Exchange 'Listing Curse' Investigation: Why 89% of New Tokens Ultimately Become Retail Investor Traps?

marsbitPublicado em 2026-03-26Última atualização em 2026-03-26

Resumo

A 2025 analysis reveals a stark reality: 89% of new tokens listed on Binance ended the year with negative returns, debunking the myth that an exchange listing guarantees profits. These tokens experienced an average price decline of 71-80%, often through a slow, steady bleed rather than a sudden crash. Listings are now widely seen as liquidity events for insiders and early investors to exit, earning the term "retail exit liquidity." Key factors driving this trend include excessive selling pressure from teams and airdrop hunters, overinflated valuations with minimal real users, weak capital flows into new altcoins, a market saturated with over 11 million new tokens in 2025, and a focus on narrative over actual product development. Projects in trend-driven sectors like Memes and RWA were particularly vulnerable due to lacking substance. Binance has also become less tolerant of underperformers, delisting tokens like A2Z, FORTH, and HOOK. The report concludes that only projects with genuine products and strong communities can survive long-term, as a Binance listing is no longer a shortcut to success but a test of sustainability.

Author: Gilmo

Compilation: Yuliya, PANews

Why have many tokens listed on Binance failed?

Recently, while browsing X or cryptocurrency communities, you might be familiar with the phenomenon of "listing on an exchange and then crashing." The days when a Binance listing meant "skyrocketing" seem to be a thing of the past. Instead, a "listing curse" has spread within the community, with many investors watching their account funds evaporate day by day, heartbroken.

So, what exactly is happening behind the scenes?

I. Overview

2025 revealed a harsh reality. Most tokens listed on Binance Spot struggled to maintain their value.

89% of tokens listed on Binance in 2025 had negative returns.

+ Price Performance

Approximately 89% to 94% of listed tokens are in severe loss. The average drawdown after listing ranges from 71% to 80%. Many tokens did not experience a sharp crash but rather a slow decline pattern, with prices gradually falling over time, quietly draining funds.

+ Reputation

A Binance listing was once a significant milestone. Now, it is often seen as a liquidity event for early investors to take profits. Due to the massive selling pressure after listing, many traders even refer to it as the "retail exit zone."

+ Attention Cycle

Most projects gain extremely high attention in the first few days. But after that, interest quickly declines. Without real products or user demand, this momentum soon fades.

+ Operations

Some projects slow down their development after reaching the listing. Subsequently, weak activity and low liquidity lead to their delisting from exchanges.

For example: A2Z, FORTH, HOOK, IDEX, LRC, NTRN, RDNT, SXP

In early 2026: ACA, CHESS, DATA were also delisted

Clearly, Binance no longer supports underperforming projects.

II. Listing Categories

In 2025, Binance listed 87 projects, covering 16 sectors.

+ Networks

Ethereum dominated with about 36%, followed by BNB Chain and Solana.

Notably, Binance has started supporting extremely nascent ecosystems like Nillion and 0G Labs, but due to the lack of real users, this is also a high-risk group.

+ Sectors

DeFi led with 18 projects, followed by AI and Infrastructure.

Trend-driven sectors like Memes and RWA can quickly gain listing opportunities, but due to the lack of core products, their failure rates are higher.

III. So Why Do These Tokens Fail?

Several key factors explain this pattern.

1. Liquidity Events for Insiders

Listings create deep liquidity. This allows teams and early investors to realize profits. Airdrop hunters also add selling pressure immediately after listing.

2. Overvaluation

Some projects launched with valuations in the billions but had small user bases. The gap between valuation and actual usage puts heavy pressure on prices.

3. Weak Market Capital Flows

During 2025, capital was largely concentrated around BTC and ETH. New altcoins received limited and short-lived capital.

4. Emphasis on Narrative Over Product

Many teams invested heavily in storytelling and marketing. Actual product development progressed slowly. Once the initial hype faded, user interest plummeted.

5. Market Saturation

Over 11 million tokens were launched in 2025. Supply increased rapidly, while user attention remained limited. Listing alone no longer drives sustainable growth.

