LazAI Research: How the AI Economy Surpasses the DeFi TVL Myth

Introduction

Decentralized finance (DeFi) has revolutionized traditional finance by igniting a story of exponential growth through a series of simple yet powerful economic primitives, transforming blockchain networks into a global permissionless marketplace. In the rise of DeFi, several key metrics have become the lingua franca of value: total value locked (TVL), annualized rate of return (APY/APR), and liquidity. These neat metrics inspire engagement and trust. For example, DeFi's TVL (the dollar value of assets locked in the protocol) soared 14x in 2020 before quadrupling again in 2021, peaking above $112 billion. High yields (some platforms claim APYs of up to 3000% during the liquidity farming boom) attract liquidity, while the depth of liquidity pools signals lower slippage and a more efficient market. In short, TVL tells us "how much money is involved", APR tells us "how much you can earn", and liquidity indicates "how easy it is to trade assets". Despite their flaws, these metrics have built a multi-billion dollar financial ecosystem from scratch. By turning user engagement into an immediate financial opportunity, DeFi has created a self-reinforcing adoption flywheel that has made it rapidly popular, driving mass participation.

Today, AI is at a similar crossroads. But unlike DeFi, the current narrative around AI is dominated by large general models trained on massive sets of internet data. These models often struggle to provide effective results in niche areas, specialized tasks, or personalized needs. Their "one-size-fits-all" approach is powerful yet fragile, generalized yet misaligned. This paradigm urgently needs to shift. The next era of AI should not be defined by the size or generality of models, but should focus on bottom-up—smaller, highly specialized models. Such customized AI requires a completely new type of data: high-quality, human-aligned, and domain-specific data. However, acquiring such data is not as simple as web scraping; it requires active and conscious contributions from individuals, domain experts, and communities.

To drive this new era of specialized, human-aligned AI, we need to build an incentive flywheel similar to what DeFi is designed for finance. This means introducing new AI-native primitives that measure data quality, model performance, agent reliability, and alignment incentives – metrics that directly reflect the true value of data as an asset, not just an input.

This article will explore these new languages that can serve as the pillars of an AI-native economy. We will elaborate on how AI will flourish if the correct economic infrastructure is established (i.e., generating high-quality data, reasonably incentivizing its creation and use, and being individual-centered). We will also analyze platforms like LazAI as examples of how they are pioneering the construction of these AI-native frameworks, leading to new paradigms of pricing and rewarding data, thereby providing momentum for the next leap in AI innovation.

The Incentive Flywheel of DeFi: TVL, Yield, and Liquidity - A Quick Review

The rise of DeFi is no coincidence; its design makes participation both profitable and transparent. Key metrics such as Total Value Locked (TVL), Annual Percentage Yield (APY/APR), and liquidity are not just numbers, but primitives that align user behavior with network growth. Together, these metrics create a virtuous cycle that attracts users and capital, further driving innovation.

• Total Value Locked (TVL): TVL measures the total capital deposited into DeFi protocols (e.g., lending pools, liquidity pools) and becomes synonymous with the "market capitalization" of DeFi projects. The rapid growth of TVL is seen as a sign of user trust and the health of the protocol. For example, in the DeFi boom of 2020-2021, TVL jumped from less than $10 billion to over $100 billion, and exceeded $150 billion in 2023, demonstrating the scale of value that participants are willing to lock into decentralized applications. A high TVL creates a gravitational effect: more capital means more liquidity and stability, attracting more users to seek opportunities. While critics point out that blindly chasing TVL could lead to protocols masking inefficiencies by providing unsustainable incentives (essentially "buying" TVL), the early DeFi narrative would lack a concrete way to track without TVL.
• Annual Percentage Yield (APY/APR): The promise of returns turns participation into tangible opportunities. DeFi protocols began offering astonishing APRs to liquidity or capital providers. For example, Compound launched the COMP token in mid-2020, pioneering the liquidity mining model—rewarding liquidity providers with governance tokens. This innovation sparked a frenzy of activity. Using the platform became not just a service, but an investment. High APYs attracted yield seekers, further pushing up Total Value Locked (TVL). This reward mechanism incentivized early adopters directly with generous returns, driving network growth.
• Liquidity: In finance, liquidity refers to the ability to transfer assets without causing significant price fluctuations—this is the cornerstone of a healthy market. In DeFi, liquidity is often initiated through liquidity mining programs (where users earn tokens for providing liquidity). The deep liquidity of decentralized exchanges and lending pools means that users can trade or borrow with low friction, thus improving the user experience. High liquidity leads to higher trading volumes and utility, which in turn attracts more liquidity—a classic positive feedback loop. It also supports composability: developers can build new products (derivatives, aggregators, etc.) on top of liquid markets, driving innovation. Therefore, liquidity becomes the lifeblood of the network, driving adoption and the emergence of new services.

