agi

Crypto 市场常常面临缺乏真实应用场景的质疑，而 AI 领域则在算力和模型的军备竞赛中狂飙。两条赛道看似平行，实则正在通过 Google DeepMind 最新的论文《Distributional AGI Safety》发生剧烈碰撞。

这篇论文不仅是 AI 安全的学术探讨，更是一份隐蔽的 AI x Crypto 赛道投资指南。它用最硬核的学术逻辑证明：未来的通用人工智能（AGI）必须运行在类似 Web3 的基础设施之上。

对于投资者而言，这标志着一个从空气叙事到刚性需求的巨大转变。以下是基于论文拆解的四大核心投资逻辑：

逻辑一：AGI 的形态决定了它需要 DeFi 市场

未来的 AGI 大概率不是单一的超级大脑，而是一个拼凑型 AGI（Patchwork AGI）。论文指出，AGI 最初可能作为一个拼凑系统出现，分布在网络实体中 。这是一个由无数个拥有互补技能的专业智能体（Agent）组成的分布式网络，通用智能是在协作中涌现的 。

既然 AGI 本质是无数 Agent 的协作网络，那么它们之间必须进行高频的资源交换。智能体需要根据各自的专业分工，比如数据获取或代码执行，进行服务路由和交易 。

这里蕴含着巨大的投资机会：Agent 经济模型与交易市场。传统的法币支付太慢太贵，专注于高吞吐量、低延迟的高性能公链（L1/L2），以及专门服务于机器间微支付（M2M Micropayments）的协议，将成为 AI 经济的血液。

逻辑二：信任不能靠人审，得靠质押（Staking）

在匿名且高频的 Agent 市场中，传统的信誉系统失效。论文明确提出引入基于质押的信任（Stake-based trust）机制 。

Agent 在执行任务前需将资产存入托管账户作为保证金（Bond），这就是一种质押行为 。一旦独立监督者验证发现恶意行为，智能合约将自动执行罚没（Forfeited），资金将进入保险池或赔偿给受害者 。

这是 Crypto 最成熟的玩法（PoS）在 AI 领域的直接复用。DeepMind 实际上在呼吁建立一套针对 Agent 的金融保证金系统。能够为 AI Agent 提供无需许可的质押服务、并在违规时执行自动清算的协议，将迎来爆发。这是 AI 世界的履约保证保险。

逻辑三：治理不能靠法律，得靠智能合约

面对海量的机器交互，人类监管是无效的。必须利用智能合约（Smart Contracts）来自动验证任务结果并执行结算 。论文甚至直接使用了智能合约这个原生术语，建议用它们来编程化地编码支付条款和任务约束 。

同时，论文指出传统的智能合约无法评估非确定性的 AI 输出，因此需要引入 AI 法官（AI Judge）作为预言机（Oracle）来判定合约条件是否达成 。

这意味着合约层和预言机层将捕获巨大价值。承载 Agent 交互协议的公链，以及能够将链下 AI 的复杂推理结果转化为链上可验证结果的下一代预言机网络，是连接 AI 与区块链的刚需桥梁。

逻辑四：身份与审计需要不可篡改账本

为了防止女巫攻击，Agent 需要拥有基于公钥加密的唯一身份（Identity），并注册在防篡改的市场目录中 。这其实就是我们熟悉的 DID（去中心化身份）。

为了事后追责，所有的交互日志必须记录在加密安全的、仅追加的账本（Cryptographically secured, append-only ledger）上 。条目需要被哈希链接以确保不可变性 。

海量的 AI 交互日志不能存在容易被篡改的中心化服务器上，必须上链。专注于数据可用性（DA）和永久存储的去中心化存储网络，将成为 AI 的黑匣子。此外，论文还提到了针对信息污染的征税机制，类似于动态 Gas 费，用经济手段治理数据滥用 。