IV. Conclusion

From 2025 to 2026, token listings on Binance will more resemble a final round for project insiders to dump their holdings rather than an opportunity for retail investors to get rich.

Only projects with real products and strong communities have a chance to survive.

You can refer to @defikadic's list to understand which are the truly quality projects:

Perguntas relacionadas

QWhat percentage of tokens listed on Binance in 2025 had a negative return?

AApproximately 89% of tokens listed on Binance in 2025 had a negative return.

QWhat is the average price drawdown for tokens after being listed on Binance?

AThe average price drawdown after listing ranges between 71% and 80%.

QWhich blockchain network dominated the token listings on Binance in 2025?

AEthereum dominated the token listings, accounting for approximately 36% of the projects.

QWhat are the main reasons cited for the failure of newly listed tokens?

AThe main reasons include liquidity events for insiders, overvaluation, weak market capital flows, prioritizing narrative over product, and market saturation.

QWhich sector had the most token listings on Binance in 2025?

AThe DeFi sector had the most token listings with 18 projects, followed by AI and Infrastructure.

Leituras Relacionadas

How Likely is it for TradeXYZ to Go Solo from Hyperliquid?

The article analyzes the possibility of TradeXYZ, a dominant project on Hyperliquid's HIP-3 real-world asset (RWA) perpetual contracts market, splitting off to build its own independent trading platform. With TradeXYZ commanding over 90% of HIP-3 volume and over 35% of Hyperliquid's total open interest, its leverage and influence are significant. The primary motive for a split is seen as capturing the underlying protocol fees, of which TradeXYZ currently receives only half. However, several factors strongly discourage such a move. First, replicating Hyperliquid's high-performance infrastructure would be challenging and could degrade TradeXYZ's product quality. Second, Hyperliquid serves as a crucial distribution channel, with most users accessing TradeXYZ's liquidity through its frontend. Third, the close, trusting relationship between the founders of both projects makes a hostile "betrayal" unlikely. The conclusion is that a split would likely result in a lose-lose scenario. Hyperliquid would lose its main growth narrative and see its valuation impacted, while TradeXYZ would face immense technical hurdles, lose its primary user channel, and damage its reputation. Instead of a full separation, the more probable path is for TradeXYZ to negotiate better terms while deepening integration, focusing on enhancing its own brand and user ownership.

marsbitHá 32m

How Likely is it for TradeXYZ to Go Solo from Hyperliquid?

marsbitHá 32m

"Teletubbies" Robot Cleaning Service, $30/Hour, Pure·Manual·Intelligence

Anthropomorphic "Teletubby" robot offers cleaning services in San Francisco at $30/hour, but it's entirely remote-controlled. The robot, created by startup Tau Robotics, can perform household tasks like washing hands, mopping floors, and taking out trash. While the initial demo videos appear impressive and are notably shown at normal speed (unlike many sped-up robot demos), the company reveals the actions are performed via human teleoperation, not autonomous AI. Tau Robotics, founded in 2024, argues this "cheat" is a strategic way to bridge the current capability gap, ensure task completion, and collect real-world home data to eventually train autonomous systems. Their service features three robot models: Chelsea for kitchens/bathrooms, Elon for regular tidying with memory, and Tony for deep cleaning. Priced at $30 per hour, it's cheaper than average human cleaners in the US. The article discusses the broader challenge of deploying humanoid robots in homes, comparing Tau's approach to others like China's Ziliang and the US's 1X Neo, which also use teleoperation. A key reason for choosing a humanoid form is to make remote control more intuitive for human operators. The piece also notes the potential "emotional value" of having a humanoid servant. The service is currently invite-only in San Francisco.

marsbitHá 46m

"Teletubbies" Robot Cleaning Service, $30/Hour, Pure·Manual·Intelligence

marsbitHá 46m

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.

marsbitHá 1h

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

marsbitHá 1h

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.

marsbitHá 1h

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

marsbitHá 1h

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.

marsbitHá 1h