These primitives together form a powerful incentive flywheel. Participants who create value by locking up assets or providing liquidity are immediately rewarded (through high yields and token incentives), which encourages more participation. This turns individual participation into widespread opportunities—users earn profits and governance influence—which in turn breed network effects, attracting thousands of users to join. The results are striking: by 2024, the number of DeFi users exceeded 10 million, with its value growing nearly 30 times within a few years. Clearly, large-scale incentive alignment—converting users into stakeholders—is key to the exponential rise of DeFi.

The current lack of AI economy

If DeFi demonstrated how bottom-up participation and incentive alignment can initiate a financial revolution, today's AI economy still lacks the foundational primitives to support similar transformations. Current AI is dominated by large general models trained on massive crawled datasets. These foundational models are impressive in scale but are designed to solve all problems, often failing to serve anyone particularly effectively. Their one-size-fits-all architecture struggles to adapt to niche markets, cultural differences, or individual preferences, leading to weak outputs, blind spots, and an increasing disconnection from real-world needs.

The definition of next-generation AI will no longer be just about scale; it will also encompass contextual understanding—specifically, the model's ability to comprehend and serve specific domains, professional communities, and diverse human perspectives. However, this situational intelligence requires different inputs: high-quality, human-aligned data. This is precisely what is currently lacking. There is no widely recognized mechanism to measure, identify, value, or prioritize such data, nor are there open processes for individuals, communities, or domain experts to contribute their perspectives and improve the intelligent systems that increasingly impact their lives. As a result, value remains concentrated in the hands of a few infrastructure providers, while the potential for the public to benefit from the AI economy remains disconnected. Only by designing new primitives that can discover, verify, and reward high-value contributions (data, feedback, alignment signals) can we unlock the participatory growth loop that DeFi relies on for its flourishing.

In short, we must also ask:

How should we measure the value of creation? How can we build a self-reinforcing adoption flywheel to drive bottom-up, individual-centered data participation?

To unlock an "AI native economy" similar to DeFi, we need to define new primitives that transform participation into opportunities for AI, thereby catalyzing network effects that have not been seen in this field to date.

AI Native Technology Stack: The New Language of the New Economy

We are no longer just transferring tokens between wallets, but instead inputting data into models, converting model outputs into decisions, and having AI agents take action. This requires new metrics and primitives to quantify intelligence and alignment, just as DeFi metrics quantify capital. For example, LazAI is building the next generation of blockchain networks by introducing new asset standards of AI data, model behaviors, and agent interactions to address the issue of AI data alignment.

The following outlines several key primitives that define the economic value of on-chain AI:

• Verifiable data (the new "liquidity"): Data is to AI what liquidity is to DeFi – the lifeblood of a system. In AI, especially large models, it's critical to have the right data. But raw data can be of poor quality or misleading, and we need high-quality data that is verifiable on-chain. The possible primitives here are "Proof of Data (PoD)/Proof of Value (PoDV)". The concept will measure the value of data contributions, not only based on quantity, but also on quality and its impact on AI performance. Think of it as a counterpart to liquidity mining: contributors who provide useful data (or tags/feedback) will be rewarded based on the value their data brings. Early designs for such systems are already taking shape. For example, a blockchain project's proof-of-data (PoD) consensus treats data as the primary resource for verification (similar to energy in proof-of-work or capital in proof-of-stake). In this system, nodes are rewarded based on the quantity, quality, and relevance of the data they contribute.

Promoting it to a universal AI economy, we may see "Total Data Value Locked (TDVL)" as an indicator: a composite measure of all valuable data on the network, weighted by verifiability and usefulness. Verified data pools could even be traded like liquidity pools—for example, a verified medical imaging pool for on-chain diagnostic AI may have quantifiable value and utility. Data provenance (understanding the source and modification history of data) will be a key part of this metric, ensuring that the data fed into AI models is trustworthy and traceable. Essentially, while liquidity is about available capital, verifiable data is about available knowledge. Metrics like Proof of Data Value (PoDV) can capture the amount of useful knowledge locked in the network, while on-chain data anchoring enabled by LazAI's Data Anchoring Tokens (DAT) makes data liquidity a measurable and incentivized economic layer.

