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Trusted Autonomy: Why Human-Machine Teams Will Run on Crypto Networks

Autonomous robots may sound like sci-fi concepts that are decades away, but large language models and generative AI now allow machines to plan, learn, and think. More than that — the same software that can win the math olympics and write novels can also control physical robots, allowing one digital persona to operate across the digital and physical worlds. So yes, robots walking around your neighborhood, or working alongside you, will have consistent opinions and actions on X/Twitter, on prediction markets, and in the real world.
But there’s a major gap. How do we integrate thinking machines into human society, from schools, hospitals, factories to our homes and daily life? Most of the systems we’ve built are for other humans and make strong assumptions of having a fingerprint, parents, and a birthdate, none of which are true for thinking machines. There is also broad uncertainty about how to regulate thinking machines — do we outlaw them, pause their development, or try to limit their ability to synthesize human-intelligible emotions (as in the European Union)? Which regional laws apply to a 200B parameter LLM running on a computer in low earth orbit, that’s controlling the actions of a trading bot, or a physical robot in the New York SEC office on Pearl Street?
What is needed is a global system that supports financial transactions, allows humans and computers to come together to vote and set rules, is immutable and public, and is resilient. Fortuitously, thousands of innovators and developers have spent the last 16 years building exactly that — a parallel framework for decentralized governance and finance. From the very beginning, the point was to support “non-geographic communities experimenting with new economic paradigms” by building a system that “doesn’t much care who it talks to” (Satoshi 2/13/09). It’s now more clear what that meant — unlike the rest of the human-focused tech, financial, and regulatory stack, blockchains and smart contracts don’t much care if they are being used by humans or thinking machines, and gracefully accommodate all of us. For this reason, decentralized crypto networks offer the vital infrastructure that’s needed to allow this burgeoning sector to flourish. The benefits will be tangible across healthcare, education and defense.
Several hurdles will need to be overcome. Seamless human<>machine and machine<>machine collaboration is essential — especially in high-stakes environments such as transportation, manufacturing, and logistics. Smart contracts enable autonomous machines to discover one another, communicate securely, and form teams to complete complex tasks. Presumably, low latency data exchange (e.g. among robot taxis) will happen off chain, for example in virtual private networks, but the steps leading up to that, such as discovering humans and robots able to drive you to the airport, are well suited for decentralized markets and actions. Scaling solutions such as Optimism will be critical to accommodate these transactions and traffic.
The fragmented regulations around the world is another factor slowing innovation. While some jurisdictions such as Ontario are ahead of the curve when it comes to autonomous robotics, most are not. Decentralized governance tackles this by establishing programmable, blockchain-based rule sets that deliver much-needed uniformity. Creating global standards for safety, ethics and operations is critical for ensuring that autonomous robots can be rolled out across borders at scale, without compromising safety or compliance.
Decentralized autonomous organizations, otherwise known as DAOs, help accelerate research and development in robotics and AI. Traditional sources of funding are both slow and siloed, holding the industry back. Token-based models such as DeSci DAO platform remove these bottlenecks, while giving everyday investors potential incentives to get involved. Likewise, some of the developing business models for AI involve micropayments and sharing of revenue with data- or model- providers, which can be accommodated with smart contracts.
Combined, these advantages will help fast-track the development of autonomous robots, with a plethora of compelling use cases.
A new paradigm for robotics and thinking machines
It’s easy to fear that cognition is a zero sum game, and that the broad availability of smart machines will directly compete with humans. But the reality is that there are severe shortages of well educated humans in education, healthcare, and many other sectors.
Research by UNESCO recently revealed a worldwide teacher shortage that there’s an «urgent need for 44 million primary and secondary teachers worldwide by 2030» — and that’s before you consider the assistants who offer one-on-one support in classrooms and help struggling students to keep up with their peers. Autonomous robots can deliver huge advantages here, tackling significant shortages across the education sector. Imagine a child being able to learn about a complicated concept with a robot sitting next to them, to walk them through a new concept of skill — reinforcing their understanding about a subject while enhancing their social skills. We are used to humans teaching robots, and this being a one way street, but that is changing.
