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, during a new cycle of bull and bear markets, the entire market is extremely anxious. After October 11th, market liquidity began to dry up, and for a period afterward, aside from a few top projects and companies still surviving, 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 began to feature more AI-related reports in their headlines, while OGs who had been fighting in this space for over a decade started to pessimistically declare “Crypto is dead.”

With the 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, and now it’s the domain of professional compliant players—ordinary people no longer have a chance; Grok will say that this is just a bull-bear transition, 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. There are no new things under the sun; we vaguely remember that in 2001, when the internet bubble just burst, the market said the same thing, and every bubble has been described this way.

So this time, we choose to study bubbles.

Even if our answer might be wrong, it is our own certainty.

1. Exploring the Cyclical Laws of History: From Railroads to the Internet, How Tech Bubbles Repeat

Glory of Railroads and Radio: The Rise and Fall of Industrial Revolution Bubbles

On September 27, 1825, the world’s first railway built in Britain—Stockton to Darlington Railway—was officially opened. Three years earlier, despite opposition from feudal aristocrats and religious groups, capitalists saw the future value of this steel giant and chose to bet on it, ultimately completing the project. They believed this technology would bring them profits, but they did not realize the profound impact it would have on the entire era.

Although the first railway was initially built as a branch of canal transportation, its convenience and cost-effectiveness led the entire industry to grow rapidly like mushrooms after rain, attracting many investors. During the late stages of the South American mining speculation bubble (1824–1825), risk investors shifted their focus to railway companies. In 1836–1837, as the overall stock market strengthened, railway stock prices doubled. The UK Parliament saw the opportunity and approved 44 new companies that year, raising more capital than all previous funding in the industry combined.

The Rise, Dissolution, and Rebirth of Bubbles

Like countless bubbles afterward, when a new technology is gradually recognized by the market, it quickly develops into a bubble and then bursts. After infrastructure improves, a new bubble often emerges even more strongly, eventually returning to normal.

After the establishment of these 44 companies, because the railway network was not yet fully built, railway transportation seemed less convenient than traditional water transport, causing the railway stock index to decline during this period. By the early 1840s, valuations rebounded and approached previous peaks. Before 1843, annual capital investment in railway companies averaged about 1 million pounds (roughly $35 million today). In 1844, this rose to 20 million pounds (20x), in 1845 to nearly 60 million pounds (60x), and by 1846, to 132 million pounds (roughly $120 billion today). That year, the total length of newly built railways reached a record 4,538 miles. Everything seemed prosperous.

The Burst of the Bubble and the Return of Value

Undeniably, early railways were successful commercial projects, but due to investor optimism, stock prices quickly far exceeded the rational valuation limits of railway stocks. The first movers had an advantage, but without barriers to entry, this advantage would disappear. Ample market capital combined with low technical and market entry barriers created excellent opportunities for subsequent competitors, compressing profit margins for existing companies and leading to a continuous decline in industry profitability, a phenomenon known as “involution.”

For investors at that time, the first sign that prosperity was ending was the disappearance of huge premiums on newly issued stocks—only companies perceived as high quality 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, leading to actual costs far exceeding initial estimates in prospectuses. Over time, these stocks turned into pure 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 this capital cycle, and the shine of technological capital suddenly faded. Overnight, many investors went bankrupt, and public praise for railway companies turned into criticism.

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

Eventually, the same story repeated later on the American continent.

Marconi and Radio

As a footnote to the era’s development, the story of railroads came to an end. With continuous advances in transportation, the distance between worlds shrank. People could travel farther, or communicate instantly via wired phones and telegraphs.

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

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

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 a trade-off for abandoning patent rights, Marconi received 15,000 pounds (about $6 million today) in cash and shares worth 60k pounds (about $28 million today), freeing him from financial worries. At just 22 years old that year.

From War to Market

As a rising star, Marconi quickly attracted attention from all walks of society. In the early days of his company, he seized the global communication needs of the British Navy and, in 1899, sold wireless equipment to both the British and Italian navies. The first order was worth 6,000 pounds (about $250,000 today), with annual revenue exceeding 3,000 pounds (about $125,000 today).

Despite securing national-level cooperation, market doubts remained about whether this technology could generate routine commercial value. After several years of trial and error, Marconi adjusted his business model from direct sales to leasing. This approach, emphasizing ecosystem building, allowed any product or enterprise to use wireless radio after paying a partial rent, with the restriction that all customers could only communicate with each other.

