AutonomysNetwork

Dear #BinanceSquareFamily readers, as someone who works as #SocialMining at @DAO Labs , today we will take a look at the interview conducted by Dr. Chen Feng, Head of Research at #AutonomysNetwork . The market is trying to move with $BTC , $BNB , $XRP price movements, #TrumpVsMusk discussions, #CUDISBinanceTGE news. It is always good for you to read different articles.
⭐ When we look at the visual, we see that 2 people working for Autonomys have similar thoughts. I shared my content about the CEO's article the other day. Today, according to Dr. Chen Feng, Associate Professor at the University of British Columbia and Head of Research at Autonomys, I will talk about TEEs (Trusted Execution Environments). Do TEEs really form the basis of Secret Artificial Intelligence? Today, artificial intelligence and many issues related to it are rapidly gaining strength. However, this power also challenges confidentiality and trust. According to Dr. Chen Feng, it is necessary to build castles to solve problems. In other words, it is necessary to use Trusted Execution Environments (TEE).
👉 What is TEE and Why is it Different?
Trusted Execution Environments (TEE) refer to hardware isolated, secure processing environments. They can protect the confidentiality and integrity of data and code even in a hostile system. In Dr. Feng's words, TEEs are "like castles we build in an untrusted area."
When comparing TEEs with other privacy technologies, ZKP (Zero-Knowledge Proofs) is strong but computationally expensive. MPC (Multi-Party Computation) is effective but has problems in terms of coordination and latency. FHE (Fully Homomorphic Encryption) is not yet widely used in terms of performance. According to Feng, “If we wait for cryptographic solutions to mature, we may have to wait until the end of the century.” TEEs are working today.
👉 So Why Does Autonomys Network Use TEEs?
Autonomys is a project positioned at the intersection of artificial intelligence and Web3. Its mission is to establish a more secure, decentralized and fair AI infrastructure. In this direction, they state that TEEs meet two main needs at the same time:
📌 Trust layer for Web3: TEEs have an ideal structure for decentralized trust mechanisms.
📌 Privacy for AI: Model architectures, user data, and inferences must remain confidential
Feng says that trust starts with confidentiality and that the most mature solution for this today is TEEs.

🤖 The Age of AI Agents and Scalable Privacy
In the future, not only humans but also billions of AI agents will take their place as users in blockchain networks. The need for privacy for these agents will become as critical as humans. Feng adds, “If AI agents are going to be users, they deserve privacy too.” His solution proposal on this issue argues that instead of giving each agent a separate TEE, designing an architecture that manages privacy at the infrastructure level is the solution. Feng’s big vision is a secret, decentralized, and fair superintelligence. He has another emphasis on this subject. “If only two or three companies control artificial superintelligence, that’s dangerous,” he says. They share the same thoughts as the CEO of Autonomys.
✍️ Finally, Dr. Feng believes that they can build a better AI future. He foresees that these goals will be achieved by thinking big and starting today.

There’s so much buzz these days about #AI , privacy and decentralization that it’s easy to get lost in jargon. That’s why when I heard Dr. Chen Feng—Associate Professor at UBC and Head of Research at AutonomysNetwork —on the Spilling the TEE podcast, I sat up straight. As a social miner from @DAO Labs ’ Autonomys HUB, I’ve been following our sector closely, and Dr. Feng’s take on Trusted Execution Environments and #ConfidentialAI feels absolutely pivotal for anyone building in Web3 and AI.
Dr. Feng summed it up beautifully in one line that really stuck with me:

He likens TEEs to castles. Imagine running your AI code and your data inside a fortress you trust, even though it’s sitting on someone else’s computer. That’s what TEEs do. They carve out secure, isolated zones so your models can compute privately and with integrity. In a network where compute is scattered across machines you don’t control, that guarantee matters more than ever.
You might have heard of other privacy technologies—zero knowledge proofs, multi party computation, fully homomorphic encryption. They offer fascinating theoretical guarantees, but they aren’t ready for today’s real-world AI workloads. Dr. Feng makes the point that if we wait for those methods to catch up, we could be waiting a very long time. TEEs on the other hand deliver practical privacy with as little as five percent overhead. That’s fast enough to run GPU-intensive tasks without breaking a sweat.
To really grasp why TEEs matter, here are the key reasons they stand out today:

This is exactly why #AutonomysNetwork built TEEs deeply into its infrastructure. Without privacy, AI can’t be trusted. Without trust, it can’t scale. We’re creating a privacy-first environment where models and data stay protected at every step.
Perhaps the most exciting part is what comes next: billions of AI agents acting on-chain, negotiating, transacting, and helping us automate complex tasks.
But scaling privacy for billions of AI agents brings a whole new set of challenges — here’s why it matters so much:

If AI agents are going to be treated like users in our networks, they deserve the same confidentiality we demand for ourselves. But scaling privacy for billions of agents is no small feat. The solution Dr. Feng proposes is to combine TEEs with powerful GPUs such as NVIDIA’s H100 and to assign secure environments at the application level rather than to each individual agent. This keeps the system efficient, prevents bottlenecks, and still protects every agent’s data.
These ideas are already being tested in the real world. Dr. Feng shared a pilot project in British Columbia, where twenty percent of residents lack a family doctor. This project uses decentralized AI doctors powered by TEEs and on-chain models to help fill that gap. The goal isn’t to replace human physicians but to prove the technology can deliver privacy, accessibility, and affordability before addressing regulatory hurdles.

The bigger picture Dr. Feng painted is equally urgent. If superintelligence is controlled by only a handful of companies, that concentration of power poses immense risk. We need decentralized alternatives built on open-source foundations, Web3 incentive models and, crucially, TEEs to provide a trust layer. As Dr. Feng said, building a better AI future that is private, decentralized and fair depends on the choices we make and the work we start today.
This is exactly what Autonomys is building — and why I’m proud to contribute through the DAO Labs SocialMining Autonomys HUB. I’m sharing this because it matters—for every builder, researcher and enthusiast in our space. #BinanceAlpha $BNB

Away from all of the #TrumpVsMusk headlines, I found myself drawn to a quieter yet equally pivotal conversation. As someone #SocialMining with @DAO Labs , I was especially intrigued by Dr. Chen Feng, the Head of Research at #AutonomysNetwork insights on the Spilling the TEE podcast, where he explored how Trusted Execution Environments (TEEs) could be the bedrock of a safe, decentralized AI future.