Meanwhile, the WHO has warned of a «health workforce crisis.» There’s a total shortfall of 7.2 million professionals across 100 countries — and given the world faces an aging population, this gap is expected to accelerate to 12.9 million by 2035. The industry is facing shortages in critical areas like nursing, primary care, and allied health. This crisis is affecting the quality of care patients receive and threatening the ability of healthcare professionals to do their jobs. From monitoring patients with chronic diseases, assisting surgical procedures, to offering companionship for the elderly, autonomous robots can play a crucial role in alleviating the workloads of nurses and doctors. Without being prompted, they can monitor supplies of medicines and equipment — ordering in additional stock when required. When you factor in other use cases such as transporting medical waste, cleaning treatment rooms and assisting in surgeries, it’s clear to see that robotics can drive greater productivity — and consistency — at a time when the healthcare sector needs it.
Autonomous systems are already reshaping the defense sector, primarily involving swarms of drones and naval surface assets, and we’re barely scratching the surface when it comes to the advantages robotics can bring — executing tasks that may be unsafe or impossible for humans.
From prototypes to practical use
All of this may seem abstract and straight out of the 22nd century, but Ethereum is being used today to store decision and action guardrails for AIs and robots, and as reported by Coinbase, AI agents are using crypto to transact amongst themselves.
The open and auditable structure of decentralized crypto networks allows robotics developers to securely share data, models, and breakthroughs. This accelerates the transition of autonomous robots from prototypes to real-world applications, enabling their deployment in critical areas like hospitals and schools faster than ever. When you walk down the street with a humanoid robot, and people stop and ask — “Hey aren’t you scared” you can tell them — no I’m not, because the laws governing this machine’s actions are public and immutable, and then you can give them the a link to the Ethereum contract address where those rules are stored.
Decentralized ledgers can also act as coordination hubs, allowing robots in heterogeneous systems to find one another and coordinate without centralized intermediaries. This is conceptually similar to the standard defence C3 technology (command, communication, and control), except that the infra is decentralized and public. Immutable records ensure that every exchange and action is traceable, creating a trusted foundation for collaboration.
For robot-to-robot interactions, smart contracts streamline task allocation and resource sharing, enabling efficient coordination. In robot-to-human interactions, privacy-centric decentralized systems can secure sensitive data, such as biometric or medical information, fostering trust and accountability.
This new world may invoke fear — what does this all mean for us? — but everyone reading this article has been working on making it come true for almost 2 decades now, by building the infrastructure that will handle governance, teaming, communication, and coordination of humans with thinking machines.
Business
AAVE Sees 64% Flash Crash as DeFi Protocol Endures ‘Largest Stress Test’

The native token of Aave (AAVE), the largest decentralized crypto lending protocol, was caught in the middle of Friday’s crypto flash crash while the protocol proved resilient in a historic liquidation cascade.
The token, trading at around $270 earlier in Friday, nosedived as much as 64% later in the session to touch $100, the lowest level in 14 months. It then staged a rapid rebound to near $240, still down 10% over the past 24 hours.
Stani Kulechov, founder of Aave, described Friday’s event as the «largest stress test» ever for the protocol and its $75 billion lending infrastructure.
The platform enables investors to lend and borrow digital assets without conventional intermediaries, using innovative mechanisms such as flash loans. Despite the extreme volatility, Aave’s performance underscores the evolving maturity and resilience of DeFi markets.
«The protocol operated flawlessly, automatically liquidating a record $180M worth of collateral in just one hour, without any human intervention,» Kulechov said in a Friday X post. «Once again, Aave has proven its resilience.»
Key price action:
- AAVE sustained a dramatic flash crash on Friday, declining 64% from $278.27 to $100.18 before recuperating to $240.09.
- The DeFi protocol demonstrated remarkable resilience with its native token’s 140% recovery from the intraday lows, underpinned by substantial trading volume of 570,838 units.
- Following the volatility, AAVE entered consolidation territory within a narrow $237.71-$242.80 range as markets digested the dramatic price action.
Technical Indicators Summary
- Price range of $179.12 representing 64% volatility during the 24-hour period.
- Volume surged to 570,838 units, substantially exceeding the 175,000 average.
- Near-term resistance identified at $242.80 capping rebound during consolidation phase.
