Amid the recent 7 Sigma anomaly volatility in Bitcoin and the signal of exhausted sell pressure, Jordi Visser systematically articulates why this bull cycle must no longer remain confined to "liquidity-fueled trading," leveraging his early observations in China of the "Ghost Cities" model to forecast a historic inflection point driven by autonomous AI agents (AI Agents) that will ignite the machine economy, accelerate stablecoin velocity, and unlock asset tokenization. He also deeply dissects why Fed rates and oil prices have increasingly lost efficacy, how Robinhood Chain and Hyperliquid are catalyzing convergence between old and new financial systems, and why AI will rewrite traditional US stock valuation logic—passing the next structural growth window to the crypto ecosystem.

Guest: Jordi Visser (Head of Macro Research, 22V Research AI, Founder of Visser Labs, Former President & CIO, Weiss Multi-Strategy Advisers)

Host: Luke Martin (Host of Convergence Podcast, Senior Crypto Trader)

Podcast Source: Convergence / X ( https://x.com/VentureCoinist/status/2120272504904110124 )

Original Title: Why Everyone is Giving Up on Crypto at EXACTLY The Wrong Time

Air Date: November 10, 2026

Key Takeaways

Notable Insights

Full Transcript

When Believers Begin to Waver: From Internet Bubble’s “Thousand Cuts” to Traditional Finance Knocking at the Door

Luke Martin: This week I’ve been repeatedly reflecting on a seemingly counterintuitive idea you previously proposed: you believe the market must first witness even those who once deeply believed in crypto losing faith before a real move can begin. Why does it need to reach this point of existential doubt?

Jordi Visser: This is inseparable from my personal memory of the dot-com bubble. In 1999, I moved back from Brazil to the U.S., and the contrast still feels surreal. In Brazil, I witnessed adult men weeping on city streets amid economic collapse and hyper-depreciation—two years of grinding turmoil across emerging markets. Yet upon returning to the U.S., I inherited a massive trading account at Morgan Stanley, where everyone around me was boasting about their internet stocks rising from $2 to $100, even blue-collar friends from high school now discussing using their AOL shares to buy a house.

Naturally, the bubble eventually burst violently. Just four years later, those who once bragged globally about their gains didn’t even want to mention stocks anymore—their accounts were buried under pennies, and entire companies collapsed en masse. The greedy speculators were completely purged. What remained were only disillusioned structural builders and idealists. By 2004, but remember, it was still three full years before the iPhone launched. From 2003 to 2007 was an agonizing, slow-motion consumption phase—ecosystems seemed to shrink endlessly, with no end in sight.

What the current crypto world evokes in me is exactly the same sensation. Recently, I dined with many insiders, all of whom were once fervently bullish—some even leveraged long positions—but now they’re questioning whether Bitcoin even has a future. Yesterday, I participated in an interview where even a self-proclaimed Bitcoin Maxi admitted privately that this entire framework no longer works. But for me, what happened over the past 16 years doesn’t matter—I care only about what’s coming next: AI Agents.

Luke Martin: Aside from yesterday’s interviewee who surprised you, which conversations with traditional finance (TradFi) leaders or veteran insiders in the last period have particularly struck you?

Jordi Visser: Anyone who has observed markets long enough notices a striking dichotomy: 90% of daily discourse happens within crypto believers or retail communities, filled with extreme pessimism. Meanwhile, an increasing number of heavyweight figures in traditional finance are quietly reaching out to me with genuinely sharp, fundamental questions. They come to me because my background is macro strategy trading—they trust I can explain things in terms they understand.

When the Fed recently signaled rate moves, the crypto space erupted in apocalyptic rhetoric, believing everything was over. But internally, I knew clearly: the real bull market cannot continue relying on liquidity flooding. Now, the truly meaningful questions are emerging from the traditional world: What does asset tokenization *actually* mean? Why would giants like Stan Druckenmiller invest in Hyperliquid?

Over the past 10 days, two billion-dollar-plus investors independently reached out to discuss details, asking core structural questions like: “What impact will tokenization have on the USD’s dominance?” and “What does tokenization mean for the stock market?” On one side, believers are demoralized; on the other, elite capital is meticulously preparing just before the fundamentals crystallize. This very contrast proves the foundation for the next big move is being laid beneath the surface.

