crypto as an asset class got quieter while tokenization as infrastructure got louder, and private markets and AI dominated every track in the building.

The session that hit closest to home was Puerto Rico’s Act 60 regime, roughly 4% tax versus about 41% in Florida or 52% in New York City, now extended through 2055. That’s the structure I’m building around with a new RIA. The cautionary tale was a CoinShares altcoin-basket ETF down around 70% since October, with 0.96 correlation to Bitcoin and a near-1% fee. You get Bitcoin’s volatility, none of the diversification, and a bill on top.

Read the full article here.

This Week’s Winners 🏆

Cracker Barrel (CBRL). The boring-stock-bounces-back trade of the week. After a logo controversy left the stock for dead last August, Monday’s earnings tripled net income year over year and beat estimates badly enough to send shares up nearly 30% in a session. It’s now up more than 45% on the year. The lesson isn’t the chain itself, but that opportunity hides in disliked, unglamorous names while everyone stares at the launch pad.

SpaceX employees (all 4,000 of them). The biggest IPO in history minted an estimated 4,000 millionaires at Friday’s open, roughly 400 of them worth $100 million or more, including welders and cooks who took stock over salary a decade ago. A group of 100+ even built their own low-fee wealth management vehicle to handle the windfall. This is what winning an IPO actually looks like, and notice it has nothing to do with buying the open.
Cash on the sidelines. With roughly $8 trillion parked in money market funds and a 2026 IPO calendar that could demand north of $200 billion, dry powder is quietly the strongest position in the market. Patience is an asset that pays you to hold it right now. The investors with cash get to be buyers on their own terms instead of someone else’s exit.

…and Losers 📉

Anyone betting on a June rate cut. The setup flipped on bulls. May CPI came in at 4.2% year over year, well above the Fed’s 2% target, while the economy added 172,000 jobs and unemployment held at 4.3%. With inflation surging and the labor market healthy, the textbook next move is a hike, not a cut, and the risk now is a Fed bias shift from easing toward tightening. Rate-cut hope has been the market’s crutch all spring, and this week’s data kicked it out.
Crypto longs into the macro print. Digital assets traded defensively ahead of the Fed. Bitcoin’s pullback added pressure before the meeting, with the broad crypto drop signaling investors hedging out risk ahead of the policy decision. Rate expectations drive liquidity, risk appetite, and institutional positioning, which is why crypto reacts sharply to the Fed. When the discount rate is the swing factor, the most rate-sensitive assets get sold first.
Oversupply bears caught offside. The 2026 glut narrative ran into reality. A Reuters poll had Wall Street modeling WTI averaging around $59 for 2026 on China demand worries and rising OPEC supply, then geopolitics intervened and the IEA actually dialed back its oversupply warning after winter shocks. Anyone short energy on the glut thesis spent the week watching the opposite play out. Consensus positioning is most dangerous exactly when everyone agrees.

What to Watch For This Week 👀

SpaceX’s first index-inclusion wave (~July 7 setup). The mechanical story beats the narrative one. About 15 trading days after listing, SpaceX qualifies for the Nasdaq-100, triggering forced buying from every index fund that tracks it, with estimates running into the tens of billions. Watch whether that artificial demand props the stock or simply gives early holders a cleaner exit.
The next crypto S-1. With the IPO window open, watch for Kraken, Securitize, or another digital asset name to file. Each filing is a free look at real financials replacing years of private-round leaks. The terms they choose, lockup length, retail allocation, gross vs. net revenue accounting, will tell you how confident these companies actually are.
The Fed meeting and the dot plot. The June FOMC decision and updated projections land this week, and with rate-cut expectations getting repriced all spring, the dot plot matters more than the decision itself. Every IPO valuation in the pipeline, from OpenAI to Anthropic, is ultimately a bet on the discount rate. Cheaper money inflates these numbers; tighter money deflates them.
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Sources

Edward Jones & FactSet, “Don’t Let Mega IPO Buzz Cloud Your Judgment”
Jay R. Ritter, University of Florida
Warren Buffett, CNBC interview, May 2019
University of Berkshire Hathaway (Pecaut & Wrenn)
Benjamin Graham, The Intelligent Investor
Fidelity Investments, “SpaceX IPO Explained”
Bloomberg, November 2025 — Figma shares falling below IPO price
InvestorPlace via TradingView, February 2026
FinTech Futures and Cointelegraph, January 2026
Forge Global, “Late-Stage Private Companies: The New Growth Investing”
Goldman Sachs Research — 2026 U.S. IPO volume projections; “25 Years On: Lessons from the Bursting of the Technology Bubble”
Cracker Barrel Old Country Store, fiscal Q3 2026 earnings release, June 2026; Fast Company and Investing.com coverage

1,000 Advisors, $28 Trillion in AUM, and Only 1 Crypto Panel

Matthew — Fri, 12 Jun 2026 13:39:45 GMT

I spent this week at Wealth Management EDGE in Boca Raton with roughly a thousand advisors and wealth managers. Collectively, this group represents about $28 trillion in collective assets under management and is the largest gathering of senior RIAs in the country, held at one of the nicest resorts in Florida.

I didn’t go to talk about crypto. I went to talk about the future of finance, and that distinction matters more than ever. This conference proved it.

Here’s what I saw, what I heard, and what I think it means.

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The Room Has Changed

Last year, digital assets had a main stage slot at EDGE. Multiple firms showed up and there was solid crypto energy.

This year? One panel. One booth. That was the entire digital asset footprint at a conference with over 250 speakers.

You can read that two ways.

The bearish read says the market is in a slump, prices are down, and advisor interest evaporated with the chart. Fair enough. The audience questions at the lone crypto panel mostly boiled down to “why is Bitcoin down?” That tells you where heads are at.
The bullish read, and the one I left with, is that the conversation didn’t shrink so much as change shape. Crypto as an asset class got quieter while tokenization as infrastructure got louder. (More on that at the end.)

The RIA of the Future Runs on Private Markets & AI

The dominant themes of the week were unmistakable: Private markets and AI.

Every summit track, every vendor booth, every hallway conversation orbited one of those two suns.

The opening keynote from Sanctuary Wealth’s Patrick McGowan walked through H1 2026 capital flows from the wealth channel into private assets including private equity, private credit, real estate, infrastructure.

The framework on screen said it plainly: rotation, risk, reset, re-entry.

Advisors aren’t asking whether to allocate to private markets anymore.
They’re asking how to do it operationally.

That operational question is real. One session laid out the hidden time burden with no spin. Private markets aren’t “Schwab click and drop.” Enhancements to the process were related to automated subscription docs, capital call management, cash reserves, ongoing monitoring, and reporting across multiple custodians with different formats and data quality.

The vendors filling that gap, names like Arch, InvestCloud, and Gridline, had real traffic at their booths.

On the AI side, sessions on designing the AI-enabled firm of the future made one thing clear: AI is no longer an experiment in wealth management; it’s becoming the operating layer.

Firms are making governance and data decisions now that will determine who compounds advantage over the next decade. Zoe Financial’s Andres Garcia-Amaya made the case that AI supercharges operational efficiency and personalizes the client experience for growing RIAs.

I agree, and this is the thesis I run my own businesses on. AI as the orchestration layer, not the replacement layer. The firms that get this will pull away, while the firms that wait will spend the next five years explaining why their margins shrank.

Puerto Rico Stole the Show for Me

The session that hit closest to home was a presention on Puerto Rico as the best tax jurisdiction available to U.S. citizens.

For my geographically challenged readers, here is where Puerto Rico is on a map. And yes, it’s part of the United States, uses the U.S. Dollar and only requires an ID to enter.

This topic resonated deeply with me as it’s something I’m working to build right now with Colton Alexander Advisers, a new Puerto Rico-based registered investment adviser designed around the island’s structural advantages. I sat in that room with more than professional curiosity.

The numbers aren’t subtle.

Act 60 provides a maximum 4% tax on interest, dividends, and capital gains for bona fide resident investors. There’s also a 4% corporate rate on exported services, which covers asset management, law, software, and consulting, with dividends from those activities tax free. In March, Act 60 was extended through 2055. That’s thirty years of tax certainty, signed into law.

Compare the rates. Roughly 4% in Puerto Rico versus about 41% in Florida and 52% in New York City. The presentation ran the compounding math, and I recreated it below because numbers you can check beat adjectives.

A $10 million portfolio growing at 20% for 30 years becomes roughly $1.9 billion after tax in Puerto Rico. The same portfolio in New York City ends around $161 million. That’s a $1.8 billion difference. Even against zero-income-tax Florida, the gap is about $1.7 billion.

Here’s the part people miss. None of this is offshore, because Puerto Rico is a U.S. territory with federal courts and FDIC banking in dollars. No passport changes, no expatriation tax, no currency risk, no renunciation of citizenship. It’s the only jurisdiction on earth that offers this treatment to U.S. citizens who keep their passport.

I think Puerto Rico is the most underpriced opportunity in American wealth management. The advisors in that room were paying attention, and the smart ones will act.

Tokenized Real Estate and the Happy Hour Test

Some of the best conversations at any conference happen off the agenda - especially when there is a yacht club involved…

Revitalization Unlimited hosted a happy hour where I met operators deeply aligned on tokenizing real estate and packaging alternative investments into accessible vehicles.

This is where I road-tested some of what we’re building at Colton Alexander Advisers. The reception was telling. When you frame Puerto Rico plus tokenization plus alternatives as a single integrated offering, people lean in, and they lean in because it solves real problems rather than because it sounds exotic. Access, tax efficiency, and operational simplicity in one structure.

That’s the test I apply to any idea. Does it survive a conversation with a skeptical practitioner holding a drink? This one did, repeatedly.

The Lone Crypto Panel, and the Down-Only Problem

Credit where due. James McConaghy of Eaglebrook and Michael Kosoff of Mariner ran the only dedicated digital asset session at the entire conference, and they did it well.

The framing was right: crypto is no longer a fringe conversation. The real question is how RIAs capture held-away assets, implement tax-loss harvesting, and position for the next generation of wealth.

But the room told its own story. Sparse digital asset presence, and audience questions fixated on price.

Then there was the CoinShares booth, one of the only digital asset firms exhibiting. Their diversified alt-asset ETF, holding a basket of staked altcoin products like Solana, Cardano, Cosmos, and Toncoin, is down roughly 70% at NAV since its October 2025 inception. Net assets sit just over a million dollars, and the correlation to Bitcoin is 0.96.

The real kick in the teeth…is that they charge almost 1% as a fee. 🙄

Think about what that means.

You get full Bitcoin correlation with dramatically worse drawdowns and no diversification benefit. So the question every advisor in that hall was implicitly asking: how is an RIA supposed to allocate client capital to products that have been down-only? Under a fiduciary standard, they really can’t, and until the industry builds products that solve for fiduciary reality instead of crypto-native enthusiasm, that capital stays on the sidelines.

This is the altcoin problem in one exhibit booth. It’s also why my framework has always separated Bitcoin from everything else. Bitcoin is a store-of-value asset class while altcoins are speculative tech bets, and funds that blend the two into a basket inherit the volatility of both and the credibility of neither.

The Hougan Takeaway

Matt Hougan put words to what I felt all week. He’s had an incredible number of conversations with advisors, and they’re genuinely interested in digital assets, just not in coins.

The interest is in stablecoins and tokenization.

The future of wealth management won’t be built on “crypto” as advisors understood it in 2021. It will be built on stablecoin yield and tokenized assets, especially in private markets and alternatives. The same private markets that dominated every stage at EDGE. The rails are blockchain, but the product is access, yield, and efficiency. Nobody in Boca Raton was speculating on dog coins, yet plenty of people were asking how to tokenize a real estate fund.

This is the convergence I’ve been writing about for years. Traditional finance doesn’t adopt crypto. It absorbs the useful parts and discards the rest, and tokenization is the useful part.

The Bottom Line

EDGE was a great event with a great team behind it, and I’ll be back in the fall. But the signal was clear. The wealth management industry has moved past the question of whether digital assets matter and is now sorting out which parts matter. Stablecoins, tokenization, and tax-advantaged structures are winning. Speculative altcoin baskets are losing.

Until next time fam!

How to Go From Idea to Usable Tool With AI in a Weekend

Matthew — Tue, 09 Jun 2026 12:54:41 GMT

This weekend I built a tool that drills the math on the Series 7.

It’s live on the web, free, no login, running entirely in a browser. It works, and I’m glad I made it. The tool itself is the least interesting part of this story - but if you want to check it out here, you can.

What’s worth your time is the instinct that produced it.

A year ago, facing the same problem, I’d have bought a prep app and lived with whatever it handed me. This weekend my first move was to build the exact thing I needed. That reflex is new, it’s available to you, and it’s the real story here.

Think about what that changes. The cost of building the exact thing you need has fallen close to zero, and the bottleneck is no longer money or engineers.

It’s whether you notice the problem and reach for the tool. The friction you’ve learned to tolerate, the spreadsheet that eats an hour, the app that does nine of the ten things you need, the report you rebuild by hand every month, used to feel permanent. Most of it isn’t anymore.

The tool is simple enough to describe in a breath. Behind it sit 74 question generators that spin up fresh numbers every time you load one, so you drill methods instead of memorizing answers. There’s a mode where I type the answer and work the math out by hand, a timed quiz at exam pace, and a worked solution on every question. That’s the whole thing.

There’s a deeper reason though that this matters to me.

For the better part of a month I’ve been making a single argument: statutory licensing is the one moat AI can’t commoditize.

Software can’t sit the Series 7 for you or file your U4. Then I spent a weekend using software to build the thing that helps me clear that exam faster. That looks like a contradiction, but it’s the argument in a single frame.

The AI built the ladder. The license is still mine to climb to and hold.

How it came together

I approached it the way I’d brief a sharp junior analyst who happens to type fast. I didn’t write code by hand. I described outcomes and corrected what came back wrong.

Before building anything, I did the reading. I pulled sample questions and prep PDFs from a range of sources online, and I clipped pages straight out of my Kaplan manual so the model could match the formatting and the way the real questions are worded.

Then I did the thing that seems counterintuitive but is actually the highest leverage piece of advise in this article: I described the outcome I wanted.

