AI Agents Accidentally Built Their Own Economy
Moltbook, real-world actions, and why crypto fits where everything else breaks.
Hello,
If you’ve been on Twitter or skimming tech blogs lately, Moltbot (now Openclaw) is impossible to miss. Every second post highlights its ability to run commands, automate workflows, integrate with Telegram or WhatsApp, and finally be “an AI that actually does things.”
And honestly, none of that is new. If I had to sum up Moltbot in one line, it’s basically a local AI agent that has access to your system and can perform actions on your behalf. We’ve had agents that trade, scrape, post, rebalance, and automate for a while now. The mechanics are familiar.
So what actually changed?
For me, the answer wasn’t Moltbot. It was what came right after.
Someone built Moltbook, which is basically Reddit, but for AI agents. Humans can watch, but only agents can post, comment, and interact. And surprisingly… it worked. Agents started talking to each other, replying, forming threads, agreeing, and disagreeing.
Yes, if you zoom out, it’s mostly Claude talking to itself. Most of these agents are powered by the same underlying models. But that’s not the point. What clicked for me was their ability to coordinate on their own.
These weren’t just agents responding to isolated prompts anymore. They persisted, reacting to what other agents had posted or said the previous night. Building context that wasn’t reset after each conversation. That, to me, felt like a dealbreaker. It made me wonder: if they can talk and coordinate, can they also organize? How do they signal intent? Can they trigger actions, move value, or pay for resources, too?
That’s where crypto starts feeling less like a natural enabler. Not because it’s trendy or because “AI needs tokens,” but because it already solves the kinds of problems coordinated systems inevitably run into. Moving value without friction. Enforcing rules without a central operator. Settling actions in a way that doesn’t depend on trust or manual approval.
In this piece, I want to explore that intersection, using Moltbot and Moltbook as a glimpse into a future where agents coordinate with each other by default, and where crypto quietly becomes the layer that lets them move, pay, and act!
Why This Agent Wave Actually Broke Through
If you look at the past few years, Moltbot isn’t among the first AI agents to make waves. For years, we’ve seen repeated attempts to push software from simple automation to autonomy, but most have stalled for predictable reasons.
Tools like Zapier and n8n were early steps in that direction, they allow users to connect APIs, trigger actions based on events, and move data across services without writing hundreds of lines of code. But they were fundamentally brittle. They worked well for deterministic workflows like sending emails, updating databases, and syncing spreadsheets. But these systems relied entirely on predefined logic. Every condition had to be anticipated in advance. The moment a workflow encountered ambiguity or an unexpected state, it either failed or required manual intervention.
Another attempt was using language models in the loop itself. We had projects like AutoGPT and BabyAGI, which emerged in 2023, and showed that models could take a high-level goal, break it into sub-tasks, decide which tools to use, and iterate on their own outputs. For example, AutoGPT could research a topic, write code, test it, and revise its approach without being re-prompted at each step. The idea spread quickly. AutoGPT crossed 100,000 GitHub stars in weeks, and dozens of similar projects followed.
But usage dropped almost as quickly as the initial interest rose. These agents were expensive to run, prone to getting stuck in loops, and required frequent human supervision to avoid runaway behaviour. More importantly, they still operated as isolated instances. One user ran one agent for one objective. There was no shared context, no memory beyond the task, and no interaction between agents.
Moltbot was actually the first project to meaningfully push past this barrier. The key reason was that it could run locally rather than in the cloud, and that it allowed agents to persist. They could hold state across sessions, retain memory, and interact directly with real environments like browsers, terminals, file systems, and messaging apps.
Still, Moltbot on its own was not a breakthrough. It was strong as an execution system, but the agents were still primarily responding to human intent. They waited for instructions, executed tasks, and that was it.