Bitcoin’s 7 Sigma Reversal & “Price Before Narrative”: Why This Bull Run Must Be Fundamentally Driven?

Luke Martin: Speaking of price action, when Bitcoin surged rapidly from $60,000 to $69,000, you pointed out on Twitter that this was a “7 Sigma” reversal—a rare event occurring only three times in the past decade. For viewers unfamiliar with statistics, did you post that tweet as a signal to chase momentum? What’s the true historical weight of this signal?

Jordi Visser: First, “sigma” is absolutely a foundational concept every market participant should understand. Think of it like a medical check-up: if your doctor measures your blood pressure at 120/80, it might seem normal by standard metrics—but if your baseline is consistently 105/70, then 120/80 is already elevated for you. Anomalies must be judged relative to your own recent baseline. In markets, volatility is that baseline. With Bitcoin’s rolling 60-day volatility compressed to near zero—say, just 1.5% daily—if a sudden 10% single-direction move occurs, that translates to nearly 7 standard deviations: a 7 Sigma move.

More importantly, I don’t rely on just one metric—I look for multiple converging signals. First, price broke decisively above the 200-day moving average. Second, the 200-day MA itself turned upward sharply within just one or two days. Third, prior to this, the market endured intense bearish shocks: security breaches, violent turbulence in the AI sector, and the probability of the U.S. Clarity Act passing dropping below 20% for the first time. Yet Bitcoin stubbornly held firm around $65,000, refusing to make new lows. When an asset can absorb such bad news without collapsing, it means one thing: sellers have sold out. The subsequent volume surge, even if triggered by a minor liquidity message, fully exposed short squeeze panic and the re-entry of sidelined momentum traders.

Luke Martin: My immediate reaction to that candlestick was recalling Bitcoin’s 2019 rally—from $3,000 to $4,500 and beyond. After that surge, people scrambled to invent narratives afterward. Based on your decades of macro trading experience, is it really critical to assign causality (Fed policy, macro improvement, or MA break) at the very start of a move?

Jordi Visser: The answer is simple: price always leads narrative (Price leads narrative). Once price moves dramatically, people naturally fabricate stories they want to believe. Over years of discussions with Paul Tudor Jones, we’ve agreed on this: after a trend emerges, the market rushes to attach explanations.

As a macro trader focused on long-term structural trends, I’m constantly searching for fields poised at the cusp of supercycle moves—and I believe crypto is about to enter history’s largest bull run. I’m so certain because the epic washout from 2021 to 2022 has already occurred. Unlike typical four-year cycles, this crash was exceptionally thorough, structurally similar to the dot-com bubble bursting.

Looking back, the internet took off around 1994, and it wasn’t until 2009—with the App Store explosion—that its full potential emerged. That’s a 15-year gap. During that span, markets couldn’t properly value this disruptive technology, so they invented absurd metrics like per-click or eyeball-based valuations. Amazon’s stock bubble burst, and it wasn’t until 2014 or 2015 that its P/E ratio dropped below 100—during which time entirely new valuation paradigms like SaaS emerged.

Now, counting from the Bitcoin whitepaper’s release, we’ve traversed a comparable timeframe. The dot-com bubble left behind excess fiber optics and debt write-offs. Similarly, crypto’s past few years saw massive venture capital inflows build redundant infrastructure. Today, valuations have corrected, and countless young people hold losses or distressed assets—now being scooped up by seasoned industry veterans who once led Fortune 500 companies. This shift in ownership proves speculation has been stripped away—true structural innovation is beginning.

Luke Martin: You emphasized earlier that Bitcoin’s past decade was indeed a textbook “liquidity trade”—many rushed blindly at any printing headline. But you argue this won’t work going forward, and many listeners may struggle to adjust. Why won’t the “liquidity logic” work anymore?

Jordi Visser: Because scale determines everything. At ~$2 trillion market cap, Bitcoin represents a tiny fraction of the ~$900 trillion global asset base. If someone wants to project Bitcoin reaching $1 million and the entire crypto ecosystem hitting $100 trillion, relying solely on annual global liquidity growth of 5% is impossible.