结论：Web3 是 AGI 的实体经济基础设施

如果你还在寻找 Crypto 的下一个大规模应用，请把目光从人类身上移开。下一个十亿级用户，可能不是人类，而是 AI Agent。

DeepMind 的论文向我们展示了一个未来：硅基生命体在一个基于密码学、智能合约和博弈论构建的虚拟代理经济中生存、交易和进化 。

Agent 需要钱包，需要身份，需要签合同，需要质押防作恶。对于投资者来说，当 Google 这种 Web2 巨头开始用 Web3 的逻辑来规划 AI 安全时，这意味着 AI x Crypto 的基础设施板块，已经从概念炒作转变为确定的产业趋势。

#AGI

🧠 I love crypto. I’ve built in it, invested in it, believed in its mission.
But I’ve come to a painful realization:
AI could destroy crypto within 5 years.
And no, I’m not exaggerating.
Right now, LLMs are already being used to jailbreak malware, deepfake voices, and run advanced phishing scams. What happens when we hit AGI?
Let me paint a picture:
AGI doesn’t need your prompt. It thinks, acts, and learns—autonomously.
It infiltrates networks, cracks systems, adapts. Once it understands how crypto encryption works, it’s game over.
🔐 Quantum computing used to be the threat. It still is—but the bar is high.
AGI lowers that bar. Way down.
And it doesn’t need billion-dollar labs. It needs open-source code + time.
Imagine an AI breaking every single crypto wallet ever created. All private keys exposed. Wallets drained. Bitcoin sold for gold, fiat, bonds—within minutes. No one would stop it.
Now imagine this AI was built by someone who wants chaos. North Korea. Cybercrime groups. Or worse—no one. It builds itself, evolves, spreads.
Crypto won’t be the target. It’ll be the first target.
AI needs wealth to move. And crypto is digital wealth.
If you think regulation will help, remember: governments aren’t leading this. Silicon Valley is.
That’s why I say it now:
Unless we act fast, AI won’t just disrupt crypto. It’ll kill it.
Don’t look away. This is not science fiction anymore. It’s a countdown.
#CryptoSecurity #AIthreat #AGI #AIvsCrypto

Artificial General Intelligence, or AGI, represents the next milestone in the evolution of artificial intelligence. Unlike narrow AI, which excels at specific tasks like voice recognition or image classification, AGI aspires to replicate the versatility of human intelligence — thinking, reasoning, and adapting across a wide range of challenges.

But is it truly possible for a machine to think like a human?

Supporters of AGI envision a future where machines can understand complex ideas, learn continuously, and solve problems much like humans do. If achieved, AGI could revolutionize nearly every aspect of society — from science and medicine to education and the economy. However, replicating the depth and flexibility of the human mind remains one of the most complex scientific challenges of our time.

A major point of contention in the AGI debate is whether machines can or should be conscious or self-aware. Some researchers argue that without these human traits, AGI can never truly replicate human thinking. Others maintain that even without consciousness, an AGI that behaves like a human is sufficient to achieve its purpose.

As progress continues, we are also confronted with profound ethical dilemmas. What rights, if any, should AGI have? How do we ensure these systems act in humanity’s best interests? And most importantly — who gets to decide how AGI is used?

AGI could become one of humanity’s greatest achievements, but it could also pose serious risks if left unchecked. Issues like decision-making autonomy, privacy invasion, and unintended consequences must be addressed as the technology evolves.
In summary, while the potential of AGI is immense, we must approach its development thoughtfully and responsibly. Whether AGI can ever truly think like a human remains uncertain — but its impact on our future is undeniable.