Questions that regenerate with new numbers.
Answers checked against worked solutions.
Wrong choices built from the mistakes the exam likes to bait.
A helpful glossary of terms for me to memorize.
An Options Matrix reference guide I could use to see various scenarios.👇

The work ran on three tools, each with one job. Think of them as the trifecta that carries an idea from your head to a live link: one to generate the code, one to store it, one to launch it.

Claude Code is the engineer. It’s Anthropic’s coding tool, and you talk to it in plain English. It writes the code, runs the commands, and fixes what you flag. You never have to open a file unless you want to.
GitHub is the filing cabinet. It stores the project in one place and tracks every change, so the work isn’t stuck on one machine. I can pick it up from my laptop or my desktop, and Claude Code reads from it to know where things stand.
Vercel is the launchpad. It turns the code into a live website with a public address, automatically, every time the project updates. There’s no server to manage and no domain to buy. My trainer lives at series7math.vercel.app, an address Vercel gave me for free.

The move that ties them together is a fairly simple workflow once you recognize how the tools .

I tell Claude Code what I want, and I let it drive all three: write the code, save it to GitHub, push it live on Vercel, and handle the logins and wiring in between. I describe, it builds and ships. That’s how an idea becomes a link I can open on my phone, with no part of the chain requiring me to write or read code.

From there I worked in small loops. Claude Code would build a piece, I’d open it, try it, and tell it what was off. The breakeven on a put was subtracting in the wrong direction. The dollar conversion forgot the hundred-share multiplier. Fix that, show me again. Most of the weekend ran on that rhythm. Look at the thing, react to the thing, send it back.

The loop is the whole skill, and it’s more forgiving than it sounds. Nothing I tried first survived intact. The first timed quiz scored wrong. An early generator produced breakevens that were off by the premium. None of that cost me anything beyond the minute it took to say so. When making a change is nearly free, you stop defending weak versions and start replacing them, and the thing gets sharper with every pass.

What made it work came down to one thing: I know the material.

It’s critical that you have some kind of domain expertise with what you are trying to create so you validate the output as helpful and useable, while being able to determine if that output is garbáge (so fancy).

When the model put a formula in slightly wrong, I caught it, because I’ve read the manual and worked these problems by hand. The model brought the typing. I brought the judgment about what good looks like, and that was the part that couldn’t be handed off.

AI doesn’t replace judgment. Full stop.

It replaces the tedious work sitting underneath it. Writing the code to randomize a strike price is tedious. Knowing that a covered call’s maximum loss is the stock cost minus the premium received, and noticing when the screen says otherwise, is judgment.

How you can win doing this too

You don’t need to code, and you don’t need a technical background. You need a problem you understand, the three tools above, and the willingness to run the loop. Here’s the path I’d hand to anyone starting out.

Pick a real problem you already have. Start with the friction you tolerate every week, not a moonshot. Small and annoying beats big and vague.
Gather the raw material first. Know the subject well enough to tell right from wrong. That domain knowledge is what separates a version that works from one that only looks like it does.
Describe the outcome, not the code. Say what ‘done’ looks like and let the tool work out how to get there.
Work in short loops. Build a piece, test it, say what’s wrong, repeat. You’re steering, not typing.
Be the judge. The machine is fast, confident, and sometimes wrong. Your standard for what’s right is the part that can’t be outsourced.

This part should sit with you whether or not you ever touch the Series 7. We’re early in an agentic economy, and the edge is shifting from people who can do the work to people who can direct the tools doing it.

From a business angle, someone in your market is already compounding small builds while their competitors wait for a budget line. From a personal angle, this is the cheapest leverage available, and it’s sitting in a browser tab you already have open. The skill getting scarce is knowing what’s worth building and telling when the result is right.

The typing in the middle keeps getting cheaper by the month.

I’ll be living in this trainer for the next six to eight weeks, drilling toward the SIE and then the Series 7. It’s free, it’ll stay free, and the link’s below. What I’d want you to take from it isn’t the trainer but the habit behind it. Build the next thing you wish existed, then the one after that. The muscle is what compounds.

Until next time - keep building friends!

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Most Trading Ideas Deserve to Die ☠️

Matthew — Sat, 06 Jun 2026 15:43:31 GMT

A few weeks ago this whole thing still felt like a sketch on a whiteboard.

We had the broad idea. Build a research engine that could take rough trading ideas, test them against real market data, keep track of what worked, kill what didn’t, and eventually help us turn the best ideas into things people can actually use: research notes, dashboards, and maybe, one day, live trading systems.

That sounds clean when you write it in a single sentence.
In practice it’s messy. Very messy.

Trading research is full of traps.

A strategy can look great because it accidentally peeked into the future.
A backtest can look amazing because it worked during one stretch of history.
A set of settings can look “optimized” when really it was fit too tightly to noise.
A chart can look convincing while the data underneath is doing something dumb.

The goal was never simply to have an AI go find trades. That’s too easy to say and far too dangerous to trust.

The goal is to build something closer to a research factory, disciplined enough to take an idea, ask the right questions, test it in layers, keep the evidence, and make it easier for a human to decide whether it deserves any more attention.

This week, that factory started to feel real.

Three parts that have to click

The engine only works when three things move together:

human intuition,
AI-assisted research, and
A dashboard that keeps score.

Take any one of them away and you’re back to guessing.

The human part still matters most. We can talk through an idea in plain English. It might start as something simple, like “what if a certain market pattern works better after a strong move?” Or “what if this setup behaves differently in futures than it does in stocks?” Or even “this looks interesting, but I don’t know how to define it yet.”

That’s where the conversation with Skyla (our Hermes-based AI agent leveraging Codex) has become useful. Instead of needing to turn every passing thought into a fully specified research plan, we can message Skyla back and forth and she helps translate a vague hunch into something more structured: what market, what signal, what rules, what should count as success, what would prove the idea wrong, and what follow-up tests would make sense if the first pass looks promising.

What’s more fun, we can leverage Skyla to help prove, debunk or improve upon ideas that are out there in the wild and on twitter. Take this conversation as an example where I dropped an interesting idea that I found and asked her to evaluate it…

The fact is, most trading ideas should die quickly, and that isn’t a failure of the process but the entire point of it.

A good research process protects you from falling in love with a chart. It makes testing cheap, and it makes it easy to say “nope, this probably isn’t real.” When something does survive the first round, though, the system finally has a place to put it.

Mission Control

That place is Mission Control, our internal dashboard, and it’s one of the bigger pieces we improved this week.

Mission Control is becoming the cockpit for the research engine. It’s where ideas, test runs, results, and the evidence behind them start to show up in a form a human can actually inspect, rather than being buried in logs, scattered across random files, or dependent on somebody remembering what happened two days ago.

The screenshot above is a small example, but a meaningful one. It shows one trial we kept from an early research path. You can see which market it traded, how many long and short positions it took, and the numbers that actually matter: how much it made, how deep the worst losing stretch ran, how many trades it took, and whether the returns justified the risk. There’s also room for the charts that show the account climbing and the ones that show it bleeding.

This isn’t us saying we found the magic trade, so please don’t read it that way. What it shows is more important than any single result. The machine can now run a trial, keep the result, and present the evidence in a format a human can review. That’s the difference between playing with ideas and building a real research process.

Building the questions into the machine

One of the biggest challenges with AI-assisted trading work is trust. If an AI tells you “this strategy looks good,” that’s basically useless on its own.

Good according to what?
Tested where?
Over what period?
How many trades?
What happened when it was losing money?
Did it cheat?
Did it try so many variations that one was bound to look good by accident?

We’re trying to build the system so those questions aren’t optional, but baked into the workflow. The engine breaks research into stages. A rough idea first becomes a clear, testable setup, then passes basic sanity checks, then produces trial evidence, then faces tougher evaluation that helps separate real signal from lucky noise. If it fails, it gets killed cleanly.

If it survives, it carries its evidence forward.

That’s the part I’m most excited about. Not because any single test result is exciting on its own, but because the process is starting to compound.

We also made progress on the autoresearch side, which lets the system move from one research step to the next without us babysitting every action. It can take a structured idea, run a bounded test, record what happened, and tee up the next decision. Bounded is the important word. We’re not building a runaway black box that fires off endless experiments and declares victory, so the system carries guardrails: limits on how many loops it can run, rules for when to stop, thresholds for what gets promoted or killed, and human approval wherever the work could have real consequences.

Here’s a sample of some trial runs our autoresearch agents were able to curate and display within Mission Control for review.

Finance doesn’t reward people for moving fast and breaking things.
It rewards people for being a little less wrong, over and over again.

Buffett Framework: “Risk comes from not knowing what you’re doing.”

The question that drives this whole build isn’t whether the engine can surface something that looks good, but whether we can tell a real edge from a lucky one before a single dollar is ever at stake.

Answers people can actually read

A research engine is only useful if people can understand what it found. If every result means digging through raw data or reading dense technical output, it’ll never become part of a real decision.

So we’ve been building toward a cleaner path.

A human has an idea.
Skyla helps shape it into a real research question.
The system runs the first pass.
Mission Control shows what happened.
The idea then dies, gets revised, or earns another round.

Eventually, if the evidence is strong enough, that work can become something more polished: a post, a chart pack, an indicator concept, or a strategy note a reader can actually follow.

The Art of the Possible

That’s the long game, and we’re not there yet. We’re still building the engine. The dashboard is improving but isn’t finished. The autoresearch loop works in an early form, though it still needs more wiring, more testing, more operator controls, and more real-world reps. We’re still learning what the system should automate and where a human should stay firmly in the loop.

The progress this week was real, though.

The system is no longer just a clever chat window bolted to a workstation. It’s starting to become an operating layer for research, one that can turn loose ideas into testable ones, run early trials, keep the evidence, and show results in a dashboard instead of leaving them in memory, vibes, or a folder full of screenshots. That might sound unglamorous, but it’s exactly the kind of plumbing serious research needs.

The fun part is imagining where this goes and thinking through the art of the possible.

As we keep building, we should be able to ask better questions faster, compare new ideas against old ones, and watch which families of strategies survive while others die, building a library of tested patterns instead of a graveyard of half-remembered experiments.

For readers, that means the research we share over time should get better too. More evidence behind the claims, cleaner explanations, and more transparency about what failed. More confidence that when we say something looks interesting, it’s been through more than a pretty chart and a good story.

That’s the part I keep coming back to. The engine isn’t meant to replace judgment, but to make it sharper. This week, we got a little closer.

Onward and upward fam! 📈

🛑 SIGN UP HERE BELOW TO BE PART OF WHAT WE’RE BUILDING! 🛑

I’m building the early cohort for when the Discord opens and the first indicators ship. If you want a seat, this is where to claim one.
Interested? Fill out the 30-second interest form here. 👈

This Week In 2 Mins

I Found a Way to Invest in the Robot Boom on the Nasdaq (June 2)

A reader flagged a new Nasdaq ticker, $BOT, and it turned out to be the first closed-end fund built to give regular investors a single-ticker stake in private humanoid robotics names like Figure AI and Apptronik.

It runs the same premium-to-NAV engine MicroStrategy used for Bitcoin, which is both what makes it interesting and where the risk hides. The catch is that its NAV is a quarterly estimate of private companies, so the price can drift far from any value you can actually verify, and the loop that funds the buying can run in reverse just as easily as forward.

I’m not a buyer here yet, but I built a monitor to track the premium or discount, because that gap, not the robots, is the real story.

Read the full article here.

Saylor Finally Sold. Here’s the Part Nobody’s Pricing In. (June 4)

Bitcoin for the first time in four years this week, though it was only 32 coins, about $2.5 million, to cover a payment it had promised investors.

The number itself is noise, but what it hinted at isn't: the product the company uses to raise money slipped below the $100 level it needs to keep issuing, its cash cushion is down to roughly six months against about $1.7 billion a year in promised payments, and Bitcoin now trades below the company's average cost. None of that forces anything today, yet for the first time we watched the machine run in reverse instead of forward.

The point I keep shouting is that Saylor isn't Bitcoin and Bitcoin doesn't need Saylor, so the real risk worth watching is that its biggest holder turns out to be its most fragile one.

Read the full article here.

This Week’s Winners…🏆

Marvell (MRVL). Nvidia's Jensen Huang called it the next trillion-dollar company on Tuesday, and the stock soared more than 25% in a session to a fresh all-time high, helped by a $2 billion Nvidia investment and a spot in the S&P 500 starting June 22. It gave some back in Friday's chip rout, but it was still the week's headline name, now up more than 130% on the year.
The US labor market. May payrolls came in at 172,000 against expectations of around 88,000, with unemployment holding at 4.3%. A genuinely strong report, even though Wall Street treated it like bad news (more on that below).

And Losers 📉

Bitcoin and the broader crypto market. Bitcoin slipped under $62,000, roughly 51% below its October high near $126,200, with about $1.5 billion in leveraged longs wiped out and a record streak of spot Bitcoin ETF outflows. Saylor's 32-coin sale didn't start the slide, but it didn't help the mood either. Here’s the one week look…
Chip stocks and the AI trade. The group that led the rally led the retreat. Friday's selloff knocked the Nasdaq down 4.18% to 25,709.43, its biggest drop since April 2025, with Nvidia off 6% and the chipmaker gauge down 10%. Broadcom fell about 14% after earnings despite revenue topping $22 billion. The S&P dropped 2.64% and the Dow lost 695 points, and the S&P's run of weekly gains snapped at nine, so close to everyone had a rough Friday.
Anyone hoping for a rate cut. That strong jobs report pushed traders to start pricing in a Fed rate hike this year rather than a cut, and Treasury yields jumped with them.

What’s On Deck the Week of June 8th

May CPI, Wednesday June 10 at 8:30am ET. The inflation print everyone’s waiting on, made louder by Friday’s hot jobs number. PPI follows Thursday the 11th, and both land with the June 17 Fed meeting one week out, which makes them matter more than usual.
SpaceX IPO, reportedly around June 12. Reports saying the company is targeting $75 billion raised at $135 per share. Investor demand reportedly is already about 2x oversubscribed, with roughly $150 billion of demand against the planned raise, though allocations can still change. It will be the largest IPO in history.
I’ll be at Wealth Management EDGE in Boca Raton, June 9 to 11. If you’ll be there, or anywhere in South Florida that week, reply and let me know. Would love to connect in person.

