Global Blockchain AI Market Growth (Status and Outlook) 2026-2032
Description
The global Blockchain AI market size is predicted to grow from US$ 704 million in 2025 to US$ 3512 million in 2032; it is expected to grow at a CAGR of 25.9% from 2026 to 2032.
Blockchain AI refers to applying machine learning, graph analytics, and generative/agentic AI to blockchain and crypto ecosystems to detect, predict, and automate decisions on on-chain transactions and address graphs, smart-contract code and runtime events, as well as market and fund-flow signals—thereby improving security, compliance, and operational efficiency. Delivered primarily as software and services, a typical architecture includes on-chain data ingestion and indexing (nodes/RPC, indexers, labeling and entity clustering), feature engineering and model training/inference, signal delivery via APIs/SDKs plus dashboards and case management, and enforcement/governance components (alert orchestration, policy constraints, audit logs, optionally backed by TEEs or zero-knowledge style verification). Key applications span smart-contract auditing and runtime monitoring, AML/KYT and transaction risk scoring, fraud and attack detection, and market intelligence with controlled automation for trading and asset management; major supply hubs commonly include the United States, the UK/Europe, Singapore, and China.
Blockchain AI is moving from ad hoc analytics into a production grade capability layer, propelled by three structural forces. First, on chain assets and transaction pathways are becoming more complex, while hacks, fraud, and cross chain spillovers raise the cost of inaction, turning monitoring, smart contract security, and real time risk control into mandatory spend for institutions. Second, compliance requirements are tightening across major markets, pushing transaction monitoring and risk scoring beyond rule engines toward model driven systems that continuously learn, expand coverage, and reduce false positives. Third, large models and agentic workflows lower the barrier to interpretation and response, enabling alert triage, case orchestration, and controlled execution to form closed loops, increasing subscription penetration and accelerating standard modules for exchanges, custodians, payment rails, and stablecoin operations.
At the same time, commercialization hinges on engineering readiness across trust, control, and auditability. On chain data is noisy, adversarial, and fast evolving, making model drift, scarce labels, and evasion tactics key drivers of lifecycle cost. Institutional buyers have low tolerance for false positives and missed detections, and demand explainability, end to end evidence trails, and jurisdiction aware data governance, forcing vendors to invest as much in data governance, traceable logs, and operational workflows as in algorithms. A second risk is tail events in automation, where agents or strategies can amplify losses under extreme volatility or network congestion, so permissioning, limits, circuit breakers, and human in the loop controls are becoming default product requirements, while trusted execution and verifiable computation are advancing from concepts into early commercial pilots.
Downstream demand is converging on platformization and multi chain coverage. Exchanges and custodians are shifting risk management upstream into onboarding and full trade lifecycle controls, prioritizing API first integration and case management, and expanding from single chain to multi chain and cross chain routes. DeFi and Web3 protocol teams increasingly focus on runtime monitoring, exploit early warning, and automated incident response to compress the time from detection to containment and reduce cascading losses. Enterprise and consortium chain users prefer auditable and explainable intelligence for risk control and data governance in supply chain finance, tokenized assets, and compliance reporting. Overall, competition is moving from point tools to integrated platforms, and vendors that combine data, models, workflows, and trust mechanisms are best positioned to secure longer contracts and higher value per deployment.
LPI (LP Information)' newest research report, the “Blockchain AI Industry Forecast” looks at past sales and reviews total world Blockchain AI sales in 2025, providing a comprehensive analysis by region and market sector of projected Blockchain AI sales for 2026 through 2032. With Blockchain AI sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world Blockchain AI industry.
This Insight Report provides a comprehensive analysis of the global Blockchain AI landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyses the strategies of leading global companies with a focus on Blockchain AI portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global Blockchain AI market.
This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for Blockchain AI and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global Blockchain AI.
This report presents a comprehensive overview, market shares, and growth opportunities of Blockchain AI market by product type, application, key players and key regions and countries.
Segmentation by Type:
Cloud Hosted
On Premises
Segmentation by Blockchain Environment:
Public Blockchain
Permissioned Blockchain
Hybrid Blockchain
Segmentation by Core AI Technique:
Graph Machine Learning
Time Series and Statistical Learning
Large Language Models and Agents
Others
Segmentation by Customer Type:
Centralized Financial Platforms
DeFi and Web3 Protocol Teams
Enterprise and Consortium Networks
Others
Segmentation by Application:
Smart Contract Security and Auditing
Compliance and Financial Crime Intelligence
Trading and Market Intelligence
Others
This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries
The below companies that are profiled have been selected based on inputs gathered from primary experts and analyzing the company's coverage, product portfolio, its market penetration.
Chainalysis
TRM Labs
Elliptic
Nansen
Arkham Intelligence
OpenZeppelin
Trail of Bits
Quantstamp
Hacken
Halborn
SlowMist
PeckShield
Beosin
Chengdu LianAn Tech Co., Ltd.
BlockSec
OKLink
Ant Group
Tencent
Baidu XuperChain
Please note: The report will take approximately 2 business days to prepare and deliver.