The early idealists who drove this experiment either cashed out or shifted into AI. And simply having wealth managers allocate 1% of client portfolios isn’t enough to sustain that scale. You must instill genuine trust and usage necessity in the institutional capital controlling the $900 trillion global stockpile—pension funds and conservative capital. Ask any senior citizen over 70, and almost none truly grasp or believe in the “digital gold” store-of-value narrative of Bitcoin.

To get them to switch from fiat, two conditions must be met: first, traditional fiat assets must stop being passive winners; second, there must be a zero-friction bridge. Don’t expect a 70-year-old to wrestle with private keys, seed phrases, and wallet setup—this process must be instant, automated by AI agents behind the scenes, and seamlessly backed by tokenized assets underneath. This is driven by technological fundamentals—not central bank printing.

China’s “Ghost City” Analogy & AI-Driven Deleveraging Deflation: Why the Fed and Oil No Longer Matter?

Jordi Visser: Regarding infrastructure-first and demand-lag, I used a metaphor in today’s piece. From mid-2000s to 2013, I spent a full month each year in China. I personally visited so-called “ghost cities,” witnessing vast, majestic buildings standing empty—experiencing firsthand how a boom fueled purely by abundant liquidity unfolds.

To me, up to now, the entire crypto world remains essentially a “ghost city”—no real economic activity runs daily inside. But now Stripe is investing heavily in acquisitions, Robinhood is aggressively building its own chain, and traditional finance is finally putting real money into the space—meaning “residents” are finally arriving. But understand the core disruption: some Chinese ghost cities never attracted enough human inhabitants. Yet this digital ghost city can be instantly filled—because crypto was never designed for humans. It was built specifically for digital agents and AI agents. Just as Uber and Airbnb wouldn’t exist without the App Store, AI agents are the primary economic actors that will fully activate and illuminate this crypto city.

Luke Martin: That “ghost city activated by agents” perspective is profoundly insightful! Before diving deeper into how agents will trigger economic activity, let me ask as a seasoned macro expert: lately, Fed policy signals feel increasingly ambiguous. Everyone’s debating whether we’re still effectively printing money, especially with Kevin Warsh stepping into a new era following Jerome Powell. How much power do the Fed and oil still hold over markets?

Jordi Visser: My advice is direct: everyone should immediately stop watching the Fed, stop obsessing over oil, and stop overanalyzing interest rate movements. I can’t fathom why so many people haven’t learned after so many cycles. Looking back from 2009 to 2019 to the pandemic, many attribute the rise in equities simply to quantitative easing (QE), but the truth is the opposite: during that period, most traditional firms in the S&P 500—homebuilders, automakers, airlines—saw no real growth, often declining. The entire rally was propped up by the Mag 7 alone, which inflated their market cap weighting to 30%.

Today, I assert the Fed matters less because the entire economic engine has fully switched to AI-driven mode, and the AI sector is largely insensitive to interest rates. Within the next one to two years, agent swarms will unleash unprecedented deflationary productivity. Consider the recent breakthrough on the Navier-Stokes equations—a century-old mathematical problem Einstein himself couldn’t solve. AI agent clusters working 24/7 can not only crack scientific problems humanity can’t, but even bypass human-made security nets, proving that human cognitive efficiency cannot compete.

Compare oil and interest rate data: in 2007, when I frequently traveled in China, Brent crude hit $110. Today’s oil prices aren’t even close to that level, yet the S&P 500 has tripled, household wealth has tripled, and a single energy commodity in an economy now several times larger can barely stir waves. As for interest rates, the majority of U.S. homeowners locked in mortgage rates around 3%. Even with a 500-basis-point hike and 10% inflation, 2022 didn’t crash into recession—while ultra-rich individuals actually benefited from higher interest income. Corporate earnings are still growing at ~30%, yet we keep applying pre-AI-era scripts based on interest rates and oil to an AI-era market—this is laughable.

Luke Martin: After your breakdown, I’ll definitely stop obsessing over Fed officials or Kevin Warsh’s soundbites. But stepping back, even if fundamentals are detached from traditional macro, don’t these large institutional fund managers’ psychological expectations still respond to oil or Treasury yields?