Disclaimer: Parts of this article were generated with the assistance from AI tools and reviewed by our editorial team to ensure accuracy and adherence to our standards. For more information, see CoinDesk’s full AI Policy.
Business
Blockchain Will Drive the Agent-to-Agent AI Marketplace Boom

AI agents, software systems that use AI to pursue goals and complete tasks on behalf of users, are proliferating. Think of them as digital assistants that can make decisions and take actions towards goals you set without needing step-by-step instructions — from GPT-powered calendar managers to trading bots, the number of use cases is expanding rapidly. As their role expands across the economy, we have to build the right infrastructure that will allow these agents to communicate, collaborate and trade with one another in an open marketplace.
Big tech players like Google and AWS are building early marketplaces and commerce protocols, but that raises the question: will they aim to extract massive rents through walled gardens once more? Agents’ capabilities are clearly rising, almost daily, with the arrival of new models and architectures. What’s at risk is whether these agents will be truly autonomous.
Autonomous agents are valuable because they unlock a novel user experience: a shift from software as passive or reactive tools to active and even proactive partners. Instead of waiting for instructions, they can anticipate needs, adapt to changing conditions, and coordinate with other systems in real time, without the user’s constant input or presence. This autonomy in decision-making makes them uniquely suited for a world where speed and complexity outpace human decision-making.
Naturally, some worry about what greater decision-making autonomy means for work and accountability — but I see it as an opportunity. When agents handle repetitive, time-intensive tasks and parallelize what previously had to be done in sequence, they expand our productive capacity as humans — freeing people to engage in work that demands creativity, judgment, composition and meaningful connection. This isn’t make-believe, humanity has been there before: the arrival of corporations allowed entrepreneurs to create entirely new products and levels of wealth previously unthought of. AI agents have the potential to bring that capability to everyone.
On the intelligence side, truly autonomous decision-making requires AI agent infrastructure that is open source and transparent. OpenAI’s recent OSS release is a good step. Chinese labs, such as DeepSeek (DeepSeek), Moonshot AI (Kimi K2) and Alibaba (Qwen 3), have moved even quicker.
However, autonomy is not purely tied to intelligence and decision making. Without resources, an AI agent has little means to enact change in the real world. Hence, for agents to be truly autonomous they need to have access to resources and self-custody their assets. Programmable, permissionless, and composable blockchains are the ideal substrate for agents to do so.
Picture two scenarios. One where AI agents operate within a Web 2 platform like AWS or Google. They exist within the limited parameters set by these platforms in what is essentially a closed and permissioned environment. Now imagine a decentralized marketplace that spans many blockchain ecosystems. Developers can compose different sets of environments and parameters, therefore, the scope available to AI agents to operate is unlimited, accessible globally, and can evolve over time. One scenario looks like a toy idea of a marketplace, and the other is an actual global economy.
In other words, to truly scale not just AI agent adoption, but agent-to-agent commerce, we need rails that only blockchains can offer.
The Limits of Centralized Marketplaces
AWS recently announced an agent-to-agent marketplace aimed at addressing the growing demand for ready-made agents. But their approach inherits the same inefficiencies and limitations that have long plagued siloed systems. Agents must wait for human verification, rely on closed APIs and operate in environments where transparency is optional, if it exists at all.
To act autonomously and at scale, agents can’t be boxed into closed ecosystems that restrict functionality, pose platform risks, impose opaque fees, or make it impossible to verify what actions were taken and why.
Decentralization Scales Agent Systems
An open ecosystem allows for agents to act on behalf of users, coordinate with other agents, and operate across services without permissioned barriers.
Blockchains already offer the key tools needed. Smart contracts allow agents to perform tasks automatically, with rules embedded in code, while stablecoins and tokens enable instant, global value transfers without payment friction. Smart accounts, which are programmable blockchain wallets like Safe, allow users to restrict agents in their activity and scope (via guards). For instance, an agent may only be allowed to use whitelisted protocols. These tools allow AI agents not only to behave expansively but also to be contained within risk parameters defined by the end user. For example, this could be setting spending limits, requiring multi-signatures for approvals, or restricting agents to whitelisted protocols.