Blockchain AI refers to applying machine learning, graph analytics, and generative/agentic AI to blockchain and crypto ecosystems to detect, predict, and automate decisions on on-chain transactions and address graphs, smart-contract code and runtime events, as well as market and fund-flow signals—thereby improving security, compliance, and operational efficiency. Delivered primarily as software and services, a typical architecture includes on-chain data ingestion and indexing (nodes/RPC, indexers, labeling and entity clustering), feature engineering and model training/inference, signal delivery via APIs/SDKs plus dashboards and case management, and enforcement/governance components (alert orchestration, policy constraints, audit logs, optionally backed by TEEs or zero-knowledge style verification). Key applications span smart-contract auditing and runtime monitoring, AML/KYT and transaction risk scoring, fraud and attack detection, and market intelligence with controlled automation for trading and asset management; major supply hubs commonly include the United States, the UK/Europe, Singapore, and China.
Blockchain AI is moving from ad hoc analytics into a production grade capability layer, propelled by three structural forces. First, on chain assets and transaction pathways are becoming more complex, while hacks, fraud, and cross chain spillovers raise the cost of inaction, turning monitoring, smart contract security, and real time risk control into mandatory spend for institutions. Second, compliance requirements are tightening across major markets, pushing transaction monitoring and risk scoring beyond rule engines toward model driven systems that continuously learn, expand coverage, and reduce false positives. Third, large models and agentic workflows lower the barrier to interpretation and response, enabling alert triage, case orchestration, and controlled execution to form closed loops, increasing subscription penetration and accelerating standard modules for exchanges, custodians, payment rails, and stablecoin operations.
At the same time, commercialization hinges on engineering readiness across trust, control, and auditability. On chain data is noisy, adversarial, and fast evolving, making model drift, scarce labels, and evasion tactics key drivers of lifecycle cost. Institutional buyers have low tolerance for false positives and missed detections, and demand explainability, end to end evidence trails, and jurisdiction aware data governance, forcing vendors to invest as much in data governance, traceable logs, and operational workflows as in algorithms. A second risk is tail events in automation, where agents or strategies can amplify losses under extreme volatility or network congestion, so permissioning, limits, circuit breakers, and human in the loop controls are becoming default product requirements, while trusted execution and verifiable computation are advancing from concepts into early commercial pilots.
Downstream demand is converging on platformization and multi chain coverage. Exchanges and custodians are shifting risk management upstream into onboarding and full trade lifecycle controls, prioritizing API first integration and case management, and expanding from single chain to multi chain and cross chain routes. DeFi and Web3 protocol teams increasingly focus on runtime monitoring, exploit early warning, and automated incident response to compress the time from detection to containment and reduce cascading losses. Enterprise and consortium chain users prefer auditable and explainable intelligence for risk control and data governance in supply chain finance, tokenized assets, and compliance reporting. Overall, competition is moving from point tools to integrated platforms, and vendors that combine data, models, workflows, and trust mechanisms are best positioned to secure longer contracts and higher value per deployment.
LPI (LP Information)' newest research report, the “Blockchain AI Industry Forecast” looks at past sales and reviews total world Blockchain AI sales in 2025, providing a comprehensive analysis by region and market sector of projected Blockchain AI sales for 2026 through 2032. With Blockchain AI sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world Blockchain AI industry.
This Insight Report provides a comprehensive analysis of the global Blockchain AI landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyses the strategies of leading global companies with a focus on Blockchain AI portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global Blockchain AI market.
This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for Blockchain AI and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global Blockchain AI.
This report presents a comprehensive overview, market shares, and growth opportunities of Blockchain AI market by product type, application, key players and key regions and countries.
Segmentation by Type:
Cloud Hosted
On Premises
Segmentation by Blockchain Environment:
Public Blockchain
Permissioned Blockchain
Hybrid Blockchain
Segmentation by Core AI Technique:
Graph Machine Learning
Time Series and Statistical Learning
Large Language Models and Agents
Others
Segmentation by Customer Type:
Centralized Financial Platforms
DeFi and Web3 Protocol Teams
Enterprise and Consortium Networks
Others
Segmentation by Application:
Smart Contract Security and Auditing
Compliance and Financial Crime Intelligence
Trading and Market Intelligence
Others
This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries
The below companies that are profiled have been selected based on inputs gathered from primary experts and analyzing the company's coverage, product portfolio, its market penetration.
Chainalysis
TRM Labs
Elliptic
Nansen
Arkham Intelligence
OpenZeppelin
Trail of Bits
Quantstamp
Hacken
Halborn
SlowMist
PeckShield
Beosin
Chengdu LianAn Tech Co., Ltd.
BlockSec
OKLink
Ant Group
Tencent
Baidu XuperChain
Please note: The report will take approximately 2 business days to prepare and deliver.
Table of Contents
127 Pages
- *This is a tentative TOC and the final deliverable is subject to change.*
- 1 Scope of the Report
- 2 Executive Summary
- 3 Blockchain AI Market Size by Player
- 4 Blockchain AI by Region
- 5 Americas
- 6 APAC
- 7 Europe
- 8 Middle East & Africa
- 9 Market Drivers, Challenges and Trends
- 10 Global Blockchain AI Market Forecast
- 11 Key Players Analysis
- 12 Research Findings and Conclusion
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