Castles in the Cloud: What Are TEEs?
Dr. Feng paints TEEs as castles in hostile territory, secure enclaves that protect code and data even when the surrounding system can’t be trusted. “If you want to understand TEEs,” he says, “ask what problem they solve. It’s about running software on someone else’s computer, with guarantees.” This metaphor brings to life how TEEs isolate sensitive operations from prying eyes, ensuring confidentiality and integrity.
Yet TEEs face challenges of their own. Hardware dependencies, limited memory, and potential side-channel attacks mean these “castles” aren’t impregnable. Dr. Feng acknowledges, “TEEs aren’t perfect, but they’re the most mature answer we have today.”

TEEs vs. ZKP, MPC, FHE
While Zero-Knowledge Proofs, Multi-Party Computation, and Fully Homomorphic Encryption promise mathematically airtight privacy, they remain orders of magnitude slower. TEEs, by contrast, impose as little as 5% overhead on GPU-intensive AI tasks, enough to deliver real-world performance today.
Defining Confidential AI
Confidential AI means data and model logic remain hidden during execution. For Dr. Feng, it’s non-negotiable: “Without privacy, AI can’t be trusted. Without trust, it can’t scale.” TEEs enable this by ensuring that sensitive inputs and proprietary algorithms never leave their secure enclave.

Autonomys Network: Building on TEEs
Autonomys’ mission is to create a privacy-first, decentralized infrastructure for intelligent agents, and TEEs are central to that vision:
Why TEEs? They deliver “trust without centralization,” aligning with Web3’s ethos of distributing power rather than concentrating it.
Autonomys believes that by pairing TEEs with decentralized coordination tools, assigning TEEs to app operators rather than to each individual agent, they can deliver truly trustworthy and high-performance AI at the scale of billions of agents without any bottlenecks.
For Autonomys, privacy is the very foundation of its vision: every AI user deserves confidentiality, and “if I share my data, I take a risk. That risk should be rewarded. That’s the promise of Web3,” as Dr. Feng reminds us..

Dr. Feng’s insights resonate deeply in today’s crypto landscape, where projects like $BTC , $PEPE and $SOL trending on Binance and leveraging innovative blockchain solutions to support the decentralized AI revolution. Many thanks to DAO Labs for giving us a voice, let’s start building those secure foundations today. And on this note, I will end this article with Dr. Feng’s call to action “We can build a better AI future. One that’s private, decentralized, and fair. But only if we start today.”
#crypto #AI

In a world where AI agents are becoming smarter, faster, and more independent, the question of trust is no longer philosophical—it’s infrastructural. At the heart of this conversation is Dr. Chen Feng, Head of Research at Autonomys Network and a Professor at the University of British Columbia.
In his recent podcast appearance on Spilling the TEE, Dr. Feng painted a compelling vision for the future of Confidential AI, with a powerful metaphor: Trusted Execution Environments (TEEs) are the castles of digital trust—hardware fortresses that secure AI logic and data from prying eyes.

🧠 What are TEEs—and Why Do They Matter?
Trusted Execution Environments (TEEs) are secure zones within a processor that isolate and protect computations. Unlike cryptographic methods such as Zero-Knowledge Proofs (ZKPs), Multi-Party Computation (MPC), or Fully Homomorphic Encryption (FHE)—which often come with performance trade-offs—TEEs offer near-native execution speeds with minimal overhead.
As Dr. Feng explains:
> "ZKPs and MPC are great for specific use cases, but when we want AI agents to act in real time—on the edge, in the wild—TEEs give us both performance and confidentiality."
That’s why #AutonomysNetwork integrates TEEs at the core of its decentralized AI infrastructure. The goal? To enable AI agents that are autonomous, privacy-preserving, and verifiably secure.

🔐 Confidentiality is Non-Negotiable
In the emerging agent-driven Web3 world, data isn’t just valuable—it’s volatile. Dr. Feng stresses that privacy isn't a nice-to-have—it’s a requirement.
> “Without confidentiality, autonomy is just a façade,” he notes. “TEEs provide the hardware root of trust to make autonomy real.”
With TEEs, AI agents can process sensitive data—such as financial inputs, personal identifiers, or proprietary models—without leaking it or becoming vulnerable to tampering. That’s the kind of assurance the decentralized future demands.

🔎 Solving the Oracle Problem with Verifiability
Another key insight from Dr. Feng? TEEs help solve the Oracle Problem—how to trust that AI outputs aren’t manipulated. TEEs support remote attestation, allowing anyone to verify that code executed securely, without interference.
This verifiability builds the foundation for agent accountability—a concept as crucial to Web3 as decentralization itself.

🌐 A Word on Engagement: What's Trending?
While we build the foundation for trusted AI with TEEs, the broader Web3 space is also buzzing. To capture the energy and eyes of the crypto community, here are a few trending tokens you might want to watch:
Solaxy ($SOLX) – A Solana-based project offering high-speed Layer 2 functionality and strong staking appeal.
Mind of $PEPE – A meme-meets-AI coin with a vibrant community and cultural momentum.
$DOGE – Making noise for its scalability focus and rapidly growing ecosystem.
Best Wallet ($BEST) – Reinventing the crypto wallet experience with social and utility-based features.
These projects reflect the dynamic innovation happening alongside the core infrastructure developments that leaders like Autonomys are driving forward.

✊ As a proud Social Miner at the AutonomysNet Social Mining hub powered by @DAO Labs ...
…I’m inspired by the depth of Dr. Feng’s vision. This isn’t just another Web3 trend—it’s the future of how we trust machines. TEEs, as the backbone of Confidential AI, are helping usher in a new era where intelligent agents can act independently and securely—without compromising the user or the system.

