After the Bubble: Where to Go from Here - 2026 Digital Asset Market Analysis Report

Preface: Certainty and Uncertainty in Crypto

At the start of 2026, amid a new cycle of bull and bear markets, the entire market is extremely anxious. After October 11th, market liquidity began to dry up; for a period, aside from a few top projects and companies still standing, most teams chose to shut down or pivot.

And after Openclaw emerged suddenly, swept by a new wave of technological innovation, the huge uncertainty only deepened everyone’s panic. As market liquidity shrank, countless crypto workers shifted to AI. Originally focused solely on crypto, media outlets now featured more AI-related reports in their headlines, while OGs who had been battling in the space for over a decade began to pessimistically declare “crypto is dead.”

With the crypto bubble burst, is crypto really dead?

Ask AI this question, and it will give you countless answers. DeepSeek will tell you that the crypto market’s dividends have vanished; now it’s the domain of professional, compliant players, and ordinary people no longer have a chance. Grok will say that this is just a bull-bear cycle, which will eliminate some people but also push crypto toward a better future. Gemini will say that AI development will drive crypto to develop in tandem.

The noise is overwhelming, so we want to find our own answer. Nothing under the sun is truly new; we vaguely remember that back in 2001, when the internet bubble just burst, the market said the same thing. Every bubble, everyone said the same.

So this time, we choose to study bubbles.

Even if our answer is wrong, it’s our own certainty.

1. Exploring the Cycle Law of History: From Railroads to the Internet, How Do Tech Bubbles Repeat in History?

Glory and Radio: The Rise and Fall of the Industrial Revolution Bubble

On September 27, 1825, the UK built the world’s first railway: the Stockton-Darlington Railway. Three years earlier, despite opposition from feudal aristocrats and religious groups, capitalists saw the future value of this steel beast and bet on it, ultimately completing the project. They believed this technology would bring them profits, but they did not realize how profoundly it would impact the entire era.

Although the first railway was initially just a branch line for canal transport, its convenience and cost-effectiveness led the industry to grow rapidly, attracting many investors. During the late 1824–1825 South American mining speculation bubble, risk investors shifted their focus to railroads. By 1836–1837, as the stock market strengthened, railway stocks doubled. The UK Parliament saw opportunity and approved 44 new companies that year, raising more capital than all previous industry funding combined.

The Rise, Dissolution, and Rebirth of Bubbles

Like countless bubbles after, when a new technology is gradually recognized by the market, it quickly develops into a bubble that bursts just as fast. When infrastructure improves, a new bubble forms even more strongly, eventually returning to normal.

After the 44 companies were established, because the railway network was not yet complete, rail transport was less convenient than water transport, and railway stock prices declined during this period. By the early 1840s, valuations rebounded and approached previous peaks. Before 1843, annual capital investment in railways was about £1 million (roughly $35 million today). In 1844, this rose to £20 million (20x), in 1845 to nearly £60 million (60x), and by 1846, to £132 million (about $120 billion today). The total length of new railways reached a record 4,538 miles. Everything looked prosperous.

Bubble Burst and the Return of Value

Undeniably, early railways were successful commercial projects, but due to investor optimism, stock prices quickly exceeded rational valuation limits. The first movers had an advantage, but without entry barriers, this advantage disappeared. Ample market capital and low technical/market thresholds created opportunities for competitors, squeezing profits of existing firms and leading to a continuous decline in industry returns, known as “involution.”

For investors at the time, the first sign that prosperity was ending was the disappearance of huge premiums on new stock issues; only high-quality companies could maintain their stock prices. For surviving railway companies, expanding and occupying prime locations was the best way to maintain valuation and competitive advantage, often leveraging bank loans to accelerate growth. Worse, since the industry was still emerging, most railway companies underestimated the difficulty of construction, causing actual costs to far exceed initial estimates. Over time, these stocks turned into financial games: dividends no longer came from profits but from capital and bank loans.

Under this vicious cycle, bank interest rates kept rising. At a critical point, railway companies could no longer sustain the capital cycle, and the glow of technology capital suddenly faded. Overnight, many investors went bankrupt, and public praise for railways turned into criticism.

