Thesis and stakes - why speculation must be harnessed, not suppressed

Trying to make animal spirits illegal doesn't pay off in the long run. Bubbles happen again and again because they are linked to discovery, risk transfer, and coordination. The goal is not to stop speculation, but to tie it to solid economic foundations so that the inevitable surges of attention and liquidity go toward productive work instead of building paper castles. That means making tools that are fun to use and markets where float, disclosure, and governance keep the worst reflexive feedback loops from happening. Private markets are big enough to matter and not liquid enough to need this treatment. By the middle of 2023, global private markets had about 13.1 trillion dollars in assets under management (AUM), but liquidity and access are still limited. Tokenization has gone from being just a presentation to being put into action. Live, on-chain funds have crossed the billion-dollar mark, and policy frameworks are becoming clearer. Those facts make it possible to make a "speculation with a conscience" playbook.

Street's thesis is relevant at this time. Its materials make a simple wedge: keep the excitement and tradability that draw capital to the frontier, but direct them to startup equity through clear wrappers, float controls, and rule sets that are easy for founders, allocators, and regulators to understand. The goal is not just to tell a story; it is also a plan for how liquidity will be earned and shared across a stack that keeps track of ownership and compliance on the chain.

Minsky’s cycle in three acts

Hyman Minsky's Financial Instability Hypothesis outlines a recurring three-act sequence throughout centuries of market innovation. Not by slogans, but by cash flow coverage and leverage, the acts are defined. In hedge finance, projects use cash from operations to pay both interest and principal. In speculative finance, cash flow pays interest but depends on refinancing the principal. In Ponzi finance, you have to borrow even the interest, so asset prices have to keep going up or the whole thing falls apart. Minsky's main point is that stability is destabilizing. When things stay calm for a long time, people take risks and stretch their balance sheets until a small shock turns the system upside down. (Minsky 1986; BIS 2024). (ResearchGate, Bank for International Settlements)

Act I - Hedge finance and apparent stability

In Act I, promises are safe and balance sheets protect themselves. Before the U.S. housing market's "reach-for-yield" era, amortizing structures that were backed by documented income and reserves were the most common type of mortgage credit. That changed as new products gained market share, but the baseline that made the later stretch possible was a time when scheduled cash flows easily covered obligations. The pattern isn't just for housing. In the early days of decentralized finance, stablecoins like DAI that were backed by collateral needed more collateral, stability fees, and programmatic liquidations. The goal was to keep the system solvent at the position level, even during drawdowns, by putting up too much collateral and charging for leverage. That is how hedge finance works.

A clear picture of how the mortgage market moved off that hedge footing in the late stages helps show the hinge. In 2006, interest-only mortgages made up about 15% of Fannie Mae's single-family purchases and 16% of Freddie Mac's. This was a big jump from the year before, which is the opposite of hedge discipline. The earlier period was stable enough for these riskier products to be used. The calm made things happen.

There is a parallel in crypto. From 2019 to 2020, Maker's multi-collateral system grew slowly. It raised and lowered savings and stability rates to keep the peg and the system solvent. That insistence on fees based on leverage and clear collateralization kept it more like hedge finance than most emissions-driven programs that came after it. In both cases, the feeling of control made room for the next step: more leverage and a need to refinance.

Act II - Speculative finance and the reach for yield

In Act II, the risk of refinancing becomes normal. The U.S. mortgage market's shift from fixed-rate to adjustable-rate, interest-only, and negative-amortization structures between 2003 and 2006 is a clear example. The borrower still pays the coupon, but they are more exposed to resets and market funding. The rise in interest-only purchases by the GSEs from 2005 to 2006 shows that refinancing dependence is starting to become a problem.

The rise of centralized and decentralized yield platforms that borrowed customer assets and chased basis and staking returns is a crypto-native version. Marketing stressed "up to 8 to 9.5 percent" APY on products that were like deposits. Later, regulators wrote down the rate cards and the risks. The yields were real when funding spreads were wide and the markets were calm, but they depended on rolling exposures and maturity transformation, which are just other names for speculative finance mechanics.

Even when the venue is decentralized, Act II can be found in derivatives. Perpetual futures use periodic funding payments between longs and shorts to keep prices in line with spot. When funding stays positive for a long time, it makes long leverage cheaper and encourages carry trades. That can only last if there are constant inflows or sellers of volatility. In formal models and market data, the economics are well understood.

Act III - Ponzi finance, margin spirals, and the Minsky moment

In Act III, cash flows can't even cover interest without borrowing more money, so prices have to go up to pay off promises made yesterday. Any shock makes feedback loops go bad. The Terra-Luna collapse in May 2022 is a well-known modern example in crypto. As more and more people tried to cash in their tokens and trust in the system fell, the algorithmic stabilizer needed to issue more and more of the equity-like token to keep the peg, which drove down the price of the equity token and sped up the run. Researchers at the Federal Reserve and the central bank have written about how the mechanics work and how they affect other things.