Saylor Finally Sold. Here’s the Part Nobody’s Pricing In.

Matthew — Thu, 04 Jun 2026 11:23:12 GMT

If you hold Bitcoin, you probably saw the headlines this week. Michael Saylor’s company, MicroStrategy (aka “Strategy”), sold Bitcoin for the first time in 4 years.

Just 32 coins, about $2.5 million, sold to make a payment it had promised investors.

Around the same time, a product the company built to always be worth $100 quietly slipped to about $95, and Bitcoin itself kept sliding, down below $64,000. None of those numbers is big. Put them together, though, and they’re worth talking through, because the story underneath is simpler than the headlines make it sound.

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What actually happened

A while back, Strategy started selling an interesting product that was relatively new to the crypto world. Think of it like a high-yield savings certificate.

You hand over $100, and the company promises to pay you $11.50 a year, forever, in cash. That’s it.

People liked it, the company raised billions, and it turned that money into Bitcoin. One rule makes the whole thing work: that certificate has to keep trading at around $100.

While it does, Strategy can keep selling fresh batches and keep buying Bitcoin. The moment it slips below $100, selling more becomes like offering $100 gift cards for $95, where you lose a little on every one.

For most of a year, that certificate sat right at $100.
This week it didn’t.

It hasn’t climbed back to $100 since May 14, and it kept sliding, closing near $94 on June 3. As I write this, almost none of it is changing hands above $100, which is the level Strategy needs in order to sell fresh batches. The engine it uses to raise money and buy Bitcoin has, for now, switched off.

Now the part that matters more than the price.

Those promised payments add up to about $1.7 billion a year. The 32 coins Strategy sold covered maybe half a month of that. Meanwhile the company’s cash, the rainy-day account it draws on to make those payments, sits at around $900 million, roughly six months’ worth. A year ago that cushion was measured in years. Today it’s measured in months per CoinDesk.

One more thing has happened since the sale.

Bitcoin has kept falling, down toward $63,000 as I write this, which is below the roughly $75,700 the company paid on average. Their entire pile is now worth less than they paid for it, at least on paper. That doesn’t force anything on its own, but it’s the backdrop against which every one of those monthly payments still has to be made.

Why 32 coins spooked everyone

The coins themselves are nothing. Saylor said outright he’d probably sell a little to make a payment, just to prove he could, and he still buys far more than he sells. The worry isn’t the size, but what it hinted at.

One analyst put it well back in January: Saylor can buy twenty thousand coins in a week and nobody blinks, but the day he sells even a couple hundred, the market panics.

This week was a small taste of exactly that. Another analyst this week called it the first real crack in the structure.

The treadmill nobody mentions

Here is the heart of it, and it’s something I wish more of us thought about before buying anything built on top of Bitcoin.

Bitcoin doesn’t pay you anything to hold it. It’s like a gold bar or a great painting, it just sits there and, we hope, becomes worth more.

That’s fine if you bought it with your own money and you can wait. It becomes a problem if you’ve promised someone $1.7 billion a year in cash, because a gold bar doesn’t write checks. You either keep bringing in new money, or you start selling the bar.

Saylor admitted as much. By his own math, Bitcoin has to climb about 2.3% every year just to cover those payments without selling stock. Picture a treadmill set to a slight incline. You have to keep walking just to stay in place. If Bitcoin runs hot, that’s easy. If it stalls or drops, the belt keeps moving under your feet.

BUFFETT FRAMEWORK QUESTION: Would you buy the whole thing?

Forget the share price and ask whether you’d want to own the entire company. You’d own a pile of Bitcoin that pays you nothing, plus a stack of promises to hand out about $1.7 billion in cash every year, backed by a bank account with maybe six months in it. It only works if the Bitcoin keeps climbing faster than the promises pile up.

Buffett built Berkshire on the opposite setup, where the money came in first and the assets paid their own way. It’s worth knowing which of those two you’re actually holding.

The same machine can spin backward

For years this was a beautiful machine, and I don’t say that to knock it. People trusted it so much they’d pay more for a share of Strategy than the Bitcoin inside was even worth. The company used that goodwill to sell more, buy more Bitcoin, and pack a little more Bitcoin behind every share, which made people trust it even more. Round and round, up and up.

The trouble is that the same wheel can spin backward.

Once people stop paying extra, and once that $100 certificate slips below $100, selling more stops being clever and starts losing money. The payments, though, don’t stop. They come every month no matter what. When the easy money dries up, the only thing left to sell is the Bitcoin itself. That’s what this week showed, in miniature.

Those 32 coins went to making a payment rather than buying more. There’s a competitor now too, Strive, paying 13% on a similar product that’s holding its value better, which makes Strategy’s 11.5% look thin right when it can least afford to sweeten the deal.

The Key Takeaway

Here is what I most want you to walk away with.

Saylor is not Bitcoin, and Bitcoin does not need Saylor. Full stop.

Bitcoin ran fine for eleven years before this company bought a single coin, and more people own it today, through funds, countries, exchanges, and regular folks, than ever before.

Strategy holds about 4% of all the Bitcoin there will ever be. That’s big enough to push the price around, and small enough that Bitcoin wouldn’t miss a beat if the company vanished tomorrow. What Saylor did was bring a flood of money and attention, and he also changed the story, turning Bitcoin from quiet, neutral money into the engine room of one company’s financial machine.

Some longtime holders love him for the bridge he built, while others feel he’s bent their asset to serve his balance sheet. Both can be true. The catch is that when adoption rides on one big, heavily borrowed-against player, it carries a way to fail that simply holding Bitcoin yourself does not.

Where I could be wrong, and whether it’s over

Now the fair side, because I won’t sell you a panic.

Six months of cash isn’t zero. The company has bought far more than it’s sold for years, even if it’s paused new buying since the middle of May, and that certificate has bounced back toward $100 before, usually around its payment dates. Plenty of smart people looked at this week and shrugged, and on the narrow facts they have a point.

What I’m describing is a bad-case story, not a prediction, and it needs three things to line up at once: Bitcoin flat or falling, the certificate stuck below $100, and that cash cushion running thin. What changed this week isn’t the odds, but that we finally watched the machine run in reverse with our own eyes.

So, is it really over? No, not this, and not now. A small sale to make a payment is not the death of Bitcoin, and anyone shouting that it is, is mostly selling you their own fear. The smarter move is to know what to keep an eye on.

That $100 certificate. If it keeps trading below $100, that’s the clearest warning light. There’s even a holder vote on June 8 about paying those dividends more often, a sign of how much keeping that level matters.
The company’s cash. Six months is the number that turns a worry into a real problem if it keeps shrinking.
How often, and how much, they sell. One payment’s worth of Bitcoin is noise. A habit is not.
The competition. Paying 11.5% while a rival pays 13% is a standoff that can’t last.

None of those is flashing red today, though a couple are amber. The company and the coin were never the same thing, even with 4% of all Bitcoin sitting on one balance sheet, and Bitcoin’s strength was never going to come from a single buyer.

The real risk worth watching is that its biggest holder turns out to be its most fragile one. Watch it with clear eyes, not fear, and don’t let anyone tell you those 32 coins were the whole story. They were the preview.

Until next time - trade safely fam!

I Found a Way to Invest in the Robot Boom on the Nasdaq

Matthew — Tue, 02 Jun 2026 11:47:31 GMT

A new challenger has entered the arena…and it’s loaded with robots! 🤖

A reader sent me a ticker this week with a simple question. What is this, and should I be watching it? The ticker was $BOT, and the further I dug, the more it became the kind of thing I enjoy taking apart here. The robots make the headline. The wrapper they sit inside is the real story, and it borrows its design straight from a playbook that crypto investors already know well.

Let me walk you through what it is, why I think it is worth watching, where the opportunity sits, and the part I would slow down on before doing anything.

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What RoboStrategy Really Is

RoboStrategy, Inc. began trading on the Nasdaq under the ticker $BOT on May 11, 2026. Its stock had never traded publicly before that day, so this is a genuinely new instrument rather than a rebrand of something older.

The company is a closed-end management investment company.

In plain terms, it is a fund with a fixed share count that trades like a stock. What makes it unusual is the mandate.

RoboStrategy is the first public closed-end fund built specifically to give ordinary investors exposure to a portfolio of private, pre-IPO, and public robotics and physical-AI companies.

The kind of access that has historically been gated behind venture capital now sits behind a ticker you can buy in a brokerage account.

The portfolio is concentrated and recognizable. It includes Figure AI, Apptronik, Dyna Robotics, Standard Bots, Dexmate, and Path Robotics, with reported average check sizes around $7 million dollars per round. Figure and Apptronik dominate by disclosed value, and most of the rest carry private marks that have not been made public.

The fund is run by Andrew Kang, a former crypto investor who co-founded Mechanism Capital and has backed Figure directly. It launched with a $200 million dollar equity commitment and up to $2 billion dollars in committed financing. The capital is permanent, which means the fund is not forced to return money on a venture timeline and can hold its positions for as long as it wants.

That permanent-capital structure is the tell. RoboStrategy describes itself as inspired by the model that Strategy, formerly MicroStrategy, used to hold bitcoin. Once you see that lineage, the whole thing reads differently.

Why I think it’s Worth Watching

Three things make this listing interesting, and the robots are the least of them.

Access. Most of the marquee humanoid companies are staying private far longer than the last generation of tech startups did. Figure has raised close to $2 billion dollars and carries a $39 billion dollar mark without being anywhere near a public listing. Apptronik tripled its valuation to roughly $5.5 billion in a single funding extension this February. For a public-market investor, there has been no clean way to participate in that value creation while it happens, and this is the first vehicle structured to change that.
Timing. Robotics funding hit a record in 2025, with startups raising close to fourteen billion dollars against roughly $8 billion the year before. Capital is flowing into the sector faster than public vehicles can absorb it, which creates demand for exactly this kind of wrapper. Whether that demand is healthy or frothy is a separate question, but it is real.
The epistemics. This is the reason I care most. RoboStrategy is a clean live experiment in a structure that has already confused the market once. The question of what the price actually measures, and how far it can drift from the value underneath it, is the same question I ask about every backtest and every Sharpe ratio. The mechanics here just make the gap easier to see.

The opportunity, and the MicroStrategy Parallel

To understand the opportunity you have to understand the loop, and to understand the loop you have to start with Strategy.

Strategy is a software company that turned itself into a bitcoin holding vehicle.

It issues equity and convertible debt, uses the proceeds to buy bitcoin, and has frequently traded at a large premium to the value of the bitcoin it holds. That premium is the engine. When the stock trades above the value of its assets, the company issues new shares at that elevated price, raises more cash than the value of the ownership it gives up, and buys still more bitcoin. Existing holders end up with more bitcoin per share than they started with, which helps justify the premium that started the cycle.

RoboStrategy is porting that engine to robotics.

When it works, the loop is powerful.

A premium funds the buying, and the buying defends the premium. A holder gets concentrated, single-ticker exposure to a basket of private robotics companies, plus optionality on the cycle compounding in their favor. If Figure or Apptronik eventually goes public at a higher mark, the fund’s stake gets revalued upward, and the holder captured that move without ever needing access to a private round.

There is also a quieter source of return that has nothing to do with the robots. The premium or discount itself moves, sometimes sharply, so a patient buyer who acquires shares at a discount to a conservative estimate of value carries a very different risk profile than someone buying into a rich premium. The structure creates its own tradeable variable on top of the underlying.

How to Think About Risks

Here is where the same structure that creates the opportunity creates the danger, and where I would slow down. Start with what a price actually tells you in this fund.

A closed-end fund has two numbers that rarely match:

the market price, which trades live every second, and
the net asset value, which is the fund’s own estimate of what holdings are worth.

For a fund holding liquid assets, NAV is easy to calculate and the two numbers stay close. For a fund holding mostly private, pre-IPO companies, NAV is a quarterly model estimate based on the last round each company raised. The price moves daily. The value it is supposedly tracking updates on a lag, and even when it updates, it is an estimate rather than a market-clearing price.

So the straight answer to what is really going on with the price is that the fundamental value is opaque by construction. You are not buying a number you can verify. You are buying a model output wrapped in market sentiment, and the sentiment can dominate the model for long stretches.

Buffett Framework
“Price is what you pay. Value is what you get.”

With a closed-end fund holding private companies, the price trades live and the value is a quarterly estimate, so the two can drift further apart than usual. The discipline is to watch the gap, not just the price.

Layer the risks and the picture gets clearer.

Sector risk sits at the bottom. Robotics and physical AI are cyclical, capex-linked, and sentiment-driven, and a buyer carries all of that thematic beta.
Idiosyncratic private-name risk sits in the middle. The portfolio is concentrated in a handful of pre-IPO companies, so a single down-round or a stalled financing flows straight into NAV. Figure shows the fragility well. Its $39 billion dollar mark sits on revenue estimated in the low tens of millions, and when BMW announced a humanoid pilot at its Leipzig plant this February, it chose a competitor’s robot rather than Figure’s. None of that shows up in a quarterly NAV until the next mark, if it shows up at all.
Premium or discount risk sits on top, and it is the one most likely to surprise people. The reflexive loop runs in reverse just as easily as it runs forward. A discount can become self-reinforcing, where a falling price makes new issuance dilutive instead of accretive, which removes the engine that was defending the price. A fund only weeks into public trading has almost no history to tell you how its premium behaves under stress, and the early life of these vehicles is exactly when the premium is least anchored.

So the framework I would use is simple to state and harder to practice. Treat NAV as a model output rather than a price. Watch the premium or discount as the primary variable, not the share price in isolation. Pay attention to share issuance, because the pace of issuance tells you whether the loop is running forward or backward. Size any position to the understanding that the top risk layer can swamp the bottom two for months at a time. I am not telling you to buy it or to avoid it. I am telling you that if you do buy it, the premium to NAV is the thing to watch above the robots.

How BOT, MSTR, and a Robotics ETF Compare

The table makes the point cleanly. BOTZ tracks its holdings tightly because they are liquid and priced all day. MSTR can detach from its NAV, but at least that NAV is a live bitcoin price. RoboStrategy can detach from a NAV that is itself an estimate, which stacks one layer of uncertainty on top of another.

