AI Bubble Underway? The Silent Impact on Web3

Source: CritpoTendencia Original Title: AI Bubble Underway? The Silent Impact on Web3 Original Link: Economic history is marked by cycles of overwhelming enthusiasm that end in abrupt corrections: the railway fever in the 19th century, the dot-com bubble in the 1990s, the ICOs of 2017, or the NFT boom in 2021.

Today, artificial intelligence (AI) seems to occupy that place of infinite promise. Multi-billion dollar investments in infrastructure, the discourse of unlimited productivity, and the narrative of total disruption have created a climate of euphoria that many analysts already compare to a financial bubble in the making.

This analysis explores how that bubble is not limited to the traditional tech sector but extends to Web3, amplifying risks and creating new forms of vulnerability.

The central thesis is clear: the disconnect between physical costs, auditable metrics, and expectations of perpetual growth in AI could drag decentralized ecosystems along with it— from the tokenization of resources to algorithmic governance in DeFi.

Anatomy of an AI Financial Bubble

Bubbles are born from a combination of narrative, liquidity, and excessive expectations. In the case of AI, three factors stand out:

1. Irrational exuberance: Investment funds and corporations are allocating billions to data centers and training clusters without clear return metrics. It is assumed that demand for computing will grow indefinitely.

2. Creative accounting: Many companies capitalize training expenses as assets, inflating balance sheets and hiding the real cost pressures. Energy subsidies and tax credits reinforce this illusion of profitability.

3. Eternal growth narrative: It’s promised that AI will transform every sector, from healthcare to finance, but tangible results remain limited and highly concentrated in a few applications.

The combination of these elements creates fertile ground for a bubble: stock prices and private valuations sustained more by expectations than fundamentals.

Energy and Hardware: The Achilles’ Heel

The infrastructure supporting AI is energy-intensive and relies on specialized hardware.

Energy dependence: Computing centers consume growing volumes of electricity, straining local grids and raising costs. In some countries, AI-related demand already competes with traditional industrial sectors.

Semiconductor bottlenecks: The shortage of high-performance chips (GPU, TPU) drives up prices and encourages speculation. Companies and governments compete to secure supply, further inflating value expectations.

Impact on Web3: The tokenization of computational and energy resources, promoted as a decentralized solution, becomes more volatile. Secondary markets for computing rights replicate financial derivative dynamics, amplifying risks.

In this context, Web3 not only mirrors the bubble but multiplies it by turning physical resources into tradable digital assets.

Web3 as Mirror and Amplifier

The narrative of decentralization and infinite liquidity in Web3 intertwines with the AI craze.

• Tokenization of computing power: Assets representing access to AI infrastructure are created. Without clear metrics, the risk of double counting and overvaluation emerges.
• Artificial liquidity: Markets for usage rights function like derivatives that create apparent liquidity, which can evaporate in a correction.
• Multiplier effect: The AI narrative integrates into Web3 as a democratizing promise, but in practice can amplify systemic risks by replicating speculative dynamics.

Thus, Web3 risks becoming a mirror that not only reflects the AI bubble but intensifies it.

Algorithmic Governance and Risk Synchronization

The integration of AI into DeFi protocols introduces a new type of vulnerability: algorithmic synchronization.

• Algorithmic crowds: AI agents tend to converge on similar strategies, reducing decision diversity.
• Amplified volatility: When all models react similarly, co-movements intensify and downturns become more abrupt.
• Implication for Web3: Protocols relying on AI to manage liquidity or risk may suffer faster and deeper financial contagion.

The promise of algorithmic efficiency thus transforms into systemic risk when decision homogeneity erodes market resilience.

Regulatory and Accounting Gaps

The lack of clear standards for AI model auditing and tokenized asset accounting creates opacity.

• Nonexistent auditable metrics: There is no consensus on how to measure robustness, drift, or security of AI models applied to finance.
• Legal risk: The use of data in training and automated decisions can lead to mass litigation.
• Impact on Web3: Protocols integrating AI without transparent auditing may face regulatory sanctions and loss of trust.

The lack of comparable metrics keeps valuations inflated and hides risks that could surface suddenly.

Possible Scenarios for Web3

The interaction between the AI bubble and Web3 could lead to different scenarios:

• Optimistic: Cost pressures drive innovation in energy efficiency and decentralized governance, strengthening the ecosystem.
• Pessimistic: The collapse of AI valuations drags down tokenized markets, DeFi liquidity, and trust in decentralization.
• Intermediate: Gradual adjustment with consolidation of players, purging of inflated narratives, and establishment of more realistic metrics.

The outcome will depend on Web3’s ability to distinguish between narrative and fundamentals, and to establish robust mechanisms for transparency and auditing.

Deflating the Bubble?

The AI financial bubble is not an isolated phenomenon: its shockwaves reach Web3 and threaten to amplify systemic risks. The disconnect between physical costs, auditable metrics, and expectations of perpetual growth could drag decentralized ecosystems down with it.

The roadmap to mitigate risks is clear:

• Develop audit metrics for AI models applied to finance.
• Increase transparency of energy and infrastructure costs.
• Design algorithmic governance that preserves strategy diversity.
• Incorporate legal risk premiums into tokenized valuations.

Web3 can be either a victim or a catalyst for resilience. It will all depend on how it faces the AI bubble: if it just replicates inflated narratives, it will be dragged down; if it manages to establish standards for transparency and efficiency, it could become a space for sustainable innovation.