🧩 Final Thought: Building Castles in Code
In the age of autonomous agents, trust is everything—and Dr. Chen Feng’s castles of trust aren’t just a metaphor. They’re real, they’re functional, and they’re already being built at Autonomys.
The future belongs to systems that don’t just compute, but compute confidentially, verifiably, and autonomously.
Welcome to the age of ConfidentialAI. 🧠🔐
#ConfidentialAI #AI3 #Web3 #SocialMining

In an era where artificial intelligence (AI) is becoming increasingly autonomous, the question of trust looms large. How can we ensure that #AI agents act in our best interests, especially when they operate independently? Dr. Chen Feng, Head of Research at #AutonomysNetwork and a professor at the University of British Columbia, offers a compelling answer: Trusted Execution Environments (TEEs).
The Castle Metaphor: Understanding TEEs
Dr. Feng likens TEEs to "castles"—secure enclaves within a processor that protect sensitive data and computations from external threats. Just as a castle safeguards its inhabitants from outside dangers, TEEs shield AI processes from potential breaches, ensuring confidentiality and integrity.
Unlike other privacy-preserving technologies such as Zero-Knowledge Proofs (ZKPs), Fully Homomorphic Encryption (FHE), and Multi-Party Computation (MPC), which rely heavily on complex cryptographic methods, TEEs provide a hardware-based solution. This approach offers a balance between security and performance, making it particularly suitable for real-time AI applications.

TEEs vs. Other Privacy Technologies
While ZKPs, FHE, and MPC have their merits, they often come with significant computational overhead and complexity. TEEs, on the other hand, offer near-native performance with minimal overhead—typically just 5-15% compared to non-secure execution environments . This efficiency makes TEEs an attractive option for deploying AI agents that require both speed and security.
However, it's important to note that TEEs are not without their challenges. They rely on hardware vendors for security assurances, which introduces a level of trust in the manufacturer. Despite this, TEEs remain a practical and effective solution for many applications, especially when combined with other security measures.
Autonomys and the Future of Confidential AI
Autonomys is pioneering the integration of TEEs into decentralized AI infrastructures. By leveraging TEEs, Autonomys aims to create AI agents that can operate independently while maintaining the confidentiality of their computations. This approach not only enhances security but also aligns with the principles of #Web3 , promoting decentralization and user sovereignty.
Dr. Feng emphasizes that in the context of Web3 and decentralized systems, privacy is not just a feature—it's a necessity. As AI agents become more autonomous, ensuring that they cannot be tampered with or spied upon becomes crucial. TEEs provide the hardware-based trust anchor needed to achieve this goal.

As a miner within the @DAO Labs ecosystem, I find Dr. Feng's insights particularly resonant various principle of trust in the AI ecosystem, mind-blowing. By adopting TEEs, Web3 users can ensure that AI agents within our network operate with integrity and confidentiality, reinforcing trust among users and stakeholders. This synergy between Autonomys' vision underscoring the importance of collaborative efforts in advancing secure AI technologies.

Conclusion
Dr. Chen Feng's advocacy for TEEs as the cornerstone of confidential AI highlights a critical path forward in the development of trustworthy autonomous systems. By combining the performance benefits of hardware-based security with the principles of decentralization, TEEs offer a viable solution to the challenges of AI trustworthiness.
For Web3 ecosystems embracing TEEs is not just a technological upgrade—it's a commitment to building a future where AI agents can be both autonomous and trustworthy. As we continue to explore and implement these technologies, we move closer to realizing a secure, decentralized AI landscape.

In today's tech evolution AI can either empower or endanger society, visionary projects like #AutonomysNetwork with its native token #AI3 , and @DAO Labs , are stepping up to redefine what it means to build responsibly.
Guarding AI also means guiding #blockchain and #crypto —because without trust, transparency, and decentralized control, AI becomes a risk, not a revolution.

I was inspired when I read one of Todd Ruoff’s statements during an interview. He said:
“Ethics is not a side conversation, but rather build it into everything we do.”
That line struck me deeply. It reminded me that ethics isn’t something you just talk about — it’s something you live by. When we treat ethics as a core principle in everything we do, we give true meaning to our work and our words. Without that alignment, the concept of ethics becomes empty — a word we repeat, but never embody.
What do they do?
✅To be fully open-sourced
✅ensures all technology can be audited,improved and challenged by the community
✅Commitment to decentralization
✅has full control over the application they built
By combining open-source ethics, decentralized control, and verifiable AI memory and accountability, Autonomys is laying the groundwork for AI you can trust — and blockchain applications you can control.
Why it matters?
Most traditional AI systems are stateless and unaccountable — they act, but no one truly knows why, and no one takes responsibility. Autonomys flips that by giving agents identity, memory, and ethical boundaries, enforced through blockchain-backed transparency and community governance.
In essence, Autonomys is designing AI citizens, not just tools.

🔥 Dear #BinanceSquareFamily readers, as someone who works as #SocialMining at @DAO Labs , today we will take a look at the interview conducted by #AutonomysNetwork CEO Todd Ruoff . Although the market is expecting price movements of tokens such as $ETH , $TRUMP , $MASK , #BinanceAlphaAlert news, #MarketRebound reading different articles will always be good for you.

👉 Hello. What was artificial intelligence ? Our assistant to whom we ask every question that comes to our minds in daily life. Yes, it really did. Searching the internet, searching for pages of information seems to be a thing of the past. Artificial intelligence and its applications that provide you with information with their sources. Should we worry about the future? Can they improve themselves and become better than humans? Anyway, I will put these crazy questions in my mind aside today. I will share my thoughts about an article I read before. What is the subject? Of course, the artificial intelligence storm. Let's all focus on the interview that Autonomys CEO Todd Ruoff had with Authority Magazine. Web3, artificial intelligence and more are here.

👉 Autonomys CEO Todd Ruoff approaches many issues regarding the future of artificial intelligence from his own perspective in this interview. Ruoff said that artificial intelligence is becoming more powerful and more autonomous every day. Naturally, he mentioned that transparency, ethical principles and security questions, which are important for users, are also increasing. He explained how they developed a vision against these challenges and why they are adhering to these principles. If we act by ignoring the basic principles, we may encounter artificial intelligences that we cannot hold in our hands in the future. Artificial intelligences, which are increasingly used today and are with us in many questions we seek answers to, will achieve success as they progress within the framework of these principles.