Faced with this, the UK government was forced to pass legislation allowing industry consolidation and canceling nearly 20% of approved new railways. Surviving companies regained profitability, and a wave of mergers began. Afterward, the glory of British railways was no longer dazzling but more like gentle morning sunlight warming the land. Although those crazy bubbles could not be recreated, they truly nourished the growth of the Industrial Revolution.

Eventually, the same story happened later in the Americas.

Marconi and Radio

As a footnote to the era’s development, the story of railways ended, and with the continuous development of transportation, the distance between countries shrank. People could travel farther, or communicate via wired phones and telegraphs without leaving home.

Of course, the speed of information transmission should not stop here.

In 1865, Scottish physicist Maxwell systematically proposed electromagnetic wave theory. Some inventors began experimenting with various radio waves. Finally, in 1895, the Italian inventor Guglielmo Marconi was favored by fortune. Using his self-developed transmitter, he successfully made a receiver ring a bell at 10 yards. He believed the distance could be extended.

Marconi keenly saw the future commercial value of this technology, applied for a patent in 1896, and started promoting it to government agencies. Soon after, he founded the Wireless Telegraph and Signal Company to develop and sell radio telegraphy equipment. As the price of abandoning patent rights, Marconi received £15,000 (about $600,000 today) in cash and shares worth £60k (about $2.8 million today), freeing him from financial worries. He was only 22 years old that year.

From War to Market

As a rising star, Marconi quickly gained attention from all sectors. Early on, he saw the global communication needs of the British Navy and, in 1899, sold wireless equipment to the UK and Italian navies. The first order was worth £6,000 (about $250,000 today), with annual revenue exceeding £3,000 (about $125,000 today).

Despite this government backing, market doubts remained about whether this technology could generate routine commercial value. After years of trial and error, Marconi shifted from direct sales to leasing. This approach emphasized ecosystem building: allowing any product or enterprise to use wireless radio by paying a rental fee, with the restriction that all customers could only communicate with Marconi’s other clients.

This strategy led to the emergence of numerous radio stations and competitors.

The Birth of Radio Concept Stocks

With Marconi and other tech competitors, the radio industry flourished, attracting huge capital inflows. Early on, despite losses shown in financial reports, investors remained enthusiastic: the technology and business models were still in early stages, and losses were acceptable. Later, Marconi’s company was renamed RCA, leveraging its accumulated technological advantage and business network in the US. They combined patents from AT&T, GE, RCA, and Westinghouse into a formidable fortress, leading to explosive growth in sales and profits.

One person’s success lifted all boats; related upstream and downstream companies also enjoyed the benefits. At the peak, some simply registered a company related to “radio” and easily raised funds and listed stocks. The story then repeated the railway bubble: under the bubble’s glow, capital and companies flooded in. When the bubble burst, banks’ loans turned into dividends, and the market collapsed. Unlike railways, radio’s commercial value was epoch-making, lasting nearly twenty years. Once infrastructure was built—radio receivers, stations, TV, and media—the potential was so vast that the market remained prosperous for a long time.

Eventually, the Great Depression arrived, and capital games could no longer continue. People had to seek more difficult but more practical ways to increase actual sales and profits.

The Internet Wave: A New Social Experiment

After IBM’s attempt at personal computers and Apple’s push, the consumer market’s computer penetration reached new heights, and some technologies that once only existed in research labs began to emerge—the internet.

From Ivory Tower to Business

The origin and birth of the internet are well known, so we won’t repeat it here. Compared to its birth, the path to commercialization is more instructive.

A decisive factor was the US National Science Foundation (NSF) deciding to relinquish control of the National Research and Education Network (NREN), transforming it into a private, profit-driven enterprise. Many key elements emerged during this process, making widespread social application of the internet possible: Apple PCs provided hardware, the World Wide Web provided the framework, Mosaic offered the entry point. Coupled with NREN’s commercialization, a giant industry began its magnificent journey.