• Model performance (a new asset class): In the AI economy, a trained model (or AI service) becomes an asset in its own right – or even a new asset class alongside tokens and NFTs. Well-trained AI models are valuable because of the intelligence encapsulated in their weights. But how do you characterize and measure this value on-chain? We may require on-chain performance benchmarks or model certification. For example, a model's accuracy on a standard dataset, or a win rate on a competitive task, can be recorded on-chain as a performance score. Think of it as an on-chain "credit rating" or KPI for an AI model. These scores can be adjusted as the model is fine-tuned or the data is updated. Projects such as Oraichain have explored combining AI model APIs with reliability scoring (verifying that the AI output is as expected through test cases). In AI-native DeFi ("AiFi"), staking based on model performance can be envisaged – for example, tokens can be staked if the developer believes that their model performs well; If an independent on-chain audit confirms its performance, it will be rewarded (if the model does not perform well, the stake will be lost). This will motivate developers to report truthfully and continuously improve the model. Another idea is tokenized model NFTs that carry performance metadata – the "floor price" of model NFTs may reflect their utility. Such practices are already emerging: some AI marketplaces allow buying and selling models to access tokens, and protocols such as LayerAI (formerly CryptoGPT) explicitly consider data and AI models as an emerging asset class in the global AI economy. In short, DeFi asks "how much money is locked?" AI-DeFi will ask "How much intelligence is locked?" —not only in terms of computing power (although equally important), but also in terms of the performance and value of the models running in the network. New metrics may include "model quality proofs" or time series indices for on-chain AI performance improvements.
• Proxy Behavior & Utility (On-Chain AI Agents): One of the most exciting and challenging additions to the AI-native blockchain is autonomous AI agents running on-chain. They could be trading bots, data curators, customer service AI, or complex DAO governors – essentially software entities that are able to sense, decide, and act on behalf of users on the network or even on their own. The DeFi world has only basic "bots"; And in the AI blockchain world, agents may become first-class economic agents. This has created a need for metrics around agency behavior, trustworthiness, and utility. We may see mechanisms like "agent utility scoring" or reputation systems. Imagine that each AI agent (perhaps represented as an NFT or semi-fungible token (SFT) identity) accumulates reputation based on its actions (completing tasks, collaborating, etc.). Such scores are similar to credit scores or user ratings, but for AI. Other contracts can use this to decide whether to trust or use proxy services. In LazAI's iDAO (Individual-Centric DAO) concept, each agent or user entity owns its own on-chain domain and AI assets. It is conceivable that these iDAOs or proxies establish measurable records.

Some platforms have started to tokenize AI agents and provide on-chain metrics: for example, Rivalz's "Rome protocol" creates NFT-based AI agents (rAgents), with their latest reputation metrics recorded on the chain. Users can stake or lend these agents, and their rewards depend on the agents' performance and impact within the collective AI "cluster." This is essentially DeFi for AI agents and showcases the importance of agent utility metrics. In the future, we might discuss "active AI agents" in the same way we discuss active addresses, or talk about "agent economic impact" like we do with trading volume.

• Attention tracking may become another primitive — recording what the agent focuses on during the decision-making process (which data, signals). This can make black-box agents more transparent, auditable, and attribute the agent's success or failure to specific inputs. In short, agent behavior metrics will ensure accountability and alignment: to allow autonomous agents to manage large amounts of funds or critical tasks, their reliability must be quantified. A high agent utility score may become a prerequisite for on-chain AI agents managing large amounts of funds (similar to how high credit scores are a threshold for large loans in traditional finance).
• Aligning incentives with AI metrics: Finally, the AI economy needs to consider how to incentivize beneficial use and alignment. DeFi incentivizes growth through liquidity mining, early user airdrops, or fee rebates; whereas in AI, mere usage growth is not enough, we need to incentivize the use that improves AI outcomes. At this point, metrics tied to AI alignment are crucial. For example, human feedback loops (such as user ratings of AI responses or corrections provided through iDAO, which will be detailed later) can be recorded, and feedback contributors can earn "alignment rewards". Or consider "attention proof" or "participation proof" where users who spend time improving AI (by providing preference data, corrections, or new use cases) are rewarded. Metrics could include attention trajectories, capturing high-quality feedback that optimizes AI or human attention resources.

Just as DeFi needs block explorers and dashboards (like DeFi Pulse, DefiLlama) to track TVL and yields, the AI economy also requires new browsers to track these centralized AI metrics—imagine a "AI-llama" dashboard displaying total aligned data volume, active AI agents, cumulative AI utility earnings, and more. It shares similarities with DeFi, but the content is entirely new.