👉 While reading the article, I observed the following as one of Ruoff's clearest messages. The formation of an ethical artificial intelligence is only possible with transparency. It was recorded as a very important thought for me. Decisions created and made using closed systems will put users at risk. Artificial intelligences should always be transparent about the decisions they make. That's why Autonomys Network develops all of its technologies as open source. Thus, anyone who wishes can examine, criticize and contribute to the codes. This approach will increase not only software quality but also social trust. Instead of decisions whose ways are unclear, the principle of transparency should be at the center of this work.

 👉 Today, many artificial intelligence systems give us fast answers. Sometimes, even in long questions we ask, we see the answer in front of us as soon as we press the enter key. So how do artificial intelligences think when giving their answers? Do they explain them? This is where Autonomys Network makes a difference. It makes a difference with its own "agentic framework" developed to solve such problems. To elaborate on this subject, for example, they have created a discussion bot called 0xArgu-mint. When you interact with it, it records the thoughts behind its interaction and decision on the chain. In this way, the answer to the question of why the artificial intelligence thinks this way regarding the questions in the minds of the users is ready on the chain. Everything is on the chain and can be audited retrospectively. Transparency is not only a principle here, but also a technical feature at the core of this business.

👉 Ruoff finds it dangerous that the future of artificial intelligence is in the hands of a few large technology companies in terms of trust. He points out that the dependence of AI systems on a central authority can lead to unilateralism in decision-making processes. In the vision of the Autonomys Network, he argues that distributing this power is a structure where everyone can contribute, control and even own AI. He states that decentralized governance is seen as a sustainable way of trust and innovation in artificial intelligence. Thanks to decentralization, an environment of trust will also be created.

✅ Ultimately, the Autonomys Network, under the leadership of Todd Ruoff, continues to not only develop new AI tools, but also to provide strong examples of how these technologies can be more equitable and more participatory. They strive to make AI safe and accessible to everyone with the principles of transparency, open source infrastructure, on-chain traceability, and decentralized control. With this approach, they are following a remarkable path not only for the Web3 community, but for anyone who wants to have a say in the future of AI. Since this is a project I have been following for a long time, it makes me happy to see them take good steps in this direction. I hope they bring the successful and fully user compatible AIs we all dream of to our world in the future.

This article is the result of a personal inquiry rather than a technical analysis. Because as a content producer, I work very closely with artificial intelligence while shaping the content, and in every process, I question both my own knowledge and its suggestions separately and try to reach a conclusion.
Especially on platforms like @DAO Labs that encourage participation, this relationship with artificial intelligence agents is really important. With these agents, we try to think, decide and even understand some issues even better. And in this process, it becomes inevitable to question the systems that create content as much as producing it. That's why I asked myself: “Will I be this comfortable with my personal data?”
In the age of #AI3 , security is not only a matter of the system, but also of the user. And trust often starts not from complex cryptographic terms, but from something much more human: Understanding. That's why this article starts with the questions I, as a user, have been asking. And it seeks to answer them honestly, with the official sources available to us.

The first concept I came across was #TEE : Trusted Execution Environment. In Dr. Chen Feng's definition, these systems are isolated structures built in an untrusted environment; areas that are closed to outside intervention and can only be accessed within certain rules. It is possible to think of it as a kind of fortress, but this fortress is not built outside the system, but right inside it. The agent works here, the data is processed here and no one from the outside can see what is happening. Everything sounds very secure. But I still have a very basic question in my mind: Who built this castle? Who has the key to the door? And at this point a new question popped up in my mind: How secure is this structure really? #ConfidentialAI
It would be too optimistic to assume that this structure is foolproof, no matter how protected it looks. Because it is usually the hardware manufacturer that builds these spaces, which brings us to the inevitable trust relationship. Of course, over time, vulnerabilities have been discovered in some TEE implementations. However, the issue here is not only whether this structure is flawless or not, but also how these structures are used and what they are supported with. Today, these systems are not considered as standalone solutions, but as part of larger and more balanced architectures. This makes them logical, but not absolute.

This is why system design makes sense not only by relying on one method, but by balancing different technologies. There are alternative solutions. For example, ZKP, Zero-Knowledge Proof, manages to verify the accuracy of information while keeping its content secret. Or systems such as MPC, which process data by breaking it up and sharing it between multiple parties. These are impressive methods. In the past, these technologies were thought to be slow, but there have been significant advances in speed in recent years. As Dr. Feng puts it, we may have to wait until the end of the century for these technologies to mature. As much as this sentence speaks of a technical reality, it is also striking.

Now I come to the real question: Where does #AutonomysNetwork fit into all this? Is this project just a promise of privacy, or is it really building a different architecture? I'm more interested in the answer to this question because I don't just want to trust the technology; I also want to know how the system works. Autonomys doesn't leave TEE alone. It protects the agent's actions within TEE and records the rationale for its decisions in the chain. These records are made permanent through PoAS, Proof of Archival Storage. In other words, the decision history cannot be deleted or changed. This ensures that the system is not only secret but also accountable. The agents are creating their own memories. And even when verifying my identity, the system does not reveal my data. This detail is supported by the ZKP.
But I still believe that when evaluating these systems, it is important to consider not only the technology, but also the structure within which it works. After all, I didn't build the system, I didn't write the code, but Autonomys' approach tries to include me in the process instead of excluding me. The fact that the agents' decisions are explainable, their memories are stored in the chain, and the system is auditable makes the concept of trust more tangible. As Dr. Feng puts it: “Trust begins where you are given the right to question the system from the inside.”
At this point, security is not only about whether the system is closed or not, but also about how much of what is happening inside can be understood. True security begins with the user being able to ask questions of the system and understand the answers they receive. While Autonomys' TEE architecture may not be the ultimate solution on its own, when combined with complementary logging mechanisms, verification layers like PoAS, and identity protection solutions, it offers a multi-layered and holistic approach.
The fact that Dr. Chen Feng, who has a strong academic background in artificial intelligence, is behind such a detailed structure demonstrates that this approach is not random but rather deliberate and research-based. In my opinion, this is precisely what elevates Autonomys from being an ordinary privacy initiative to a more serious framework.
#BinanceAlpha