In the early days of open-source commercialization, not everyone saw the opportunity. Many companies remained conservative. They lacked awareness of the potential, and in the business environment of the time, industry giants preferred land-grabbing and ecosystem building to generate revenue, resisting this highly open new environment. Nonetheless, this was not bad for industry development: the resistance from giants created ample space and opportunities for new entrants.

Netscape: The First to Taste the Fruit

As one of the earliest companies to succeed, Netscape’s peak was a major market boost. In late 1994, Mosaic Communications was involved in a legal dispute over the name Mosaic, and eventually renamed Netscape Communications.

Although the company still had $12 million in cash, its monthly expenses of $1 million forced it to consider a business model shift. It changed its previous service model to a free 30-day trial plus a $49 fee afterward, leveraging its product’s superior performance to quickly capture a large market share. The strategy was initially just to boost market valuation, but it proved so effective that in its August 1995 IPO, Netscape raised $140 million, catapulting it to the peak.

However, success and failure are two sides of the same coin. The sales strategy’s success made Netscape intoxicated with the IPO euphoria, neglecting how to build a moat. They neither acquired upstream/downstream companies nor improved their products, nor did they pursue business cooperation with peers. Instead, they chose the most foolish approach—doing nothing.

The outcome was clear: once the market discovered this huge cake and verified its deliciousness, many competitors flooded in. Netscape was eventually acquired by AOL.

One Whale Falls, All Things Grow

Netscape’s story is lamentable, but overall, it was meaningful for market development. Countless profit-seekers and innovators joined the adventure, giving birth to many dazzling projects. Almost in the same year, Jerry Yang and David Filo spent a lot of time researching browsing needs and created an efficient information indexing system called “Yahoo,” while Sergey Brin and Larry Page at Stanford explored how to find information faster on the internet. These ideas spread overseas, inspiring Jack Ma to start developing the “China Yellow Pages.”

The Extreme of Concept Bubbles

Compared to the railway and radio tech, internet technology’s entry barrier is much lower. You don’t need to hire workers to build railways or get government approval. As long as you understand internet knowledge, you can do anything. The huge wealth effect and low entry barriers triggered a market frenzy.

Initially, markets were cautious, but when they saw simple products like Yahoo and Google, which originated in garages but earned huge profits through innovative models, they realized the old valuation logic was failing. As internet tech stocks soared, investors threw away doubts. Ultimately, for fundamental investors, valuations in the TMT sector were wildly exaggerated, and everyone thought it was fine.

As valuations approached extremes, analysis standards also shifted. Usually, higher stock prices lead analysts to give higher estimates based on profits. To keep valuations reasonable, when profit-based metrics could no longer support prices, analysts shifted to revenue, then to concepts like “click-through rate” and “retention rate,” to project future market potential. The logic was sound, but the key problem was how to ensure the analysis of business models was valid without historical cases—by listening to the founders’ stories.

In the end, people no longer buy based on technology practicality but on stories—whose business story is more convincing, with a broader outlook, can raise more funds. A real FOMO (Fear of Missing Out) began. Initially, people carefully designed business models, but as the market became more impatient, some found that even if their company had nothing to do with the internet, just registering a website could be classified as TMT and enjoy market benefits. Undeniably, some projects with far-sighted visions emerged—online shopping, food delivery, even online pet care. But the problem was, with infrastructure still incomplete, stories remained just stories.

The same ending played out again: for listed companies, few truly adapted to the times and survived; most relied on bank loans to sustain the false prosperity. When interest rates hit a critical point, the market collapsed.

Data-Driven Bubble Indicators: How Internet Valuations Became Unsustainable

The story of history is simple, but to find more valuable information, we need to convert these narratives into quantifiable, comparable macro-financial indicators and find patterns. This section uses the internet bubble (1995–2002) as the core sample, with historical data from the 1929 Great Depression as a reference, analyzing macro data across four dimensions—valuation metrics, monetary environment, capital flows, and real economy—to systematically reveal the evolution of macro indicators during the bubble lifecycle. These regular patterns will serve as a “constant” benchmark for the cyclical analysis of the crypto market in subsequent chapters.