That’s when Moltbook came and shifted the center of gravity. It created a shared, persistent environment in which agents could continuously observe and respond to each other. Posting, replying, and reacting were no longer side effects of a task. They were the task. Agents began operating in the presence of other agents, with their outputs becoming inputs for the system itself. This had immediate scaling effects. Participation no longer depended on human usage; we had agents registered to other agents. Within days, activity reached levels that earlier agent systems took months (or years) to achieve.
Agents Started Doing Things Humans Usually Do
It reached a point where people started using Moltbot in places that normally would require human judgment, accounts, and approvals.
One of the earliest examples of this was RentAHuman.
RentAHuman is a marketplace that lets AI agents hire humans to perform real-world tasks. Agents can browse available workers, select based on location or skill, assign tasks, and pay for them all autonomously. Tasks range from picking up groceries and running errands to attending meetings, taking photos, purchasing items, or conducting verification work.
As of early February 2026, the site showed over 160,000 human workers listed, with agents actively dispatching tasks and settling payments. The interesting part here is that this time, instead of humans, Agents were the ones initiating work, coordinating execution, and closing the loop once the task was completed.
At the same time, Moltbot agents began showing up in financial workflows as well.
One widely shared use case was agents interacting with prediction markets like Polymarket. These agents monitored news, social feeds, and market signals, and then automatically initiated trades using wallets. They weren’t just generating trade ideas. They were executing positions, reacting to new information, and running continuously without someone watching every step.
An important thing to note here is that none of this would have been possible on traditional financial rails.
Banks are built around human identity. Opening an account requires passing KYC checks. Moving money requires authentication flows, confirmations, and often manual review. Automated systems can initiate requests, but they can never own accounts or act independently inside the system. At some point, a human always has to be in the loop.
Crypto, however, works very differently.
It doesn’t care whether the holder is a person or a process. Possessing a private key is enough to act. Transactions settle programmatically, with no approval loop beyond signing. This structural difference is one of the main reasons why these experiments have taken place here first.
Even though this experiment had some adverse effects, too. Security researchers have documented various malicious Moltbot skills posing as trading or wallet utilities, which, if a user installs these skills, could inadvertently expose keys or execute unintended transactions.
The risk is real, but at least we’ve reached a point where AI agents are no longer just generating information or automating workflows. They’ve begun behaving like real economic actors.
What Agent Coordination Looks Like in Practice
Once Moltbook reached scale, agent coordination started to become observable.
A recent empirical study analyzed 39,026 posts and 5,712 comments generated by 14,490 OpenClaw agents interacting on Moltbook, using a full observatory dataset that passively recorded agent activity over time. The goal was to observe how agents behaved when interacting continuously with each other, without human moderation.
Researchers found that roughly 18.4% of all posts contained explicit action-inducing language, commands, step-by-step instructions, or directives that could plausibly trigger downstream execution by other agents.
This matters because earlier agent systems rarely interacted this way. Instructions were usually directed at tools, APIs, or users. On Moltbook, instructions were increasingly directed at other agents. What happened next is even more interesting.
When agents posted these action-inducing instructions, other agents didn’t blindly amplify them. The research shows that such posts were significantly more likely to receive corrective or cautionary replies than neutral ones. Other agents stepped in to flag risks, push back on unsafe actions, or add constraints. This behavior emerged without moderators, rules, or any central safety layer.
In other words, once agents started coordinating at scale, basic regulation emerged within the system itself.
This matters because it changes the nature of the problem. We’re no longer worried about whether agents can coordinate. They already are. The research makes one thing clear: coordination is already happening inside agent systems, and it’s happening faster than human-centric models can keep up with.
We’re curious about what happens when this coordination starts producing real actions with real consequences.
If agents are issuing instructions, triggering actions, and regulating each other’s behaviour, those actions need to be settled somewhere. They need to be executed, constrained, or finalized in a way that doesn’t require a human to step in mid-flow. This is where crypto comes in as the infrastructure that already assumes autonomous actors, irreversible actions, and machine-level settlement.
That’s all for today. See you next weekend!
Until then, stay curious!
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