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

The Birth of Radio Concept Stocks

With Marconi and other tech competitors entering, the entire radio industry began to thrive, attracting massive capital inflows. In Marconi’s early days, although financial reports showed losses, investor enthusiasm was undampened: the technology and business model were still in early development, and losses were acceptable. Later, Marconi’s company was renamed RCA, leveraging the technological advantages and business network built in the US. They pooled patents from AT&T, GE, RCA, and Westinghouse, forming an impregnable business fortress, which caused RCA’s sales and profits to explode.

As one succeeds, others benefit. Related upstream and downstream companies also enjoyed this wave of technological dividends. At the market’s peak, some people merely registered a company related to “radio” and easily raised funds and listed stocks. The story then resembled the railroad boom: under the bubble, countless capital and companies flooded in. When the bubble burst, and dividends disappeared, banks’ loans were used as dividends, and the market collapsed. Unlike railroads, radio technology’s commercial value was epoch-making, lasting nearly twenty years. Once the infrastructure was complete—from radios, broadcasting stations to television and radio media—the potential was so vast that the market could stay prosperous for a long time.

In the end, 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 Peak of the Internet Wave: A New Social Experiment

With IBM’s attempt at personal computers and Apple’s push, the mass market’s computer penetration reached new heights, marking the emergence of technologies once confined to research labs—namely, the Internet.

From Academia to Business

The origin and birth of the Internet are well-known topics, so we won’t elaborate here. Compared to its origin, the path of commercialization is more instructive.

A decisive factor was the US National Science Foundation (NSF) deciding to relinquish control over the National Research and Education Network (NREN), transforming it into a privatized, profit-driven enterprise. During this process, many key elements emerged that enabled the Internet’s widespread societal adoption: Apple PCs provided hardware, the World Wide Web offered a framework, and Mosaic provided an entry point. Coupled with the commercialization of NREN, a giant industry began its magnificent journey.

In the early days of open-source commercialization, not everyone saw the opportunity. Many related companies adopted conservative approaches. On one hand, their knowledge and insights did not reveal the potential of the Internet; on the other, in the business environment of the time, industry giants preferred to expand their ecosystems through land grabbing and self-built networks, naturally resisting this highly open new environment. Nonetheless, this was not bad for industry development: the resistance from giants created ample market space and opportunities for new entrants.

Netscape: The First to Take a Bite

As one of the earliest companies to seize the opportunity, Netscape’s peak truly invigorated the entire market. In late 1994, Mosaic Communications was embroiled in a legal dispute over the same name, and eventually renamed Netscape Communications Corporation.

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 subscription fee, leveraging its product’s superior performance to quickly capture a large market share. Its goal was simply to boost market valuation, but this tactic 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 success of this sales strategy made Netscape intoxicated with the IPO euphoria, neglecting how to build a moat. They neither acquired upstream and downstream companies to strengthen their supply chain nor deepened their products to improve usability. Even their industry collaborations were dismissed, choosing instead to do nothing.

The outcome was clear—once the market discovered this huge cake and confirmed its deliciousness through Netscape’s example, many competitors flooded in. Netscape was eventually acquired by AOL.

A Whale Falls, All Things Grow

Netscape’s story is lamentable, but overall, it was a meaningful event for market development. Countless profit-seekers and innovators joined this adventure, giving birth to many dazzling projects. Almost in the same year as Netscape’s success, Jerry Yang and David Filo spent much time researching browser 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. When these ideas spread overseas, Jack Ma was inspired and began developing the “China Yellow Pages.”

The Extreme of Concept Bubbles

Compared to the previous railroad and radio technologies, internet technology’s entry barrier was much lower. It didn’t require hiring workers to build railways or obtain government licenses. As long as you understood internet-related knowledge, you could do anything you wanted. The huge wealth effect, combined with low barriers, ignited a frenzy in capital markets.

Initially, markets remained cautious during the bubble’s early stages. But once they saw that simple garage-born products like Yahoo and Google could generate enormous profits through innovative business models, they realized the previous valuation logic was failing. As internet tech stocks’ prices soared, investors threw all doubts aside. Ultimately, for fundamental investors, valuations of the TMT sector were wildly exaggerated, with almost everyone believing it was justified.