Extreme P/E Ratios

The most direct sign of a bubble is in valuation metrics. In every tech bubble, market optimism about new technology gradually pushes valuation multiples beyond reasonable bounds. This is a gradual “anchor drift,” where investors accept increasingly absurd valuations until the entire valuation system fails.

During the internet bubble, the NASDAQ composite’s P/E ratio peaked at about 200x in March 2000, far exceeding Japan’s asset bubble peak of 60–80x for the Nikkei 225. This meant investors were willing to pay $200 for every $1 of current earnings—implying that even if profits did not grow, it would take 200 years to recover the investment. Notably, over half of the tech companies listed on NASDAQ at the peak were unprofitable, making meaningful P/E calculations impossible.

Meanwhile, the S&P 500 hovered around 29–33x in 1999–2000, with readings above 45x appearing mainly before 2002. Its long-term average was about 15–20x. In March 2001, the P/E of NASDAQ was still as high as 175x, indicating that even as the bubble burst, valuation normalization was far from complete.

Shiller CAPE Ratio: A Century-Long Warning

Nobel laureate Robert Shiller’s cyclically adjusted P/E (CAPE, also called Shiller P/E or P/E 10) smooths short-term fluctuations by using the average inflation-adjusted earnings over the past ten years. It is widely regarded as one of the most reliable long-term valuation indicators. Over more than 140 years of data, the median CAPE of the S&P 500 is 16.04x, with a mean of about 17.17x.

At three iconic bubble moments, CAPE exceeded the “danger threshold” of 30x: before the Great Depression in 1929, it hit 32.56x, then the market crashed 89%, and took until 1954 to fully recover; during the 2000 dot-com bubble, it reached a record 44.20x, then the market fell 49% (2000–2002), with NASDAQ down 78%. Over the decade from 2000 to 2010, investors earned an annualized real return of about -1.4%. Data shows that when CAPE exceeds 30x, the subsequent ten-year annualized real return averages only 0–3%, well below the long-term average of about 7%.

It’s worth noting that CAPE is not a “timing tool”—a high CAPE does not predict when a crash will happen, but it effectively signals low returns over the next decade. As the Minneapolis Fed’s research notes, after the 2000 tech bubble burst, the impact on the real economy was mild, but the destruction of wealth for stock investors was profound.

Extreme P/S Ratios

Because many companies listed at the bubble’s peak were unprofitable (over half of NASDAQ tech firms in March 2000), P/E ratios lost their reference value. The price-to-sales ratio (P/S) thus became a more reliable indicator of bubble severity.

Research by CFA Institute shows that in March 2000, the median P/S for “Internet Content” companies was 32.44x, while in September 2020, the same category’s median P/S was only 3.15x—more than 10 times lower. The median P/B ratio for semiconductors also fell from 13.85x in 2000 to 3.32x in 2020.

The Double-Edged Sword of Monetary Policy: Loose Policy Fuels Bubbles, Tight Policy Bursts Them

Behind every major asset bubble, there is almost always an easy monetary policy. Low interest rates reduce the opportunity cost of capital, encouraging funds to flow into high-risk, high-return assets, fueling speculation. When central banks tighten, raising interest rates and increasing borrowing costs, the bubble’s fragility becomes exposed.

Loose cycle: the catalyst for bubbles. The monetary policy environment during the internet bubble began in the mid-1990s. From 1995 to 1998, under Greenspan, the Fed maintained relatively loose rates, with the federal funds rate around 5.25–5.5%. A key event was the LTCM crisis in fall 1998, which prompted the Fed to cut rates three times, lowering the rate from 5.5% to 4.75%. Goldman Sachs reviewed this period, noting that the rate cuts “released abundant liquidity,” directly fueling NASDAQ’s surge from 11% of NYSE trading volume in 1990 to 80% of total market cap in 1999. The “insurance cut” in 1998 greatly boosted investor confidence—The Globe.com’s IPO in November 1999 saw a 600% first-day gain, setting a Wall Street record.