A bigger Minsky moment happened when platforms that had lent money based on the perceived stability of Act II and had taken token collateral with limited borrow liquidity met margin spirals. Bankruptcy courts, trustees, and law enforcement cleaned up the mess and wrote down how mixing up balances and reflexive collateral made unsecured exposures at scale, which led to big judgments and court-ordered investigations.

Beauty contests and narrative markets

Chapter 12 of Keynes is the shortest description of second-order expectations ever written. It says that markets are like a beauty contest in a newspaper where the best strategy is to pick the faces that the average person thinks the average person will like. Prices can go up or down based on more than just the basics. They can also change based on what people think others will soon believe. That logic works with institutional incentives, the media, and social graphs to make trend following happen at the same time.

Robert Shiller called this "narrative economics." Stories that spread, like "the new economy" and "digital gold," change the ways that investors judge plausibility and risk, which changes flows and discount rates. You can see narrative shifts in time series and in the language used in earnings calls, news articles, and social media. George Soros' reflexivity creates a feedback loop: prices go up, which makes collateral and confidence better, which makes financing easier, which raises prices again, until expectations change.

There are real-time examples from 2024 to 2025. The US's approval of Bitcoin's spot ETFs didn't just bring in passive flows. They brought in more investors, made liquidity more concentrated during U.S. trading hours, and changed how things worked during the day and on weekends. Data providers kept an eye on the changing share of altcoins, the rotation of volumes, and the strange persistence of positive sentiment. All of these things affected the financing conditions for listed crypto companies and the launch calendars for new tokens. The story changed the flow regime. (Kaiko 2024; Kaiko 2024). (Kaiko Research) [Graphic Suggestion 1—Parallel Minsky timelines: Goal: to make the three-act structure real. What to plot: two timelines that line up, one for the historical housing credit cycle from 2002 to 2009 and one for the crypto-native cycle from 2020 to 2022. Add information about leverage types, interest coverage proxies, and funding spreads to each one. Axes or schema: a horizontal time axis with markers for hedge, speculative, and Ponzi phases, as well as overlays for interest-only mortgage share and perpetual funding rates.

Thin-air supply and reflexive blow-ups

When the outstanding market value of a token looks big on a fully-diluted basis but the circulating float is small, borrowable supply is even smaller, and unlock schedules make sure that emissions go up when demand is uncertain, you can call it "thin-air supply." Small forced flows can then change the price a lot, which raises paper values, which speeds up listing and market-making incentives, which narrows spreads and draws in leveraged longs - until unlocks and basis reversals hit and the loop starts over.

It is possible to measure the mechanics. Researchers in the field think that about $155 billion worth of token unlocks will happen between 2024 and 2030. The median market-cap-to-FDV ratio for 2024 cohorts stayed low, which means there were big overhangs. When unlocks happen, the effect depends on how deep they are, how easy it is to borrow, and whether or not you can short through perps without having to pay extra. The main point is that structures with a low float and a high FDV are more sensitive to changes in supply and more likely to be funded by one party.

Traditional finance confirms the way things are going. When IPO lockups end and seasoned equity offerings happen, more shares are available, which usually lowers prices in the short term. The classic study of 1,948 IPOs found that trading volume went up by 40% permanently and that there was a statistically significant negative abnormal return of about 1.5% around the time the lockup ended. This was especially true for deals backed by venture capital. Every month, thin-float tokens find the same supply-elasticity mechanism.

Microstructure in the crypto market adds reflexive amplifiers. There isn't enough liquidity in all venues, many altcoins don't have enough borrow markets, and the risk of manipulation goes up when depth is concentrated on a few offshore exchanges. Positive funding can make shorts pay to hedge into unlocks, and inventory limits make it more expensive for market makers to lean against flows. Empirical research and data analyses illustrate these frictions in practice.

A real-world example of crypto: Big increases in free float often go hand in hand with Bitcoin's poor performance in the next window. For example, one project saw its circulating supply rise by hundreds of millions of dollars at the same time as its price fell. The direction is in line with what equity literature says and what on-chain measures of borrow availability say. In other words, thin-air supply gives the dice an unfair advantage.

Aligning excitement with fundamentals - equity-backed, fee-accrual tokens

A promise of a dividend is not the same as a fee accrual. In simple terms, a protocol or platform charges users fees and sends some of that money to a treasury or to rules that limit the supply of tokens or reward people who keep the network safe. The important thing is that cash-like flows link the value of tokens to real-world actions instead of just emissions. Ethereum's EIP-1559 is a good example of how to tie value to activity without promising profits. It burns the base fee on each transaction, which means that net issuance is based on how much the network is used. Analyses and formal models validate the mechanism and its constraints.

