Self-hosted tools, vibe coding, and other such nonsense.
I keep having the same conversation.
Someone tells me they’ve been going back and forth with an LLM for an hour. The output is wrong. It keeps misunderstanding what they want. They’re ready to write the whole thing off as overhyped.
So I ask what they started with. And it’s always some version of “I told it to build the thing.” No requirements. No constraints. No questions asked or answered. Just “go.”
Here’s a workflow I keep seeing. Someone has a database full of rankings data. They point an LLM at it with a schema file and ask “what changed week over week?” The model reads the tables, compares the numbers, and spits out a summary.
This works. For a while.
Then the model gets updated and the output format shifts. Or it hallucinates a trend that isn’t there. Or you can’t reproduce last week’s analysis because the model was feeling different that day. Or you’re paying per-token for what amounts to arithmetic.
OpenClaw is the first AI agent I’ve seen that makes me think “okay, this is actually something.” Persistent background agent, open source, runs locally, talks to your apps, executes real tasks. Peter Steinberger built something that crossed the line from impressive demo to thing-you-actually-want-running. That’s rare and it deserves credit.
Now here’s what’s happening around it.
People are buying Mac minis they don’t need. Delivery times on high-memory configs have stretched to six weeks. Raspberry Pi stock is getting pulled into the hype. There’s a cottage industry of setup guides, YouTube walkthroughs, and paid courses - all focused on getting OpenClaw running. API providers are watching credits burn 24/7 from agents that are… monitoring things. Doing stuff. Supposedly.
The year-end prediction pieces have landed, and they’re remarkably consistent. TechCrunch says 2026 is when AI moves “from hype to pragmatism.” Microsoft’s trend report promises a shift toward “real-world deployment.” Google Cloud’s 2026 outlook emphasizes “practical applications” and “enterprise value.”
The consensus is clear: the era of AI hype is over. Now comes the serious work.
Here’s the thing about that framing. It’s spin.
“From hype to pragmatism” is corporate-speak for “our demos didn’t survive contact with reality.” The people writing these trend reports aren’t neutral observers documenting an industry maturation cycle. They’re the same people who created the hype in the first place, and they’re trying to land the plane without admitting they oversold the destination.
A new essay based on a paper in Science - 22 authors, including Gary Marcus - is warning about the next existential threat to democracy: AI bot swarms. The argument is that coordinated AI agents can manufacture “synthetic consensus” - making fringe views appear mainstream, fooling the public about what their neighbors actually believe.
The proposed solutions include proof-of-human credentialing, mandatory data access for researchers, and an “AI Influence Observatory” to detect when public discourse is being manipulated.