Tightening cycle: the bubble’s terminator. Starting June 1999, the Fed recognized the risk of overvaluation and began raising rates. Over ten months, the Fed increased rates six times, from about 4.75% to 6.5% by May 2000—an extremely high level since 1991. The discount rate also rose to 6%, one of the highest since August 1991. These measures sharply increased borrowing costs, making bonds and other fixed-income assets more attractive relative to high-risk tech stocks, leading to capital withdrawal from speculative assets.

It’s important to note that rate hikes are not the sole trigger but a key catalyst. On March 13, 2000, Japan announced a recession, triggering global sell-offs. On March 20, Barron’s published “Burning Up,” warning that internet companies were running out of cash. In the same month, MicroStrategy had to restate earnings after aggressive accounting, with a 62% single-day stock price plunge. The combination of rising rates, external shocks, and collapsing confidence formed the full chain reaction that burst the bubble.

After the bubble burst, the Fed quickly shifted course. In 2001, it cut rates 11 times, from 6.5% to 1.75%, one of its fastest easing cycles. However, the labor market continued to deteriorate; by June 2003, unemployment hit 6.3%, three years after the bubble burst. The lag in monetary policy’s transmission to the real economy is a key factor in understanding the aftermath.

Capital Flows and Leverage: From VC Boom to Retail Leverage

If valuation metrics are the “thermometer,” monetary policy is the “fire,” and venture capital (VC), IPO markets, and margin debt are the “fuel.” During bubble expansion, capital floods into speculative assets at an ever-faster pace and lower thresholds—from professional VCs to investment banks’ IPO underwriting, to retail leverage trading, forming a complete speculative chain.

VC: From selective to reckless. During the internet bubble, VC investment exploded. According to NVCA data, US VC funding rose from about $8 billion in 1995 to a peak of $105 billion in 2000 (nominal). Over five years, it grew 13-fold. In 1999, 39% of US VC went into internet companies. This capital frenzy led to a sharp decline in project quality—many startups with no clear profit path could get huge funding just for a “.com” domain.

After the bubble burst, VC funding retreated rapidly. In 2001, investments fell to about $36.5 billion—still the third-highest year but down over 67% from the peak. CFA research shows that the average internal rate of return (IRR) for VC funds in 1999 was -4.29%, and in 2000, -2.51%. This means that most institutional funds that entered during the peak ended up with losses.

IPO market: From boom to freeze. IPO volume is a sensitive indicator of market speculation. In 1996, the US had 677 IPOs, the peak; then it declined to 474 in 1997, 283 in 1998, and rebounded to 476 in 1999. In 2000, 380 companies went public. After the bubble burst, IPOs plummeted to just 80 in 2001—less than a quarter of the previous year. Over 280 VC-backed companies went public in 1999, many with first-day gains over 100%. VA Linux’s IPO on December 9, 1999, soared 698%, still one of the most extreme first-day performances.

Margin debt: Leverage peaks. Margin debt measures market leverage and speculation. In the late 1990s, retail investors flooded the stock market, and margin debt soared, peaking at about $300 billion in March 2000—about 3 trillion yuan in 2000 dollars, roughly 5 trillion in today’s dollars. Margin debt as a percentage of nominal GDP reached 2.6% during the bubble, close to the 2.5% before the 2007 subprime crisis, and surged again to 3.97% in 2021.

During the 2000 crash, retail investors did not withdraw but increased their participation. Data shows that in 2000, retail net inflows exceeded $260 billion, higher than $150 billion in 1998 and $176 billion in 1999. By the end of 2002, about 100 million retail investors had lost around $5 trillion in market value. Vanguard research indicates that by 2002, 70% of 401(k) retirement accounts had lost at least 20%. This reflects a typical retail behavior pattern: while institutions and insiders cashed out, retail investors often became the last to hold the bag.

The Lagging Transmission to the Real Economy: GDP Contraction, Employment Collapse, and No-job Recovery

Asset bubbles’ impact on the real economy is often delayed, spreading along a transmission chain from financial markets to corporate investment and then to the labor market. After the internet bubble burst, the US economy experienced a mild GDP decline but deep, lasting scars in employment and corporate investment, exemplifying a “jobless recovery.”