As company valuations approached extremes, professional analysis standards also distorted. Usually, higher stock prices led analysts to assign higher valuations based on profit models. To keep valuations reasonable, they shifted from profit-based metrics to revenue, then to “click-through rates,” “retention,” and other concepts, projecting future market potential. The logic seemed sound, but the fatal flaw was the lack of historical cases to validate these analyses. The only way was to listen to the founders’ stories—“storytelling.”

In the end, people stopped paying for technology’s practicality and started paying for stories—whose business story was more convincing, with broader prospects, and thus more likely to attract funding. A real FOMO (Fear of Missing Out) frenzy began. Initially, people carefully designed business models, but as the market grew more impatient, some realized that even if their business had nothing to do with the internet, registering a website could still be classified as a TMT project and enjoy market benefits. Undeniably, some visionary projects emerged, such as online shopping, food delivery, and even online pet care. But the problem was, with infrastructure still incomplete, stories remained just stories.

The same ending repeated: for companies in the stock market, only a 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 historical narrative is simple, but to find more valuable insights, we need to convert these stories into quantifiable macro-financial indicators and identify patterns. This section uses the 1995–2002 internet bubble as the core sample, supplemented by data from the 1929 Great Depression period, analyzing macro data across four dimensions—valuation metrics, monetary environment, capital flows, and real economy—to reveal the evolution of macro indicators during bubble cycles. These regular patterns will serve as a “constant” benchmark for the cyclical analysis of the crypto market in subsequent chapters.

Extreme Price-to-Earnings Ratios (P/E)

The most direct signal of a bubble is in valuation metrics. In every tech bubble, market optimism about new technologies gradually pushes valuation multiples higher, eventually detaching from any reasonable fundamentals. This process is a gradual “anchoring drift,” where investors accept increasingly absurd valuations until the entire valuation system collapses.

During the internet bubble, the NASDAQ Composite’s P/E ratio soared to about 200x at its peak in March 2000, far exceeding the 60–80x peak of Japan’s asset bubble (Nikkei 225). This meant investors were willing to pay $200 for every $1 of current earnings—implying that even if profits remained flat, 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’s CAPE Ratio: A Century-Long Warning

Nobel laureate Robert Shiller’s cyclically adjusted P/E ratio (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: 32.56x before the 1929 Great Depression, which then declined 89% and took until 1954 to fully recover; 44.20x during the 2000 dot-com bubble, followed by a 49% decline in the S&P 500 and a 78% drop in NASDAQ from 2000–2002. Over the decade from 2000 to 2010, investors earned an annualized real return of about -1.4%. Historical 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 important to note 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 states, 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 Price-to-Sales Ratios (P/S)

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. Therefore, the price-to-sales ratio (P/S) became a more reliable indicator of bubble severity.

Research by the CFA Institute shows that in March 2000, the median P/S for “Internet Content” companies reached 32.44x, while in September 2020, the same category’s median P/S was only 3.15x—more than ten 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

Every major asset bubble is closely linked to loose monetary policy. The level of interest rates determines the opportunity cost of capital. When risk-free yields are very low, funds naturally flow into high-risk, high-return assets, fueling speculation. When central banks tighten and raise rates, bubbles become vulnerable.

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 hedge fund collapse in fall 1998, which triggered systemic risk concerns. The Fed cut rates three times, lowering the rate from 5.5% to 4.75%. Goldman Sachs reviewed this period and noted that the rate cuts “released abundant liquidity,” directly fueling the NASDAQ’s rise 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 saw a 600% surge on its first day, setting a Wall Street record.

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

It’s crucial to understand that rate changes are not isolated triggers but part of a chain of catalysts. On March 13, 2000, Japan announced a recession, triggering global sell-offs. On March 20, Barron’s published the cover story “Burning Up,” warning that internet companies were running out of cash. That same month, MicroStrategy was forced to restate earnings after aggressive accounting, with its stock plunging 62% in a single day. The combination of rising rates, external shocks, and collapsing confidence formed a complete trigger chain for the bubble’s burst.

After the crash, the Fed quickly shifted course. In 2001, it cut rates 11 times, bringing the federal funds rate down from 6.5% to 1.75%, one of its fastest easing cycles. However, the labor market continued to weaken—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 of bubbles.