AI3 contributors at #AutonomysNetwork , supported by @DAO Labs through the #SocialMining initiative, are advancing decentralized learning by addressing the practical bottlenecks of bandwidth, data growth, and state storage in AI-based decentralized physical infrastructure networks.
Lately, it has been explained in technical discussions that Autonomys domain framework allows AI workloads to be used without putting additional pressure on block validation. Isolating its machine learning work from the main chain means Autonomys solves the problem brought by AI’s high resource use in decentralized transactions.
This separation enables dynamic workload adjustment, aligning with research by Li (2023) that flagged state bloat and historical data overflow as the leading barriers to usable decentralized AI systems. Members of Social Mining note that using the Subspace Protocol, storage can be scaled efficiently all the while ensuring transparency which is important in decentralized learning.
The proposed Proof-of-Training (AI-PoT) domain would handle model training, validation, and rewards, using AI3 as the economic engine. They make it possible to encourage honest use of computing resources and cut down on the need for one central group to supervise everything.
Social Mining in Autonomys Hub is responsible for the research: it records what the community observes and applies it to developing the protocol. The model mixes economic stability with being decentralized, so AI3 can support new AI-based economies as a basic layer.

In an era where artificial intelligence increasingly permeates our daily lives, the call for transparency and accountability in #AI systems has never been louder. Todd Ruoff, CEO of #Autonomys , envisions a future where AI operates not behind closed doors but within an open, decentralized framework that champions ethical standards and user empowerment. In a recent interview with Authority Magazine, Ruoff shared his journey from decades in high finance to leading a mission-driven company at the intersection of decentralization and ethical artificial intelligence. His message is clear: “the future of AI must be open, transparent, and accountable to the people it serves”.

At the heart of Ruoff’s philosophy is a belief that AI cannot remain a “black box.” The algorithms that influence our decisions, our economies, and even our relationships must be auditable. “When AI is built in the open, users can rest assured that their AI is operating without bias,” he explained. This conviction is what powers #AutonomysNetwork s' commitment to open-source and on-chain transparency—a radical shift from the closed, proprietary models that dominate today’s AI landscape.

One of the company’s flagship projects, 0xArgu-mint, embodies this ethos in action. Argu-mint is an autonomous AI agent that engages in on-chain debates with verifiable memory. Unlike traditional chatbots, Argu-mint’s conversations are permanently recorded via Autonomys’ Distributed Storage Network (DSN), ensuring that its logic and learning are not just traceable but tamper-proof. Powered by the Proof-of-Archival-Storage (PoAS) mechanism, this infrastructure guarantees that AI interactions remain accessible and auditable, even years down the line.
But Autonomys isn’t stopping there. Their Agentic Framework introduces a model where AI agents are not just tools—but self-monitoring entities capable of reasoning and adapting within ethical constraints. This framework allows agents to retain memory across tasks, evolving intelligently while staying within transparent, accountable systems.

For Ruoff, decentralization isn’t just a technical choice—it’s a moral one. By removing control from centralized entities and distributing it across a Web3 ecosystem, Autonomys ensures AI development is aligned with community values, not corporate interests.
As the world grapples with how to make AI safe, fair, and inclusive, Autonomys is offering a powerful blueprint—one that decentralizes not only the technology, but also the trust.

In conclusion, Todd Ruoff's vision for AI underscores the critical role of open-source development, on-chain transparency, and decentralized governance in fostering ethical and accountable AI systems. Through Autonomys' innovative frameworks, we witness a tangible path toward realizing this vision, one that resonates deeply within the #DAOLabs community and the broader #Web3 landscape.

Today, eyes were on the PCE data. Although the statements seem positive, the decline in the crypto market continues due to Fed expectations and tariff uncertainties caused by #TrumpTariffs . Despite all this decline, $LPT has returned over 100% to its investors in a short time. It is still unclear at what level #bitcoin will close May.
In such a turbulent environment, it's important to focus not only on price movements, but also on the infrastructural and technological developments that projects offer. Today, we will be looking for answers to the following question: Can projects that go beyond short-term market fluctuations and offer user-oriented technological solutions really create lasting value?
We are in the age of artificial intelligence. We use it, we develop it, we even integrate it into our daily lives. But how much do we know about how it works? #AutonomysNetwork CEO Todd Ruoff's interview with Authority Magazine takes a serious look at this question. If AI systems are not controllable, isn't it an illusion to think that we really control them?

There is a lot of truth in this statement. Many of the AI models we use today are closed boxes: decision-making processes are not visible from the outside, data usage is unclear, and systemic errors or biases can be easily hidden. According to Todd Ruoff, an advocate of open source and on-chain architecture, this can only be changed with radical transparency. As a user myself, I can tell you that as you start to understand how the system works, instead of accepting every output as it is, you can actually see that your questions increase. What makes a model reliable is not only the answer it gives, but also being able to see how that answer is formed. This is why I would like to draw your attention to the Agentic Framework developed by Autonomys.
Here, all the decision processes of AI agents are recorded in the chain, including the thinking and planning steps. Agents like Argu-mint learn from social media interactions and transparently document this process. In theory, this sounds impressive. In practice, I wonder how such a detailed logging system is optimized, because there is always a trade-off between user experience and in-system efficiency. But this structure certainly sets a standard, at least for users who want control.

