AI Bubble and Web3 Not one story. Public market AI and AI infrastructure leaders appear to be highly regarded, but they are supported by real revenue growth and significant capital spending. By contrast, many AI, DePIN, and blockchain data projects still trade mostly on anecdotal momentum, with valuations often leading fundamentals by a wide margin. At the same time, a smaller subset may be structurally undervalued if the market lacks durable cash flows, defensible data moats, or network effects.
This article explains where Web3’s AI-related assets may be overvalued, where they may be undervalued, and how professionals and organizations can assess risk using a fundamentals-first framework.
Macro context: What people mean by the “AI bubble”
The AI boom has been intense and uneven. In 2025, AI-related companies led a significant share of US stock gains, and sharp one-day drawdowns in leading chip stocks reinforced bubble narratives. However, investing in AI is not purely speculative. Big tech companies are committing very large budgets to AI over the next few years, and demand for AI data centers is expected to grow approximately 19% to 22% annually through 2030, driven by high-performance compute and storage needs.
There are two competing explanations that could be valid:
- Excessive growth, not a pure bubble: Much of AI infrastructure spending is tied to real adoption and revenue, as profitable companies fund capital expenditures from strong balance sheets.
- Excess speculative riskSome economists and researchers argue that the returns from scaling up large language models may diminish, and that infrastructure construction could outpace actual productivity gains.
For Web3, this is important because cryptocurrency markets often amplify key narratives. When optimism for AI is strong, token AI and DePIN can become highly experimental expressions of the same theme, even when real use of the underlying token projects is limited.
Capital Flows: Why the Hype Extends to Token AI and DePIN
AI has absorbed a significant share of venture funding. In 2025, AI startups received about 41% of private equity funding, a sharp rise compared to 2021, with the majority of this capital concentrated in a small number of companies. Big technology companies, meanwhile, reported significant increases in spending on data centers, and memory suppliers reported sharp profit growth and shortages linked to demand for artificial intelligence.
This combination creates a familiar dynamic in cryptocurrency markets:
- Huge demand and visible in the real world For computing, storage and data
- The smaller, more volatile token floats That can move quickly on narrative stimuli
- Evaluation gaps Between common stocks priced based on dividends and tokens priced based on options
What can be considered a token for AI, DePIN and on-chain data?
Distinctive artificial intelligence
Tokens associated with AI services or infrastructure – such as decentralized inference, model markets, AI agents, or tokenized access to model outputs. The key question is whether the token captures value from real use or simply acts as a speculative shell.
DePIN (Decentralized Physical Infrastructure Networks)
Protocols that use token incentives to coordinate real-world resources such as GPU compute, storage, bandwidth, sensors, or power. AI-focused DePIN typically targets compute and storage bottlenecks.
Data on the chain
Data that is stored, referenced, authenticated on-chain, or verifiably linked via an oracle. In AI, this often includes source-based datasets, licensing records, market labeling, and verifiable inference outputs.
A practical bubble framework for AI-Web3 codes
To evaluate whether AI Bubble and Web3 Real in a particular segment, an organized checklist is helpful. A proper diagnosis examines economic pressures, industry pressures, revenue growth, valuation temperature, and financing quality. When applied to token AI, DePIN, and on-chain data, the picture often resembles a small bubble sitting on top of a real AI demand cycle.
- Economic pressure: Web3 AI is not of great importance yet. The pressure is showing up in crypto withdrawals more than broader economic indicators.
- Industry strain: Many tokens point to significant future adoption while current usage remains modest – a classic mismatch.
- Revenue growth: Mixed. Some protocols show early attraction of fees; Many of them reported close to zero revenue.
- Evaluation temperature: Tokens often trade at large multiples of annual revenues, or no revenues at all.
- Financing quality: Ranging from well-established cryptocurrency support to short-term retail flows and incentive farming.
Tokenized AI, DePIN, and on-chain data may be overpriced
1. Fully diluted extreme valuation relative to protocol revenue
One common overvaluation pattern is a fully diluted multi-billion dollar valuation coupled with low millions of dollars, or less, in annual protocol revenue. Even allowing for early-stage discretion, this gap is often wider than public markets can bear for AI leaders with consistent profits.
2. Narrative driven price action
Many AI tokens are clustered around broad AI titles – model releases, chip announcements, or “AI Series” marketing. When prices move more based on global AI news than on project-specific usage metrics, the risk of a bubble is high.
3. Low utilization versus theoretical capacity
In the DePIN calculation, the gap between “available GPUs” and “paid jobs executed” is large. If the network cannot consistently match demand with supply, token incentives risk creating idle capacity and inflated expectations.
