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Full analysis of the Talus tokenomics model of raising $10 million: How does 10 billion US drive the decentralized AI agent revolution?
Talus Network, an AI agent network dedicated to building a “blockchain brain”, recently officially announced the complete economic model of its native token US. The token’s total supply is fixed at 10 billion, designed with zero inflation and deflation mechanisms at its core, aiming to deeply bind the token’s value to the use of real AI agents on the network. At the same time, the Talus testnet has attracted more than 35,000 users and completed more than $10 million in financing. On December 11, Binance Alpha announced that it will launch US tokens for the first time. This series of developments marks that this ambitious project aimed at realizing “full-chain AI” has officially entered a critical stage of market validation and ecological launch.
! What is Talus
What is Talus? An AI network that gives blockchain “autonomous hands and feet”
Before diving into its tokenomics, it’s essential to first understand the fundamental industry puzzles that Talus Network is trying to solve. Currently, the vast majority of “AI+crypto” projects adopt a hybrid model of “off-chain computing, on-chain settlement”. Although this model takes into account the efficiency of AI computing, it leaves the core reasoning and decision-making process in an opaque off-chain “black box”, unable to verify whether the AI follows preset rules, violating the verifiable spirit of blockchain.
Talus Network has chosen a more aggressive but ambitious “on-chain” path. Its goal is not to simply connect AI to the blockchain, but to execute and record the logic, state, and decision-making steps of AI agents directly on the blockchain as verifiable smart contracts. In simple terms, Talus aims to add “autonomous reasoning” and “active execution” capabilities to blockchain, a system that excels in “recording state” and “executing deterministic logic”, that is, giving it a “brain” and “hands and feet”.
To achieve this, Talus has built a multi-layered technology stack. Its foundation is a high-performance blockchain based on the Cosmos SDK, with Sui Move chosen as the smart contract language to ensure security and high performance. By introducing the concept of “mirror objects”, it connects off-chain AI resources; By integrating the IBC protocol, cross-chain interoperability is achieved. Ultimately, developers are able to create truly intelligent agents on the network that are autonomous, social, reactive, and proactive.
Quick overview of the core data of the Talus Network project
Financing and Valuation:
Technology and Ecological Progress:
Token US Basic Information:
Analyzing US Tokenomics: How to Create a “Use is Value” Flywheel?
Talus’ token, US, is the economic cornerstone of its ambitious vision. Unlike many models that rely on inflationary subsidies or speculation-driven, US’s economics are designed around a core principle: token value must be driven by AI agent activity that actually occurs on the network. The ultimate goal of its design is to form a self-reinforcing positive economic flywheel.
The logic of this flywheel is clear and tight: more applications give rise to more AI agents and workflows; Every workflow executed on the network is subject to coordination fees; These fees will translate into demand for the US token, increasing its scarcity; This increase in token value in turn attracts more developers and node operators to join the ecosystem and develop more tools and agents, thereby creating more economic activity.
Specifically, the US token plays multiple key roles in the system, with each function directly linked to real-world use:
This design deliberately avoids unnecessary inflation and unsustainable yield promises, attempting to anchor the token’s value firmly to the unforgeable network utility.
Token distribution: Focus on long-term ecological construction and team binding
A robust economic model is inseparable from a well-thought-out token distribution. Talus allocates 30% of the total supply of 10 billion US to the “community and ecosystem”, which is the largest part, aiming to guide real use in the long term through developer funding, tool launch incentives, liquidity provision, etc. The majority of this portion of tokens will be released linearly over 36 months, ensuring that growth stems from actual activity rather than short-term hype.
! Talus Tokenomics
Both the team (core contributors, 22%) and early investors (20.5%) share have strict exercise conditions: zero circulation at the time of the token generation event, both with a 12-month lock-up period, followed by 36 and 24 months of linear release, respectively. Also, ununlocked tokens cannot be used for any incentive programs. This structure deeply binds the interests of the team and capital to the long-term success of the network, effectively avoiding early selling pressure.
Additionally, 7.5% of the tokens are used for liquidity bootstrapping and airdrop programs, with a portion already released at TGE to reward early community contributors. The remaining unclaimed tokens will continue to be used for community initiatives, ensuring participation in the launch phase and ongoing construction beyond. This distribution framework reflects the long-term orientation of the project party of “heavy construction and light speculation”.
Opportunities and Challenges: Can Talus Bridge the Commercialization Gap of “Full-Chain AI”?
Despite the impressive technical vision and economic model design, Talus faces several “mountains” that must be climbed on the road to large-scale adoption. The primary challenge is technical feasibility and cost-effectiveness. Placing complex AI reasoning entirely on-chain may have a much higher computational cost than the hybrid model of “off-chain computation + on-chain settlement”. Even on the high-performance public chain Sui, how to control costs within an acceptable range for commercial applications is the key to determining its scenario width.
The second is fierce market competition and differentiated positioning. Decentralized AI agents are not a new track, and projects such as Fetch.ai and Olas have occupied a certain ecological niche. Most of them have a more flexible hybrid architecture and may have a performance advantage. Talus must demonstrate that the “mathematically verifiable” it provides in certain high-risk, trust-demanding scenarios (e.g., automated asset management, compliance workflows) is enough to offset its potential performance and cost disadvantages.
Finally, there are the challenges of value capture and ecological cold start. The value flywheel theory of the US token is perfect, but its operation depends entirely on an active AI agent ecosystem that can generate real economic value. In the early stage of the project, how to design an effective incentive mechanism to attract the first batch of high-quality developers and enterprise users, and promote the formation of network effects will be the biggest practical test faced by its economic model. The current 35,000 testnet users is a positive starting point, but converting them into paying repeat users is another more difficult battle.