85% of Tokens Launched in 2025 Have Fallen Below Their Market Entry Price

RBK-cryptoPublished on 2025-12-23Last updated on 2025-12-23

Abstract

According to an analysis by Memento Research, 85% of tokens launched in 2025 have fallen below their initial listing price. The study of 118 token generation events (TGEs) found that 84.7% of assets are trading below their starting valuation, with two-thirds losing over 50% of their value. A significant 38% have experienced a devastating 70-90% decline, entering what analysts term the "token graveyard zone." Notably, all 28 tokens with a high initial fully diluted valuation (FDV) of over $1 billion are in the red, with a median decline of 81%. The worst-performing category was infrastructure projects, which formed the bulk of the sample and fell an average of 72-82%. The DeFi sector performed relatively better, with 31.6% of its tokens still trading above their TGE price. The report concludes that purchasing tokens at launch in 2025 was largely unprofitable. Success was limited to assets with a low starting valuation; in this group, 40% traded above their launch price. For all other segments, the median decline ranged from 70% to 83%, indicating that the TGE often marked a price peak followed by a sharp correction.

Analysts at Memento Research analyzed 118 token launches (token generation event, TGE) since the beginning of the year and recorded massive declines. According to their data, 84.7% of assets are trading below their initial valuation.

Two-thirds of the tokens in the sample have lost more than 50% of their value, and 38% have a current market capitalization that is 70–90% below the initial level. The authors refer to this range as the "token graveyard zone" (highlighted in red on the chart). The authors rely on data from aggregators CoinGecko and CoinMarketCap, their own calculations, and data from public blockchains, with prices recorded as of December 20, 2025.

Large TGEs with inflated initial valuations performed particularly poorly. Out of 28 launches with an initial valuation (fully diluted value, FDV) of $1 billion or more, none are in profit. The median decline was about 81%.

The authors analyzed token launches in categories such as infrastructure projects (Infra, 46 launches in 2025), artificial intelligence (AI, 23 tokens), decentralized financial platforms (DeFi, 19 tokens), consumer services (Consumer, 14 tokens), gaming projects (Gaming, 6 tokens), stablecoins and related projects (Stablecoin, 4 tokens), decentralized futures platforms (Perp DEX, 3 tokens), data providers (Data, 2 tokens), and one token from a scientific crypto project (DeSi). For the 30 largest tokens, the median initial valuation was $1.58 billion, while for another 28, it was around $680 million.

The largest losses were recorded in infrastructure projects. They made up the bulk of the sample and showed an average decline of 72% to 82%. The DeFi sector performed relatively better, with 31.6% of tokens trading above their TGE price. The perp DEX segment stands out from the rest with an average increase of 213%, but the result is heavily skewed by the launch of the Aster DEX platform, whose token ASTER surged in price due to prolonged aggressive support from the largest crypto exchange Binance and its founder Changpeng Zhao.

Projects with high initial valuations (FDV) failed to meet expectations and were revalued by the market significantly downward. This particularly affected infrastructure and AI-focused projects, which accounted for the majority of the decline.

Buying tokens at launch in 2025 meant betting on rare exceptions, the authors write. Most launches turned out to be unprofitable, and only assets with low initial valuations showed significantly better results. In this group, 40% of tokens traded above their launch price, and the median decline was about 26 percent. For all other segments, the average declines ranged from 70% to 83%, and there were almost no successful examples.

Thus, for most tokens in 2025, TGE was an unfortunate entry point. The median decline was about 70%, and in the case of inflated initial valuations, the market perceived the token launch as a local price peak, followed by a sharp decline.

Experts allowed for a Bitcoin drop to as low as $56k. Where did this conclusion come from?

Bitcoin's price fell short of 2025 forecasts. Who predicted what and why?

Coins of the year. How and why Tron's TRX token outperformed almost the entire crypto market.

Related Questions

QAccording to the analysis, what percentage of tokens launched in 2025 are trading below their initial valuation?

A84.7% of the tokens are trading below their initial valuation.

QWhat is the median drawdown for tokens with a high initial fully diluted value (FDV) of over $1 billion?

AThe median drawdown for tokens with an initial FDV of over $1 billion is approximately 81%.

QWhich token category performed the worst in terms of price decline, and what was the average drop?

AInfrastructure projects (Infra) performed the worst, showing an average price decline between 72% and 82%.

QWhich specific sector showed an average price increase of 213%, and what was the primary reason for this outlier performance?

AThe perp DEX sector showed an average increase of 213%, primarily due to the strong performance of the Aster DEX token, which was aggressively supported by the Binance exchange and its founder, Changpeng Zhao.

QWhat was the key factor that distinguished the performance of tokens with a low initial valuation from the rest?

A40% of tokens with a low initial valuation traded above their launch price, with a median decline of only about 26%, significantly outperforming other segments which saw average declines of 70% to 83%.

Related Reads

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit7m ago

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit7m ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit12m ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit12m ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit12m ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit12m ago

Trading

Spot
活动图片