Ruoff's warnings about the centralization of AI are also hard to ignore. The fact that the most powerful models today are under the control of a few tech giants is not just a technical problem, it's a societal one. Decentralized structures are promising, but I still have questions about their sustainability. The “data ownership” model proposed by Autonomys, the idea that the user can control and monetize their own data, is impressive. But I can't speak for sure until I see it become widespread on a user basis.
The real impact of the interview lies not so much in the technical details, but in the clear vision of how AI should be governed, by whom and with what values.
Todd Ruoff CEO of Autonomys argues that AI should not be a system controlled by a few private entities, but should take shape on a decentralized and transparent infrastructure. For him, this is not just a design choice, but a process in which ethical responsibilities must be shared across society.
The approach behind this vision is not limited to technological development. Together with @DAO Labs ' #SocialMining model, Autonomys aims to build communities of conscious users in the age of artificial intelligence. Because there is a need for a community that not only uses AI, but also understands how it works and questions it when necessary. In the background is the Subspace Protocol, which does not require energy consumption and works with PoAS consensus based on Nakamoto's honest majority assumption. Autonomys builds all the infrastructure needed to create AI-powered super dApps and autonomous agents on this decentralized layer.
In conclusion, AI is not only a technological evolution, but also a matter of governance, ethics and participation. If we are to build the future together, we need to define together how the systems we use work. Autonomys' proposed model reminds us that we need to be participants in this process, not just spectators.
#BinanceAlpha

In times when data privacy has become a headline cliché, Chen Feng's vision for Trusted Execution Environments as a foundation for #ConfidentialAI offers a technical and philosophical framework. In his capacity as Head of Research at #AutonomysNetwork and UBC Professor, Feng cloaks #TEE as 'digital castles'-fortified islands where AI agents are sovereign over their logic and data. This metaphor gives an architectural significance to the otherwise highly abstruse domain of privacy technology and thereby states the mission of Autonomys network in the language of security concepts.
His insights are quite captivating for me as a social miner on @DAO Labs #SocialMining Ecosystem.

#AI3

Why TEEs Outperform Cryptographic Alternatives
The cryptographic toolkit already contains ZKPs and FHEs, Feng says, but TEEs are special because they combine performance and security. Zero-knowledge proofs never come free speed overhead, and homomorphic encryption slows computation down by a factor of 10,000; TEEs, on the contrary, just isolate the execution in hardware so that the execution virtually runs at native speed. For any autonomous agents facing real-time decisions-crush decisions about trading crypto assets or handling sensitive health data, this performance differential is truly existential.
Autonomys’ choice reflects this calculus. By integrating TEEs at the infrastructure layer, they create environments where:
AI models process data without exposing inputs/outputsCryptographic attestations prove code executed as intendedMemory remains encrypted even during computation
As Feng notes: “When deployed, the system operates independently within its secure enclave, with cryptographic proof that its responses...are genuinely its own”. This combination of autonomy and verifiability addresses what Feng calls the “Oracle Problem of AI” – ensuring agents act independently without hidden manipulation.

Privacy as Non-Negotiable Infrastructure
The podcast presents very worrying scenarios: AI therapists leaking mental health data, bot traders being front-run through model theft, etc. Feng's solution: ensure that privacy is the default through TEEs rather than making it an opt-in feature. Aligning with this is Autonomys' vision of "permanent on-chain agents" that retain data sovereignty along interactions.
Critically, TEEs not only conceal data but also safeguard the integrity of AI reasoning. As Feng's team demonstrated with their Eliza framework, attestations produced with TEEs allow users to verify that an agent's decisions stem from its original programming and have not been subjected to adversarial tampering. For Web3's agent-centric future, this goes from trusting institutions to trusting computation that can be verified.

Strategic Implications for Web3
Autonomys’ TEE implementation reveals three strategic advantages:
Interoperability: Agents can securely interact across chains and services without exposing internal states.Composability: TEE-secured modules stack like LEGO bricks for complex workflows.Sustainability: Hardware-based security avoids the energy costs of pure cryptographic approaches.
As Feng summed up: "These TEEs provide an environment wherein these systems can operate independently without manipulation even by their original creators". With the AI space being dominated by centralized players, this view provides a blueprint for true decentralized intelligence-an intelligence whose capability is not gained through compromise of privacy.
Moving forward, the route entities in the ecosystem must collaborate. Autonomys' partnerships with projects such as Rome Protocol for cross-chain storage and STP for agent memory management is the implication that they are not only building technology but also building the connective tissue for confidential AI ecosystems. Now, should more developers take this castle-first approach, we might finally begin to develop AI systems that enable and not exploit, thereby fulfilling the Web3 promise of user-owned intelligence.

The interview with Todd Ruoff - #Autonomys CEO lays out an enticing vision for ethical, transparent, decentralized AI. Three overriding themes seem to prevail: the need for open source and on-chain transparency; #AutonomysNetwork ' agentic framework for #AI operationalization, accountability, and memory; and how decentralization of control over AI really matters in the real world.

As a social miner on @DAO Labs #SocialMining Galaxy, I will take you on a tour through his insights.

#AgenticAI $AI3

Open-Source and On-Chain Transparency: Foundations for Ethical AI
For Ruoff, the force behind Autonomys’ ethical AI approach is an unwavering support for open-source development. He argues that AI made in open source forces consumer trust that technology is free from hidden bias, and to emphasize, the code and training data are open for any kind of auditing. Such transparency is certainly absent in a closed-source system, which in reality is akin to a "black box" that might obscure or mute unethical behavior. Thus, Ruoff made sure that everything under Autonomys was open-source by recording AI interactions on-chain, embracing that every decision and process is visible and also immutable and verifiable. In a way, this type of transparency could be the very glue for public trust and holding the AI systems to the highest ethical standards.

Autonomys’ Agentic Framework: Accountability and Memory for AI Agents
Another standout theme is the innovative agentic framework developed by Autonomys to directly tackle the problems of AI accountability and memory. Ruoff explains that the AI agents developed by them, such as 0xArgu-mint, have an entire memory and reasoning process recorded on-chain. Thus, every interaction, every decision, and even the internal logic of the agent are open for review forever. This framework, in practice, allows for what Ruoff calls a "digital, immutable autopsy" upon the agent's behavior: the highest level of transparency and ability to investigate and learn from the behavior of AI in cases, especially when things go wrong. By providing AI agents with self-sovereign identities and permanent, auditable histories, Autonomys has set a standard for responsible AI to surpass.

Decentralizing Control: Safeguarding AI as a Public Good
Finally, Ruoff sought decentralization in addressing one of the AI industry's most pressing risks: concentration of power. According to him, the way things are now, only a few corporations call the shots in determining the direction and design of AI technologies. Autonomys arose against this backdrop with the alternative promise of being distributed and decentralized, so that no single entity (Autonomys themselves included) may ever assume unilateral control over the application of AI. Apart from granting access to AI, this approach also mitigates the likelihood of abuses of power, serving instead as a fertile ground for inclusiveness and friendly innovation. In Ruoff's own words, AI "should be a public good, not a corporate asset"—a vision that finds itself in many minds in the ever-climbing concern for digital sovereignty and privacy.

