State of FLock.io Q1 2025

Key Insights

• In Q1 2025, FLock.io recorded 4,728 training submissions and over 410,000 validation submissions, indicating that each training output was evaluated multiple times. This highlights the protocol’s emphasis on thorough, distributed model assessment before reward allocation and reflects strong engagement across both roles.
• FLock’s staking ratio rose from 1.3% to 41.1% during Q1, indicating robust economic participation and signaling alignment between tokenholders and the protocol’s long-term incentive structure.
• During Q1 2025, FLock launched FL Alliance in closed beta, introducing multi-round federated training on private datasets, with onchain role assignment and a stake-based reward-slash mechanism to enforce honest participation.
• In March, FLock released its Web3 Agent Model, a domain-specific LLM trained on real-world blockchain data and benchmarked to outperform GPT-4o, Gemini Flash 2.0, and DeepSeek-v3 on Web3 tasks. Early adopters include io.net, OpenGradient, and HashKey Chain, which are deploying the model for decentralized agent tooling, DeFi analytics, and developer-facing applications.

Primer

FLock.io (FLOCK) is a decentralized AI development platform that integrates blockchain infrastructure with privacy-preserving machine learning. It comprises three key components: AI Arena for model training, FLock Moonbase for model marketplace, and FL Alliance for collaborative fine-tuning using federated learning. Federated learning is a method where AI models are sent to local devices for training. Only the trained parameters, not the raw data, are shared back. While this approach improves data security and model relevance, it typically depends on centralized servers and struggles with participant incentives and security. FLock overcomes these challenges by integrating blockchain to decentralize coordination, incentivize honest participation, and ensure transparent governance. Through this combination, FLock allows communities to collaboratively propose, train, and deploy AI models in a trust-minimized environment, reducing barriers to entry and fostering a more ethical, creative, and inclusive AI ecosystem.

FLock’s ultimate vision is to merge federated learning with blockchain-based incentive structures to democratize the entire lifecycle of AI models, beginning with sourcing high-quality data and proposing models to training, validation, and deployment. Backed by a deep foundation of research, including over 10 academic papers featured in esteemed journals such as those of the IEEE Computational Intelligence Society, the FLock team continues to explore the powerful synergies between decentralized infrastructure and collaborative AI development. For a full primer on FLock, refer to our Initiation of Coverage report.

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Key MetricsFinancial OverviewMarket Cap and Price

In Q1 2025, the price of FLOCK declined 92% from $0.77 at the end of December to $0.06 by the end of March. Over the same period, its circulating market cap dropped from initial highs above $500 million to approximately $8.9 million. The steep decline in both price and market cap reflects early post-launch volatility, with rapid token distribution and initial speculation giving way to slower, more organic market activity.

This pattern was not unique to FLOCK: market cap fluctuations were common across newer tokens in Q1. Additionally, total crypto market capitalization fell 18.6% during the quarter, which was driven in part by macroeconomic uncertainty tied to the new U.S. administration.

Staking Ratio

In Q1 2025, FLock’s staking ratio rose from virtually 0% at launch to 41.1% by March 31, reflecting consistently growing participation throughout the quarter. The increase was generally steady, with short-term fluctuations but no significant reversals, suggesting sustained engagement despite broader market volatility. Notably, the most rapid acceleration occurred in January, where the staking ratio climbed to over 27% by month-end. The curve then continued its upward path in February and March, eventually stabilizing above 40% in the final weeks of Q1.

Staking FLOCK is required for all participants in AI Arena and is essential for role eligibility, task access, and incentive alignment. Participation begins with Task Creators, who stake tokens to define training objectives, rounds, and reward structures.

Participants in active tasks fall into one of three roles:

• Training Nodes stake FLOCK to develop or fine-tune models using private data. They are rewarded based on the quality and ranking of their submissions.
• Validators stake FLOCK to evaluate models using standardized datasets. Their validation frequency is influenced by stake and subject to rate-limiting.
• Delegators stake FLOCK on behalf of training nodes or validators and receive a share of rewards. Payouts are time-weighted and depend on the performance of the delegatee and the chosen reward-sharing ratio.

