L'essor de l'intelligence artificielle décentralisée : comment le rapport financier du premier trimestre de Nvidia confirme-t-il la tendance de fusion entre IA et cryptographie ?

Local time May 20, Nvidia announced its financial results for the first quarter of fiscal year 2027, ending April 26, 2026, delivering a comprehensive and better-than-expected performance. Revenue for the quarter reached $81.615 billion, up 85% year-over-year, and increased 20% quarter-over-quarter, setting a new company quarterly revenue record. GAAP net profit was $58.321 billion, a 211% increase year-over-year, and up 36% quarter-over-quarter.

But the core driver of the outperformance remains the data center business. This segment achieved $75.2 billion in revenue for the quarter, up 92% year-over-year and 21% quarter-over-quarter, accounting for over 92% of total revenue. Specifically, approximately $38 billion came from hyperscale data center operators, representing over half of total data center revenue; the remaining 50% came from AI cloud, industrial clients, enterprise deployments, sovereign AI, and other diverse channels.

The structural driver behind this performance is the global wave of infrastructure expansion—accelerated construction of AI compute factories—representing the largest infrastructure build in human history. Nvidia CEO Jensen Huang explicitly stated during the earnings call that the era of intelligent AI has arrived, with relevant technologies already deployed in real business applications and creating value. Notably, Nvidia’s forward-looking guidance remains strong, with Q2 revenue projected at $91 billion, significantly exceeding market expectations of $87.3 billion, indicating that this compute-driven growth trend is still accelerating.

Explosion in AI compute power: how does it spill over into decentralized infrastructure in the crypto industry?

As hyperscale data centers invest over $1 trillion annually in capital expenditures to build centralized AI compute clusters, a neglected but crucial trend is unfolding: the supply and demand structure of AI compute is fostering a new paradigm of decentralized infrastructure. Traditional tech giants’ centralized AI training models face a massive “silicon ceiling”—the cost of training modern large language models has become so high that ordinary developers and enterprises can hardly afford it.

This structural contradiction provides a clear entry point for decentralized computing networks. Take Render Network as an example: it has successfully transitioned from a professional CGI rendering platform to an essential infrastructure provider for AI startups, with a market cap of about $5.1 billion. Its core mechanism tokenizes the compute cycles of high-end GPUs, allowing developers to access decentralized compute resources on demand without bearing huge capital costs, effectively breaking the traditional cloud service providers’ centralized pricing barriers.

Bittensor represents another technological path—the tokenization of decentralized intelligent models. In this network, machine learning models compete and collaborate on a peer-to-peer basis, with nodes rewarded in TAO tokens based on the objective value their models provide to the network, forming a competitive meritocracy incentive mechanism. As of April 2026, Bittensor’s market cap remains the leader in this space, surpassing $4.2 billion.

What are the technological and governance evolutions in the decentralized AI (DeAI) track?

In early 2026, the DeAI track entered a critical phase of simultaneous technological performance and governance model evolution. On the technical architecture front, the Zero Gravity (0G) project proposed a groundbreaking solution that fundamentally addressed the historic challenge of running large-scale AI models in Web3 environments through disruptive low-level design, achieving comprehensive GPU-level optimization. 0G also launched the “Gravity Foundation 2026” special fund to support the development of DeAI inference frameworks and data crowdfunding platforms.

On the other hand, governance issues have become the most contentious point in DeAI. In April 2026, a serious internal governance crisis emerged within the Bittensor ecosystem—one of its top development teams, Covenant AI, suddenly announced its withdrawal from the network. After successfully training a large model with 720 billion parameters in a decentralized environment, network validators cut off token rewards to that subnet, causing the token price to plummet 15% to 25% in a single day.

This incident reveals a deeper insight: in the AI field with highly concentrated compute capital, there may be a significant gap between the proclaimed “decentralized governance” of token economics and the actual power structure. If early investors and foundations control key validator nodes, the actual control of the network remains highly centralized—founders may be both rule-makers and ultimate arbiters. This raises a critical question for the entire DeAI track: how to establish a truly verifiable, auditable, and anti-monopoly decentralized governance framework?

From concept to execution: how does crypto economy become the “operating system” for AI Agents?

2026 is becoming a pivotal year where AI and crypto industries deeply intersect. If 2025’s market focus was on AI tokens, decentralized compute, and concept coins’ speculative value, 2026’s narrative has fundamentally shifted—projects no longer just discuss “how AI will change crypto,” but start embedding AI Agents directly into wallets, exchanges, payment protocols, and on-chain execution workflows.

Significant events are happening intensively: in February 2026, Uniswap released 7 Agent Skills, enabling structured on-chain function calls by AI. In April, several leading wallets and public chains launched dedicated wallet frameworks and open payment protocols designed for AI Agents, covering quoting, negotiation, escrow, settlement, and dispute resolution. These technological deployments mark the transition of AI Agents from concept validation to actual operational and payment-capable execution layers.

Ethereum Foundation established a decentralized AI team as early as September 2025. Vitalik Buterin published a systematic AI strategic framework in early 2026, proposing that Ethereum should become the “trust layer” of the AI world—providing verifiable identities, secure payment channels, reputation records, and programmable economic relationships for AI Agents. This vision is guiding the industry: when AI Agents need identities, payments, and verification, blockchain could become their underlying operating system.

Core disputes and controversies in AI + crypto: what risks deserve cautious assessment?

Rapid development of any emerging track involves deep disagreements and controversies, and “AI + crypto” is no exception. Currently, at least three core disputes warrant ongoing attention.