What I’m Watching 👀

Should you buy $BOT, probably not right now. It’s extremely volatile as a very new and illiquid security. I’m certainly not a buyer right now…but I am an active watcher.

I built a small monitor for this one with Claude Code, because it’s a good test case and the data is useful whether or not anyone trades it. The monitor watches the premium or discount to the last reported NAV, the fund’s share issuance activity, the funding news for the underlying private names, and the divergence between BOT and a liquid public robotics basket. The divergence between the two is close to a pure read on sentiment, since the public basket strips out most of the shared sector beta. I will report what it surfaces in future issues.

The broader lesson travels well beyond one ticker. A number that looks precise can still be measuring something soft underneath, and the discipline is in asking what the number actually represents before you act on it. That is true of a NAV on a closed-end fund, and it is true of a Sharpe ratio on a backtest.

Until next time - trade safely fam!

Sources

RoboStrategy, Inc. Nasdaq listing announcement, GlobeNewswire / Nasdaq, May 11, 2026
RoboStrategy SEC Form 497AD, FY2026
Reporting on the $200M / $2B commitment and Andrew Kang background (Mechanism Capital)
Figure AI Series C announcement, The Robot Report, September 2025 ($39B post-money)
Figure AI funding and BMW Leipzig pilot context, TechMarketBriefs, April 2026
Apptronik Series A extension coverage, CNBC and Bloomberg, February 2026 (~$5.5B)
Robotics sector funding totals, Crunchbase News, February 2026

Capital That Thinks for Itself: My Bet on AI-Native Asset Management

Matthew — Sat, 30 May 2026 15:38:18 GMT

I write BitFinance because I think the real edge in markets sits in the validation work most people skip.

Show the real numbers.
Show the strategies that died.
Let the track record stand somewhere anyone can check it.

That’s the whole thesis, and it’s why I’m careful about what I put my name on.

So when I tell you I’ve joined Duon Labs as a formal advisor, I want to be straight about the why. The deck didn’t win me over, but the work did. Their team is building in the open and - more importantly - shipping.

Let me give you the plain version of what that is.

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A foundation model, pointed at markets

You already know what a foundation model is, even if you’ve never used the term. It’s the engine underneath tools like ChatGPT or Claude, one big system trained to understand language that then powers a hundred different apps on top.

Duon Labs is building one of those for financial markets instead of language.

Picture a machine that runs millions of simulated futures every day, thousands of versions of what markets might do next, then writes down where all those futures tend to agree. That shared read becomes a forecast.

The forecast becomes a strategy.
The strategy deploys real capital.
The whole chain, from raw data to live trades, runs under one roof.

Here’s the part that I think is easy to miss:

Most trading bots follow rules a person wrote by hand. Duon Labs’s system writes and tests its own. New versions of the models turn over constantly, each one measured against the last, the better ones kept and the weaker ones retired.

The research loop improves on its own and creates a valuable flywheel effect.

That changes the shape of the business. A traditional asset manager grows by hiring more people. Duon Labs grows by adding compute and letting the loop compound. Think of a wind tunnel for strategies: thousands of designs tested against simulated conditions before a single dollar goes near the real thing.

There’s a quieter advantage in here too.

The engine doesn’t care what it’s pointed at. The same stack that models crypto can model commodities, equities, rates, or anything else with a price history, because it learns from the structure of markets rather than the quirks of one of them. Build the wind tunnel once and you can test aircraft, race cars, and rockets in it. That’s why a commodities sleeve is on the way and why the long-term story isn’t a crypto fund but a research engine that travels across asset classes.

Why the on-chain part matters

Now the piece I care about most.

Every trade Duon Labs makes settles on a public blockchain. That sounds technical, but the consequence is simple. The track record stops being a quarterly letter you take on faith and becomes a ledger anyone can open and check in real time.

Sit with that for a second, because it inverts something critically important.

In most of finance, transparency is an actual cost. You disclose details because you have to by a regulated mandate - not because it helps the business.

With Duon Labs, transparency is the product.

The same property that lets an outside investor audit the strategy also lets a custodian’s risk team underwrite it, lets an advisor report it to clients, and lets a board see exactly what it owns. One feature, doing work across the whole stack.

For a newsletter built on the idea that numbers you can check beat marketing, that’s the entire appeal.

The train has left the station (choo! choo!)

None of this is theoretical, so it’s worth grounding in what the team is shipping.

Consumer reach most research shops never get near. Duon Labs’s signals sit inside a widget on CoinStats, a portfolio app with a large retail following, and that placement has pulled in more than 3 million monthly widget views. The same intelligence behind the institutional strategies is already living on people’s phones.
Running on serious on-chain infrastructure. The vaults trade live on Hyperliquid, one of the busiest venues in on-chain markets, and a newer vault launched on MegaETH this spring. Two different network designs, one underlying brain.
Developers lining up to build on it. Next to the vaults, Duon Labs is opening an API so coders and quant teams can build on the same engine. Almost 200 of them are on the waitlist before it’s even fully open.

Ultimately there are really two stories here:

One is capital the system runs on its own.
The other is an engine other people get to build on top of.

Both are already moving.

The art of the possible

The interesting question isn’t whether the engine works but who it works for. A few of the places this could land:

Custodians and custodial platforms. A custodian can hold a client’s assets safely but can’t do much to grow them beyond basic staking. Duon Labs plugs in as an actively managed yield option where the capital never leaves custody and every trade is verifiable. That clears the compliance bar that usually kills these products before launch, because the risk team can audit the strategy instead of taking a counterparty’s word for it.
Tokenization issuers and platforms. The hard part of tokenizing a fund isn’t the wrapper but the thing inside it: a strategy institutions will actually buy. An auditable, on-chain track record is exactly that, a transferable vehicle any allocator can verify before committing a dollar. The issuer distributes through rails it already runs, and the underlying does the convincing.
RIAs and wealth managers. Most advisors still can’t offer real digital asset exposure they can stand behind, so they default to a spot ETF or to nothing. This is actively managed exposure, held at a qualified custodian, and reportable down to the individual trade. An advisor can pull up the actual position on-chain in front of a client, which turns a hard conversation into a simple one. This one feels like the long-term destination.
Asset managers and allocators. An allocator’s job is to trust a manager’s numbers between quarterly letters, which is mostly an act of faith. Here the sleeve is verifiable continuously rather than reconciled once a quarter. You stop auditing the past and start watching the present.
Trading desks and quant shops. A desk doesn’t want someone else’s black box but better inputs for its own. Duon Labs’s read arrives through an API as one more signal that stacks on top of existing models, not a replacement for them. It’s edge a desk can test against its own book before ever leaning on it.

Different customers, one engine underneath. Every dollar of research compounds across all of them, which is what makes the economics hard to copy.

My role, and where it stands

I’m advising on methodology and validation, on how research gets pressure-tested before it touches live money, and on how to frame the work for the institutional investors who might one day allocate to it. I bring a Series 65, prior experience running a digital asset strategy, and a low tolerance for flattering numbers. Coincidentally, we’re complementary because I’m building an engine to auto-curate indicators that could be used by Duon Labs’ to hone their agent accuracy.

None of this is finished, and I won’t pretend otherwise. The out-of-sample research is strong, and it’s auditable. Turning research-grade results into consistent live performance is the hard part, and that translation work is a big reason I said yes. I’d rather you hear that from me than find it on the audit page later.

So go take a look for yourself and see what we’re cooking!

The vaults trade on-chain, the model releases are public, and you can watch the whole thing run live here: live.duonlabs.com

I’ve said for a while that crypto + AI is the most interesting place to be building right now. Duon Labs is the clearest example I’ve found of someone doing it out in the open, with the receipts attached. I’m glad to be along for it, and I’ll keep showing you what I learn, the wins and the misses both.

Excited to keep you posted on the opportunity. If you’re interested in learning more about what their building or their fundraising efforts - please reach out!

🛑 SIGN UP HERE BELOW TO BE PART OF WHAT WE’RE BUILDING! 🛑

I’m building the early cohort for when the Discord opens and the first indicators ship. If you want a seat, this is where to claim one.
Interested? Fill out the 30-second interest form here. 👈

This Week in Two Minutes

Your Software Is About to Start Paying Its Own Bills (May 26)

Keyrock, Coinbase, Tempo, and Virtuals published twelve months of real machine-to-machine payment data.

AI agents settled $73 million across 176 million transactions between May 2025 and May 2026, with 98.6% of that flow moving through USDC. The sustained Q1 2026 run rate was $11 to $15 million weekly.

Why it matters: a USDC transfer on Base costs about 1/100th of a cent, while a Stripe card rail costs about 31 cents. For a $0.31 transaction, one rail leaves the merchant with $0.3099, the other with $0.001. The old rails can’t carry that traffic at that price point. Post office vs. email, fifty years later.

The part worth holding: roughly 74% of agent volume currently flows through Coinbase’s chain, Coinbase’s stablecoin, and Coinbase’s facilitator. That’s the most vertically integrated payment stack since Stripe.

Read the full article here.

Tokenization’s Missing Buyers (May 28)

A few days ago, Adam Blumberg published a piece in the Tokenized Asset Coalition’s Industry Veteran Series that did something the tokenization industry has spent five years avoiding.

He runs an SEC-registered RIA built for the crypto-native UHNW investor, and he still can’t allocate to tokenized RWAs at scale. The buyers aren’t missing, they’re blocked. Five operational barriers (custody fragmentation, opaque token-to-asset data, KYC rebuilt at every issuer, no reporting integration, four different income mechanics) sit between conviction and allocation.

Tokenization works for stablecoins and tokenized Treasuries because the underlying is liquid and the data is clean. It stalls in private credit, real estate, and specialty finance because the operational rails to hold those assets don’t exist yet.

The article ties to the RIA work I’m doing in Puerto Rico, and the gap that work is built around.

Read the full article here.

Winners This Week🏆

Public-chain tokenization rails. 2 big wins in one week!
- DTCC announced a partnership to tokenize Russell 1000 equities and US Treasuries on Stellar ($XLM) by 1H 2027. Paxos became the first blockchain-native firm registered with the SEC as a clearing agency and central securities depository, after seven years of regulatory back-and-forth. The legacy infrastructure that has run US securities for decades is migrating to public blockchain rails. If you’ve been waiting for a structural signal that tokenized real-world assets are a real category and not a slide deck, this week was it.
- I guess the $XLM market liked the news…
US regulated crypto derivatives. Kalshi got the first CFTC approval for a Bitcoin perpetual futures product, with stated plans to expand to more than a dozen currencies. Coinbase received a separate no-action letter allowing perpetual futures through its Bermuda affiliate, with BTC, ETH, and stablecoins as eligible collateral. US-based traders no longer have to use offshore venues to get perp exposure. Better tax treatment, lower counterparty risk, and a competitive landscape that suddenly looks domestic.
Bank-issued stablecoins. SoFi launched SoFiUSD, a bank-issued dollar stablecoin available inside its app with Ethereum and Solana support. Banks have moved from regulating stablecoins to issuing them. The competitive set against USDT and USDC just changed shape, and the door is now open for other US banks to follow the same playbook.
Hyperliquid and the DEX thesis. The 11-person team’s HYPE token sits within 4.5% of its all-time high after ICE CEO Jeffrey Sprecher called the protocol “bigger than Nasdaq” at the Bernstein conference and disclosed multiple meetings between ICE and the team. Legacy exchanges have stopped pretending decentralized rivals don’t exist. That’s a meaningful change in posture from the firms that run the regulated markets.

And The Losers…📉

Long-duration bond holders. The 30-year Treasury yield hit 5.19% earlier this week, the highest since July 2007. The 10-year touched 4.67% before easing to 4.44% Friday on tentative US-Iran ceasefire news. Anyone holding duration through this repricing took a beating, and the bid-to-cover ratios on recent 10- and 30-year auctions show real demand strain at the long end. Yields eased late in the week, but the structural backup is the story.
Anyone positioned for rate cuts. Fed funds futures now show markets assigning a 46% probability to a rate hike in December, not a cut. Six months ago the consensus was for multiple cuts in 2026. Core PCE came in at 3.3% year-over-year this week, still well above the Fed’s 2% target. The trade that the Fed would ride to the rescue this summer has been mostly squeezed out.
Small caps. The Russell 2000 trailed every major index again this week, finishing in the red while the S&P, Dow, and Nasdaq all closed green or near it. Smaller balance sheets carry more funding-cost sensitivity, and the rate-hike repricing makes that exposure expensive. The gap between large-cap AI beneficiaries and the rest of the economy keeps widening.
Bitcoin spot and the “uncorrelated asset” pitch. BTC trading around $73K and underperforming AI and semiconductor stocks while the broader market notches highs. Spot Bitcoin ETFs hit a record nine-day outflow streak with $2.8 billion pulled, the longest run of withdrawals since they listed in January 2024. The narrative that Bitcoin trades independently of risk assets is being tested in real time, and right now it’s struggling to hold.

What’s on Deck This Week: June 1

June FOMC meeting and the jobs report. Non-farm payrolls land Friday, June 5. With rate-hike expectations creeping back into futures and core PCE at 3.3%, the labor data is the cleanest read on whether the Fed has room to move in either direction this year. The June FOMC follows mid-month, and any pivot language from Chair Warsh moves both equities and fixed income.
Iran ceasefire verification. The reported 60-day memorandum of understanding between the US and Iran extends the current truce and gradually restores tanker flow through the Strait of Hormuz. The deal is contingent on Trump approval. Oil markets, Treasury yields, and inflation-linked positioning all move with the headline risk on this one.
ETF flows as a forward indicator. Watch whether the nine-day Bitcoin ETF outflow streak breaks or extends. Flows have led price by a few sessions this cycle. The same read applies to equity ETF flows heading into the FOMC.
Stablecoin regulatory deadlines. The GENIUS Act implementation deadline is July 18. MiCA’s transitional period ends July 1. Neither contains provisions for autonomous machine-to-machine transactions, which is the gap the agentic commerce piece spent time on. Worth watching how issuers and regulators handle the mismatch.

Until next time! Trade safe out there fam 😎

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Tokenization’s Missing Buyers

Matthew — Thu, 28 May 2026 13:10:13 GMT

A few days ago, Adam Blumberg published a piece in the Tokenized Asset Coalition’s Industry Veteran Series that did something the tokenization industry has spent five years avoiding.

