2026 Cryptocurrency Exchange Listing Decision Questionnaire Survey Report

marsbitPublished on 2026-01-21Last updated on 2026-01-21

Abstract

The 2026 Cryptocurrency Exchange Listing Decision Survey Report, conducted by RootData, gathered 313 valid responses from professionals including Listing BD personnel, researchers, and listing committee members. Key findings reveal that over 69% of respondents are directly involved in or responsible for listing decisions, with many handling over 50 projects annually, leading to significant information overload. Major pain points in the decision-making process include fragmented and outdated data, with approximately 50% of respondents citing these issues. High "hidden costs of trust" and data inaccuracy often prolong the review process. Over 30% of respondents noted that data delays significantly impact decisions, potentially causing missed opportunities or errors. Transparency of project information—such as details about institutional investors, valuation, team, and product roadmap—is critical. More than half of the respondents rely on third-party data platforms like RootData (used by 88.9% of participants) for verification. Projects listed on authoritative platforms with detailed information can improve listing efficiency by at least 30%. Conversely, low transparency often triggers extended defensive reviews, with 16.7% of respondents likely rejecting such projects outright. The report concludes that data transparency is vital in listing approvals, significantly affecting both the efficiency and outcome of a project’s capitalization efforts.

Source: RootData

Recently, RootData initiated a survey questionnaire focusing on cryptocurrency exchange listing decisions, collecting a total of 313 valid responses. Participants included Listing BD personnel, researchers, and listing committee members, among others. The survey results are now compiled into this research report for reference.

Respondent Profile: Covering Frontline Practitioners and Decision-Makers in Listing

Over 69% of respondents are involved in or directly decision-makers for Listing work. Survey participants were primarily from exchange Listing BD and research institute/investment analysis roles. They are the "value discovery" and "access control" departments of exchanges, and decision-makers face immense information processing pressure.

Decision-Making Pain Points: Fragmented Data and Delayed Updates

Approximately 50% of respondents evaluate over 50 projects annually. Decision-makers are in a state of severe "information overload." Among the vast number of projects, those that can provide structured and transparent data significantly reduce the cognitive cost for decision-makers. This also indicates that "transparency" has become one of the important metrics for projects to stand out within the extremely short evaluation window.

Distribution of Core Work Responsibilities

Due diligence and decision-making are highly overlapping functions. This means that data platforms are no longer just auxiliary tools but are integrated into the decision-making chain.

The "Stumbling Block" to Decision-Making Efficiency

"Trust cost" is the most expensive hidden cost for exchanges. Uncertainty in data can cause the decision-making process to repeatedly backtrack. As the compliance trend further intensifies, the accuracy and effectiveness of asset information disclosure will become important factors affecting the exchange listing cycle.

The "Hidden Penalty" of Data Delay

Over 30% of respondents believe delays have a significant or极大 impact, potentially leading to decision-making errors, missed opportunities, or even质疑 project transparency. Even though 60% of respondents表面 "can accept it," delayed information updates from projects may result in a hidden penalty during the Listing evaluation.

Handling of Outdated Information

50% of respondents indicated that if project data is not transparent, it will trigger the exchange's "defensive due diligence," prolonging the review time. 16.7% of respondents explicitly stated they would stop the review process or even directly reject the project's Listing application.

The "Required Course" for Listing Review

The historical track record of institutional investors, valuation, team, product roadmap, and other asset-related "essential dimensions" constitute the credit cornerstone of Web3 projects. In reality, this information is also very easy to falsify. Therefore, over half of the respondents indicated a strong need for third-party data platforms to help them cross-verify information.

Preferred Commonly Used Data Platforms

88.9% of respondents stated they choose to reference RootData's data, making it a "desktop essential" tool for exchange Listing teams. This is particularly evident for projects with lower token capitalization (primarily those with their first TGE or not yet listed on major global crypto exchanges). This high penetration rate signifies that the data structure and quality control established by RootData for Web3 projects are becoming an industry standard. For projects with very high token capitalization, 94.4% of respondents会选择 Coingecko or Coinmarketcap platforms for data cross-verification.

Efficiency Boost from Detailed Project Information

91.4% of respondents explicitly stated that a project being listed on authoritative third-party data platforms like RootData and Crunchbase with detailed information will significantly improve Listing efficiency and好感度,至少可以带来 30% 的审核效率的提升 (at least bringing a 30% improvement in review efficiency).

The Role of Data Platforms in Web3 Development

Only 2.7% of respondents believe projects do not need to focus on data transparency. Listing, being one of the most mysterious links in the industry, has over 80.6% of users agreeing that data platforms are very important for their Listing decisions. This further indicates that whether a project values data information disclosure will directly affect the effectiveness and efficiency of its capitalization.