In addition to base-layer burns, some exchanges and protocols send fees to validators, LPs, or token stakers. On the dYdX chain, validators get trading and gas fees, which they can share with stakers through protocol modules. GMX gives some of its fees to stakers and its liquidity providers. Independent dashboards keep an eye on the fee run-rates and the revenue of holders. Whether any of these tokens are securities in a certain place depends on rights and marketing, but the economic logic is clear: the more stable the use, the stronger the valuation anchor.

Design trade-offs are important. Fee-to-burn raises the number of claims per token without making a clear creditor, but it makes valuation sensitive to changes in fees and can be used in thin markets. If you market fee-to-treasury plus periodic buybacks carelessly, they can look like dividends by another name. Sharing fees directly with stakers makes security economics stronger, but in some cases it can make the case for securities classification stronger. The goal of the design is to link token payoffs to activities that can be verified while keeping legal claims non-contractual and up to the user. Global standard-setters and bank supervisors are coming to the same conclusion: programmability is useful, but rights and disclosures are what really matter for classification.

Street as a channel - curation plus float policy

Street's wrapper routes speculative energy into startup funding by giving three different groups different jobs. A domestic operating company does brand and advisory work but doesn't own any equity or issue any tokens. The startup's operating company is in charge of running the business. A DAO or offshore foundation gets a negotiated equity stake, issues the project's token, and takes care of the treasury and rewards. This keeps the token from being an equity stake while giving the DAO a real asset to manage. It also sends any exit proceeds to a treasury that can reward the community at its own discretion.

Programmable compliance and allow-listed distribution control who can hold and trade at launch, while on-chain cap tables make ownership auditable. Trading fees are the funding rail: projects charge between 50 and 150 basis points on token venue volume and send the money to the treasury to be burned, given as grants, or used as rewards according to published rules. This doesn't create demand out of thin air; it pays real users and market makers to provide depth in exchange for clear, activity-linked economics.

The rest is done by curation and float policy. Street sets standard times for launches, initial floats, and unlocks. High-float, low-FDV starts make prices less flexible and help people find the right products. Staged unlocks based on product and revenue milestones keep emissions behind adoption. When perps list, counterparties are encouraged to lend out inventory or work with venues to keep early funding imbalances from getting too big. The goal is not to get rid of volatility, but to steer it toward productive financing instead of reflexive liquidation spirals.

Rail map, in order: technology, capital, governance, and culture. Technology rails: on-chain cap tables, programmable compliance, and smart contracts let you control distribution and make fee routing clear without giving up profit rights. Capital rails: trading makes money that goes toward development, grants, and, at the foundation's discretion, burns or rewards. There are also curated float and borrow programs to keep supply from getting too thin. Governance rails: the separation of Street OpCo, Startup OpCo, and the Foundation-DAO keeps regulatory exposure to a minimum and keeps token rewards discretionary and non-contractual while still linking them to real work. Narrative and culture rails: distribution goes through aligned creators and founder channels, with regular updates on usage, fees, and treasury actions. This way, curated stories with verifiable metrics last longer than launches that only include memes. The end result is a channel that encourages speculation but only on equity-backed projects, with rules that keep float in check and excitement tied to measurable activity.

Conditional forecasts

If high-float, low-FDV launches combined with fee accrual become the norm for new projects, we should see lower unlock-related drawdowns, tighter funding-rate extremes around emissions dates, and a stronger link between protocol fees and token valuation multiples within 12 to 24 months. The mechanism is a lower price elasticity and a value anchor.

If tokenized money funds and bond funds keep growing on public chains and bring compliance tools with them, then more projects will use allow-listed distribution in 12 to 18 months. This will make stablecoin and RWA venues have better borrow inventory, which will stop one-way squeezes in altcoins. The mechanism is infrastructure spillover.

If regulatory harmonization continues to move forward, as it is already happening in Europe with MiCA and other places, we should see more consistent disclosure templates for fee accrual and token supply within 24 months. This will lower legal uncertainty discounts. The mechanism is standardized claims and procedures.

If projects keep using low-float high-FDV and cliff unlocks while perps stay one-sided, you can expect to see repeated reflexive blow-ups around unlock windows in the next 6 to 12 months. There will also be a wider gap between protocol fees and token prices. The mechanism is when supply from thin air meets funding problems.

Conclusion - taming, not banning, the cycle

When guided, speculation is a renewable resource that pays for exploration; when left to chase its own reflection, it is a wrecking ball. Minsky talks about why cycles come back, Keynes and Shiller talk about why stories guide flows, and market microstructure talks about how thin-air supply and derivative funding make blow-ups happen over and over again. Engineering, not moralizing, is the answer. Equity-backed, fee-accrual tokens link price to use without guaranteeing profits; curation and float policy make prices less flexible and open up shocks; programmable compliance brings legal discipline into code. Street's method is one clear example of this idea. This is how to make bubbles that leave something behind when they pop if the industry wants them.