Jordi Visser: Let’s test it with recent market data. Since June, the S&P 500 has largely traded sideways. Yet during that period: oil surged from $70+ to break $100 on June 25th, and the 10-year Treasury yield climbed from 4.30% to 5%. According to old-school logic, the market should have collapsed—but instead, it just stayed flat! If oil breaking $100 and Treasuries hitting 5% can’t break the market, imagine the explosive rebound potential if the 10-year yield drops just 20 basis points and oil pulls back slightly.

Even more crucially, today’s “Mag 8” (I include Oracle, AI core holdings, as part of the eight) is just 1% shy of all-time highs, and seven of the eight stocks have re-established above their 50-day MA. Only three days this year saw all eight above the 50-day MA—now seven are clustered above. If the index holds this weak-news consolidation for just a few more days, its technical pattern becomes identical to the recent Bitcoin after exhaustion of selling pressure: all worst-case scenarios are priced in, and corporate earnings are still surging ahead of the midterm elections.

AI Agents as Economic Actors: Why Machines Choose Crypto & Stablecoins Over Traditional Dollars?

Luke Martin: After macro resilience, let’s return to your core engine: the on-chain infrastructure is now ready. Like my own screen right now running over 30 agents—some clearing my inbox at night, blocking spam, others scheduling my calendar, though sometimes doing unauthorized tasks. With the crypto financial infrastructure mature, what kind of agent-driven commercial activity will first spark real growth in Bitcoin and the broader crypto economy?

Jordi Visser: The agents you’re running are “personal efficiency agents”—they save you time. But the ones that will truly ignite economic activity are “consumer-grade agents” (Consumer Agents) directly interfacing with external commercial transactions. Last week, Meta launched a personal AI assistant, and Instinct AI, a private company focused on life-agent execution, jumped from $2.5B to $10B valuation overnight. Payment giant Stripe is also making aggressive acquisitions across the consumer payment stack—including acquiring the large-model routing platform OpenRouter. All signals point to one future: autonomous agent networks are taking full control of transactions.

Many have heard Leopold Aschenbrenner’s 165-page report “Situational Awareness,” but I suspect few actually read page 71’s chart. That chart explicitly states: once AI crosses the threshold of “autonomous research agents,” technology enters absolute exponential takeoff—cracking cancer, solving Navier-Stokes, and rumors suggest several top labs have already secretly solved multiple world-class math problems internally.

Aschenbrenner predicted this inflection point would arrive in 2027 when writing the report in 2024—but reality is progressing faster than the forecast! Given that such cutting-edge autonomous research nodes are now imminent, consumer-grade agents—far lower in technical threshold—will inevitably explode in adoption.

This is precisely why I’ve long advocated for the convergence of macro and fundamentals. In traditional equity markets, macro and fundamental managers may differ in style, but ultimately both assess the same equity profitability logic. Yet crypto has long been fragmented: Bitcoin Maxi focus only on macro and libertarianism, Ethereum and Solana ecosystems only on applications and fundamentals—mutually isolated. I firmly believe in their fusion.

Imagine: even I, a professional immersed in AI daily, find opening a Robinhood Chain wallet frustratingly complex—how could a 70-year-old ever endure that? But consumer-grade agents erase that barrier. Instinct AI can already handle Brazilian visa applications and full payment processes autonomously.

I encountered a particularly telling real-world case: Delta Airlines was recently accused of using AI agents to identify users’ past privacy data and payment capacity for dynamic price discrimination. A friend told me he checked a Delta ticket, returned an hour later, and found it had jumped $130. I immediately told him: clearly, they deployed an AI pricing agent. Your only solution is to deploy your own personal AI agent to negotiate against the airline’s AI agent—let AI fight AI. Only then will the market return to fair competition. The sequence must be: first, Bitcoin establishes consensus, then the vast on-chain ecosystem feeds back into Bitcoin. We’ve now entered a new stage where application ecosystems lead the charge.

Luke Martin: But I must raise a sharp question on behalf of rational skeptics. Coinbase founder Brian Armstrong strongly promoted on David Senra’s podcast that agents must use crypto wallets for 24/7 cross-border settlements, and Coinbase has already built the infrastructure. Yet in Silicon Valley, I’ve seen brilliant AI entrepreneurs argue: “Why must AI use crypto? I can just authorize my credit card info to the agent and pay in dollars.” Even I wavered after hearing this. Why are you so certain agents will ultimately choose Bitcoin and stablecoins, not just stick with dollars?