Summary

The survey results reflect that over half of the professionals in exchange listing departments regard project information transparency as a crucial part of the listing review process, especially information regarding institutional investors, valuation, team, and product roadmap. Sufficient information transparency on third-party data platforms can effectively speed up the review progress (by over 30%), while the review cycle for projects with low transparency will be prolonged.

In the current state of industry development, a large number of projects are陷入 "launching the token only for it to break issue price immediately"窘境, and users have lost trust in the vast majority of crypto projects. The reasons include both the projects' own lack of highlights and reliable business models, as well as many projects being in an information-opaque "black box" state. The disclosure status of a project's core information has become one of the core factors affecting its capitalization progress and effectiveness.

Related Questions

QWhat percentage of survey respondents are directly involved in or make decisions about exchange listings?

AOver 69% of respondents are directly involved in or make decisions regarding exchange listings.

QWhat is considered the most expensive hidden cost for exchanges during the listing process, according to the report?

A"Trust cost" is considered the most expensive hidden cost for exchanges, as data uncertainty leads to repeated backtracking in the decision-making process.

QWhich data platform is used by the vast majority (88.9%) of listing teams for reference, especially for projects with low token capitalization?

A88.9% of respondents use RootData as a reference, making it a 'desktop essential' tool for exchange listing teams, particularly for projects with low token capitalization.

QHow does having detailed information on authoritative third-party data platforms like RootData impact the listing efficiency?

A91.4% of respondents stated that having detailed information on platforms like RootData significantly improves listing efficiency and favorability, increasing audit efficiency by at least 30%.

QWhat are the 'mandatory dimensions' or core information that form the credit foundation for a Web3 project during listing reviews?

AThe 'mandatory dimensions' include institutional investors, valuation, team, product roadmap, and the asset's historical evolution, which form the credit foundation for a Web3 project.

Related Reads

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

NEAR Returns to AI Origins: From Payroll Struggles to Blockchain, Now Focusing on AI Agents and Privacy NEAR Protocol's journey began not with grand blockchain ambitions, but from a practical hurdle: its AI startup founders, including Transformer paper co-author Illia Polosukhin, couldn't efficiently pay international developers in 2017. This led them to pivot and build a high-performance, scalable blockchain. After years navigating various crypto narratives like sharding and cross-chain interoperability, NEAR is now leveraging its AI roots to re-enter the AI arena. A key driver is its "NEAR Intents" layer, which abstracts complex cross-chain transactions. Users simply state their goal (e.g., swap BTC for ETH), and a solver network finds the optimal route. This system has processed over $20B in cross-chain volume, generating significant fee revenue. A major growth area is private transactions via "Confidential Intents/Swaps," which hide trade details until settlement to protect against MEV and front-running. Remarkably, private swaps recently accounted for over 40% of NEAR's transaction volume, highlighting strong demand but also potential regulatory scrutiny. With its AI-founder pedigree, NEAR is positioning itself at the intersection of blockchain, AI agents, and privacy, aiming to become infrastructure for the emerging agent economy while navigating the challenges of its rapid adoption.

marsbit1h ago

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

marsbit1h ago

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

In recent discussions, Vitalik Buterin has frequently emphasized the concept of "CROPS," a framework defining core values for Ethereum's development. CROPS stands for Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. Initially outlined in the Ethereum Foundation's "EF Mandate," it represents a commitment to user sovereignty, ensuring that the network resists external control, remains open, protects privacy, and prioritizes security. The relevance of CROPS extends beyond Ethereum's foundational principles, becoming crucial in the context of AI integration. As AI agents begin handling wallet operations and automated transactions, the risk increases that users may cede control over their digital assets, privacy, and intentions to centralized AI service providers. A "CROPS AI" would therefore emphasize local execution where possible, privacy-preserving remote model calls (e.g., using zero-knowledge proofs), and transparent, verifiable processes to maintain user agency. Vitalik highlights a significant convergence between "CROPS Ethereum access layer" and "CROPS AI." Both address the same fundamental challenge: how users can access powerful services—be it blockchain data via RPCs or AI models—without exposing sensitive information or relinquishing ultimate control. This intersection points toward a future digital entry point that is more private, secure, and user-controlled. Ultimately, CROPS is not merely an abstract ideal but a practical guidepost. It steers development—from protocol resilience and wallet design to AI agent safety—towards a future where users retain self-sovereignty even as digital systems grow more complex and powerful. In an era of accelerating AI adoption, these "slow variables" of censorship resistance, openness, privacy, and security may define Ethereum's enduring value.

marsbit1h ago

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

marsbit1h ago

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbit3h ago

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbit3h ago

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbit3h ago

Token Inefficient, Economy Tokenless

marsbit3h ago

Trading

Spot
Futures
活动图片