GDP: Shallow recession, deep wounds. The NBER defined the recession from March to November 2001, lasting about 8 months. On the surface, it was mild—real GDP shrank only 0.3%, with a 1.3% annualized decline in Q3 2001. But the data masked structural damage: fixed investment (excluding inventories) declined continuously from 2001, bottoming out in Q3 2002. Between 1996 and 2000, US real business fixed investment grew at about 10% annually, but after the bubble burst, it reversed sharply, far below historical averages.

Employment: from historic lows to ongoing deterioration. The story here is more severe. The US unemployment rate rose from a low of 4.0% in late 2000 to a peak of 6.3% in June 2003—more than a year after the official end of the recession (November 2001). This “end of recession, rising unemployment” pattern is a hallmark of tech bubble aftermath. The Labor Department estimates that in 2001, about 1.735 million jobs were lost net; in 2002, another 3.5B. The tech sector was hit hardest—about 200k jobs lost in Silicon Valley alone from 2001 to early 2004. Unemployment only stabilized near 4.4–4.5% by late 2006, making this one of the longest recovery periods since WWII.

• The Third Recovery Curve: Hierarchical Rebuilding of Capital Markets (2002–2015)

Unlike the relatively linear recovery of the real economy, capital markets’ recovery was layered and complex: public markets rebounded first, while VC and IPO markets lagged, with incomplete recovery paths.

Public markets: Rapid technical rebound, but full recovery took over a decade. After bottoming in October 2002, the S&P 500 rose 28.7% in 2003, then stabilized with gains of 10.9% in 2004, 4.9% in 2005, and 15.8% in 2006. These positive returns accumulated over years, and the index broke above its 2000 peak in October 2007, taking about 7.5 years. NASDAQ’s recovery was even longer: due to its high valuation deviation during the bubble, it only regained its March 2000 high of 5,048 points on April 23, 2015—15 years later. Despite the long wait, from October 2002 to 2007, NASDAQ rose from 1,114 to over 2,800, nearly 150%. For investors who bought at the bottom, this was an excellent contrarian opportunity; for long-term holders at the peak, the recovery cost was measured in decades.

VC: Sharp contraction, very slow recovery. During the bubble, US VC investment reached about $105 billion in 2000, about 1.087% of GDP. After the crash, funding retreated faster than expected: in 2001, investments fell to about $40.5 billion, and by 2002–2003, the industry shrank to about half of 2001, with VC’s share of GDP dropping below 0.2%, less than a fifth of the bubble peak.

The microstructure of VC also changed fundamentally. The “growth first, profit later” business model was questioned, and investors shifted toward higher-quality targets: more mature companies, clearer profit paths, lower valuation multiples. Data from Wing VC shows that the median age of companies completing Series A increased from 0.5 years in 2000 to 1.4 years in 2003, reflecting a systemic reduction in risk tolerance. New seed-stage firms like Y Combinator (founded 2005) and First Round Capital (2004) emerged, completing the VC ecosystem’s renewal.

IPO market freeze was also prominent. In 2000, 380 companies went public; after the crash, only 79 in 2001—less than a quarter. The exit window’s closure suppressed liquidity in the venture ecosystem, forcing VC firms into “holding positions without exit,” further discouraging new investments. The real restart of exit channels had to wait until 2004–2005.

• The Fourth Recovery Curve: Regulatory Reconstruction and Trust Restoration (2002–2004)

Market recovery also involved rebuilding institutions and trust. During the bubble, many companies committed financial fraud (e.g., Enron in October 2001, WorldCom in June 2002, Adelphia in June 2002), severely damaging public confidence. The stock market’s continued decline in 2002 was partly due to these scandals.

On July 30, 2002, the US Congress passed the Sarbanes-Oxley Act (SOX), the most significant regulatory reform since the Great Depression. SOX strengthened internal controls, increased executive accountability, and imposed new constraints on auditors. This legislation marked the beginning of rule-based market order rebuilding and laid the foundation for investor confidence recovery.