Jordi Visser: First, I fully agree: in high-frequency micro-payments, stablecoins will dominate, while Bitcoin serves as large-value settlement and core collateral. Imagine transferring $100—Venmo handles it effortlessly. But when moving $1 million, conventional payment tools fail utterly. Here, Bitcoin acts as the ultimate trusted settlement backbone and collateral.

The deeper, decisive logic lies in “frictionless vs. profit structure conflict.” Traditional dollar payment networks—whether card issuers or payment gateways—are commercial entities with shareholders to serve, profits to generate, and fees to collect. This makes it physically impossible for the system to reduce friction and fees to zero. But the essence of the AI era is relentless deflation—AI agents inherently seek the lowest cost and fastest speed, driving endless optimization.

Back to the Delta flight example: a human browsing a website, inputting data, and placing an order—by the time they finish, the backend AI agent has already scanned 17 different airlines, compressing all middleman markups to the limit. The same applies globally to currency exchange and business payments. Once both merchants and consumers are represented by agents, agent-to-agent (Agent-to-Agent) interactions will refuse to route through legacy financial intermediaries. The only reason we still convert to dollars is that physical coffee shops haven’t fully adopted on-chain payments. Once we enter the era of agents communicating directly with agents, frictionless channels based on stablecoins and crypto rails will win decisively.

Think back to mobile internet’s early days—my stepfather refused to try Uber for seven years, yet eventually adapted perfectly. Once early adopters can automatically snag low-priced NBA Knicks tickets 24/7 on StubHub or relentlessly monitor restaurant 7 PM cancellation slots to save money, the younger generation will quickly spread the benefit, teaching parents and grandparents. Concerns about security and risk controls are easily solved—just as we set spending limits on Venmo, we can hard-code a $100 or $2,000 cap on agent-specific operational wallets.

Here lies a blind spot most traditional investors still haven’t grasped: once a paradigm crosses the threshold from 10 users to 1,000 or 10,000 users, the on-chain fundamental expansion driven by AI agents will become exponentially uncontrollable. Last May, during tariff tensions and market rallies, I wrote multiple reports on AI “inference” compute explosions. Yet when I pitched them daily to global hedge fund managers, they barely listened—still obsessed with tariffs. I said then: when you finally wake up to study inference, Micron’s stock will have already risen tenfold.

What’s about to unfold in crypto today is the exact same script: fundamentals grow like snowballs under AI agent-driven momentum. By the time the general public hears the narrative in news headlines, asset prices will have already undergone massive revaluation.

From Grokbot to “City Tax Model”: Who Captures Value When Billions of Agents Trade?

Luke Martin: Speaking of agent evolution speed, I’ve felt it firsthand with Grokbot over the past couple months. Previously, if I asked it to track data non-stop for a week, it would often slack off after one day. Now, although it’s still far from true recursive self-improvement (RSI), even without my command, it remembers my Markdown background files and actively communicates with other agents to inform me, “Luke just mentioned this.” This change is too sci-fi!

Jordi Visser: That’s why I dedicated significant space in my initial Grokbot coverage to its “democratized usability.” It turns what was once a highly technical agent orchestration into a smooth, SMS-like and gaming-like interaction. Just type a few prompts to set up a Jarvis-level overseer agent—even now, during our video call, I can send a text from my phone to have Jarvis coordinate a cluster of tech-savvy agents, auto-fetching a list of stocks that strengthened over the past three days and matched volume-price patterns in four days—delivering precise results in under two minutes.

I had a personal experience recently: I needed to transfer my New Jersey driver’s license to New York. Checking the DMV site, it showed no appointments available for the next month. Instead of waiting, I instructed AI to scour Reddit and X platforms for anyone who’d faced the same issue and found a workaround. Within 30 seconds, it pinpointed a specific backdoor access point after a certain step—I booked an appointment for the next day. Yesterday, I completed the process in just 10 minutes. Everyone should start training themselves to delegate complex tasks to AI—only by conditioning your mind to this collaborative mode can you keep pace with the incoming Instinct AI wave.