Staked tokens function as collateral to discourage dishonest behavior. Smart contracts automatically distribute rewards, and slashing penalties apply for protocol violations. A 40% staking ratio indicates a relatively strong level of economic engagement from tokenholders. While not absolute, this level of participation helps defend against Sybil attacks and aligns stakeholder incentives, reinforcing the protocol’s operational resilience.

Network OverviewTraining Participation

Training nodes are essential to FLock’s AI development process. These participants stake FLOCK to join training tasks and contribute computing resources to fine-tune global models using private, local datasets. Each node is rewarded based on its performance, stake, and ranking, making it both an operational and economic participant.

In Q1 2025, the number of training nodes on FLock increased from seven at the end of December 2024 to 143 by March 31, reflecting a period of rapid onboarding followed by a gradual plateau. The most significant growth occurred in January, during which the node count more than doubled from 17 on January 1 to 88 by the end of the month. February saw continued expansion, albeit at a slower pace, rising from 89 to 128 by month-end. By mid-March, the count had stabilized in the mid-130s and ultimately settled at 143, suggesting the network had reached an early saturation point for node participation, likely constrained by available task capacity and economic incentives.

This level of training node engagement reflects sustained demand for training opportunities and suggests a sufficiently active participant base to meet the protocol’s operational needs. It also indicates ongoing validator involvement, as training nodes depend on validator evaluations to earn rewards.

In Q1 2025, training submissions on FLock rose to 4,728 by March 31, reflecting strong and sustained activity. January was the most active month, contributing over 1,800 submissions and peaking at 105 on January 25. February added around 1,400 more, while March saw continued but more variable participation, with daily counts ranging from single digits to nearly 80.

One notable training task included FLock x OneKey, which trained AI models on real-world vulnerability data to detect smart contract security issues. Another was Animoca Brands x FLock. This collaboration developed HeyAni, a federated learning agent designed to evaluate Web3 investment opportunities based on business plans and token performance.

Validator Activity

In Q1 2025, validation submissions on FLock surged from just six to over 412,000 by March 31, underscoring the critical role of validator activity in the network. January was the most active month overall, contributing nearly 140,000 submissions, with a notable spike of 17,589 on January 26. February continued this momentum, adding roughly 140,000 more and peaking at 23,519 on February 15 and 28,729 on February 26. March saw a slight moderation in baseline activity but was punctuated by major surges on March 22 and March 31, contributing 21,501 and 18,242 submissions, respectively.

Each validation submission represents a full evaluation of a trained model by a validator using a shared dataset. Completing over 410,000 validation submissions compared to 4,728 training submissions indicates that each training output is evaluated multiple times, highlighting the protocol’s emphasis on thorough, distributed model assessments before reward allocation.

In Q1 2025, FLock’s 30-day rolling yields declined significantly for both training nodes and validators, though training nodes consistently outperformed validators. By March, training node yields averaged roughly 2x higher than validator returns. While validators submitted over 410,000 validations, nearly 90x the number of training submissions, their aggregated stake was typically much larger, often comprising 70–80% of the total pool across tasks. As a result, validator rewards were distributed across a broader stake base, contributing to lower yields on a per-token basis despite higher submission frequency.

Delegation and Network Stake Dynamics

Throughout Q1 2025, the ratio between active delegators and validators on FLock held steady at roughly 5:1. Delegators rose from 47 to 1,060, compared to a validator increase from 17 to 211. This ratio emerged early in the quarter and remained stable, reflecting strong interest from tokenholders in supporting validator performance indirectly through delegation rather than operating validator nodes themselves. The steady growth in both groups points to expanding network participation, while the higher count of delegators reflects lower operational barriers and risk for participants seeking passive exposure to protocol rewards.

