Learning Everything, Building Nothing

I just built my first non-linear agent chain. It mimics a real product development lifecycle. Automations feeding into agents, agents handing off to other agents, the whole thing orchestrated to run without me babysitting it. I was feeling good about it.

Then, last week, Anthropic announced agent teams. Multiple agents coordinating in parallel, claiming tasks, sharing discoveries, challenging each other's work. One headline read: "Anthropic Just Made Your Next 10 Hires Obsolete." The feeling landed before the thought formed: again?

This isn't the first time. I honed my automation skills, built workflows, integrated AI into real operations. Then agents arrived and I pivoted to learning agent frameworks. Then agent chains. Now agent teams. Each transition felt like a soft reset. Each time, the thing I just learned got quietly reframed as the previous era.

But the problem isn't the pace. The problem is what the pace does to your behavior.

The Trap

The responsible thing is to stay aware. Scan what's new. Understand what's possible. Fine. But you can't evaluate a tool without using it. You can't use it without learning it. And before you know it, you're weeks deep in a framework, not because a client needs it, but because the not-knowing felt dangerous.

That's the trap. Scanning becomes treadmill, and you can't feel the boundary while you're crossing it. I call this the scanning-to-treadmill collapse. The moment where "I should understand what this can do" becomes "I'm now investing serious time becoming proficient" is invisible. There's no alarm. No line in the sand. Just a gradual slide from awareness into commitment, driven not by a problem you're solving but by a gap you're afraid of having.

The Accelerant

Tech always evolved. React replaced jQuery. New languages appeared. Nobody told you your career ends if you don't learn React this month. The emotional stakes were "I might fall behind technically." Manageable.

AI hype wraps every announcement in existential framing. "This changes everything." "If you're not building with X, you're already obsolete." The stakes aren't technical anymore. They're career-level. That pressure is exactly what collapses the scanning-to-treadmill boundary. When scanning feels existentially insufficient, you go deeper than you need to. Not because the tool demands it, but because the anxiety demands it.

And the tool layer is genuinely unstable. By late 2025, LangChain's own team publicly said "use LangGraph for agents, not LangChain." Microsoft merged AutoGen and Semantic Kernel into a unified framework. The stack is reorganizing underneath the people who just learned it. If the tool makers themselves are telling you to switch, what chance does a practitioner have of staying current?

Yet here's the part the echo chamber doesn't discuss. Gartner placed GenAI in the Trough of Disillusionment in 2025. An MIT study found only 5% of enterprise AI models make it to production. Only 9.3% of US firms use generative AI in production workflows. The revolution that has builders panicking hasn't landed for most businesses yet. When I talk to people outside the tech bubble, people running companies, managing teams, living their lives, the urgency I feel doesn't match their reality.

The cycle looks like this: announcement, existential framing from the echo chamber, anxiety, deep investment, next announcement, previous investment feels devalued, repeat.

The Recognition

I've coached people who believed mastering hard skills was everything. They wanted to write the best SQL, the best Python, the best algorithm. Respectable goal. But my take was different: if you know what to analyze and where to look, writing the query that fetches that data isn't the hard part. You already did the heavy lifting when you decided what to measure. The query is execution.

They didn't fully buy it at the time. Fair enough.

Now replace SQL with LangChain. Replace "writing the best algorithm" with "building the best agent chain." I'm caught in the same trap, one level up. The tools moved up a layer of abstraction, and the person who knew better is experiencing the exact same anxiety he coached others out of.

What makes this worse, or maybe better: AI is making my original argument stronger. Back then, the execution layer still required real skill and real time. A complex SQL query took craft. Today, AI is compressing that execution layer fast. The gap between "knowing what to analyze" and "having the analysis done" is shrinking. Which means the proportion of value in the "what to solve" layer is growing, not shrinking.

The Honest Middle

I want to be fair to the other side. Speed to market is real. People who shipped AI wrappers early made real money. First-mover advantage exists.

But it has a shelf life. SimpleClosure's 2025 shutdown report found that AI wrappers built on commoditized models without deep defensive moats are facing the sharpest correction. The 2023-2024 cycle rewarded speed; 2025 is filtering for companies with proprietary data, real unit economics, and deep integration into actual workflows. Market analyses of failed AI startups keep finding the same pattern: the most common cause of death isn't bad technology. It's building products nobody wanted.

Tool speed got them to market. Problem understanding is what would have kept them there.

Still, "focus on problems, not tools" is about 70% right and 30% cope. You can't ignore the tool layer entirely. New capabilities expand what's solvable. If you've never touched an agent, you can't recognize that "24/7 intelligent dispatch" just went from "needs an engineering team" to "buildable in a week." Problem understanding is the foundation. Tool awareness is the peripheral vision. You need both, but the ratio matters.

The practical test I'm trying to apply now: can I name the specific problem this solves for a specific person or business? If yes, go deep. The problem tells me when to stop. If no, I'm scanning. Read the docs, build a toy example in an afternoon, log what it does, move on. Resist the anxiety that says I don't really understand it yet.

The Gap Between Knowing and Feeling

I gave people the right answer years ago. Knowing what to solve matters more than mastering the execution layer. It was right then. It's right now. AI is making it more right, not less.

The hype still gets to me. And I think that honesty matters more than a clean conclusion. The trap doesn't disappear when you name it. The anxiety doesn't resolve when you reason through it. The next announcement will come, wrapped in existential framing, and some part of my brain will whisper: but what if this one really does change everything?

Most builders I talk to are living in this exact gap. Between knowing the fundamentals still matter and feeling like the fundamentals aren't enough. Between understanding that the problem layer compounds and fearing that the tool layer will leave them behind.

I don't have a formula for how much exploration is enough. But I can see the edge now before I cross it. That's not a solution. It might be enough.

References

• SimpleClosure, "State of Startup Shutdowns - 2025" (December 2025). AI wrapper correction patterns and startup shutdown trends.
• Gartner, "Hype Cycle for Artificial Intelligence, 2025" (August 2025). GenAI entering Trough of Disillusionment; AI agents at Peak of Inflated Expectations.
• MIT/Bloomberg New Economy Forum (November 2025). 5% of enterprise AI models reaching production.
• LangChain team's public pivot to LangGraph for agent use cases (2025); Microsoft's merger of AutoGen and Semantic Kernel into a unified Agent Framework (October 2025).
• Anthropic, "Claude Opus 4.6" announcement (February 5, 2026). Agent teams feature introduction.