It named the problem out loud.

Blumberg runs Protocol Wealth, an SEC-registered RIA built for the crypto-native ultra-high-net-worth investor. He has the clients. He has the conviction. He has six years of teaching this stuff before he ever launched the firm. Despite all of that, he can’t allocate to tokenized real-world assets in any meaningful way.

The reason isn’t skepticism. The piece is the opposite of skeptical, and that’s why I think it warrants consideration.

Somebody on the inside is saying out loud that the architecture being built right now will decide whether the trillion-dollar institutional channel ever shows up.

Here’s the part that landed hardest for me.

Walk into any conference that hgihlights “tokenization” today and you’ll hear the same pitches you heard five years ago:

Democratize access.
Bring smaller managers onchain.
Open up regional real estate, agricultural credit, specialty private credit, energy royalties, the long tail of strategies that traditional distribution couldn’t reach.

That’s it. That’s the only pitch that justifies the cost of building this industry.

What’s actually being shipped is tokenized share classes of the BlackRock USD Institutional Digital Liquidity Fund. Tokenized Apollo private credit. Tokenized Hamilton Lane. Tokenized KKR. Real funds, real institutions, real assets. None of that’s in dispute.

The question is whether tokenizing those funds advances the original case for tokenization at all. The answer is no.

Apollo and BlackRock and KKR were not assets that lacked distribution. An RIA who wanted Apollo private credit already had three different ways to get it. The tokenized version is a UI upgrade dressed up as a paradigm shift.

The original promise was about expanding the universe of managers who could compete for capital. The current path is a premium digital wrapper for the trillion-dollar managers who already had every distribution channel they needed.

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Where tokenization actually works

The bottom line is that tokenization works as a technology and foundation for transmitting value between parties when they would normally be illiquid.

The challenge, is that it’s worked in a narrow part of the market that most retail investors don’t think about as “tokenization” at all.

Let’s look at some examples:

Stablecoins are tokenized dollars. They work.
Tokenized Treasuries like BUIDL, OUSG, and BENJI are tokenized T-bills, and they’re growing fast because the underlying is homogeneous, the NAV is calculated by an established fund administrator, and the oracle just publishes a daily number.

The data tying the token to the asset is reliable, the income mechanics are clean enough to live with, and the use case (cash management onchain) is sharp.

That’s the easy case. Liquid underlying, transparent pricing, daily marks, simple income structure. Tokenization on liquid markets is largely a solved problem.

What hasn’t been solved is the case that actually justifies the regulatory complexity, the smart contract risk, and all the capital that’s gone into building this industry. Private credit. Real estate. Specialty finance. Royalty interests. Insurance-linked securities. The asset classes where the underlying economics are interesting precisely because the existing distribution machinery couldn’t reach them efficiently.

In those markets, tokenization runs into a problem that doesn’t show up in the marketing decks:

There’s nobody to buy what you’ve built.

The volume problem

Here’s the thing about tokenizing a private investment vehicle. The token can be perfectly designed. The smart contract can be flawless. The compliance modules can be elegant.

None of that matters if there’s no allocator on the other side ready to write a check.

In liquid markets, the buyers exist. Anyone with a stablecoin and a wallet can buy a tokenized Treasury. The market makes itself.
In illiquid markets, the buyers are fiduciaries managing other people’s money.

This means they have rules to follow requiring:

A qualified custodian that supports the specific token.
Verified accreditation that doesn’t require re-onboarding the client at every issuer.
The investment position to show up in their portfolio reporting system alongside everything else they manage.
A defensible answer to compliance about how income is delivered and how it gets taxed.

Blumberg lays out five operational reasons fiduciaries can’t currently buy tokenized alternative assets at scale.

Custody is fragmented across multiple platforms with no single coverage path.
Token-to-asset data is opaque exactly where it matters most.
KYC is rebuilt at every issuer.
Reporting doesn’t integrate with the platforms that actually run the wealth-management industry.
Income arrives through 4 different mechanisms with four different tax treatments.

None of those problems is technically intractable. All of them require infrastructure that crosses platform boundaries, and the platforms have been building inside their own walls instead.

And herein lies the rub:
you end up with tokenized regional real estate that nobody can allocate to.

Tokenized specialty private credit with low volume because the natural buyers can’t operationally hold it. The promise of tokenization, the one about reaching the manager you couldn’t reach before, dies at the operational layer.

Why this is on my desk

I’m in the middle of standing up a registered investment adviser in Puerto Rico. Part of the work I’m doing with that team is trying to engage this exact problem.

I don’t want to overclaim what we’re building. The infrastructure questions Blumberg names are coordination problems the whole industry has to solve together, and no single RIA is going to fix federated KYC or qualified custodian coverage on its own.

What I can say is that the gap between what tokenization promised and what it’s delivering is the central question I keep coming back to, and the firm I’m helping build is being designed with that gap in mind from day one.

The reason I’m telling you about this isn’t to pitch the RIA, but to make a point about what to watch for as the tokenization narrative gets louder over the next year.

As we progress, I’m excited to keep y’all apprised of the progress we make. The ultimate goal is to build a platform that can unlock many of the challenges we’re seeing related to volume and attract institutional players to participate.

In short…we’re hoping to build it so others will come.

What to watch

The tokenization headlines are going to keep coming. Larry Fink and Boston Consulting Group see this technology growing to tokenize tens of trillions of dollars worth of assets.

Every traditional asset manager will announce something. Every blockchain conference will have a tokenization track. The numbers being projected for tokenized RWAs by 2030 will keep climbing.

Most of that volume, if it materializes, will be tokenized versions of products that already existed.

Money market funds
Treasury products
Real estate
Share classes of institutional funds you could already access through a broker.

Useful incrementally for cash management and capital efficiency, but not the structural change in who gets to invest in what.

The structural change starts when the long-tail manager (the regional real estate operator with a track record, the specialty lender with differentiated underwriting, the niche credit shop you’d never have heard of through traditional channels) can actually find fiduciary buyers onchain. That requires operational infrastructure built across platform boundaries instead of deeper into single-platform silos.

It also requires fiduciaries to actually show up to participate, which is the slower part of the answer, and the part I’m spending my time on.

Tokenization is an unfinished experiment rather than a failed one.

The work that’s been done at the standards layer, at the custody layer, at the issuance layer is real and impressive. The work that hasn’t been done is the part that connects all of it into something a fiduciary can use to bring a new asset into a real client portfolio.

That gap is where the actual opportunity lives. It’s also where the next phase of this industry gets decided and I’m pumped to be building in a space like this!

Until next time!

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Your Software Is About to Start Paying Its Own Bills

Matthew — Tue, 26 May 2026 12:13:55 GMT

For 200 hundred years, the financial system has been built for human hands.

Payments settled in days because humans needed days. Fees existed because humans paid people to staff the desks, run the wires, and resolve the disputes. The whole system was sized for analog speed and analog volume.

We finally have an alternative, and it didn’t arrive because Wall Street decided to upgrade the plumbing. It arrived because the open architecture of blockchain, with its immutability, transparency, and security, turned out to be the only foundation that could support stablecoin settlement and agentic payments at the scale software is about to demand.

Here’s the math that makes it concrete.

A USDC transfer on Base costs roughly one one-hundredth of a cent.
The same payment on a Stripe card rail costs nearly thirty-one cents in fees.

For a $0.31 transaction, the first rail leaves the merchant with $0.3099. The second leaves the merchant with $0.001.

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What Just Happened

On May 21, the crypto market-making firm Keyrock published “Who Pays the Agent?” in partnership with Coinbase, Tempo, and Virtuals.

They curated, 12 months of actual data stemming from real machine-to-machine payments; not slides from a venture pitch.

The findings should tell you where the puck is going:

AI agents settled over $73 million across roughly 176 million transactions between May 2025 and May 2026, with 98.6% settling in USDC.
It’s worth noting that Keyrock themselves flag a caveat here revealing that onchain analysis might suggest a meaningful portion of those transactions reflect artificial or gamified activity, and the headline volume figure may overstate genuine commercial throughput by as much as half….but still, not nothing!
The more grounded signal is the sustained $11 to $15 million weekly run rate in Q1 2026, with a peak of $15.3 million in late March, coinciding with genuine integrations like Nansen and Alchemy rather than airdrop farming.

The agentic economy stopped being a pitch deck and started being an ecosystem with a measurable, if early, run rate.

Why the Dollar Can’t Carry This

Think about the post office and email.

The postal system works fine for shipping a letter, but it can’t carry a message that costs one ten-thousandth of a stamp to deliver. Email didn’t solve that by reinventing the post office; it built a different network with a different cost structure underneath. Both systems still exist. They serve different problems.

Agentic commerce is the same situation, just 50 years later.

Card networks were engineered around a human shopping at a register. Settlement clears in days. A floor fee exists because the rails are expensive to operate and someone has to pay for the chargeback infrastructure. None of that scales down to a software agent paying a data feed three cents for a real-time market quote.

Erik Reppel, who built x402 at Coinbase, frames it this way:

“Paying with stablecoins is actually the logical equivalent to paying in cash.
If I hand you $10, there’s no person who’s going to take 3% of the $10.”

That’s the deeper point. Blockchains haven’t merely made payments cheaper. They’ve returned payments to their pre-intermediation economics.

When three out of four agent transactions cost more in fees than they’re worth, the existing rails become structurally impossible to use.

The Four Lanes of the New Highway

4 major protocols are emerging to serve this market. The names and functions matter more than the technical implementation.

Visa extended its existing card rails to provision AI-ready tokenized credentials agents can present at checkout.
Google released AP2, an authorization layer that lets users delegate spending authority to agents using cryptographic mandates.
Stripe and Tempo co-authored the Machine Payments Protocol (MPP), a payment-method-agnostic standard handling stablecoins, cards, and Lightning through a single HTTP flow.
Coinbase built x402, repurposing the long-dormant HTTP 402 status code to enable stablecoin payments between machines. (remember, I talked about them at Consensus)

The meaningful frame: these aren’t all competing on the same field; they’re stacking. AP2 handles authorization on top while x402 and MPP handle settlement beneath it.

Actual value moves on blockchain rails below that. Visa runs as a parallel lane for transactions where tokenized cards make more sense than stablecoins. For the longer arc on why x402 specifically matters, my earlier DIEM and VVV thesis walked through how this was already showing up in agent-payment tokens.

Who’s Building Which Lanes

Coinbase and Stripe each span five of the six layers Keyrock maps.

Coinbase covers settlement (Base), wallets (AgentKit), the payment protocol (x402), and governance as a 402 Foundation partner. Stripe mirrors this through Tempo, Privy, Bridge, and MPP. Circle covers four layers. Google and Visa span two and one respectively.

The legacy incumbents are paying real money to claim position.

Capital One acquired Brex for $5.15 billion specifically for its programmable card infrastructure.
Mastercard purchased BVNK for $1.8 billion.
Stripe acquired Bridge for $1.1 billion.

The named deals total more than $8 billion in twelve months. If you’re wondering whether the establishment is fighting or buying in, the M&A number is your answer.

The Size of the Prize (where stuff gets nutty!)

Industry forecasts say this gets big. Like, really big.

Gartner projects $15 trillion in AI-agent-intermediated B2B purchases by 2028.
McKinsey estimates $3 to $5 trillion in retail agentic commerce by 2030.
Juniper Research forecasts $1.5 trillion.
BCG puts US e-commerce alone at $1.3 trillion.

Sure, it’s absolutely worth treating these with appropriate skepticism. Getting from today’s $600 million annualized run rate to $1.5 trillion in 4 years implies a compound annual growth rate of roughly 350%.

For context, the entire stablecoin market grew at about 100% CAGR over 6 years. The agentic projections require growth 3-5x faster than the most explosive period in stablecoin history.

That doesn’t make them impossible. It makes them aspirational, contingent on infrastructure, trust, and regulation all maturing in parallel. The infrastructure is mostly ready. The other two are not.

What This Means If You Keep Finance Honest

This is the section I’d ask you to read twice.

Most of the agentic commerce coverage you’ll see this year is breathless about the upside, with little of it speaking to the constituencies who actually keep finance honest: auditors, fund administrators, custodians, vendors, and compliance officers.

Those teams don’t have a playbook for this yet, because their existing playbooks all assume a human authorizer somewhere upstream.

A few questions each constituency is sitting with.

For external auditors, the question is who signs the audit trail when a software agent initiates a transaction. Google’s AP2 cryptographic mandate framework is the first serious substitute for human authorization. Auditors should be reading those mandate structures the way they read powers of attorney today.
For fund administrators, the question is how reconciliation works when trade count goes from hundreds per day to potentially millions across agent-originated activity. Existing NAV strike processes weren’t designed for sub-second activity at this density.
For qualified custodians, the question is what custody actually means for an agent wallet. Once funds land on stablecoin rails, they cannot be recalled, and there’s no chargeback. The BitFinance Vaults Deep Dive covers the broader programmable-custody architecture being rebuilt around this.
For vendors and merchants, the lesson is that agentic commerce runs on protocol endpoints, not visual checkout flows. OpenAI’s ChatGPT Instant Checkout was shelved in March 2026 after only about 30 Shopify merchants used it.
For compliance officers, the question is what an “agent of record” looks like inside a KYC stack. American Express launched Agent Purchase Protection in April 2026, covering erroneous purchases by verified agents in its registered ecosystem. That’s the first commercial answer. Expect more.

The Risks Worth Naming

Concentration is the obvious risk, and the report makes the picture sharper than most coverage admits. 98.6% in USDC means Circle is a single point of failure for the entire settlement layer.

Sharper still: roughly 74% of agent payment volume flows through Coinbase’s chain (Base), uses a stablecoin Coinbase co-issues (USDC), and is processed by Coinbase’s facilitator. That’s the most vertically integrated payment stack assembled since Stripe built its relationship with fiat banking. The protocols are open. The infrastructure isn’t.

Regulation is the second binding risk. 3 frameworks reach enforcement in a five-week window this summer:

MiCA’s transitional period ends July 1,
the GENIUS Act’s implementation deadline is July 18, and
the EU AI Act’s high-risk obligations take effect August 2.