Capital Flows and Leverage: From VC Frenzy to Retail Leverage

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

Venture Capital: 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 roughly $105 billion in 2000 (nominal). Over five years, it grew 13-fold. By 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 simply raised huge sums with a “.com” domain.

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

IPO Market: From boom to freeze. The number of IPOs is a sensitive indicator of market speculation. The peak was 677 IPOs in 1996, then a brief decline to 474 in 1997, 283 in 1998, and a rebound 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, surged 698% on the first day, still one of the most extreme IPO performances in US history.

Margin debt: Leverage peaks. Margin debt, a key indicator of market leverage and speculation, soared during the late 1990s as retail investors flooded into stocks. It peaked at about $300 billion in March 2000—coinciding with NASDAQ, VC investment, and other bubble indicators—equivalent to roughly $500 billion today. Margin debt as a percentage of nominal GDP reached 2.6% during the bubble, approaching the 2.5% level before the 2007 subprime crisis and surpassing 3.97% in 2021.

During the 2000 crash, retail investors did not withdraw but instead increased their market exposure. 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 roughly $5 trillion in market value. Vanguard’s research indicates that by 2002, 70% of 401(k) retirement accounts had lost at least 20%, illustrating the typical retail behavior during bubbles—while institutions and insiders cashed out, retail investors often became the last bagholders.

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

Asset bubbles’ impact on the real economy is often not immediate but propagates 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 and lasting scars in employment and corporate investment, exemplifying a “jobless recovery.”

GDP: Shallow recession, deep wounds. The NBER defined the 2001 recession as lasting from March to November—about 8 months, one of the shortest postwar recessions. Real GDP grew only 1.0% in 2001, far below the pre-bubble expansion rate of 4.8%. It did not turn negative. In 2002, driven by consumer spending and housing, GDP grew 1.7%. With loose monetary policy and fiscal stimulus, growth accelerated to 2.8% in 2003 and peaked at 3.8% in 2004, showing a gradual recovery pattern.

Employment: Severely lagging. The 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 recession was officially over. This “end of recession but rising unemployment” pattern is a hallmark of the bubble’s aftermath. The Labor Department estimates that 1.735 million jobs were lost in 2001, and another 508k in 2002. The tech sector was hit hardest—about 200k jobs lost in Silicon Valley alone from 2001 to early 2004. It took until late 2006 for unemployment to return close to pre-bubble levels (around 4.4–4.5%). The entire employment recovery lasted over six years.

Capital Market: Layered reconstruction. Unlike the relatively linear recovery of the real economy, capital markets showed a more complex layered pattern: public markets (stock indices) rebounded quickly, while VC and IPO markets lagged, with incomplete recovery paths.

Public markets: Rapid technical rebound but slow full recovery. As shown, after bottoming in October 2002, the S&P 500 rose 28.7% in 2003, then continued modest gains (10.9% in 2004, 4.9% in 2005, 15.8% in 2006). It took about 7.5 years for the index to surpass its March 2000 peak of 5,048 points, finally recovering fully in October 2007. NASDAQ’s recovery was even longer: due to its high valuation deviations during the bubble, it only regained its March 2000 high in April 2015, after 15 years. During this period, from October 2002 to April 2007, the index rose nearly 150% (from 1,114 to over 2,800). For long-term investors who bought at the bottom, this was an excellent contrarian opportunity, but for those holding at the peak, the wait was a decade.

VC: Sharp contraction, slow recovery. Compared to public markets, VC’s recovery was more tortuous. During the bubble, US VC funding reached about $105 billion annually, accounting for 1.087% of GDP. After the crash, funding plummeted—down to about $40.5 billion in 2001, roughly half of the peak, and remained below 2001 levels in 2002–2003. The VC-to-GDP ratio fell below 0.2%, less than a fifth of the bubble peak.

The microstructure of VC also changed fundamentally. The “growth first, profitability later” model was questioned, and investors shifted toward higher-quality targets—more mature companies, clearer profit paths, and 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 during this period, 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 severely restricted liquidity for venture capital, and many VC firms faced “holding positions without exit,” further dampening new investments. The real restart of exit channels came only around 2004–2005.