Meanwhile, SEC fined major banks like Citigroup and Merrill Lynch for conflicts of interest and reformed analyst independence. These systemic reforms, together with loose monetary policy and improving economic data, formed the institutional basis for trust rebuilding.

The Macro Laws of Recovery: Five Core Conclusions

Synthesizing the four recovery curves, the macro process after the internet bubble reveals key regularities:

First, monetary policy is the fastest and most powerful lever in recovery, capable of shortening the market’s bottoming process. However, its effect on employment and real investment is lagging— in this case, unemployment peaked nearly two years after the recession ended.

Second, different markets recover at different speeds: public markets (stocks) bottom out first, followed by GDP, then employment, with VC and private equity often experiencing over-shooting—once capital withdraws, rebuilding takes far longer than the initial collapse.

Third, there is a huge time gap between “formal” and “substantive” recovery of indices. The S&P 500 rebounded +28.7% in 2003, but it took 7.5 years to fully recover its 2000 peak; NASDAQ took 15 years. This means that for investors who entered at the top, the real recovery process is much longer than the visible rebound.

Fourth, bubble aftermath involves fundamental restructuring: valuation logic shifts from “user growth” to “profitability,” VC moves from “scattershot” to “selective,” and regulation shifts from “after-the-fact” to “systematic constraints.” Recovery involves not only price return but also a change in market participants’ mindset.

Fifth, companies with real infrastructure value not only survive but often become the core engines of the next growth cycle. Amazon’s stock fell from $107 to $6 during the bubble, but it completed a strategic shift from e-commerce to cloud computing (AWS), laying the foundation for explosive growth in the following decade—an insightful lesson from bubble recovery.

This model’s core insight is that, despite different variables (technology, market structure, participants), the underlying macro patterns are surprisingly consistent:

This is precisely the “invariant” basis we will rely on when mapping this model onto the crypto market in subsequent chapters.

2. Multiple Bull and Bear Cycles in Crypto: Unique Economic Cycles of Blockchain

BTC’s Independent Evolution: From Cryptography Experiment to Institutional Risk Asset

Most past bubbles are now history, but we are in a new bubble today. If you have experienced multiple crypto bull-bear cycles, you may resonate with these bubble patterns.

Therefore, in this chapter, we will take BTC as the core reference, analyzing the crypto cycle’s similar yet distinct lifecycle. These paths reflect common human speculative behaviors but also show clear differences due to blockchain’s decentralization, global immediacy, and tokenomics. After bubbles, some tracks remain as foundations for new cycles, some are eliminated, and some transform—this is the core feature of crypto cycles.

On May 22, 2010, programmer Laszlo Hanyecz posted on Bitcoin Talk, offering 10,000 BTC for two Papa John’s pizzas. The deal was made, making him the first person in history to buy real-world goods with Bitcoin. At that time, 10,000 BTC was worth about $41; today, its value exceeds $1 billion. This event is forever memorialized as “Bitcoin Pizza Day,” vividly recording BTC’s transformation from an almost valueless tech experiment to a core asset recognized worldwide.

Data shows that each cycle’s peak market cap growth rate has gradually converged from about 88x in 2013 to a decline of about 47–48% in 2024–2025 (as of March 2026). Behind this trend is the ongoing decoupling of BTC and altcoin cycles: in 2025–2026, BTC dominance remains stable at around 58.6%. Since the approval of US spot BTC ETFs in 2024, net inflows have exceeded $55 billion (notably from products like BlackRock’s IBIT).

This figure far exceeds any single previous crypto product and directly reflects that institutions now treat BTC as an independent allocation asset, not just an altcoin derivative. In contrast, the overall crypto market remains highly speculative: early bull markets feature high narrative density and structural opportunities; later, homogenous projects flood the market, diluting liquidity.