4. Weak ditches and easy fork
Projects that operate as thin orchestration layers on off-chain providers, without defensible data, robust reputation systems, or meaningful integration depth, are fragile at high valuations. If users could switch providers with minimal friction, obtaining long-term token value would be uncertain.
5. Token emissions that dilute the fundamentals
High emissions can attract hardware and attention temporarily, but can also suppress sustainable token value if organic demand does not materialize before rewards diminish. Investors often underestimate how emissions for an effective valuation will change over time.
Some projects may be undervalued
Even in a frothy market, there can be a drop in value when a token is trading as a narrative asset but acting like production infrastructure.
1. DePIN with real, priced resources and recurring demand
Demand for AI infrastructure is structurally strong, with demand for data centers expected to grow approximately 19% to 22% per year through 2030. DePIN networks that provide reliable compute or storage with verifiable usage, competitive pricing, and consistent quality of service can become important suppliers. If markets price it as an AI experimental rather than infrastructure with potential cash flow, it may not be undervalued.
2. On-chain source for compliance and licensing
Companies are placing greater importance on training data sources, copyright integrity, and auditability. On-chain certification and licensing records could become more valuable as regulation and procurement processes require traceability. The tokens supporting these records may seem unremarkable today, which is precisely why they can be mispriced compared to long-term demand.
3. Transparent fee collection and controlled token economies
Some protocols build clear fee models, limit emissions, and publish metrics that allow analysts to track true adoption. In a sector where many valuations ignore cash flow-like signals, transparent value capture can be a source of structural undervaluation.
Real-world use cases: where value is created or destroyed
DePIN for AI compute and storage
- Decentralized GPU Markets: Inference and training functionality can be monetized and bottlenecks reduced, but reliability, scheduling, and compliance must be proven.
- Decentralized storage of datasets and model artifacts: Storage and retrieval fees can be charged, but must compete on integrity, performance, and enterprise-wide safeguards.
- Edge computing and sensor networks: Can create unique data flows that are difficult to replicate, potentially forming defensible moats.
Token AI services and model markets
- Typical markets: The value depends on whether the market provides aggregation, quality assurance and distribution – not just listings.
- Artificial intelligence agents as actors on the chain: Promising for automation in DeFi and payments, but most deployments remain early and experimental.
- AI-powered decentralized finance: Must demonstrate measurable improvements in performance and strong risk controls, not just the “internal AI” branding.
On-chain data and verifiable inference
- Data source and license records: Can support compatible training paths and creator compensation models.
- Labeling and validation markets: Requires strong resistance to divination and conflict resolution, otherwise incentives are misdirected.
- Verifiable inference and zkML: It can become critical for on-chain trusted agents and high-risk workflows where auditability is essential.
How professionals and businesses can evaluate prices in AI-Web3
For developers, analysts, and enterprise teams, the goal is to separate infrastructure-like networks from narrative-driven tokens.
Due diligence checklist
- Use: What percentage of compute, storage, or data capacity is paid for and used regularly?
- Revenue quality: Are the fees paid by external users, or are they incentives that are mostly recycled?
- Unit economy: Are service providers profitable at market prices without emissions subsidies?
- Capture the token value: Are fees accruing to token holders, shareholders or treasuries in a measurable way?
- trench: Is there a defensible data set, reputation layer, compliance posture, or integration feature?
- Emissions and mitigation: How does rolling supply evolve over 24 to 48 months?
- Compliance and Abuse Prevention: How does the network prevent illegal use of computing and meet enterprise requirements?
Professionals looking to build expertise in this field can explore Blockchain Council training across AI, Web3, DeFi, and blockchain security. Related programs include Certified Artificial Intelligence (AI) Expert, Certified Blockchain Developer, Certified Web3 Professional, and Certified DeFi Expert certifications.
Bottom line: Bubble signals are real, but mispricing works both ways
AI Bubble and Web3 The debate is best resolved through hashing out. AI infrastructure in public markets is expensive, but supported by revenue, capital spending, and measurable overall impact. In token markets, many AI and DePIN assets display recognizable bubble traits: extreme valuation multiples, narrative-driven rallies, low usage, and unclear moats.
However, the image is not completely foamy. DePIN networks with real usage and solid unit economics, coupled with on-chain data and provenance protocols in place for compliance-based ordering, can be undervalued when markets treat them as short-term narrative trades. The practical opportunity lies not so much in calling the entire sector a bubble, but in identifying the protocols that build the infrastructure that companies will actually pay for.