First, the governance paradox in DeAI is repeatedly validated. The internal conflict event in Bittensor exposed the fragility of tokenomics under high-stakes competition—when compute contributors discover that token distribution can be dominated by a few validators, “decentralization” may regress into a centralized power structure cloaked in decentralization. This is not unique to Bittensor but a systemic risk in DeAI.

Second, the trustworthiness and black-box challenges of inference layers remain severe. How decentralized AI networks verify that on-chain large model inference results are genuine and unaltered is still an unsolved technical challenge. Various zero-knowledge proof (ZK) schemes and verifiable computation frameworks are actively explored, but large-scale commercial application is still distant.

Third, the alignment of tokenomics with real business value is problematic. Some DeAI projects’ valuations are driven more by narrative expectations than by verifiable user adoption and revenue data. Investors should carefully distinguish between projects with real business deployment and those relying solely on conceptual narratives.

How do institutions like Raoul Pal view the long-term logic of “AI + crypto”?

Against Nvidia’s confirmation of ongoing AI compute expansion, institutional investors are forming a more systematic understanding of the “AI + crypto” fusion’s long-term logic. In a deep analysis published in May 2026, Raoul Pal, founder of Real Vision, states that humanity is entering an “exponential era,” where AI, cryptocurrencies, and tokenization technologies are rapidly merging into a new foundational layer of the global economy.

Pal’s core framework offers three long-term investment perspectives. First, he emphasizes that for the first time in crypto, ordinary investors can own the infrastructure of the future economy before institutional entry—meaning, in this exponential era, the “ownership layer” itself may hold broader investment value than simply holding certain AI concept tokens.

Second, Pal predicts that the overall size of the crypto market could grow from about $2.7 trillion today to $100 trillion within the next decade. This forecast is not about the valuation of individual projects but reflects confidence in the long-term growth logic of “AI-driven + blockchain infrastructure.”

Third, Pal personally experiences how AI tools have significantly improved daily productivity—reducing tasks that once took days to just hours. This suggests that when evaluating “AI + crypto” assets, investors should focus not only on short-term price fluctuations but also on how technology fundamentally changes productivity and efficiency.

Risk warning: The “AI + crypto” fusion track is still in early stages, with high uncertainty in technology maturity, governance, and regulation. Token price volatility may far exceed traditional assets; investors should conduct cautious risk assessments based on their own risk tolerance.

How will Ethereum become the “trust layer” and settlement foundation in the AI Agent era?

If decentralized compute and AI Agents are the front end of “AI + crypto,” Ethereum is attempting to become the back-end “trust layer” and settlement infrastructure. In a systematic AI strategy framework published in early 2026, Vitalik Buterin explicitly states that Ethereum should not be viewed as a competing “alternative technology path” to AI, but as a foundational layer enabling AI to operate in a verifiable, auditable, and decentralized environment.

This framework includes four pillars: trusted AI interaction tools, economic coordination layers for AI, AI as an interface for Web3, and AI-augmented governance systems. Practically, Vitalik himself has run a 35-billion-parameter open-source large model on a local device equipped with NVIDIA 5090 GPUs, attempting to free AI inference from reliance on cloud giants.

Meanwhile, protocols and standards for AI Agent identity, payments, and execution are being launched on Ethereum mainnet, accelerating the deployment of this technical framework. For investors interested in “AI + crypto,” these underlying protocols and standards’ evolution in the Ethereum ecosystem are key indicators of the long-term value of this track.

Summary

Nvidia’s strong Q1 FY2027 revenue of $81.6 billion not only consolidates AI as a core narrative in global capital markets but also sends a clear signal to the crypto industry: the continuous expansion of AI compute power is driving large-scale development of decentralized compute networks and AI Agent infrastructure from the supply side. From massive data center investments, to commercialization of decentralized compute networks, to the technical capabilities of autonomous on-chain economic activities by AI Agents, a transmission chain from “AI compute supply” to “crypto infrastructure demand” is gradually forming. Meanwhile, issues of governance effectiveness, technological maturity, and validation cycles remain key variables in the industry’s evolution. For investors, understanding the technical logic and risk boundaries, and focusing on projects with real infrastructure value and ongoing network effects, may be the rational approach to grasping this long-term trend.

FAQ

Q: How does Nvidia’s outperformance directly impact the crypto industry?

Nvidia’s Q1 revenue of $81.6 billion, with data center growth of 92% YoY, reflects explosive global demand for AI compute. This indirectly boosts attention to decentralized AI tracks, including infrastructure for decentralized compute networks and AI Agents.

Q: What are the main technical challenges facing decentralized AI (DeAI)?

Key challenges include: efficient operation of large-scale AI models in decentralized networks, verifiable and trustworthy inference results, and incentive and governance mechanisms for compute contributors.

Q: What role does Ethereum play in the AI Agent era?

Ethereum Foundation has established a dedicated decentralized AI team, and Vitalik Buterin has proposed that Ethereum should become the “trust layer” of the AI world—providing verifiable identities, secure payment channels, reputation records, and smart contract environments for AI Agents.

Q: What key metrics should be monitored when evaluating “AI + crypto” projects?

Focus on whether the project has genuine technological breakthroughs or infrastructure value, the degree of true decentralization in governance, whether tokenomics can effectively close the loop with real business revenue, and the team’s ongoing development capacity.

Q: What is Raoul Pal’s long-term view on AI + crypto?

Raoul Pal believes AI and blockchain are merging into a new foundational layer of the global economy, predicting the crypto market could grow from about $2.7 trillion today to $100 trillion in the next decade. He emphasizes that crypto can serve as an “ownership layer,” enabling ordinary investors to benefit from infrastructure development before institutional participation.