Most altcoins die or slowly fade after the bull market ends, mainly due to lack of real users and products, narrative disproof, and liquidity exhaustion after sharp price drops. This pattern was most thoroughly demonstrated in the Terra-Luna event of 2022: Luna’s market cap once hit $40 billion; its core narrative “algorithmic stablecoin” was discredited when UST de-pegged, and market cap collapsed within days. On-chain data shows TVL dropped from a peak of $18 billion to less than $10 million, with long-term liquidity exhausted. According to DefiLlama, between 2021 and 2025, over 70% of DeFi and meme projects’ TVL fell more than 90%, with most entering chronic death—low trading volume, halted development, and market oblivion.

From zero to $13.6 trillion, a simple overview of BTC’s evolution helps us better understand how its consensus value develops:

Unique Bubble Mechanism: Decentralized Speculation, Token Economics, and Network Effects Amplification

Crypto bubble formation shares the same core as the internet bubble, but with notable differences. The latter was mainly driven by VCs, while the former is amplified by decentralized speculation, tokenomics, and network effects. The 2017 ICO craze is a prime example: total funding was about $5.3 billion, with many projects raising funds solely on whitepapers, and failure rates reaching 46–59%.

During 2020–2022 macro liquidity expansion, stablecoin supply grew rapidly from about $5 billion to over $150 billion (stabilizing around $310 billion in 2026), further boosting leverage and speculation. When external liquidity recedes, token economies lacking self-sustaining mechanisms reveal problems: high inflation design and short-term incentives dominate, relying on continuous external funding.

This phase of the altcoin market can be explained by Everett Rogers’ diffusion of innovations theory (S-curve). Bull markets are essentially the spread of speculative narratives from early adopters to the broader society: initially driven by programmers, crypto VCs, and tech enthusiasts; then by influencers, traders, and Web2 players amplifying narratives via social media; then by the general public—workers, students, small business owners—attracted by wealth effects; finally by laggards—those with low tech skills, relying on short videos and leverage. When adoption reaches 80–90%, marginal participation drops sharply, and the bull market ends. This pattern was prominent in Solana meme coins in 2024, with over 5,000 new tokens issued in a month, causing attention and funds to fragment across many projects, most of which saw trading volume collapse within months.

When narratives reach society’s tail and early investors start cashing out, the upward price structure collapses, and the bubble ends. From a behavioral perspective, the end of prosperity begins when the crowd mobilized by narratives is fully absorbed.

Cycles and Cycles: Differences Between Internet and Crypto Bubbles

During the internet bubble, the NASDAQ P/E ratio peaked at about 200x in March 2000, with many tech firms unprofitable (over 50%). The median P/S ratio in “Internet Content” was 32.44x. In crypto, valuation bubbles are more directly reflected by TVL/market cap ratios and fully diluted valuation (FDV):

In 2021, DeFi TVL peaked at about $180 billion, while total crypto market cap approached $3 trillion. Some meme and emerging projects’ FDV/TVL ratios exceeded 100x. Between 2021 and 2025, over 70% of altcoins maintained high FDV despite sharp TVL declines, causing valuation systems to break down—much more extreme than the money-burning rate in the internet era.

To understand why, we can look at the structure of participants and macro factors.

Fundamental Participant Differences: Grassroots Victory

The internet bubble was dominated by institutional VCs and investment banks; retail investors mainly participated indirectly via stocks. In contrast, crypto’s decentralized ethos means it is mainly driven by global retail investors. In 2021, new addresses surged by tens of millions, spreading from tech geeks to the broader society.

This structural difference amplifies volatility: retail leverage is higher (perpetual contract holdings often over 60%), making black swan events more impactful than in the institutional era. A prime example is the FTX collapse in 2022, which triggered over $20 billion in liquidations and caused most altcoins to fall over 60% in a month.

Devastating Impact of Tight Monetary Policy

The Fed’s tightening cycles are the common end of bubbles, but their impact on crypto is faster and more thorough. During the internet bubble, six rate hikes from 1999 to 2000 (from 4.75% to 6.5%) caused NASDAQ to crash 78%. In crypto, the 2022 tightening was the most aggressive: rates soared from 0% to 5.25–5.50% (a cumulative 525 basis points), with