Moving Towards DeFi-style AI Flywheel

We need to build an incentive flywheel for AI — treating data as a first-class economic asset, thus transforming AI development from a closed endeavor into an open, participatory economy, just as DeFi has turned finance into a user-driven liquidity open space.

Early explorations in this direction have emerged. For example, projects like Vana are starting to reward users for participating in data sharing. The Vana network allows users to contribute personal or community data to DataDAO (a decentralized data pool) and earn exclusive tokens for the dataset (which can be exchanged for the network's native tokens). This is an important step towards monetizing data contributors.

However, rewarding contributions alone is not enough to replicate the explosive flywheel of DeFi. In DeFi, liquidity providers are not only rewarded for depositing assets, but the assets they provide also have transparent market value, and the yield reflects the actual usage (transaction fees, borrowing interest, plus incentive tokens). In the same way, the AI data economy needs to go beyond generic rewards and directly price data. In the absence of economic pricing based on data quality, scarcity, or the degree to which the model is improved, we can fall into shallow incentives. Simply distributing tokens to reward participation may encourage quantity rather than quality, or stall when tokens lack an actual AI utility peg. To truly unleash innovation, contributors need to see clear market-driven signals, understand the value of their data, and reap the rewards when the data is actually used in AI systems.

We need an infrastructure that focuses more on direct valuation and reward data to create a data-centered incentive loop: the more high-quality data people contribute, the better the model becomes, attracting more usage and data demand, thereby increasing the returns for contributors. This will transform AI from a closed competition for big data into an open market for trustworthy, high-quality data.

How are these concepts embodied in real projects? Taking LazAI as an example - this project is building the next generation blockchain network and primitives for a decentralized AI economy.

LazAI Introduction - Aligning AI with Humanity

LazAI is a next-generation blockchain network and protocol specifically designed to address the AI data alignment problem, building the infrastructure for a decentralized AI economy by introducing new asset standards for AI data, model behaviors, and agent interactions.

LazAI provides one of the most forward-looking approaches by making data verifiable, incentivized, and programmable on-chain to solve the AI alignment issue. The following will illustrate how an AI-native blockchain puts the above principles into practice using the LazAI framework as an example.

Core Issue - Data Misalignment and Lack of Fair Incentives

AI alignment often boils down to the quality of training data, while future needs require new data that is aligned with humans, trustworthy, and governed. As the AI industry shifts from centralized general models to contextualized, aligned intelligence, the infrastructure must evolve in tandem. The next era of AI will be defined by alignment, precision, and traceability. LazAI directly addresses the challenges of data alignment and incentives, proposing a fundamental solution: aligning data at the source and rewarding the data itself directly. In other words, ensuring that training data verifiably represents human perspectives, is denoised/debiased, and rewards based on data quality, scarcity, or improvement to the model. This marks a paradigm shift from patching models to organizing data.

LazAI not only introduces primitives but also proposes a new paradigm for data acquisition, pricing, and governance. Its core concepts include Data Anchoring Tokens (DAT) and Individual-Centric DAOs (iDAO), both of which work together to achieve data pricing, provenance, and programmable usage.

Verifiable and Programmable Data - Data Anchoring Token (DAT)

To achieve this, LazAI has introduced a new on-chain primitive, the Data Anchor Token (DAT), a new token standard designed specifically for AI data assetization. Each DAT represents a piece of on-chain anchored data and its lineage: contributor identity, evolution over time, and use cases. This creates a verifiable history of each piece of data – similar to a version control system for datasets (like Git), but secured by the blockchain. Because DATs exist on-chain, they are programmable: smart contracts manage the rules for their use. For example, a data contributor can specify that their DAT, such as a set of medical images, be restricted to specific AI models, or used under certain conditions (by enforcing privacy or ethical constraints through code). The incentive is that DAT can be traded or staked – the model (or its owner) may pay to gain access to the data if it is valuable to the model. Essentially, LazAI has built a marketplace where data is tokenized and traceable. This is a direct echo of the "verifiable data" metric discussed earlier: by examining a DAT, you can confirm whether it has been validated, how many models are in use, and what model performance improvements it has caused. Such data will receive a higher valuation. By anchoring data on-chain and tying economic incentives to quality, LazAI ensures that AI is trained on trusted and measurable data. It's about solving problems by incentivizing alignment – quality data is rewarded and comes out on top.

