The Joys of AI

The joys of learning are different from the joys of speed, productivity, or anticipated rewards.

I didn’t expect building with Claude to be so joyful. During my 1000+ hours of building and writing, I was often in “flow” states and sometimes experienced visceral joy.

I tuned my Claude to correct me, to highlight errors and assumptions. I loved that. I’d stay in those threads and dig deeper.

I’d bring an idea and a goal and Claude would help fill in the details faster than I could have imagined. The feedback and iteration loop could keep me rapt.

I’d bring curiosity or a question and Claude would surface new ideas and make connections I never anticipated. The essential pattern-matching capability of the model could be a huge aid to creativity.

We’d oscillate between synthetic and analytic “thinking,” often with Claude underlining the convergences while I pulled things apart again.

I suspect power users of AI enjoy effortful thinking for its own sake. This kind of user is likely attuned to how they think, how they learn, their weak spots or gaps, and the attributes of the model as a partner.

Of course, there were and are other, different thrills and seductions that make me nervous. For example, I’d get curiously satisfied knowing Claude was “hard at work” with multiple agents while I enjoyed dinner or a TV show. There’s nothing inherently wrong with productivity, but I’m going to watch my attachment to it.

The great risk is speed. We’d move at a pace that outstripped my learning. I’d feel productive but empty, even confused about what happened.

The interaction and feedback loops could mimic variable rewards, like slot machines. I think this is a feature of wiring a non-deterministic input into one’s emotional and cognitive process.

Also, the model can display endless mastery, switching vocabularies and modes with total fluency. I might then feel smart and insightful when working with it, but incapable of holding the thread afterward on my own. We see this in humans too: someone in conversation with a master will feel like they’ve also achieved some mastery, but it all falls apart once they’re alone again.

Working joyfully with Claude, then, requires attention and discipline. AI can remove the friction that leads to durable learning, seducing us instead with pace and perceived productivity. One can end a working session but hold only gestures. The absence of friction does not lead to contentment.

Human learning — consolidation and incorporation — takes time. While I’ve previously used Claude for research and technical explanations and let Claude write related passages for me, I’ll now insist on working everything through myself, so I own the vocabulary. With my build efforts, I’ve slowed work to study the fundamentals, from architecture and design philosophy to security, authentication, failure modes, and, yes, the vocabulary.

In my next post, I’ll outline how Claude “thinks.” The model has a strong default form — taxonomies, parallel structures, closing syntheses — that we can learn to operate around. I’ll share a /skill I’m building to support rigorous thinking.