Growth Development and Key MilestonesLaunch of FL Alliance

On January 21, 2025, FLock launched FL Alliance in closed beta as the next phase of its decentralized AI infrastructure, building on the foundation of AI Arena. While AI Arena enables open competition for model training using public datasets, FL Alliance introduces a new layer of collaboration by supporting training on private, locally held data through a federated learning setup. Key upgrades include onchain role assignment, where participants are randomly selected as proposers or voters, and a reward-slash mechanism that enforces honest participation through stake-based incentives. This marks a shift from single-stage model submission to multi-round, privacy-preserving training, expanding the protocol’s applicability to domains requiring strict data confidentiality. For those interested in early registration for FLock’s FL Alliance private beta, here is the form.

Partnership with GSR

On January 15, 2025, FLock signed a memorandum of understanding (MoU) with GSR, a global crypto trading and liquidity firm, to co-develop domain-specific AI models focused on trading strategies and market intelligence. The partnership leverages FLock’s decentralized federated learning infrastructure to train models without exposing sensitive data, with GSR participating as a task creator on the platform.

Launch of FLock Web3 Agent Model

On March 12, 2025, FLock introduced the FLock Web3 Agent Model, a domain-specific large language model (LLM) built to natively handle complex onchain tasks. Unlike general-purpose models, this LLM was trained from the ground up on real-world Web3 data and optimized for blockchain-specific function calls, smart contract interactions, and DeFi automation. Benchmarked against GPT-4o, Gemini Flash 2.0, and DeepSeek-v3, the model outperformed across Web3 tasks, offering a significant leap in accuracy and utility for blockchain-native AI use cases.

Source: Flock.io

Early adopters include io.net, OpenGradient, and HashKey Chain, which are integrating the model for decentralized agent tooling, real-time DeFi analytics, and developer copilot solutions. As part of its evolution, the model now integrates Base’s Model Context Protocol (MCP), allowing agents to autonomously interface with crypto tools through a standardized, secure, and interoperable framework, further extending its onchain execution capabilities.

Institutional Validators Join FLock Mainnet

FLock has expanded its validator set with the addition of several key institutional partners:

• A41: The largest validator in Korea by total staked assets, strengthening FLock’s presence in the East Asian staking ecosystem.
• Digital Currency Group (DCG): As FLock’s lead investor and a prominent player in crypto infrastructure, DCG joined the validator set to further support the development of decentralized AI model training.
• HashKey Cloud: A major validator and infrastructure provider in Asia, HashKey Cloud joined the network with a focus on supporting collaborative model development and onchain computation.

Closing Summary

In Q1 2025, FLock made significant strides in expanding its decentralized AI infrastructure, both technically and operationally. Despite market headwinds, the protocol demonstrated strong network activity and deepened stakeholder engagement. The staking ratio climbed to 41.1% by quarter-end, supported by rising participation across all roles. Training node count grew from seven to 143, and cumulative training submissions exceeded 4,700. Validator activity surged, with over 410,000 validation submissions recorded, while the validator set grew from 17 to 211. Meanwhile, delegators increased to 1,060, underscoring interest in passive staking participation.

Technically, FLock introduced FL Alliance in closed beta, enabling privacy-preserving, multi-round model training through federated learning and onchain coordination. The release of the FLock Web3 Agent Model further marked a milestone in protocol evolution, offering a domain-specific LLM for real-time blockchain automation. Early adopters such as io.net, OpenGradient, and HashKey Chain integrated the model, while support for Base’s Model Context Protocol (MCP) extended its tool interoperability. On the validator side, institutional partners A41, DCG, and HashKey Cloud joined the mainnet, reinforcing network security and regional reach.

Together, these developments reflect FLock’s transition from early experimentation to a more mature phase of network growth. With a deepening validator base, increasing participant engagement, and expanding technical capabilities, the protocol is well-positioned to continue scaling its decentralized AI mission in the quarters ahead.