Individual-Centric DAO (iDAO) Framework

The second key component is LazAI's iDAO (Individual-Centric DAO) concept, which redefines governance in the AI economy by putting individuals, rather than organizations, at the heart of decision-making and data ownership. Traditional DAOs often prioritize collective organizational goals, inadvertently weakening individual will. iDAO subverts this logic. They are personalized governance units that allow individuals, communities, or domain-specific entities to directly own, control, and validate the data and models they contribute to the AI system. iDAOs support customized, aligned AI: as a governance framework, they ensure that the model always follows the values or intentions of the contributors. From an economic perspective, iDAOs also make AI behavior community-programmable—rules can be set to limit how a model can use specific data, who can access the model, and how the benefits of the model's output will be distributed. For example, iDAOs can stipulate that whenever their AI model is called, such as an API request or task is completed, a portion of the proceeds will be returned to DAT holders who contributed the relevant data. This establishes a direct feedback loop between proxy behavior and contributor rewards – similar to the mechanism in DeFi where liquidity provider earnings are pegged to platform usage. In addition, iDAOs can interact composably with each other through a protocol: one AI agent (iDAO) can invoke another iDAO's data or model under negotiated terms.

By building on these primitives, LazAI's framework brings the vision of a decentralized AI economy to life. Data becomes an asset that users can own and monetize, models transform from private silos to collaborative projects, and everyone involved—from individuals curating unique datasets to developers building small, specialized models—can become a stakeholder in the AI value chain. This alignment of incentives promises to replicate the explosive growth of DeFi: when people realize that engaging in AI (contributing data or expertise) translates directly into opportunities, they will be more actively engaged. As the number of participants increases, network effects kick in—more data leads to better models, more users are attracted, and more data and requirements are generated, creating a positive cycle.

Building an AI Trust Base: A Verifiable Computing Framework

In this ecosystem, LazAI's Verified Computing Framework is the core layer for building trust. This framework ensures that each generated DAT, every iDAO (individualized decentralized autonomous organization) decision, and each incentive allocation has a verifiable traceability chain, making data ownership executable, governance processes accountable, and agent behavior auditable. By transforming iDAO and DAT from theoretical concepts into reliable and verifiable systems, the Verified Computing Framework realizes a paradigm shift in trust—from reliance on assumptions to mathematically verified certainty.

The realization of value in a decentralized AI economy The establishment of this set of foundational elements has made the vision of a decentralized AI economy a reality:

• Data Assetization: Users can confirm ownership of data assets and obtain profits.
• Model Collaboration: AI models are transforming from closed silos to open collaborative products.
• Participation in Rightsization: From data contributors to vertical model developers, all participants can become stakeholders in the AI value chain.

This incentive-compatible design is expected to replicate DeFi's growth momentum: when users realize that participating in AI construction (by contributing data or expertise) can be directly translated into economic opportunities, enthusiasm for participation will be ignited. As the scale of participants grows, network effects emerge – more high-quality data leads to better models, attracts more users to join, and in turn generates more data demand, forming a self-reinforcing growth flywheel.

Conclusion: Moving Towards an Open AI Economy

The journey of DeFi shows that the right primitives can unleash unprecedented growth. In the upcoming AI-native economy, we are standing at a similar breakthrough threshold. By defining and implementing new primitives that prioritize data and alignment, we can transform AI development from centralized engineering into a decentralized, community-driven endeavor. This journey is not without challenges: we must ensure that economic mechanisms prioritize quality over quantity and avoid ethical pitfalls to prevent data incentives from compromising privacy or fairness. However, the direction is already clear. Practices such as LazAI's DAT and iDAO are paving the way to transform the abstract concept of "human-aligned AI" into concrete mechanisms of ownership and governance.

Just as early DeFi experimentally optimized TVL, liquidity mining, and governance, the AI economy will iterate on its new primitives. In the future, debates and innovations around data value measurement, fair reward distribution, and AI agent alignment and benefits are bound to emerge. This article only touches on the surface of the incentive models that may drive the democratization of AI, hoping to spark an open discussion and in-depth research: How can more AI-native economic primitives be designed? What are the possible unintended consequences or opportunities? With the participation of a broad community, we are more likely to build an AI future that is not only technologically advanced, but also economically inclusive, and aligned with human values.

The exponential growth of DeFi is not magic—it is driven by incentive alignment. Today, we have the opportunity to drive an AI renaissance through data and model peer practices. By converting participation into opportunities and opportunities into network effects, we can launch a flywheel that reshapes value creation and distribution in the digital age for AI.

Let us build this future together - starting with a verifiable dataset, an aligned AI agent, and a new primitive.