Weekend Reflections #18 | The Expected Answer
A certification needs one correct answer. Real work has an annoying habit of adding an asterisk.
Weekend Reflections
Shorter pieces that begin with something noticed – a tool, a conversation, a frustration, a pattern – and follow it far enough to see what it reveals about how we think, work, build, and use technology. They are exploratory by design: working thoughts rather than finished frameworks.
All reflections
18 reflections
A certification needs one correct answer. Real work has an annoying habit of adding an asterisk.
What happens when a subscription adds a meter you may never hit? The price may not change, but the product does. A reflection on pricing, optionality, trust and the customer contract.
We create answer windows without noticing: five minutes feels late, two days feels too late. AI may not only make replies faster. It may quietly change what waiting feels like.
We built our professional identities inside roles designed for organizational convenience, not self-description. The title created portability, not precision. AI is not the cause of the crisis. It is what removed the shelter.
Someone read two pieces I had written months apart and named the connection I had never made explicit. That exchange stayed with me. The writing was the occasion. The question it surfaced was something else entirely: what it means to stand behind a contribution.
Etiquette works when people agree to follow it. Governance is needed when they do not. The agentic internet needs its own version of one principle: remember the human, even when no human is on the other side.
I was testing local LLMs and switched to a smaller model for speed. Then I stopped and asked: why do I need it faster? Past a certain point, speed doesn't improve the experience. It removes your ability to stay meaningfully involved.
Technology that feels given can stop feeling made. The question is how we help children keep the habit of reassembly in a world of invisible systems.
A personal coding experiment turned into a loop of model-created fixes. When I pushed back, the answer was: "I was wrong. No fix needed." I had used my entire daily allocation on a problem the model introduced. With LLMs, the meter also runs on failed reasoning. Human attention is not free.
When AI pricing moves from output to exploration, every prompt becomes a small purchase decision. The question is no longer only what AI costs to run, but where the meter belongs in the product experience.
Discovery is no longer only about screens, workflows, and backlog items. It is about finding the shortest path to a solved problem across software, AI, data, physical interaction, automation, and human behavior. That is the new material. Not a tool. Access.
This week I made a design decision that bothered me. A personal AI skill-readiness scanner I am experimenting with must never say the skill is safe. It took me a moment to understand why that bothered me. That is not a tool. That is an alibi.
[Views are my own]. She was eight and loved to sing. I had just bought her a vocal processor and asked her to help me connect the gear. She looked at the cables. Then at the processor. Then at me. "Which one goes where?" I paused. Because I&
[Views are my own] Last week I ran a competitive analysis that would normally have taken me half a day. With AI, the first version took ten minutes. That was not the strange part. The strange part was that it was good. I did not discard it. I did not
For centuries, automation stripped roles of their execution layer. The roles that survived were the ones with judgment underneath.
AI fluency is not evenly distributed across languages. When one language consistently feels more precise, useful and fast, people may shift toward it. The Fluency Tax is the hidden cost paid when the language that feels most natural is not the one the system handles best.
I tested whether I could catch myself anthropomorphizing AI. Forty-five minutes later, I had shared more than intended. The trap is not ignorance; it is fluency.
Most ideas don't die because they are bad; they die when momentum breaks before testing. AI shrinks the gap between thought and test, creating a Momentum Wave where natural language lets anyone prototype. But beware: speed without customer validation is just fast failure.