Global Artificial Intelligence for Blockchains Market Growth (Status and Outlook) 2026-2032

The global Artificial Intelligence for Blockchains 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.

Artificial Intelligence for Blockchains 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, and market/fund-flow signals—improving security, compliance, and operational efficiency. It is delivered primarily as software and services, typically composed of on-chain data ingestion and indexing (nodes/RPC, indexers, entity clustering and labeling), feature engineering plus model training/inference, signal delivery via APIs/SDKs with dashboards and case management, and enforcement/governance components (alert orchestration, policy constraints, permissions and limits, traceable audit logs, optionally backed by trusted execution environments or zero-knowledge style verification). Key use cases include 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.

Artificial Intelligence for Blockchains is evolving from an auxiliary analytics tool into a core productivity layer for blockchain systems, driven by the convergence of rising on chain complexity, tightening regulatory requirements, and rapid advances in AI capabilities. As multi chain deployments, cross chain interactions, and high frequency smart contract calls become standard, manual and rule based approaches are no longer sufficient to identify risks in a timely manner, prompting institutions to embed machine learning, graph analytics, and agent based automation into transaction monitoring, contract security, and operational optimization. At the same time, regulators across major jurisdictions are strengthening requirements around anti money laundering, traceability, and risk disclosure, accelerating investment in intelligent compliance solutions. The maturation of large models and orchestration technologies further reduces the cost of interpretation and response, enabling AI systems to move beyond detection toward actionable decision making.

Despite strong momentum, the market faces notable challenges and risks. On chain data is inherently noisy and adversarial, requiring models to remain robust amid constantly evolving attack vectors and business patterns, which raises the bar for data governance and lifecycle management. Institutional buyers have limited tolerance for false positives, missed detections, and opaque decision logic, pushing vendors to enhance explainability, auditability, and operational controls alongside algorithmic performance. In addition, automated execution can amplify losses during extreme volatility or network congestion, making the balance between efficiency and safety a central design consideration for Artificial Intelligence for Blockchains.

Downstream demand is increasingly shaped by platformization and full lifecycle coverage. Exchanges, custodians, and payment providers are shifting AI capabilities upstream into onboarding and end to end transaction controls, favoring standardized solutions that integrate quickly and support multi chain environments. Decentralized application and protocol teams prioritize runtime monitoring and automated incident response to shorten the window between detection and containment. Enterprise and consortium chain users are expanding adoption of auditable and explainable intelligence for risk control and data governance in tokenized assets, supply chain finance, and compliance reporting. Overall, competition is moving from point solutions to integrated platforms, and providers that combine data, algorithms, and governance mechanisms are best positioned to capture sustained global growth.

LPI (LP Information)' newest research report, the “Artificial Intelligence for Blockchains Industry Forecast” looks at past sales and reviews total world Artificial Intelligence for Blockchains sales in 2025, providing a comprehensive analysis by region and market sector of projected Artificial Intelligence for Blockchains sales for 2026 through 2032. With Artificial Intelligence for Blockchains sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world Artificial Intelligence for Blockchains industry.

This Insight Report provides a comprehensive analysis of the global Artificial Intelligence for Blockchains 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 Artificial Intelligence for Blockchains portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global Artificial Intelligence for Blockchains market.

This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for Artificial Intelligence for Blockchains 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 Artificial Intelligence for Blockchains.

This report presents a comprehensive overview, market shares, and growth opportunities of Artificial Intelligence for Blockchains 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. Show more