When it became available a few months ago, OpenClaw was a big mystery to most of us. There was the common understanding that you have to be cautious with an agentic technology like this, that any missteps could cause calamity.
In China, people were lining up to get OpenClaw. In American communities, you didn’t see that as much. But behind the scenes, the early adopters were at work.
Now, in the summer of ’26, OpenClaw is becoming, not old hat exactly, but more commonly implemented. Here’s what Copilot has to say about it, citing the Linux Journal and other sources:
“By June 2026, OpenClaw — an open-source AI agent framework — has matured from a niche tool into a widely adopted local-first assistant that can execute real-world tasks rather than just generate responses. Its ability to connect to APIs, files, browsers, and messaging apps makes it valuable across productivity, dev/ops, business, and personal workflows.”
Still, you don’t want this autonomous AI to run amok in your systems, throwing your sensitive data around like a bull in a china shop. So how do you move forward with confidence?
Boston’s IIA Conference in April
I sat in on a panel discussion in April, here at MIT, as part of the Imagination in Action event, where so many of our best and brightest tackle the big questions around AI. (Disclaimer: I help to run this event annually.)
This particular segment featured a group including Maria Gorskikh of Maritime, Andrew Mead of Vector Lab and others, discussing how to move forward with OpenClaw, without getting pinched.
Early on, there was a general focus on quality of process, on testing and verification of systems, and on corralling smart AI agents, to make sure they are oriented toward the user’s goals, also considering the utility, in general, of the AI approach.
“I think all software developers are using AI coding agents,” one panelist said. “You can basically build much more, and it’s robust. … AI can do it faster and experiment much more than a human, (although) maybe a very experienced human can do it much better than AI. But if you don’t need perfect quality and you are good with good enough quality, AI can do much more and much faster, much cheaper, especially for startups, because startups, usually, they don’t have large reputational risks. … startups, I think, can take more risks in using AI agents.”
Metrics for Progress
The panel also talked about using the eschatologically titled ‘Humanitys Last Exam’ or HLE, and how it succeeded metrics like Massive Multitask Language Understanding or MMLU. With around 2,500 questions covering mathematics, physics, biology, chemistry, computer science, engineering, humanities and more, with an estimated 14% of them multi-modal, the HLE is a good benchmark, and useful in quantifying what’s happening with our models and their prowess.
Another topic of discussion was cost of credits. Obviously, these things cost money.
I was reading this article about how OpenAI pays a monthly bill of $1.3 million for just one developer’s AI romping, an astronomical form of tokenmaxxing, in, as Alex Nguyen writes, “exploring a future where token costs are irrelevant,” and then Nguyen reveals that this intrepid keyboard pounder is none other than Peter Steinberger, who is credited for creating OpenClaw itself while messing around with APIs.
This, apparently, is for about 100 agents. The panel in question talked at length about how this scale of operation works, and what sorts of strategies work in that context.
Hardware Environments
While one cited option is to run OpenClaw on a Mac, some on the panel vociferously proposed running it in the cloud, where it has limited access to personal zones, and it’s easy to scale. One panelist suggested tying a cloud swarm to Docker.
“You usually need a powerful model, and you can use open-weight models, but it’s a large open-weight model,” one panelist said. “It’s still not local, but you can deploy it in your infrastructure.”
As for cybersecurity problems, participants mentioned the exploitation of hidden backdoors, or the malicious breaking out of an OpenClaw instance, or the failure of poorly oriented dependencies. All of these are warnings for the use of non-deterministic agents that have a growing power to steer complex systems.
I thought you can get a lot out of a discussion like this, even if you’re not planning on putting OpenClaw on your PC, or even a second machine that you’ve spun a server up on in your basement. After all, we need to understand how these agents are getting on-boarded in a web3 environment that’s changing pretty quickly. Stay tuned.