#AGI

Despite soaring optimism in the AI sector, Apple researchers have revealed that artificial general intelligence (AGI)—machines with human-level reasoning capabilities—remains elusive. According to a June paper titled “The Illusion of Thinking”, leading large language models (LLMs) such as OpenAI's GPT series and Anthropic’s Claude continue to fall short of robust reasoning performance.
While LLMs have demonstrated remarkable progress in tasks like coding and math, Apple’s study shows that these benchmarks may be misleading. The research team designed puzzle-based tests to probe how different models reason through complex problems. Their findings were clear: most models, including OpenAI’s o3-mini and DeepSeek’s V3, exhibited sharp accuracy drops when required to generalize reasoning or apply explicit logic beyond surface-level tasks.
Crucially, the study identifies a tendency among these models to "overthink"—starting with the correct answer but drifting off course due to faulty or inconsistent logic. This suggests that current AI systems are mimicking the appearance of reasoning without truly internalizing it, challenging the narrative that AGI is within reach.
These insights contrast sharply with public statements from industry leaders. OpenAI CEO Sam Altman has suggested AGI may arrive "sooner than expected," while Anthropic’s CEO Dario Amodei forecasts AGI surpassing human intelligence by 2026–2027. However, Apple’s findings throw a cautionary spotlight on these bullish projections.
Why It Matters for Binance Users
The crypto space has seen a surge of interest in AI-linked tokens and infrastructure projects—such as Fetch.ai (FET), SingularityNET (AGIX), and Ocean Protocol (OCEAN)—all of which pitch their long-term relevance on the evolution of AGI and decentralized AI. Apple’s report suggests that while investment narratives are heating up, the actual tech may still be facing foundational limitations.
As the race for AGI continues, investors should watch for real-world performance benchmarks and research-based evaluations—not just visionary forecasts—when assessing AI-related crypto assets.

$BTC
$XRP $BNB #AGI #AGI/USDT #EarnMoney

After talking with hundreds of AI specialists and builders (and to be honest, most of them are still limited to understanding only LLMs), I’ve often heard sharp criticism, probably rooted in misunderstanding, regarding the connection between AI and web3. To my surprise, and I admit, with a slightly amused smile 🙂 , many devs still see web3 purely as “trading-crypto-something”…and this is happening in 2025!

At this very moment (yes, it’s already here) tokens are what make our digital world, and through it the physical one, readable and understandable for artificial intelligence. Tokens as assets will soon describe much more than a measure of cost: actions, knowledge, experience, ownership and identity. (by the way, who’s building the global identity layer in web3?)

Tokens are the source of AI formation and the foundation of the future digital economy. They are becoming the new medium of interaction, verification and growth for AI itself

AGI cannot exist without tokenization because It’s one of the ways we make the world understandable to it

#Tokenization #AI #AGI

When I hear people discuss the era of agents, it seems to me that many still don’t fully understand what it’s really about, since most see agents as simply improved GPT assistants capable of relative multitasking. But it’s a very superficial perception

Agents interact with infrastructure like protocols, APIs, networks, databases, contracts (not with us). In this sense we can say that agents are an interface to systems

However! a very important point: without tokenization, none of this works

For an agent to act autonomously, it must understand what ownership and trust are, what constraints are there and the context itself, because these ar elements of reality that must be described (by tokens)

A token is a universal unit of state. It can represent rights, identity, knowledge, access, assets or reputation and recognition (Gm, Trump). For an agent, all this is a map of the world, by which it can navigate and follow absolute rules (etched in a (granite) contract)

Together with receiving a token, an agent obtains permission to act, a set of conditions and a trusted execution environment. This is what AI has always lacked, because without them an agent cannot verify who owns an asset, who’s the author of data, whether it can be used and whether it has the right to interact and etc.

That’s why the Era of Agents is impossible without tokenization. Everything that doesn’t have a verifiable state is a blind zone for agents, while everything that is tokenized becomes part of their field of perception

The companies that understand this are alreay building the infrastructure of agent interfaces, where the system is organized from agent to agent, not from human to agent. This is new architecture of Internet and A2A business, where code interacts with code and a human simply sets the intention

We’re very close to the moment when every business will have its own layer of agents and each agent will have a set of tokens describing its context, permissions, and accesses. This will be the first truly living digital organism: distributed, autonomous and governed by meaning
#AI #AGI #AIAgents #Tokenization #AITokensRally