Reaching mid-2026, the narrative logic of the global artificial intelligence capital market is undergoing a quiet yet profound dramatic change. Once upon a time, the grand narrative of computing power being king and brutally stacking GPUs dominated the entire AI investment cycle, but entering H2 2026, this growth model driven purely by hardware investment and burning money on foundation models is facing severe valuation bottlenecks. In the secondary market, traditional AI computing power rental concept stocks showed obvious performance peaking signals during the interim reporting season. The huge scissors gap between high capital expenditures and commercial closed loops that cannot be validated for a long time has caused massive institutional funds to start withdrawing from heavy-asset computing power sectors. However, this does not mean the end of AI investment dividends. On the contrary, a strategic great migration of capital main forces is unfolding—accelerating penetration from basic computing power facilities to the application layer and financial scenarios. The DeFAI (Decentralized Finance AI) track, straddling the two major outlets of AI and Web3, especially the AI-driven crypto lending and smart risk control fields, is becoming the core reservoir undertaking this massive capital overflow.
The Ebb of Computing Power Narrative and the Rise of DeFAI Application Layer
Reviewing the AI investment frenzy over the past two years, the core driving force of the capital market lay in the arms race of computing power infrastructure. But entering 2026, with the production capacity ramp-up of new-generation chips by giants like Nvidia completed, the scarcity of computing power for global large model training has significantly alleviated, and the gross margin of the computing power rental market has seen a visible decline. The latest earnings season data shows that although revenues of multiple listed companies focusing on bare computing power rentals maintained superficial growth, their free cash flows continued to be negative due to high depreciation costs and continuous server room maintenance investments. This typical heavy-asset model is being ruthlessly debunked in valuation models.
Meanwhile, the capital market's evaluation criteria for AI have fully shifted from technological belief to commercial implementation capabilities. Investors are no longer paying for empty large model parameters, but are closely watching whether AI can truly cut into the capillaries of the real economy and financial transactions, achieving cost reduction, efficiency enhancement, and asset restructuring. Under the evolution of this logic, the DeFAI track stands out with its clear and quantifiable profit model. DeFAI is not simply mechanically applying AI concepts to blockchain, but utilizing AI's deep learning, large language models, and complex logical reasoning capabilities to thoroughly reconstruct the underlying operating mechanism of decentralized finance protocols. Among numerous DeFi sub-tracks, crypto lending, because it touches the most core financial links of collateral, liquidation, and credit expansion, has become the preferred frame for AI technology implementation.
AI Smart Risk Control: Reconstructing Asset Pricing Power in On-Chain Lending
Traditional DeFi lending protocols have long been constrained by a fatal flaw: low capital efficiency caused by over-collateralization. On public chains like Ethereum, for users to borrow stablecoins, they often need to collateralize blue-chip assets with values far exceeding the loan amount. This causes a large amount of high-quality assets to sleep in protocols, unable to generate actual economic leverage effects. The fundamental reason for this phenomenon is that traditional DeFi lacks the ability to assess borrowers' dynamic credit risks, and can only defend against market volatility risks through static collateral ratios.
In 2026, the deep involvement of AI smart risk control is completely rewriting this industry pain point. A new generation of DeFAI lending protocols, by introducing AI-driven on-chain credit scoring models, can capture and analyze borrowers' multi-dimensional on-chain data in real time, including historical interaction records, liquidation frequency, asset portfolio health, and even cross-chain fund flows. AI algorithms can not only complete dynamic modeling of a borrower's credit profile in milliseconds, but also automatically adjust risk premium parameters based on macro market volatility.
This technological breakthrough makes uncollateralized or under-collateralized credit lending a reality in the DeFi space. For institutional funds, this means they can release funds originally沉淀 in over-collateralized protocols, significantly improving capital turnover rates. From a valuation logic perspective, the P/E ratio anchoring of lending protocols with AI risk control capabilities is no longer just TVL (Total Value Locked), but the reduction in bad debt rates and the multiplier increase in capital turnover rates brought by AI models. This valuation paradigm shift from capital scale-driven to technology efficiency-driven is exactly one of the core logics for top capital to heavily invest in DeFAI in H2 2026.
RWA Track Explosion: AI Acting as the Core Engine for Real-World Asset On-Chainization
Besides the lending of on-chain native assets, another explosion point for DeFAI in 2026 lies in the full takeoff of the RWA (Real-World Assets) track. As global regulation gradually clarifies tokenized assets, a massive amount of traditional financial assets such as government bonds, private credit, and commercial real estate are accelerating on-chainization. However, the on-chainization of real-world assets faces extremely complex compliance reviews, asset valuations, and default disposal issues, which is precisely the soft spot that traditional DeFi protocols cannot solve independently.