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

Market recovery also involved rebuilding institutions and trust. During the bubble, many companies were caught in accounting scandals (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 legal responsibilities for executives, and imposed new constraints on auditors. This legislation marked a rule-based effort to restore market order and laid a foundation for investor confidence.

Simultaneously, the SEC imposed hefty fines on 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 a comprehensive basis for restoring market trust.

Macro Patterns of the Recovery Path: Five Core Conclusions

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

First, monetary policy is the fastest and most powerful lever in recovery, capable of quickly bottoming the financial markets. 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: stock markets (public markets) bottom first, followed by GDP, then employment, with VC and private markets often experiencing over-shooting—withdrawals of capital take longer to rebuild than the initial collapse.

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

Fourth, bubble aftermaths often involve fundamental restructuring: valuation logic shifts from “user growth” to “profitability,” VC moves from “broad deployment” to “selective mature projects,” and regulatory frameworks evolve from “after-the-fact accountability” to “systemic 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 remarkably consistent:

And this will serve as the “invariant” basis for mapping this model onto the crypto market in subsequent chapters.

2. Multiple Bull and Bear Cycles in Crypto: Unique Economic Trends 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 one. If you’ve experienced multiple crypto bull-bear cycles, you might resonate with these bubble patterns.

Therefore, in this chapter, we will use BTC as the core reference, analyzing BTC and the overall crypto market to systematically explore their similar yet distinct lifecycle paths. 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 characteristic of crypto cycles.

On May 22, 2010, programmer Laszlo Hanyecz posted on Bitcoin Talk, offering 10,000 BTC for two Papa John’s pizzas. This transaction was completed, making him the first person in history to buy real-world goods with Bitcoin. At that time, 10,000 BTC was worth about $41, but today it 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 by global institutions.

Data shows that each cycle’s peak market cap growth rate has gradually converged from about 88x in 2013, while the crash amplitude has decreased from 93% to about 47–48% in the 2024–2025 cycle (as of March 2026). Behind this trend is the continued divergence of BTC dominance from Altcoins: in 2025–2026, BTC dominance remains stable around 58.6%, and since the approval of the US spot BTC ETF in 2024, net inflows have exceeded $55 billion (notably from products like BlackRock’s IBIT).

This figure not only surpasses any single previous crypto product but also 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 phases feature high narrative density and structural opportunities; later, homogenous projects flood the market, diluting liquidity.

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

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

[Graph or data omitted for brevity]

Unique Bubble Mechanisms: Decentralized Speculation, Tokenomics, and Network Effects Amplification

Crypto bubble formation mechanisms are similar to those of the internet bubble but with notable differences. The latter was mainly driven by VC, while the former is amplified through decentralized speculation, tokenomics, and network effects. The 2017 ICO boom exemplifies this: total funding reached about $5.3 billion, with many projects raising funds solely based on whitepapers, and failure rates as high as 46–59%.

During 2020–2022, macro liquidity injections caused stablecoin supply to grow rapidly from about $5 billion to over $150 billion (stabilizing around $310 billion in 2026), further fueling leverage and speculation. When external liquidity recedes, tokens lacking self-sustaining mechanisms reveal problems: high inflation designs and short-term incentives dominate, relying fundamentally on continuous external funding.

This phase of the altcoin market can be explained by Everett Rogers’ diffusion of innovations (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 easy leverage—becoming the last wave. When adoption reaches 80–90%, marginal participation drops sharply, and the bull ends. This pattern was prominent in the Solana meme coin season of 2024, where over 5,000 new tokens were issued in a single month, diluting attention and funds across many projects, most of which saw trading volume collapse within months.

When narratives reach society’s tail end and early investors start to take profits, the upward structure collapses, and the bull market ends. From a behavioral perspective, the end of prosperity begins when the crowd mobilized by speculative narratives is fully absorbed.

Cycles and Differences: Internet Bubble vs. Crypto Bubble

During the internet bubble, the NASDAQ P/E ratio peaked at about 200x in March 2000, with many tech companies unable to calculate meaningful P/E ratios (over 50% in loss). The median P/S ratio in “Internet Content” was as high as 32.44x. In contrast, crypto valuations 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—far exceeding the money-burning rates of the internet era.

To understand these differences, we can look at participant structures and macro factors.

Fundamental Participant Structure Differences: Grassroots Victory

The internet bubble was dominated by institutional VCs and investment banks, with retail investors