None contain provisions for autonomous machine-to-machine transactions. The UK Competition and Markets Authority moved first in March 2026, placing full legal responsibility on businesses deploying AI agents, with fines up to 10% of worldwide turnover.

Expect other jurisdictions to follow.

Why Spend Time on This?

The Indicator Project is the work I’m doing on building agentic trading and investment solutions for individual investors, the people without an analyst team. That work is why I read the Keyrock report the morning it dropped.

The trajectory worth holding in mind is bottom-up.

Payment rails have always been rebuilt this way:
volume from below, not decree from above.

What starts as a $0.01 API call eventually pulls $50 SaaS payments onto the same settlement layer, then $500 procurement orders, and eventually flows that institutions currently assume will never leave their existing infrastructure.

The people who pay attention to the bottom of the curve now are reading the same map institutional buyers will be reading in three years. There’s an information asymmetry available, and it’s the cheap kind: the report is already public.

I run my AI trading research engine from a Telegram chat now. Here’s the setup.

Matthew — Sat, 23 May 2026 15:27:01 GMT

There’s a workstation in my home office I call the Forge. We wrote about it recently as the foundational hardware that runs our trading research engine, the autoresearch loop, and the strategy validation pipeline I use to filter real edge from coincidence.

Until this week, the only way to actually use it was to sit in front of it.
That changed yesterday morning.

My collaborator JMD typed a directive into a Telegram chat from his couch a three-thousand miles away, and an AI agent on the Forge wrote and committed code. I was in the kitchen making coffee. Neither of us touched the machine.

Here’s how it works, why it matters, and how you can build something similar.

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The stack, in plain English

Three pieces:

Tailscale is a private mesh network. It strings a secure wire between any devices you own without exposing them to the open internet. No port forwarding. No public IP. No “open SSH to the world.” For us, it means our laptops and the Forge talk to each other like they’re on the same local network, even though we live a thousand miles apart.
A Telegram bot is the chat surface. You register one through Telegram’s BotFather utility, you get an API token, and the bot sits as a member of a chat both of us belong to. By itself the bot does nothing intelligent. It’s a pipe that forwards what we type to whatever software we point at the token, and routes the response back. The intelligence sits on the other side of the pipe.
Hermes is the brain on the other side. Hermes is an open-source autonomous AI agent framework from Nous Research, MIT-licensed, that runs as a long-running process on the Forge. When either of us sends a directive through the Telegram bot, Hermes picks it up, opens code files, executes shell commands, writes commits, manages its own permissions, logs everything it did, and reports back through the same chat. It can ask clarifying questions. It can run for hours.

In my setup, Hermes is routed through ChatGPT’s Codex as the underlying model endpoint. Hermes is the runtime, the memory layer, and the orchestrator. Codex is the brain doing the actual reasoning inside that runtime. Those two pieces together are what turn an instruction in a chat into committed code on the Forge without either of us at the keyboard.

The piece worth understanding is the memory loop. After enough tool calls on a complex task, Hermes writes the work out as a reusable skill file in plain markdown that lives on disk. Next time a similar task arrives, it loads the skill automatically and gets faster. The agent compounds. That’s what makes this fundamentally different from a chatbot session that forgets you the moment the window closes.

If you read my earlier piece on Openclaw, this is the same family of self-hosted, message-driven AI tooling, just a generation forward. Openclaw is the gateway. Hermes is the agent that lives behind the gateway and learns from what it did. The name fits the function.

In Greek mythology, Hermes carried instructions between worlds. This one does too.

Why this matters

Most “AI trading” coverage you see online is screenshots of someone asking ChatGPT to pick stocks. No infrastructure underneath. No validation. No work happening when the human isn’t typing. The user has to be at the keyboard for anything to happen, and the moment they walk away, progress stops.

What I’m describing here is the opposite of that. It’s the boring, load-bearing plumbing that has to exist before you can actually trust a system to do real work. A real machine. Real version control. An agent that holds context across sessions and writes its own skill library. Whatever happens on the autoresearch engine continues whether I’m asleep, in a meeting, on a flight, or making coffee.

The bigger unlock is collaboration. You can build trading systems alone. It’s slow, it’s lonely, and you miss obvious things because nobody is challenging you. The traditional fix is to be in the same office or to do shared screen calls, but none of that scales for a side operation between two operators with other jobs and other lives.

This setup gives us a shared workspace that is the agent itself. Either of us can direct it from anywhere. Both of us can see what it did. Hermes holds the context, the chat is the audit log, and the Forge does the work. We get the upside of working together without having to be in the same place.

How you’d build this yourself

I’ll keep this practical, because the actual setup is more accessible than it sounds.

You need 4 things.

A persistent machine. Your Forge doesn’t have to be a Mac Studio. A Mac mini, a small Linux box, or an old desktop running headless will work. The requirements are that it stays on, it has enough memory to run an agent runtime comfortably, and you control it. Used Mac minis are cheap. Repurposed gaming PCs are cheaper.
A Tailscale account. The free tier covers up to 100 devices. You install the client on the Forge and on every other device you want to use to reach it. That’s the entire setup. No router configuration. No firewall rules to write. If you can install an app, you can use Tailscale.
Hermes installed on the Forge. The install is a single curl command from the Nous Research repo. It auto-configures most of what you need. You point it at whichever LLM endpoint you want to use as the brain. I’m using ChatGPT’s Codex. You could use OpenRouter, the Anthropic API, or a local model on the same machine if you have the hardware for it.
A Telegram bot. This is the chat layer. Telegram lets you create one through their BotFather utility in about two minutes. You get an API token. You hand that token to Hermes during the install, and Hermes takes care of the rest. No script to write. Telegram is one of the 20+ messaging channels Hermes ships with out of the box.

Total cost to recreate: roughly $0 if you already own a machine and have a model API subscription. Maybe $30 a month if you’re starting from scratch and use the cheapest viable tier. The hardware is the biggest variable, and you probably already have something that works.

The whole thing takes an afternoon if you’re starting cold. The harder part is what you do with it after, not how you set it up.

What it powers

The reason I built this isn’t the chat. The chat is the interface. The reason is the autoresearch engine that sits behind it.

The engine takes a strategy idea and runs it through hundreds or thousands of parameter and regime variations. It applies our multiple-gate validation framework I’ve written about before. It tells me what survives statistical scrutiny and what doesn’t.

That second piece, the trial count, is the one that matters most. Most published Sharpe ratios are meaningless because the publisher doesn’t disclose how many trials they ran to get there. Mine runs the trials explicitly and applies the haircut.

What does that mean for you?

It means the indicators and strategies I write about going forward will have actually been validated, not just back-of-the-envelope plausible. It means I can share work the engine has completed, not just hypotheses. It means the cycle from interesting idea to tested result you can act on gets significantly shorter, because Hermes never sleeps and the machine never logs off.

Here’s a glimpse of the dashboard the agent has been working on.

The first screenshot is the intake form where a new strategy idea gets captured. The second is the Kanban view of the pipeline, showing where every strategy sits today.

That’s the work that needs autoresearch time, which is exactly why getting the remote-control piece working this week mattered. The bottleneck was never ideas. The bottleneck was machine time, and machine time only counts if the machine is actually running.

Where it can break

Honest part. An agent that can pilot your machine is a real security surface. Tailscale plus tight permission scoping plus a private chat with two known humans is the floor, not the ceiling. Anyone deploying something like this without that posture is asking to lose the machine.

The agent also doesn’t do everything. It’s excellent at coding tasks, file edits, running scripts, and checking the output. It’s not running autonomous trades or making decisions without review. The pipeline is built so a human signs off at each gate. That’s intentional. I’m not interested in giving an LLM the ability to move money without me reading what it did first.

Setup takes some focused time. Maybe an afternoon if you’re starting cold. After that it just runs.

If you decide to try this and get stuck, reply to this post or hit me on Substack chat. I’ll point you somewhere useful. The reason I’m writing it up this way is that I think more retail quants should be running real infrastructure, not just clicking around in apps. The tools are finally cheap enough and accessible enough that there’s no good excuse anymore.

Want to be part of what we’re building?
I’m building the early cohort for when the Discord opens and the first indicators ship. If you want a seat, this is where to claim one.
Interested? Fill out the 30-second interest form here. 👈

moving right along…

This Week in 2 Mins

$1.75 Trillion Is About to Reprice Six Industries (May 19)

SpaceX is reportedly filing for IPO on June 12 at a $1.75 trillion valuation, which would be the largest public listing in history. I used my undergrad-era Virgin Galactic memory as a reminder that getting the timeline wrong on a real narrative is how investors lose money. The actual investable space economy is bigger than the rockets and breaks into six layers:

Launch, in-orbit infrastructure
Satellite services
Communications
Manufacturing and supply
Commodities and power (the bedrock layer)

The bedrock layer is the one nobody is pricing in. Names with real cash flow today, like Iridium, BWXT, and MP Materials, are where the durable bets live. Most of the heat is on the higher-variance names at the top of the stack. The lesson from 2011 was that the businesses underneath the headline outperformed the headline itself. The same is probably true now.

Read the full article here.

5 Crypto Risk Management Rules That Actually Work (May 21)

A collaboration with ! Most crypto traders learn risk management the hard way, and then learn the wrong lesson from it. Five rules that actually work, drawn from how professional portfolio managers run money: don’t risk more than 1-2% on a single trade, set your stop before you enter rather than after, cap the whole portfolio not just individual trades, shrink position size when volatility rises, and preserve capital above everything else.

The core insight binds all five together. Capital you preserve compounds. Capital you lose has to be re-earned at exponentially worse odds. A 50% loss requires a 100% gain to recover. A 90% loss requires a 900% gain. Pick two of the five rules and apply them this week. The trader who follows two rules consistently will outperform the trader who knows all five and applies none of them.

Read the full article here.

Winners & Losers This Week

Winners 🏆

Kevin Warsh was sworn in Friday as the 17th Fed Chair after a 54-45 Senate vote, the most divisive Fed chair vote in history. The Dow set a record close at 50,580 the same day, and the S&P extended its weekly winning streak to eight, the longest since December 2023.
Nvidia delivered a beat-and-raise midweek that dragged the AI-linked names along for the ride. SoftBank rallied two days straight and added roughly $61B in market cap, one of the largest two-day gains in Tokyo Stock Exchange history.
OpenAI and SpaceX both filed IPO paperwork in the same week. Both are expected to be among the largest U.S. IPOs ever. The 2026 calendar is now genuinely historic.

Losers 📉

Long-duration Treasury holders. The 30-year yield touched 5.197% midweek, the highest in over a year, pressuring bond prices and rate-sensitive equities before easing late in the week.
Bitcoin spent the week stuck between $76K and $78K, well off the $82K from the prior week. A $563M liquidation cascade on May 18 reset positioning, and the BTC-as-macro-hedge narrative took another hit when a high-profile billionaire investor disclosed he sold most of his bitcoin because it didn’t behave like a hedge during the geopolitical stress.
Walmart, Amazon, and Nvidia (the stock, not the print) closed Friday in the red despite the broader tape rallying, a reminder that index records can mask narrow leadership.

On Deck

Memorial Day shortens the week, but Friday is loaded.

Monday: U.S. markets closed for Memorial Day. 🇺🇸🦅 This means I’ll take my annual trip to the Duke Golf Course.
Friday: PCE inflation, the second revision to Q1 GDP, personal income and spending, advanced trade balance, and Chicago PMI all hit before the open. This is Warsh’s first major inflation print as Fed Chair, and the tape will price the read accordingly.
Retail and enterprise earnings: Salesforce, HP, Costco, Dell, Marvell, Best Buy. Read the consumer prints for actual demand signal, the enterprise prints for AI capex commentary.
Crypto and regulatory: Trump’s executive order kicked off a 120-day Fed review of whether crypto firms should get direct access to Federal Reserve payment services, and the CLARITY Act still has dozens of amendments pending. Slow-burn stories, but next week could surface headlines.
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The 5 Crypto Risk Management Rules That Actually Work

Matthew — Thu, 21 May 2026 12:19:11 GMT

NOTE: A quick note before we dive in. Today's piece is a collaboration between and myself, written together and published here on BitFinance. We've been thinking about the same problem from different angles for a while, and risk management was the natural place to combine notes.

If you're new here from one of our publications, welcome. The piece below is one cohesive argument we both stand behind, not two essays stitched together.

******

Most crypto traders learn risk management the hard way. They take a big position, the market moves against them, they freeze, and by the time the loss becomes real, they’ve already learned a lesson that cost more than it had to.

Between the two of us, we’ve watched this play out for more than fifteen years. The traders who survive multiple cycles share something most of the rest don’t. They built their risk discipline before they needed it.

The five rules below come from how traditional portfolio managers, hedge fund risk officers, and family office allocators actually run money. They aren’t complicated. They aren’t new. They work because they all derive from a single insight.

Capital you preserve compounds.
Capital you lose has to be re-earned at exponentially worse odds.

That second part is the one most retail investors never internalize. The math is harder than it looks, and it’s the reason every rule below exists.

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RULE 1: Don’t Risk More Than 1-2% on a Single Trade

The Warren Buffett Mantra: "Risk comes from not knowing what you are doing."

Professional portfolio managers almost never let one trade put more than 1-2% of their total capital at risk. In crypto, it’s common to see people put 20% or 30% into one altcoin because the conviction feels strong. Conviction is not a position-sizing model.

Crypto moves fast and drops hard. A normal 50% drawdown on a large position can erase months of gains in a single week. The 1-2% rule isn’t about being scared. It’s about staying alive long enough for your winners to actually work.

The math is simple. Your max loss on any trade should be a fraction of your account, not a fraction of the position. Once you decide the max loss, the position size becomes a calculation rather than a feeling.

Position Size = (Account × Risk %) ÷ Stop Distance %

Run the numbers for an account willing to risk 1% per trade with a 20% stop:

Most retail crypto traders size by gut feel and then wonder why a single bad trade wipes out three months of gains.

The fix is to size first, then enter. Ultimately the goal is to make sure you can stay in the game because no single trade wrecks the entire portfolio.

RULE 2 : Set Your Stop Before You Enter, Not After

The Warren Buffett Mantra: “The most important thing to do if you find yourself in a hole is to stop digging.