AI technology acts as a bridge connecting the real and crypto worlds at this moment. Advanced AI large models can parse unstructured legal contract documents in real time, automating the verification of the authenticity and compliance of RWA underlying assets. In the lending process, the AI risk control engine, by accessing global macroeconomic data, regional interest rate fluctuations, and the market trading prices of underlying assets, conducts 24-hour uninterrupted dynamic valuation adjustments on RWA assets. Once underlying assets show precursors to default risk, the AI system can trigger early warnings on-chain and adjust the risk exposure of the lending pool in advance, before the off-chain liquidation mechanism is activated.
This deep integration of AI+RWA not only greatly broadens the asset boundaries of the crypto lending market, but also attracts massive traditional Wall Street funds to enter the DeFi space through compliant channels. For investors, investing in DeFAI protocols mastering core AI risk control technology is equivalent to investing in an on-chain infrastructure capable of accurately pricing global trillion-level real-world assets, whose growth potential is far beyond comparable to simple computing power rentals.
Crossing Bull and Bear: Why DeFAI Has Become the New Safe Haven Anchor for Institutional Funds
Entering August 2026, the global macroeconomic environment remains full of uncertainty. Geopolitical frictions, the misalignment of European and American interest rate cycles, and the high-frequency volatility of traditional financial markets make top hedge funds and family offices urgently need to find anti-risk assets capable of crossing macro cycles. The reason the DeFAI track can become a safe haven for funds at this node stems from its unique risk-return structure.
First, the income sources of DeFAI protocols are highly diversified and counter-cyclical. In a bull market, lending demand is旺盛, and protocols earn substantial interest spreads and liquidation fees through efficient AI fund scheduling; in a bear or oscillating market, the AI risk control system, through precise hedging strategies and robust conservative asset allocations, can still create stable protocol income for the protocol. This endogenous blood-making ability to maintain positive cash flow regardless of market ups and downs is unattainable by traditional AI hardware stocks in down cycles.
Secondly, from the perspective of tokenomics design, mainstream DeFAI projects have generally introduced AI-driven yield distribution mechanisms. Protocols dynamically predict market liquidity needs through AI models, intelligently adjusting the buyback-and-burn ratios and staking yields of native tokens, thereby forming strong deflationary expectations on the token supply side. This innovation, which extends the application of AI technology from pure risk control to the token governance level, greatly enhances the confidence of institutional funds to hold long-term.
H2 2026 AI Investment Layout Strategy: Track Selection from the Underlying Logic
Facing the strong rise of the DeFAI track, how should investors adjust their AI investment layout strategies in H2 2026? The core lies in grasping the two main lines of technological barriers and commercial implementation certainty.
- Focus on protocols with deep data moats: The core of AI risk control models lies in data feeding. Prioritize paying attention to DeFAI projects that have already established deep data partnerships with头部 public chains, centralized exchanges, and even traditional credit reporting agencies. The on-chain and off-chain multi-dimensional data mastered by these protocols constitutes a moat difficult for latecomers to cross, and the accuracy and robustness of their AI models will far exceed the industry average.
- Screen real revenue versus token inflation bubbles: In DeFAI investment, one must penetrate the accounting appearance of token issuance and deeply analyze the protocol's non-token incentivized real revenue. Only those protocols capable of charging stable technical service fees or risk control profit-sharing to institutional borrowers through AI risk control services possess long-term investment value. Beware of pseudo-DeFAI projects that rely entirely on high APY token issuance to maintain TVL scale.
- Pay attention to sub-segment leaders combining RWA and AI: Among numerous DeFAI sub-segments, vertical protocols focusing on the tokenization and AI risk control of specific RWA assets (such as private credit, specific supply chain finance) are often easier to achieve commercial closed loops than large and comprehensive general-purpose platforms. Their deep AI model accumulation in specific industries will bring higher asset pricing power and profit margins.
In summary, 2026 is a key watershed for AI investment to move from infrastructure frenzy to the deep water zone of applications. The capital story of traditional computing power rental is nearing its end, while the DeFAI lending track, centered on smart risk control, on-chain credit scoring, and RWA asset pricing, is reshaping institutional funds' perception of AI investment value with its solid commercial blood-making ability and counter-cyclical characteristics. At this node of capital narrative reconstruction, deeply understanding how AI technology empowers the underlying logic of finance, and grasping the valuation leap opportunities of the DeFAI track, will become the key to winning in the second half of the AI investment game. Capital ebbs from computing power, and dances in finance; DeFAI, with its unique posture, is defining the new trend of AI investment in the next era.