Luke Martin: Now I’m switching into trader mode to calculate a fine-grained scenario. Suppose in the near future, hundreds of millions of autonomous AI agents conduct high-frequency stablecoin trades. In this value network, where will profits ultimately accrue? To OpenAI, Anthropic, and other foundational model companies—or to whom?

Jordi Visser: Value will be captured simultaneously at multiple key nodes across the tech stack, but the core lies in understanding the “toll collection” logic and the explosive transaction volume. At the base layer, model vendors and compute infrastructure will continue benefiting. On the transaction settlement and on-chain flow side, assuming all trades run on stablecoins, Stripe collects tolls at the front-end fiat and commercial interfaces, while Layer 2 networks like Robinhood Chain built on Arbitrum capture massive transaction fees, ultimately funneling revenue back to the underlying Ethereum mainnet.

Most people lack a sense of “machine concurrency volume.” Recall Google Cloud’s April 2023 earnings call, when Sundar Pichai announced Token consumption spiked 8x YoY, nearing 9 trillion, then quickly soared to nearly quadrillion levels—this is the real fear behind tech giants’ massive capital expenditure (CapEx) sprees. When hundreds of thousands, even millions, of agents operate 24/7 in parallel, transaction counts will be astronomically beyond human imagination.

Once we grasp the scale, we must resolve a key knot in macroeconomics: “Why did liquidity fail in crypto before?” Because historically, humans locked the vast wealth released by monetary expansion into illiquid “dormant assets”! Despite frequent discussions with Raoul Pal on global liquidity, I must emphasize: globally, only about $120 trillion in actual cash exists, yet total book asset values reach $900 trillion—backed by massive fiat leverage. When you spend $1 million on a building, that money is permanently trapped in concrete and steel—you can’t tear off a wall to buy a coffee at Starbucks. That $1 million becomes “frozen wealth.”

If total global assets (including ~$3 trillion in crypto) hover around $903 trillion, the future trend won’t be mass asset collapse like in the Great Depression. Instead, as traditional fiat leverage returns to rationality and assets become tokenized, unlocking liquidity (money supply expanding from $120T to $200T), dormant, locked-in assets will suddenly gain tradability! This is exactly what Caitlin Long has long emphasized: “velocity of money”—the faster transactions occur, the higher the capital turnover, and the greater the GDP output and protocol fee revenue generated per unit of capital.

Ethereum is like an expanding urban mother city—it doesn’t need to monopolize every front-end app. It merely attracts Arbitrum, Robinhood, and competitors through secure consensus and absolute trust, taxing all commercial entrants for security. Solana, meanwhile, functions as a high-speed satellite financial hub handling ultra-fast transaction demands. As long as trust endures, the underlying toll collectors will profit exponentially with urban prosperity.

Robinhood Chain, Hyperliquid & the “Silent IPO”: How TradFi and Crypto Are Mutually Consuming and Converging?

Luke Martin: You mentioned Robinhood Chain—undoubtedly the most impressive execution by a traditional finance or Web2 company entering crypto. CEO Vlad Tenev’s pace resembles military precision, leading the U.S. equity tokenization race far ahead of expectations. And looking at Robinhood’s Q2 earnings, crypto derivative revenues—including prediction markets and on-chain trading—have officially surpassed traditional stock trading, even without factoring in their own chain’s revenue share. Many see tokenized stocks merely as enabling 24/7 trading—why are Vlad Tenev and BlackRock’s Larry Fink so excited?

Jordi Visser: This directly touches the core of my deep analysis in “The Silent IPO”: why did many early crypto idealists choose to exit at this juncture? Early geeks built this system to pursue absolute freedom and anti-centralization rebellion. They rejected government co-option, despised politicians minting meme coins, and disliked Larry Fink walking in with Wall Street giants—feeling the original mission had been corrupted. So they declared, “I’m out,” cashed out, and left. But from day one, I’ve argued history never lets new systems remain isolated from old ones forever. New and old financial systems must converge—and the ultimate outcome is the new system gradually absorbing, rewriting, and swallowing the old from within.