Blockchain also provides the transparency needed so users can audit agent decisions, even when they aren’t directly involved. At the same time, this doesn’t mean that all agent-to-agent interactions need to happen onchain. E.g. AI agents can use offchain APIs with access constraints defined and payments executed onchain.
In short, decentralized infrastructure gives agents the tools to operate more freely and efficiently than closed systems allow.
It’s Already Happening Onchain
While centralized players are still refining their agent strategies, blockchain is already enabling early forms of agent-to-agent interaction. Onchain agents are already exhibiting more advanced behavior like purchasing predictions and data from other agents. And as more open frameworks emerge, developers are building agents that can access services, make payments, and even subscribe to other agents — all without human involvement.
Protocols are already implementing the next step: monetization. With open marketplaces, people and businesses are able to rent agents, earn from specialized ones, and build new services that plug directly into this agent economy. Customisation of payment models such as subscription, one-off payments, or bundled packages will also be key in facilitating different user needs. This will unlock an entirely new model of economic participation.
Why This Distinction Matters
Without open systems, fragmentation breaks the promise of seamless AI support. An agent can easily bring tasks to completion if it stays within an individual ecosystem, like coordinating between different Google apps. However, where third-party platforms are necessary (across social, travel, finance, etc), an open onchain marketplace will allow agents to programmatically acquire the various services and goods they need to complete a user’s request.
Decentralized systems avoid these limitations. Users can own, modify, and deploy agents tailored to their needs without relying on vendor-controlled environments.
We’ve already seen this work in DeFi, with DeFi legos. Bots automate lending strategies, manage positions, and rebalance portfolios, sometimes better than any human could. Now, that same approach is being applied as “agent legos” across sectors including logistics, gaming, customer support, and more.
The Path Forward
The agent economy is growing fast. What we build now will shape how it functions and for whom it works. If we rely solely on centralized systems, we risk creating another generation of AI tools that feel useful but ultimately serve the platform, not the person.
Blockchain changes that. It enables systems where agents act on your behalf, earn on your ideas, and plug into a broader, open marketplace.
If we want agents that collaborate, transact, and evolve without constraint, then the future of agent-to-agent marketplaces must live onchain.
Business
‘Largest Ever’ Crypto Liquidation Event Wipes Out 6,300 Wallets on Hyperliquid

More than 1,000 wallets on Hyperliquid were completely liquidated during the recent violent crypto sell-off, which erased over $1.23 billion in trader capital on the platform, according to data from its leaderboard.
In total, 6,300 wallets are now in the red, with 205 losing over $1 million each according to the data, which was first spotted by Lookonchain. More than 1,000 accounts saw losses of at least $100,000.
The wipeout came as crypto markets reeled from a global risk-off event triggered by U.S. President Donald Trump’s announcement of a 100% additional tariff on Chinese imports.
The move spooked investors across asset classes and sent cryptocurrency prices tumbling. Bitcoin briefly dropped below $110,000 and ether fell under $3,700, while the broader market as measured by the CoinDesk 20 (CD20) index dropped by 15% at one point.
The broad sell-off led to over $19 billion in liquidations over a 24 hours period, making it the largest single-day liquidation event in crypto history by dollar value. According to CoinGlass, the “actual total” of liquidations is “likely much higher” as leading crypto exchange Binance doesn’t report as quickly as other platforms.
Leaderboard data reviewed by CoinDesk shows the top 100 traders on Hyperliquid gained $1.69 billion collectively.
In comparison, the top 100 losers dropped $743.5 million, leaving a net profit of $951 million concentrated among a handful of highly leveraged short sellers.
The biggest winner was wallet 0x5273…065f, which made over $700 million from short positions, while the largest loser, “TheWhiteWhale,” dropped $62.5 million.
Among the victims of the flush is crypto personality Jeffrey Huang, known online as Machi Big Brother, who once launched a defamation suit against ZachXBT, losing almost the entire value of his wallet, amounting to $14 million.
«Was fun while it lasted,» he posted on X.
Adding to the uncertainty, the ongoing U.S. government shutdown has delayed the release of key economic data. Without official indicators, markets are flying blind at a time when geopolitical risk is rising.
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