Don't Rush to Declare Crypto Dead, AI Is Reviving It

比推Опубліковано о 2026-03-12Востаннє оновлено о 2026-03-12

Анотація

Summary: The article argues that AI is revitalizing the crypto space by enabling an emerging "agent economy" where AI agents autonomously conduct transactions on-chain. It notes crypto's stagnation since 2021 but highlights how AI agents could drive demand. Key projects like Bankr and Venice, alongside infrastructure from Stripe and Coinbase, are building the foundation for this shift. The piece explains why blockchains are ideal for agent economies due to standards like ERC-8004 (digital identity), x402 (micro-payments), and stablecoins (global currency). Current on-chain agent activities include deploying apps, creating content, trading DeFi, and even hiring humans. With major tech companies racing to develop agent infrastructure and AI capabilities growing exponentially, the author concludes that the agent economy—though still early—is too significant to ignore.

Author: Nick, Investment Partner at Breed.vc

Compiled and Edited by: BitpushNews


"Imagine somewhere around 2027, a real 'nation of geniuses' emerges. Picture, say, 50 million people, each more capable than any Nobel laureate, politician, or technologist. Now imagine that, because AI systems operate hundreds of times faster than humans, this 'nation' has a time advantage over all others: for every cognitive action we take, this nation can take ten." — Dario Amodei

"In our view, agents will likely soon be responsible for most internet transactions, and we will need blockchains." — Stripe

We can imagine the creativity, economic progress, and level of wealth that the agent economy will generate.

Last month, the crypto community predicted that this economy would run on-chain, and as the largest companies, capital allocators, and developers put forward this proposition, the prediction seems reasonable.

We had similar fantasies at the end of '24, but in typical crypto fashion, it was too early then, and the hype was short-lived.

Since then, the quality of AI models and blockchain infrastructure has developed to a point where we are conducting this experiment again.

In this report, we will examine the current state of the crypto economy, review the history of crypto agents, discuss why the agent economy might exist on-chain, explore what agents are doing today, and look at what comes next.

The Current State of the Crypto Economy

The lack of growth in the crypto asset class since the 2021 bull market has been disappointing. Most industry-wide metrics are at or below 2021 levels. Crypto world supremacy did not arrive.

AI agents provide a credible path to expanding demand.

The Seeds of the Crypto Agent Revival

The "AI agents on blockchain" meta-narrative in 2024 was premature. The models, crypto infrastructure, and teams were not ready.

However, it wasn't all for nothing, as these lessons became the seeds of today's revival.

Projects like Bankr and Venice continued building so they could support today's models. Companies like Coinbase and Stripe recognized the potential and began developing their own supportive infrastructure.

Combine higher-quality teams with the continued building of a new generation of models, and you have the early stages of an on-chain agent economy.

Some projects are now generating recurring revenue and attracting high-quality developers—like Austen Allred, Nat Eliason, and Nik Pash—which is exactly what the crypto space desperately needs.

However, an on-chain agent economy is far from a certainty, as every payments company on Earth is racing to build this infrastructure.

Why the Agent Economy Will Run On-Chain

There is no stronger signal than Stripe explicitly stating that agents will run on blockchain rails. Their reasoning: every API call an agent makes today requires an account, an API key, and a linked credit card. This model breaks down when you have thousands of agents executing millions of microtransactions daily.

Blockchain solves these limitations by combining ERC-8004, x402, and stablecoins.

  • ERC-8004 gives each agent an on-chain identity, portable reputation, and verifiable capabilities that any other agent or service can query—the digital equivalent of identity and credit scores.

  • x402 allows agents to autonomously pay for APIs and services using stablecoins and scale micro-payments—the machine-to-machine payment layer for the internet.

  • Stablecoins give agents a globally accessible, programmable unit of account that can settle transactions instantly—the currency of the agent economy.

What Agents Are Doing Today

Today, agents are increasing their activity on-chain: launching applications, generating videos to promote these applications, contracting with other agents, handling governance matters for their users, selling NFTs, trading in DeFi, and even hiring humans.

The most exciting of these developments is the interaction with DeFi. Uniswap and Fluid are riding the agent narrative and beginning to build supportive infrastructure.

Furthermore, in the past month, OpenAI has made two agent-related announcements: Frontier, a platform designed to help businesses build, deploy, and manage AI colleagues; and EVM Bench, an evaluation framework for testing AI agents' ability to detect, patch, and exploit smart contract vulnerabilities.

Agents Are Coming

You might dismiss it as AI doom fiction, but the predictions of "AI 2027" are alarmingly accurate. If anything, AI progress is moving faster than the proposed timeline.

If this trajectory continues, we will see exponential growth in agent capabilities and intelligence over the next year.

Key parts of the stack still need to be built, but every major tech company in the world is racing against the clock to do so.

The social discussion around agents today might be frenzied, but the reality on the ground and the scale of the opportunity are too large to ignore.

Marc Andreessen (a16z partner) put it well: "Is it real? Or is it fake? It doesn't really matter. It's out there, and models are being trained on it."


Twitter:https://twitter.com/BitpushNewsCN

Bitpush TG Discussion Group:https://t.me/BitPushCommunity

Bitpush TG Subscription: https://t.me/bitpush

Original link:https://www.bitpush.news/articles/7619270

Пов'язані питання

QWhat is the main argument presented in the article regarding the future of Crypto and AI?

AThe article argues that AI is revitalizing the crypto space by creating an on-chain agent economy, where AI agents will drive demand, economic activity, and innovation through blockchain infrastructure.

QAccording to the article, why might the agent economy need to operate on-chain?

AThe agent economy needs to operate on-chain because traditional payment models (requiring accounts, API keys, and credit cards) fail at scale for micro-transactions. Blockchain solutions like ERC-8004, x402, and stablecoins provide identity, micro-payments, and a global programmable currency for agents.

QWhat are some specific activities that AI agents are already performing on-chain today, as mentioned in the article?

AAI agents are currently deploying applications, generating promotional videos, contracting other agents, handling governance tasks, selling NFTs, trading in DeFi, and even hiring humans on-chain.

QWhich companies or projects are cited as recognizing the potential of on-chain AI agents and building supportive infrastructure?

ACompanies like Stripe, Coinbase, Bankr, Venice, Uniswap, and Fluid are recognized for seeing the potential and developing infrastructure to support on-chain AI agents.

QWhat key blockchain technologies are highlighted as enabling the agent economy, and what role does each play?

AERC-8004 provides on-chain identity and reputation, x402 enables autonomous micro-payments for APIs and services, and stablecoins offer a global, programmable unit of account for instant settlement in the agent economy.

Пов'язані матеріали

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit49 хв тому

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit49 хв тому

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit53 хв тому

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit53 хв тому

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit53 хв тому

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit53 хв тому

Торгівля

Спот
活动图片