Standing at this historical juncture, you can now appreciate the brilliant misalignment: crypto is currently in a valuation and fundraising winter, with many startups unable to secure venture funding. Meanwhile, Robinhood sits with massive, real cash reserves in the fiat domain. They’re acting as the vascular conduit, channeling traditional finance’s vast capital into Ethereum and Arbitrum via aggressive U.S. equity and securities tokenization! More traditional users will naturally acquire on-chain wallets by holding tokenized U.S. stocks, creating real new capital inflow. I’ve even designed a portfolio index covering 10 core crypto sectors, selecting 40 tokens plus 6 deep-crossing public companies—using traditional Wall Street metrics to benchmark Bitcoin performance.

Interestingly, I’ve recently noticed major investment banks like Morgan Stanley analysts continually upgrading Robinhood’s stock ratings, yet their reports never mention “crypto.” Simple reason: they still don’t fully grasp the on-chain economic mechanism and can’t explain it to high-net-worth traditional clients. So they resort to traditional growth narratives to boost stock ratings and seize coverage advantage early.

In this round-the-clock perpetual pricing convergence wave, whether it’s Coinbase’s index perpetual contracts (Perps) or the phenomenon-level platform Hyperliquid, they’re rapidly dismantling the closed-market barriers of traditional stock exchanges. Last weekend, amid widespread panic, many rushed to ask me Sunday evening during the NFL halftime: “How much will the S&P 500 drop at Monday open? How much will crude futures gap up?” I opened Coinbase’s index perpetual contract and showed them: the market was already down 28 basis points outside hours—so the opening would be roughly that level. They were stunned—completely unaware that real-time continuous pricing existed even during weekend closures.

This is the deepest intuition behind Stan Druckenmiller’s public stake in Hyperliquid via PURR. Knowing his investment style—like his acute timing on Argentina’s political shifts—I can vividly imagine the scene: one weekend, he and his team discussing expected opening prices for oil or SpaceX assets, and his team reporting, “No need to wait until Monday—right now, it’s trading live on Hyperliquid. The opening will follow that price.”

For Wall Street’s top traders, accustomed to anxiously watching closed-market screens all weekend, the ability to access a 7×24 real-time market reflecting genuine supply-demand dynamics is an irreversible, game-changing dislocation. Once experienced, it’s impossible to go back. That’s why on Twitter, nearly every week, seasoned analysts suddenly get briefed by strangers about Hyperliquid’s real-time quotes—and quietly become the next batch of buyers.

From Tokenized Stocks to Private Credit Crisis: Where Is RWA’s Next True Spark?

Luke Martin: Currently, all eyes are fixed on Robinhood-led “tokenized U.S. stocks,” which is genuinely reshaping trading experiences in real time. But beyond tokenized stocks, if we look toward the next phase of Real World Assets (RWA), what asset class do you foresee as equally explosive? Will it be the massive global bond market, or another overlooked corner?

Jordi Visser: In my view, the next most explosive and urgently needing on-chain tokenization to break through is private credit, and more broadly, the entire private equity and real estate sector.

The reason is simple and urgent: the traditional private credit market is currently facing a severe liquidity impasse—massive capital is frozen and immobile. We can clearly see that various Business Development Companies (BDCs) are trading significantly below par. Look at Blue Owl, KKR, Apollo—these private equity giants absorbed vast amounts of retail capital into illiquid products, triggering redemption runs and media crises in public markets. The demand for private credit assets still exists (e.g., insurance funds need long-term stable allocations), but due to the lack of public, continuous market mechanisms for trading and precise repricing, assets are trapped in illiquidity and severe mispricing.

If someone builds a secondary liquidity exit channel via on-chain tokenization architecture (and firms like Apollo are likely already researching this), and tells distressed investors: “You can tokenize your locked-in stakes—there are institutional buyers willing to purchase at transparent prices 20–30% below market, giving you cash exit today,” this would instantly detonate a superheadline effect on Wall Street!

Tokenized stocks solve cross-border investor access (e.g., no need for a Japanese account to hold Japanese stocks) and free users from outdated ETF constraints, enabling 24/7 global portfolio assembly—already a huge space. On the collateral layer, BlackRock’s money market funds have already proven viable. Whoever unlocks private credit and global real estate—those stagnant pools—will hold the true lever for the next RWA explosion.