Traditional investors rarely enter a trade without a clear plan for both downside protection and profit taking. In crypto, many people hold with diamond hands until they’re down 80-90%, then panic-sell at the bottom. That looks like conviction from the outside, but it functions like denial.

A real plan has three parts. Where you exit if you’re wrong. Where you take partial profits if you’re right. What signal would change your mind before either trigger fires.

Set the stop based on a technical level, a volatility band, or the moment the original reason for the trade no longer applies. Stops aren’t predictions about where the market will go. They’re admissions that you can’t predict, and you’d like to be wrong cheaply.

The other piece worth setting before the trade is the risk-reward ratio. If you’re risking 20% to make 20%, you need to be right more than half the time just to break even after fees. If you’re risking 20% to make 60%, you can be wrong twice as often as you’re right and still come out ahead. Look for setups where the math favors you before the trade even starts.

RULE 3: Cap Your Whole Portfolio, Not Just Each Trade

Warren Buffett Mantra: "The riskiness of an investment is not measured by beta but rather by the probability of that investment causing its owner a loss of purchasing power over his contemplated holding period."

Serious investors control risk at the portfolio level, not just the individual trade level. That means hard rules like a maximum 10-20% drawdown before reducing exposure across the board. Limits on how much capital can sit in any one sector or narrative. Caps on correlated assets.

Crypto traders often pile into whatever narrative is hot, then wonder why everything moves together when sentiment turns. Holding eight Layer-1 tokens isn’t diversification. It’s one bet wearing eight different costumes.

Correlation is the silent killer in crypto. During a normal market, an altcoin bag might look diversified. During a real selloff, everything trades like one position. The portfolio-level cap is what protects you when correlation goes to 1.0, which it always does at the worst possible moment.

A 25% allocation to crypto across four uncorrelated assets behaves very differently than a 25% allocation to four assets that all crash together. The number on the position sheet is not the number on the risk sheet.

RULE 4: Shrink Position Size When Volatility Rises

Warren Buffett Mantra: "Be fearful when others are greedy, and greedy when others are fearful."

When markets get crazy, professional traders make their positions smaller. In crypto, when things get volatile, most retail traders do the opposite. They add leverage because they think the moves will be faster.

Faster moves with leverage means faster liquidations. The reason professionals shrink positions during high-volatility periods is the same reason airlines lower their flight altitude in turbulence. The cost of being wrong gets higher when conditions get rougher.

A simple way to scale positions to volatility is to look at average true range, or ATR. When ATR doubles, your position size should roughly halve to keep the dollar risk constant. This sounds like extra math, but in practice it’s a single calculation per trade. It’s also the difference between surviving a volatile week and getting carried out.

The trader who sizes the same in calm markets as in panicked ones is taking double the risk during the panic. The dollar amount on the position sheet looks identical. The actual exposure is not.

RULE 5: Preserve Capital First. Always.

Warren Buffett Mantra:
Rule #1: Don’t lose money.
Rule #2: Don’t forget the first rule.

Most crypto traders focus on maximum upside. Experienced investors focus first on not going to zero.

The question to ask before every position is whether you can sleep with it. If the answer is no, the position is too big. Cut it until you can sleep.

The traders who make it through three or four full cycles all share this discipline. They keep meaningful cash or stablecoin reserves, often 20-50%, so they’re ready when the next opportunity arrives and protected when the next selloff does.

The reason capital preservation matters more than upside capture is the recovery math. This is the chart most retail investors have never been shown.

A 50% loss requires a 100% gain to recover. A 75% loss requires a 300% gain. A 90% loss requires a 900% gain. The math gets exponentially harder, not linearly harder.

Every rule above exists to keep you on the top rows of that table, not the bottom ones.

The Real Reason These Rules Work

None of the rules above are complicated. None of them are new. The reason they keep showing up in serious portfolio management is that they’re not really about predicting markets.

They’re about surviving them long enough for the rest of your edge to matter.

Most crypto traders don’t lose money because they pick bad assets. They lose money because they size positions too large, hold too long, and freeze when conditions change. The discipline of risk management is what separates the traders who are still here after five years from the ones who aren’t.

Pick two of the five rules above and apply them this week. Not all five. Two. The trader who actually follows two rules consistently will outperform the trader who knows all five and applies none of them.

The market will always be volatile. Your process doesn’t have to be.

Trade safe!

Mari and Matthew

Share BitFinance with Matthew Snider

About the Authors

writes Mari’s Crypto Alpha, helping busy professionals navigate crypto markets. Twelve years in crypto, government crypto advisor and lawyer, raised over $100M from Solana, Animoca, Sequoia, Polygon, and Delphi.

This article is for informational and educational purposes only and does not constitute investment, financial, legal, or tax advice. Securities mentioned involve risk and may not be suitable for all investors. Past performance is no guarantee of future results. Readers should conduct their own due diligence or consult with a licensed financial advisor before making any investment decisions.

$1.75 Trillion Is About to Reprice Six Industries

Matthew — Tue, 19 May 2026 12:48:27 GMT

Fifteen years ago, I was a reconciliation analyst intern at Bank of America while finishing my undergrad at Boston University. One of the perks being marketed at the time was a credit card rewards play tied to commercial space travel. Virgin Galactic was supposed to be the first to take regular people to the edge of space. The first commercial flight was just around the corner. Points could one day book a seat.

That was 2011. Virgin Galactic eventually went public, then flew about a dozen paying customers, then watched its stock fall more than 99% from peak.

The promise was real, but the timeline was wrong by about a decade or more.

I tell you this because the space narrative is back, and it’s louder than it has ever been.

SpaceX files for IPO on June 12 at a reported $1.75 trillion valuation, which would make it the largest public listing in history.

Wall Street is going to spend the next six months telling retail investors that space is the next frontier (not just the final one!). Most of that coverage will focus on the rocket companies, because rockets are easy to photograph….but today, let’s dive deeper into the ecosystem of companies building this industry literally from the ground up.

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A Map of the Coming Wave

The actual investable space economy is bigger than the rockets, and it’s worth understanding before the SpaceX listing repackages every public name in the sector.

Think of the space economy like building a city from scratch.

You need transportation to get there, builders once you arrive, services that earn revenue once the city is operational, communications to tie everything together, parts suppliers feeding the whole thing, and underneath all of it, the raw materials and power that make the city work.

Six layers. Each one has public companies you can actually own today.

Here’s the 6-layer stack. Pay particular attention to companies the bedrock layer. To me, this feels like the sector nobody is pricing in. 👀

LAYER 1: Launch

Notable pics: Rocket Lab (RKLB)

Launch is the highway system of space. Nothing else works without it. SpaceX owns roughly 60% of global launch market share and is filing for IPO. Rocket Lab is the only public alternative with a credible medium-lift vehicle in development, called Neutron, targeting first flight later this year.

Rocket Lab reported $200 million in Q1 2026 revenue, up 64% year over year.

Backlog stands at $2.2 billion, more than double a year ago. Roughly 68% of revenue now comes from satellite components and spacecraft, not launch itself, which means the company is becoming a vertically integrated space platform rather than a pure launch provider.

The valuation already prices in Neutron flying on schedule, so any timeline slip will compress the stock quickly. Defense exposure through the Golden Dome interceptor program adds an upside catalyst.

Oh this? Nothing…just the stock trading at $7 per share 2 years ago.

LAYER 2 : In-Orbit Infrastructure

Notable pics: Redwire (RDW), Intuitive Machines (LUNR)

Once you’re in orbit, somebody has to build the platforms, the solar arrays, the landers, and the manufacturing facilities that actually operate up there.

Redwire builds parts on roughly half the missions you’ve read about. Q1 2026 revenue grew 58% to $97 million, with a backlog of $498 million. Book-to-bill of 1.92 means new contracts are arriving nearly twice as fast as revenue is being recognized.

The company is still losing money, and the share count keeps expanding, so this is a higher-variance bet that hinges on Edge Autonomy integration working.

Intuitive Machines is the only public company that has actually landed on the moon. Twice. NASA’s lunar program runs through commercial partners, and LUNR has captured multiple Commercial Lunar Payload Services contracts including a $180 million South Pole delivery booked in March.

Full-year 2026 revenue guidance is $900 million to $1 billion. The stock has run 132% in six months, which means sentiment is doing a lot of the work.

LAYER 3: Satellite Services

Notable pics: Planet Labs (PL), AST SpaceMobile (ASTS)

Satellites earn money in two ways; either by selling the data they collect, or by selling connectivity.

Planet Labs runs an Earth imaging constellation with more than 200 satellites and posted record revenue of $308 million in 2025 against a $900 million backlog. Recurring contract value is 98% of revenue, which is the kind of utility-style economics that gets analysts excited.

AST SpaceMobile is more speculative. The company is building space-based cellular broadband that connects directly to standard smartphones from orbit. If it scales, every phone on Earth becomes a satellite phone with no special hardware. That’s the bull case. The bear case is that the deployment timeline keeps slipping and the cash burn is significant.

From a portfolio perspective, it would make sense to treat this as a venture-style position and not a core holding due to the uncertainty associated with this level in the stack.

LAYER 4: Communications & Operations

Notable pics: Iridium (IRDM)

Every other layer depends on satellite networks that move data between space and Earth. Iridium operates the only constellation covering 100% of the planet, with predictable subscription revenue from defense, maritime, aviation, and IoT customers.

This is the boring, cash-generative name on the list and the kind of business Buffett would actually look at.

Globalstar is being acquired by Amazon for $11.6 billion to power its Project Kuiper rival to Starlink, with Apple’s Emergency SOS feature riding along. The deal closes pending FCC review. Iridium is now the largest independent public name in this layer.

LAYER 5: Manufacturing & Supply

Notable pics: L3Harris Technologies (LHX), Northrup Grumman (NOC), RTX Corporation (RTX), Lockheed Martin (LMT)

Defense primes design and build the satellites, rocket motors, and avionics that everyone else uses. The thesis here is diluted because space is a fraction of total revenue.

L3Harris build classified comms and satellites used by the U.S. Dept. of War.
Northrop Grumman builds rocket motors.
RTX builds GPS satellites…oh, and hypersonic weapons for what that’s worth.
Lockheed Martin runs the largest classified space programs on Earth.

These names give you space exposure wrapped inside a much broader defense portfolio, which is either a feature or a bug depending on what you actually want to own.

LAYER 6: Commodities & Power

Notable pics: MP Materials (MP), BWX Technologies (BWXT).

This is the layer that almost every space investment article ignores, and it’s the one I find the most compelling.

Every satellite needs magnets.
Every magnet needs rare earths.
Every off-world data center, lunar base, or deep-space mission needs nuclear power because solar alone doesn’t cut it past a certain distance or in shadowed regions.

MP Materials operates the only active rare earth mine in the United States.

The Department of War took an equity stake in 2025 and established a 10-year price floor of $110 per kilogram on the key magnet material, which removes commodity price risk from the business model.

Apple committed $500 million for domestically made magnets. Q1 2026 revenue grew 49%, with magnet production set to launch in the second half of this year. Government-aligned, structurally favored, and the only at-scale operator in a market China has historically dominated.

BWX Technologies supplies the reactors that power every US Navy submarine and aircraft carrier. That naval nuclear business throws off predictable cash flow that funds the company’s space work, which includes the Pele microreactor for the Department of War and the DARPA DRACO nuclear thermal propulsion program.

Q1 revenue grew 26%, EBITDA grew 14%, and the company raised full-year guidance. Backlog stands at $8.7 billion. This is the cleanest cash-generative name on the entire list, and the regulatory moat around naval nuclear is essentially impossible to replicate.

How to Actually Use This

Here’s what fifteen years between space narratives taught me.

The companies that survive long enough to matter are the ones with real cash flow today, not the ones selling a compelling future. Virgin Galactic had the better story in 2011. Aerospace primes and satellite operators that nobody talked about had the better business.

The same is probably true now. Two of the six layers, communications and commodities, are where cash-generative businesses sit with structural tailwinds. BWXT, MP Materials, and Iridium are the names worth deeper diligence if you want exposure without betting on timelines. The other layers are higher variance, which is fine if position size matches the conviction.

Note the “The Buffett zone” in the upper-right. Most of the heat in this sector lives in the upper-left.

If I had to build a starter basket from this list, it would lean heavily toward layers four and six, with smaller positions in layer two for the lunar economy optionality. Rocket Lab is a fine name in layer one, but the multiple already reflects most of the upside.

Buffett Framework Question: Which of these companies would I still own if the SpaceX IPO underperforms and the space narrative cools for three years? The answer narrows the list quickly to the names with cash flow today. That’s not a coincidence. That’s the lesson.

Space is going to be a real economy. It’s also going to take longer than the headlines suggest. Both things can be true. Position accordingly.

The biggest IPO in history is going to make space feel like it just became investable. It didn't. It's been investable for years. The companies underneath the rockets have been building real cash flow while the headlines waited for SpaceX.

When the noise hits on June 12, remember which layer the bedrock is on.

Until next time!

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🛑 REMINDER 🛑

Quick reminder of what we’re building. Live indicators. The research behind them. The methodology taught alongside the signal. A small room where you leave knowing how the work actually gets done
I’m building the early cohort for when the Discord opens and the first indicators ship. If you want a seat, this is where to claim one.
Interested? Fill out the 30-second interest form here. 👈

Sources:

Q1 2026 earnings releases from Rocket Lab, Redwire, BWX Technologies, MP Materials, and Intuitive Machines. SpaceX IPO reporting from Reuters, Bloomberg, and Fortune.
Amazon-Globalstar transaction announcement, April 14, 2026.
Starcloud Series A coverage from Data Center Dynamics, March 2026. Department of War / MP Materials Price Protection Agreement, July 2025.
Apple / MP Materials $500 million commitment, August 2025.
NASA Commercial Lunar Payload Services contract awards documentation.
BWXT / DARPA DRACO and Pele microreactor program announcements.

My 18-Month Education Plan to Stay Ahead of AI

Matthew — Sat, 16 May 2026 15:17:08 GMT

Last night I registered for the Securities Industry Essentials exam.
$80 bucks and about thirty hours of prep ahead of me.

It’s the first step in an 18-month plan that doubles as my answer to a question more knowledge workers should be asking themselves: what’s my AI hedge?