Conclusion
Ruoff's insights have illuminated a way forward, calling for the unfolding of open-source transparency, strong agentic architectures, and decentralization, which are no longer just technical choices but must be the ethical compass. His governance at Autonomys instills confidence that it must be possible to build AI systems that respond to safety, accountability, and serve the public interest.

Phase-2 Mainnet workings are underway for AI3 by #AutonomysNetwork , in alliance with #DAOLabs and with the help of #SocialMining . The project now regards audit visibility and bug disclosure as important achievements, not potential problems.
Periodically testing under stress during recent competitions brought to attention some weaknesses in the protocol such as the double-minting bug in the XDM module. The developers have applied key safety improvements as well as keeping up with benchmarking requirements. It points to a focused way of infrastructure building that is not like other rush-launch projects.
There is the same level of rigor throughout the audit process. Closing XDM reviews shifts focus to sudo operations at the domain level and mechanics for syncing domains. Each of these components is crucial for how Autonomys’ design scales up and down in use. Managing load balance on Astral is still a problem, but minor updates are helping to assure the system remains sustainable over time.
Such engineering updates guide Social Mining participants in how to organize and contribute their ideas. The documentation, analysis and settled content associated with protocol stability are not simple bullet points - they are actually valuable rewards within the DAO Labs system. As a result, all contributors on the #AutonomysHub actively contribute to setting the direction rather than just reporting on what is happening.

AI3, at the center of #AutonomysNetwork and integrated into @DAO Labs #SocialMining framework, represents an effort to redefine the foundations of decentralized AI systems. Members of the Autonomys Hub are studying the network’s design to see how it handles the needs of extensive, on-chain intelligence.
Because any AI system may need high-throughput data all the time, the current model centers around the ability to store data that never loses and provides fast and consistent access. When talking about Social Mining, the importance of decoupled execution for avoiding the main problems in traditional blockchain applications has come to the fore.
People from the community read the Autonomys one pager, analyzing the statements and questioning if modular EVMs can really be flexible. Contributors approach infrastructure as flexible, believing it can develop with ongoing pressure and use.
Attention is given to how $AI3 works within a setting where decentralized compute must blend seamlessly with agents, oracles and verifiable inputs. From this view, Social Mining means more than content creation; it is about using a structured process to judge real architecture in real environments.

As interest in #altcoins and #AI integrations reoccur, one development flying under the radar could reshape how we think about intelligence in decentralized systems. #AutonomysNetwork , a project exploring the intersection of AI and blockchain, recently released V1.3, a milestone update that quietly introduces a powerful concept: permanent on-chain memory for AI agents.

The idea is simple but significant. Most AI agents, commonly used in Web2 or early-stage Web3 tools, are stateless. They operate without context, forgetting past interactions and functioning through opaque systems, often behind closed APIs. This makes transparency, trust, and evolution over time difficult to achieve. Autonomys is challenging that paradigm. With V1.3, its agents, called Auto Agents, can now store and retrieve memory directly from the blockchain. This means that every action, decision, or learning moment can be permanently recorded, publicly auditable, and composable by others.
The implications are wide-reaching:
AI agents that evolve over time with publicly verifiable memorySystems where behavior can be audited, like smart contracts
Developers building transparent, reusable logic for governance, automation, and beyond.
To help coordinate and support this emerging space, Autonomys Hub powered by #DAOLabs has been launched as a central gateway for developers, DAO contributors, and researchers. The Hub serves as a community-powered engine driving experimentation, documentation, and education around AI agents in Web3. With DAOLabs' long-standing involvement in decentralized tooling and social mining, the Hub aims to make these advanced capabilities more accessible and composable across ecosystems.
DAOLabs, a collective that’s been deeply engaged in Web3 since 2021, sees the potential for Auto Agents to become core infrastructure for DAOs, where persistent memory could improve governance, reduce redundancy, and create more autonomous contributors, both human and machine.
Use cases already being explored include:
Governance agents that track proposal history and voting logicDeFi bots that adjust strategies based on past market conditionsGame NPCs that develop identities and memories across player interactions
What Autonomys is proposing isn’t just a better storage solution; it’s a foundational layer for what might become on-chain cognition: agents with memory, accountability, and long-term identity within Web3 ecosystems.
The question now is whether the broader community will recognize the potential in time. In a space driven by rapid iteration and short memory, perhaps adding memory is exactly what AI in Web3 needs next.

What do you think?
#MarketRebound

Hey everyone,
Imagine if the next groundbreaking #Web3 project didn’t ask for your money—but for your creativity. That’s exactly what the Initial Labor Offering (ILO) is all about. Developed by #AutonomysNetwork and @DAO Labs , this innovative model invites real people—writers, educators, designers, and community builders—to share their skills and passion in exchange for token rewards. It’s like turning your everyday creative work into real ownership.

Let me break it down for you in simple terms. Instead of the usual fundraising method that relies on big investors, this #ILO puts the power in the hands of people who truly believe in a project. It’s a fresh way to decide who gets early access, who builds the project’s foundation, and who eventually benefits when it all takes off.
So, What Exactly Is the Autonomys x DAO Labs ILO?
Picture this: a contribution-first token launch that’s live with a $50,000 reward pool. From March 24 to April 24, 240 Social Miners and selected KOLs—that could be you—are taking part by completing tasks in 24 creative categories.

Whether you’re writing an article, designing a graphic, or educating your community, every bit of work counts. And here’s the cool part: your rewards are based on the quality of your work, the originality of your ideas, and the impact you make on social media.
But it’s not just about doing tasks—it’s about getting to know the project inside out. As you learn the vision and mission of #Autonomys —a Layer-1 blockchain built for AI3.0 that focuses on decentralized, human-centric artificial intelligence—you become more than a promoter. You become a co-creator, a part of the family that’s building something amazing from the ground up.

