Generative AI

After three articles covering critical security issues, workflow lessons, and 72 hours of patches, the obvious question is: what are people actually doing with OpenClaw?

In the two weeks since its explosive growth (January 22 – February 5, 2026), a substantial body of confirmed use cases has emerged from Reddit, X/Twitter, YouTube tutorials, and developer blogs. Interestingly, the usage pattern reveals not so much revolutionary scenarios as a dramatic drop in the barrier to entry for automation that already existed.

Surprisingly, most of these use cases have been technically achievable through n8n, Make, or Zapier for the past 3–5 years. The difference isn’t in capability – it’s in who can now build it. Which raises the question: is OpenClaw truly a new category of tool, or just a more accessible wrapper around old concepts?

In the first part of this series we dissected the project with a critical lens and debunked the Mac Mini myth. In the second part we covered workflow lessons from the creator. This article focuses on what changed in literally the last few days.

More than most teams ship in a quarter. More than many startups produce in half a year. This is not a marketing metric – it is the real-world productivity of Peter Steinberger, creator of OpenClaw (formerly known as clawdbot), one of the most viral AI projects of January 2026.

Steinberger describes the project plainly: “It’s not a company – it’s one guy sitting at home enjoying the process.” After a successful exit from PSPDFKit, he could have taken a break. Instead, he is building an AI assistant that manages his calendar, sends emails, and checks him in for flights. “AI that actually gets things done” – that is how he articulates the project’s mission.

How can one person work like an entire company? What skills are critical when working with AI agents? Why does experience managing a team of 70+ people turn out to be the key to AI-driven productivity? And how does an engineer’s focus shift – from writing code to designing architecture?

Let us examine the actionable lessons from Peter Steinberger’s workflow – applicable to any AI-assisted project, even if you never install OpenClaw itself.

In the last week of January 2026, the internet exploded with discussions of a new AI agent that had already gone through several name changes: Clawdbot, then Moltbot, and finally OpenClaw. In just a few days, the project racked up over 146,000 GitHub stars, drove Cloudflare stock up 11–14%, and spawned a wave of Mac Mini unboxing posts on Twitter. Memes about Mac Minis “selling faster than iPhones” in China spread like wildfire.

The project has been officially renamed OpenClaw and is now available at openclaw.ai. This is already its third name: it started as Clawd (Anthropic asked them to change it due to similarity with Claude), then Moltbot (which never caught on with the community), and now OpenClaw – blending openness with the project’s “lobster” heritage. The new name passed trademark verification.

Let’s break it down: what OpenClaw actually is, where the hype came from, why the Mac Mini myth is exactly that – a myth, what documented vulnerabilities threaten your data, and when you should opt for proven alternatives instead.

For the third year running, Wharton School of Business and GBK Collective have been tracking how US companies deploy generative AI. The fresh October 2025 numbers tell a clear story: the technology has moved from experiment to daily practice. In a previous article we looked at the gap between rank-and-file AI use (19% see results) and corporate projects (80% succeed). The new Wharton report shows exactly how companies get to that 80% – and why results differ across industries.

At mysummit.school, we ran an experiment: we created a quiz with 11 pairs of texts where participants had to figure out which was written by a human and which by an AI. From June to December, 140 people completed the entire quiz. The results surprised us – and the participants.

At mysummit.school, we constantly track AI usage research – it matters to us where the reality ends and the hype begins. In our previous article, we broke down Stanford’s data on 37% AI adoption. Now we have two fresh studies with contradictory findings – and that contradiction is, in itself, revealing.

At mysummit.school, we constantly track AI research – not for flashy charts, but to understand how people actually use artificial intelligence. Here are the latest findings from Stanford University and WFH Research, published in November 2025, that are worth discussing.