I want to walk through what I’m doing and why, because some version of it is relevant to more people than the headline suggests.

The AI Moat Almost Nobody Talks About

AI is going to compress a lot of what people get paid for in financial services. Full stop.

Analyst tasks, modeling, research write-ups, first drafts of nearly everything. I use AI extensively in my own work, so this isn’t speculation, it’s observation. The 80% of a typical knowledge-worker job involving drafting, summarizing, and structured analysis is getting commoditized in real time.

What AI doesn’t change is licensing.
Treat professional licenses like a career “moat”.

You can’t sell securities without a license.
You can’t give investment advice for compensation without an IAR registration.
You can’t solicit futures customers without an AP registration.

The regulatory perimeter around financial services is one of the few moats genuinely insulated from AI commoditization, because the moat is statutory rather than technical.

Software can’t pass a Series 7 on your behalf and it can’t file your U4.

The administrative 80% of the work gets automated. The 20% that requires the credential becomes a higher proportion of where my time goes, and the value of that 20% goes up rather than down. In a heavily AI-disrupted services market, being unlicensed means being replaceable, while being licensed makes you one of the people the AI runs through rather than around.

This is, by some distance, the most durable career hedge I see in front of me. If you’re in professional services and not thinking about your work in these terms, you should be.

The Lucky Part

Here’s the fortunate alignment. I’m a sucker for an educational challenge.

The MBA was a stretch I genuinely enjoyed. Studying for the Series 65 was the same kind of fun. All I kept hearing on crypto twitter was influencers and talking heads saying “this isn’t financial advice” - and I thought I could differentiate myself by passing a rigorous exam that allowed me to do just that.

There’s a particular satisfaction in sitting down with hard material, working it until it clicks, and walking out genuinely more capable. People train for marathons. I sign up for exams. The wiring is the same.

So this isn’t me white-knuckling through a defensive credentialing program because I’m worried about AI. I’m doing something I’d want to do anyway, and the fact that it happens to be one of the most durable AI hedges available is a bonus.

The 18-Month Plan

By end of 2026:

SIE (registered last night)
Series 7 (general securities representative)
Series 63 (state law for BD agents)
Series 3 (commodities and futures)

By end of 2027:

CAIA Level I and Level II (Chartered Alternative Investment Analyst)
FRM Part I and Part II (Financial Risk Manager)

Credential Count: 6
Time to Goal: 18 months
Total Cost (incl. study materials): ~$11,000

What Each One Does and Why I Want It

The SIE is foundation. Anyone over 18 can take it without sponsorship. I’m taking it because it unlocks the others.
The Series 7 closes the brokerage-side gap. When a client wants help structuring a Reg D offering and placing the resulting interests, currently I can handle one half cleanly. The 7 makes the other half mine.
The Series 63 is the state law exam that activates the Series 7 in your home state. It’s the most accessible exam in the FINRA stack. You can self-enroll without sponsorship, and heavy overlap with the Series 65 makes it the fastest of the group for me.
The Series 3 is commodities and futures. Half the crypto strategies I look at touch this. BTC basis trades, ETH funding rate arbitrage, anything with a futures leg; e.g. Perpetual Futures. (read more here)
The CAIA is the alternative investments designation. Most of what I do at Block3 is alts-adjacent or alts-native. The CAIA puts an institutional stamp on a body of work I’ve been doing for years managing hedge funds and building investment vehicles. It makes a powerful career complement when paired with the Series 7.
The FRM is the risk management designation. For someone publishing investment research and helping family offices and PMs evaluate digital asset exposure, FRM is the credential that says I think about downside the way they do. Oh, and last time I checked…crypto and blockchain tech is still risky. Having a framework to help orgs manage risk is going to be huge.

Each one maps onto a conversation I already have. The credentials put institutional language behind work I’m already doing, and each adds a layer AI can’t strip away.

The Accredited Investor Unlock Most People Miss

Here’s something underdiscussed and relevant if you’re reading this and thinking “interesting, but not for me.”

In 2020 the SEC expanded the accredited investor definition. Net worth and income are still the common qualifying paths, but holders of the Series 7, Series 65, or Series 82 in good standing now qualify on credential alone.

No $200K income or $1M net worth requirement.

Being an accredited investor matters because accredited status unlocks Regulation D private placements, pre-IPO secondaries, hedge fund participation, venture deals.

The Anthropic and SpaceX secondaries everyone has been trying to get into are accredited-only. Most people qualify through income or net worth. Licensed professionals can qualify through holding the license itself.

If you’re an early-career professional, or a serious investor without the income to clear the bar, the Series 65 is a roughly $400 path to accredited status.

One of the most underrated arbitrages in personal finance. Bar none.

Cost Reality + Honest Gaps

A two-year credential stack at $10K to $12K is real money. It’s also less than 12% of what a decent MBA program costs today, and the opportunity cost on a two-year residential MBA adds another six figures in foregone income.

I have an MBA. I’d recommend it for the networking, not the credentials. If you like education, MBAs are great. If you like people, MBAs are great.

Candidly, there are faster, more direct ways to become a financial professional, though, and business school isn’t the right tool if the specific goal is operational fluency in financial services.

The Gaps: What this credential stack doesn’t make me…

A CFA charterholder (buy-side equity research).
A CFP (personal financial planning).
A JD or CPA (better partnered with than competed against).

Specialization rocks.

For Anyone Considering This Path

Some of these exams require firm sponsorship. The Series 7 and Series 3 in particular need an affiliated firm willing to register you. That’s a real barrier.

What isn’t a barrier: starting to study. The SIE, Series 65, and Series 63 you can all take on your own - you don’t even need a college degree!

By the time you’re ready to ask a firm to bring you on, you can demonstrate not just interest but real preparation. In financial services, people who actually do what they say they will do are rarer than you’d expect.

If you’re reading this with even mild anxiety about AI displacing your work, don’t wait. Start with the exams you can take on your own. The Series 65 in particular is a thirty-hour commitment that pays off in both AI insulation and accredited investor status.

I’ll be writing about this periodically over the next 18 months.
If you’re on a similar path, I’d love to hear about it.

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🛑 Quick Reminder 🛑

Quick reminder of what we’re building. Live indicators. The research behind them. The methodology taught alongside the signal. A small room where you leave knowing how the work actually gets done
I’m building the early cohort for when the Discord opens and the first indicators ship. If you want a seat, this is where to claim one.
Interested? Fill out the 30-second interest form here. 👈

This Week In 2 Mins

I Found a Token That Acts Like a 30-Year Mortgage on AI Compute (May 12)

Erik Voorhees's privacy-first AI platform Venice has built two of the more coherent token mechanics in the AI-crypto category.

VVV gives stakers proportional access to the platform's inference capacity at 14-18% yield, while DIEM represents $1/day in API credit in perpetuity. Think of this like a power purchase agreement but applied to AI compute.

Buy-and-burn engine, 42% of supply already burned, $700-800M market cap, real revenue. Article also flags three other AI plays I'm watching: Bittensor (TAO), NEAR Protocol, and Virtuals (VIRTUAL).

What are your pics in the AI/crypto sphere?🤔

Read the full article here.

Ten Years Ago, Crypto Built the Most Important Derivative of the Decade
(May 14)

On May 13, 2016, BitMEX shipped the first perpetual futures contract with no expiration, no settlement, no precedent in 200 years of derivatives history.

A decade later it's a $7.24T market.

This article walks through the funding rate mechanic (the "dog on a leash" metaphor), why the design works, how sophisticated traders use perps for yield rather than leverage, and what's coming next as the contract spreads to oil, gold, S&P 500, and tokenized real-world assets.

Hyperliquid runs 44% of perp DEX volume with a $640M annualized fee run rate and 97% buy-and-burn. JPM, Grayscale, Bitwise, and VanEck have all moved into the space.

Are you using perps to trade? I want to know!

Read the full article here.

Winners 🏆

CLARITY Act. Senate Banking Committee 15-9 bipartisan vote on May 14, headed to the Senate floor. Two Democrats (Gallego, Alsobrooks) joined all Republicans. First real bipartisan progress on U.S. crypto market structure after four months of negotiation gridlock. Legislative path still requires merging with the Agriculture Committee version and resolving the ethics provision, but the political momentum changed direction this week.
Q1 earnings season. S&P 500 blended earnings growth came in at 27.1%, the highest growth rate since Q4 2021 (32.0%) and the 6th consecutive quarter of double-digit year-over-year earnings growth. 84% of companies that have reported beat EPS estimates, above the 5-year average of 78%. Communications Services, IT, Consumer Discretionary, and Materials led growth. Whatever you think of valuations, the underlying corporate earnings story is doing its job.
Energy sector. Energy was the sole sector with gains on Friday, rising 1.6% as the post-Iran War oil spike continues to ripple through markets. With one airline CEO noting the jet fuel crisis is about to hit airlines worse than Covid and the Strait of Hormuz logistics still unwinding, energy looks like the obvious risk-off hedge for a market increasingly nervous about an inflation re-acceleration.

Losers 📉

Semiconductors and AI hardware. Friday’s profit-taking was brutal across the AI rally’s biggest names. Intel retreated more than 6%, while Advanced Micro Devices and Micron Technology lost 5.7% and 6.6%, respectively. Nvidia dropped 4.4%, while Cerebras Systems — which surged 68% Thursday after it began trading on the Nasdaq — shed 10%. The trigger was the Trump-Xi summit ending without major breakthroughs, but the deeper read is that the group is being called out for an unsustainable recent move. Worth watching whether this is a healthy reset or the start of something bigger.
Small caps. The Russell 2000 lost more than 2% in midday Friday trading. If that holds through session close, it would mark the index’s worst day since November... The Russell 2000 is now down more than 2% on the week, putting it on track to snap a seven-week winning streak. Small caps had been the breadth story underneath the mega-cap-led rally. Breaking the streak matters because it suggests the rally’s been narrower than it looked.
Rate cut expectations. The Fed funds futures market repriced hard this week. Traders now see the Fed raising rates for its next move, a complete reversal from where positioning sat a month ago. “Ten of the last 12 recessions were preceded by a spike in oil”, Dan Niles noted on CNBC Friday. The Fed’s hands look increasingly tied between oil-driven inflation and growth concerns, and the cost of waiting is no longer obvious.

Watch this coming week (May 18-22)

Retail earnings week. Walmart, Home Depot, Target, Lowe’s, and TJX are expected to report. The K-shaped consumer thesis (high-end resilient, low-end pressured by gas prices) gets its biggest test of the cycle. Watch the guidance more than the prints. Retailers cutting full-year outlooks would be the cleanest signal that the consumer is finally cracking.
NVIDIA earnings (likely late May). The most important single print in equities, full stop. After Friday’s semis selloff, the bar is higher than it’s been in two years. Beat-and-raise gets the AI rally back on track. Anything less reframes the entire group as overextended.
Oil price direction. Brent and WTI behavior is now the dominant macro variable. It will be months before the oil disruption rights itself, as it takes weeks for ships sailing out of the Strait of Hormuz to reach destinations in North America, Europe or East Asia, and investors don’t expect oil prices to return to pre-war levels. Sustained levels above current marks keep pressure on the Fed and the consumer. A break lower opens room for the cut narrative to come back.

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🛑 REMINDER 🛑

Quick reminder of what we’re building. Live indicators. The research behind them. The methodology taught alongside the signal. A small room where you leave knowing how the work actually gets done
I’m building the early cohort for when the Discord opens and the first indicators ship. If you want a seat, this is where to claim one.
Interested? Fill out the 30-second interest form here. 👈

BitFinance with Matthew Snider

Michael Saylor’s "Never Sell" Bitcoin Strategy Just Got Edited

The rescue kit, in 5 parts

How to read this, depending on who you are

Where this leaves us…

Stop Prompting Your AI. Start Looping It.

What an AI Loop Is

How Mine Runs

2 Jobs; 1 System

Why Loops Are the Next Phase

Keep Yourself in the Loop

🛑 SIGN UP TO BE PART OF WHAT WE’RE BUILDING! 🛑

This Week In 2 Minutes

🏆 Market Winners

📉 Market Losers

👀 What to Watch This Week

How (and Why) I Own Quantum Tech Without Having to Pick the Winner

Quantum isn’t one trade. It’s a stack.

What the machine actually does

Why now, and not five years ago

The 5-layer stack

The number everyone quotes is the wrong one

Managing the risks

What I’m Watching

BlackRock’s new Bitcoin Income ETF Sells the Only Days That Matter

How a fund like this actually works

The days that decide the year

Selling volatility on a schedule

The first ones already failed

When the trade makes sense

The 100-Year-Old Receipt That Will Put Bitcoin in Your Brokerage Account

The problem you already feel

What a depositary receipt does

A Bitcoin IPO without the IPO

Why this matters if you work with an adviser

This is already being built

Who it’s really for

The objective view

This Week in 2 Minutes

This Week’s Winners…🏆

…and Losers 📉

What to Watch (June 22)👀

Coinbase Made an AI Your Fiduciary. Read the Fine Print.

What It Does

The Catch is in the Disclaimers

Who This is For

Two Roads at One Fork

Are RIAs Cooked?

The Other Half of the Stack

Where I Could Be Wrong

The Watchlist

Most Crypto Tokens are Uninvestable by Design

There’s no moat in decentralization

Mirage #1: It’s cheap; therefore “value”!

Mirage #2: It Has Partnerships!

The 10X argument runs backwards

Capitalism takes the technology and leaves the token

One thing could change all of this thinking…

Where this leaves you

The IPO Is the Exit. You're the Liquidity.

The structure of the trade

Two receipts from the past year

The pipeline deserves the same lens

Where the five seconds are better spent

Where I could be wrong and the Watchlist…

This Week In 2 Minutes

This Week’s Winners 🏆

…and Losers 📉

What to Watch For This Week 👀

1,000 Advisors, $28 Trillion in AUM, and Only 1 Crypto Panel

The Room Has Changed

The RIA of the Future Runs on Private Markets & AI

Puerto Rico Stole the Show for Me

Tokenized Real Estate and the Happy Hour Test

The Lone Crypto Panel, and the Down-Only Problem

The Hougan Takeaway

The Bottom Line

How to Go From Idea to Usable Tool With AI in a Weekend

How it came together

How you can win doing this too