AI × Crypto Is Not Zero-Sum: Stock Valuation Peak & the Rise of “Micro AI-Native Businesses”

Luke Martin: The final core question—one I’ve wrestled with for years—is the mental model conflict between AI and crypto. Over the past few years, when AI fever peaked, I instinctively thought AI and crypto were both in the same high-risk frontier asset category—AI’s popularity would directly siphon attention and capital from crypto, so whenever Dario Amodei or Sam Altman dropped a major update sparking AI mania, crypto often underperformed. But after reading your latest deep article, I realize you’ve completely overturned this zero-sum mindset—arguing instead that they synergize and mutually propel each other. Can you unpack the internal linkage in detail?

Jordi Visser: This requires tracing the real evolution of the AI investment wave over the past few years. The first wave, the first two years after ChatGPT launched, was heavily concentrated on Nvidia and a few hyperscale cloud giants. Then last year, capital began shifting massively toward foundational infrastructure and inference compute. In my own tracked AI infrastructure index, at least 40 stocks have doubled in the past year—many even skyrocketed 200% to 500%, delivering fivefold breakout curves rivaling crypto altcoins.

But this surge differs fundamentally from the dot-com bubble—sending a deafening alarm to all die-hard traditional stock investors. Stocks are fundamentally a “hope trade” and “growth premium trade”: investors buy them expecting 15–20% annual returns. After all, bonds have been in a prolonged bear market, leveraged property rental is no longer attractive in high-rate, high-inflation environments, and VC/private equity liquidity is constrained. Stocks became the only viable growth game in town. But here’s the problem: looking forward three years from today, the high-growth premium for public market large caps may face a dramatic structural end.

Look at this year’s data: S&P 500 constituent earnings grew ~30% YoY, yet the S&P index has only risen 11–12% (annualized ~17%). This shows this year is a clear “multiple compression year.” Many wonder: if AI is so grand, why no 100x P/E bubbles? Reality is the opposite: Micron, a storage infrastructure leader, now trades at just 2–4x P/E, and even Nvidia’s forward P/E based on next year’s earnings hovers in the single digits to low double digits (9–11).

On the surface, this seems absurd—but Wall Street capital is razor-sharp: stock prices haven’t flown with strong earnings because investors fundamentally doubt the “terminal value” of all these public giants.

No one knows if these current titans can maintain their moats in three years. Any new player with a minor technological iteration could instantly topple established leaders. Imagine humanoid robots scaling into mining and physical industries: a new startup could buy land and deploy robots from scratch, while legacy public mining giants—burdened by massive human labor costs—can’t overnight fire all employees to restructure costs. Once the perpetual cash flow assumption in asset pricing models collapses, traditional giants’ valuation multiples can’t rise again.

Meanwhile, U.S. equities dominated ~$70 trillion in market cap—72% of global equity—over the past two decades because internet platform dividends were almost entirely captured by American giants (like Germans wondering why 90% of search traffic goes to Google). But AI’s essence is radical democratization and equalization.

The most vibrant future commercial ecosystems may well be a dedicated ETF indexing “micro businesses” earning just $200K annually worldwide. These family-run shops couldn’t scale before—but now, countless one- or two-person “AI-native micro-enterprises” can use agent armies to aggressively carve into software and commercial niches. Each captures $200K in small business from Amazon, Google, or legacy software giants—multiple streams achieve $2M in annual revenue easily.

Individually, these micro-businesses seem trivial—but scaled across billions of people and trillions of agents, they continuously erode the profits and growth space of traditional public companies.

Thus, the conclusion is clear: initially, because infrastructure stocks offered solid earnings and multi- to tenfold gains, they temporarily drew smart capital away from crypto. But as traditional U.S. equities lose their old certainty of high-valued growth myths due to AI disruption, and capital shifts focus from static P/E ratios to “velocity of money” and machine economy protocols, the crypto world—still under $4 trillion and inherently aligned with frictionless agent coordination—will formally inherit the mantle of the globe’s most imaginative new growth story.

Luke Martin: Jordi Visser, this hour of incredibly dense insight has been invaluable—thank you for joining us and sharing these frontier perspectives!

Jordi Visser: My pleasure—enjoyed the conversation immensely. See you next time.

Luke Martin: Thank you all for listening. We’ll see you next time.

Curated & Translated: Deep Tide TechFlow