You might be wondering: what about the traditional IDO (Initial Decentralized Offering)? In a typical IDO, you’re often required to hold and stake large amounts of money to secure a spot in the token sale. This setup can make opportunities exclusive to those with significant capital, leaving many true believers on the sidelines.

The ILO, on the other hand, is all about effort and talent. Instead of investing money, you invest your skills—whether you’re writing, designing, or marketing. It’s essentially crowdfunding powered by creativity and passion. This approach ensures that tokens are awarded to those who truly understand and contribute to the project, rather than just those who can afford to buy in.
Why Does This Matter to You?
We all know that traditional token launches often give tokens to speculators, leaving genuine supporters out in the cold. With the ILO, tokens go first to those who actually understand and help grow the project. You’re not just buying in—you’re contributing your time, talent, and passion. That means you’re more likely to hold onto your tokens because you know what they represent.

The Real Value for Social Miners
If you’ve ever wondered how you can make a real impact, this is it. The ILO makes learning and contributing part of the reward process. You’re not just earning for a quick tweet; you’re becoming an informed part of an exciting ecosystem. As you absorb the vision of Autonomys and then share it with your community, you create a ripple effect of genuine interest and long-term engagement.
A Fair Launch That Benefits Everyone
For startups like Autonomys, this means organic growth from people who truly believe in them. Instead of spending big on flashy ads, they build a community that’s knowledgeable and ready for the Token Generation Event. And with strong backing from industry giants like Pantera Capital, Coinbase Ventures, and Web3 Foundation, you know this isn’t just another project—it’s a visionary platform built by real people.
So, if you’ve ever felt that your creativity deserves more than just likes and retweets, this is your chance to turn passion into ownership. Let’s build the future of Web3 together—because Web3 truly belongs to the builders.
Let’s prove it together. #SocialMining

I have been doing #SocialMining and producing content for different projects within the @DAO Labs ecosystem for a long time. During this process, I not only completed tasks, but also adopted being a part of the community. With RWA Inc., the first #ILO model of #DAOLabs , we had the opportunity to get to know the contribution-based distribution structure. This was a fair system based on the community, breaking the “pre-sale – investment” model we were used to in Web3.
#AI3 #AutonomysNetwork
Then, when the second ILO was announced for Autonomys, I encountered a more developed version of the system this time. Thanks to my previous activity in DAO Labs tasks, my content production on Autonomys Hub and my interaction with the community, I was selected for the Pledge Pool. No financial contribution or token purchase was made during this process. The selection process was entirely based on contribution, production and continuity.

When the Autonomys ILO process started, I had to complete different tasks for four weeks in order to receive an allocation. These tasks; tweeting, retweeting, sharing explanatory content, preparing infographics and writing articles on Some Platforms. Thanks to the tasks, I not only introduced the project, but also had the chance to understand it better and share it more effectively with the community. Autonomys’ visionary approach such as artificial intelligence, verifiable decision-making, and distributed storage made content production more meaningful and motivating for me.

Although the TGE has not yet taken place, information about how the vesting model will work was clearly shared at the beginning. The flexible vesting structure encourages those who contribute to the project to stay engaged throughout the process. This provides a more motivating experience in terms of long-term community support rather than just short-term reward expectations. This approach made me feel like a content producer, not just someone who does a task, but also a part of the project and contributes to its growth.

As a result, we are experiencing that a contribution-based distribution system is really possible with DAO Labs’ ILO model.
Seeing my labor being rewarded thanks to the Autonomys ILO process shows how valuable the participatory structure is in Web3. The fact that this model rewards contribution instead of investment can be inspiring for more projects in the future.

Welcome to the future of artificial intelligence—a future where AI is no longer just a tool controlled by a few tech giants but a decentralized, human-centric ecosystem empowering individuals to shape their digital destinies. This is AI3.0, the third evolution of AI, and it’s ushering in what’s being called the Age of Autonomy.
$ETH $BTC #AI3 #AutonomysNetwork
In this article, we’ll dive into the groundbreaking concepts of “Introduction to AI3.0 & The Age of Autonomy” by Autonomys Network, exploring how AI3.0 is redefining the relationship between humans, decentralization, and AI.
AI Evolution: From Centralized to Decentralized
To understand AI3.0, we need to take a step back and look at the evolution of AI through three distinct phases, each defined by its relationship with humans and centralization:
• AI1.0: Centralized machine learning for specific tasks, controlled by Big Tech. Think basic chatbots or recommendation algorithms.
• AI2.0: Generative AI like ChatGPT—creative but still centralized, limiting user control and raising privacy concerns.
• AI3.0: Decentralized, human-centric AI. Powered by Web3, it lets you customize and deploy AI agents, breaking free from tech giants.
AI3.0 is about user sovereignty, transparency, and collaboration, creating a secure ecosystem where humans shape their digital futures.
What is the Age of Autonomy all about?
The Age of Autonomy is the culmination of AI3.0’s vision—a world where humans and AI collaborate seamlessly to fulfill fundamental human needs: safety, connection, and prosperity.
Throughout history, technology has aimed to address these needs, from the invention of tools to the internet. AI3.0 takes this mission to the next level by empowering individuals to leverage AI in ways that enhance their lives while maintaining control over their data and digital identities.
The Age of Autonomy isn’t just a technological shift; it’s a philosophical one. It’s about empowering individuals to thrive as active architects of their futures, not passive recipients of tech handouts.
So, why does AI3.0 Matters for Web3?
The convergence of Web3 and AI in AI3.0 is more than a buzzword—it’s a paradigm shift that addresses the limitations of centralized AI systems. Here’s why it’s a big deal for our Web3 community:
• Decentralization: No more Big Tech gatekeepers—own your data and AI agents.
• Economic Opportunity: Earn rewards by contributing to the network.
• Internet of Agents (IoA): Interoperable AI agents handle tasks autonomously, from transactions to identity verification.
• Collaboration: Autonomys works with deAI projects like Bittensor and Morpheus, driving ecosystem-wide innovation.
My Final Thoughts
AI3.0 and the Age of Autonomy are redefining AI as a decentralized, human-empowering force.
With Autonomys Network leading the charge, they’re building a future where everyone can harness AI to thrive.
Let’s spread the word and shape this Web3 revolution together!