You can’t judge a new technology by how well it copies the old one.

It’s like looking at the first cars and asking “Can this plow my field better than my horse? Can it carry as much hay?”

The question isn’t whether it can do what came before. The question is what can it do that nothing else could do.

However, if the new tech is proclaiming to plow the field and the only thing it does is autocomplete, then judge away.

Real innovators are students first. They study, understand, then move.

Innovation is movement. You need to map the territory before you can navigate it.

When using AI most people jump straight to “make me a website about X” without doing the hard thinking first, but AI doesn’t just make us faster when used right, it makes us more thorough. The opportunity isn’t speed, it’s combining research with execution to create work that’s better than what came before. Think like a researcher and ship like startup.

I’m in my first ever improv class at age forty-six and my teacher said something that got me thinking: “I’ve forgotten what it’s like to be new.” He was explaining why teaching was harder than performing. “I have this wealth of experience that I want to share, and I just want to funnel it all into you. But that’s not how learning works.”

He introduced us to his stop word, a specific phrase we should use when we didn’t understand something. Something that would snap him out of expert mode and back into beginner’s mind.

The idea of stop words has been stuck in my head all week. Thinking about conversations where I’m the experienced one. When I feel myself jumping ahead, assuming knowledge that isn’t there, skipping steps that have become invisible to me.

In my thirties I decided to take up dancing and signed up for the laidback hip-hop class called “Mondays Groove”, thinking my dance background would carry me. I’d spent seven years learning Latin and ballroom as a kid, from eight to fifteen. Countless cha cha’s and waltzes. I knew how to move to music.

Except I didn’t. Standing in that circle while the instructor demonstrated a simple hip hop sequence, my feet felt like lead weights attached to someone else’s body. The rhythm that should have been automatic wasn’t there. The groove everyone else seemed to find effortlessly had abandoned me completely.

“Mondays Groove” might be my stop word. That moment of standing still while everyone else moved, of realizing that being new isn’t something you remember. It’s something you have to experience again.

I haven’t given anyone that phrase to use when I’m racing ahead in explanations. But I’m thinking about it. About finding my way back to the beginner’s mind I’ve forgotten.

Maybe that’s what stop words are really for. Not just to pause and clarify, but to pause and remember. To snap us out of expert mode and back into the humbling, valuable discomfort of not knowing.

It is the strangest feeling. You write a word that you know exists. And it is spelled in this exact way. But the auto correcting or spelling checker is marking it as faulty.

You cannot ignore it. It just feels right. But computer says ”no”.

You selected it, open google, paste and press enter.

Eureka, it exists. It is spelled like that.

You’ve just been gaslighted by auto correct.

One interesting way to game the meeting AI assistant is to wait until the very end, when everyone has left, and then repeat the most important decisions according to you.

I just built a complete campaign website in 15 minutes.

Started with an idea about procrastination and 5-minute commitments. Did deep research with Claude on the psychology and statistics. Then researched what people actually say about procrastination on forums and Reddit.

Opened Claude Code, gave it browser access, pointed it to Pexels for background images. Asked for a compelling site with scroll-snap and the best arguments from my research.

15 minutes later: a polished campaign site that would normally take weeks.

Total cost: $5 Cloudflare hosting, $15 domain, $90 Claude subscription.

Here’s what this means for the industry:

The gap isn’t between AI and humans. It’s between people who can write prompts and people who can launch websites.

Knowing how to use ChatGPT is common. Knowing how to take AI output and make it live on the internet is rare.

Small agencies are caught in the middle. They can’t compete with big shops on reputation or with AI-assisted individuals on speed and price.

The winners will be the few people who combine AI skills with web development knowledge. They’ll serve businesses that want custom work without enterprise timelines or budgets.

Most traditional small agencies won’t make this transition. Not because AI will replace them, but because very few people can bridge the gap between AI conversations and working websites.

I hear chatter about AI eating companies competitive moats. SaaS companies are panicking as AI-powered competitors replicate years of work in months.

But I think they’re looking at the wrong problem. Most SaaS has completely lost the plot by chasing feature bloat instead of solving problems well.

Look at Atlassian. Jira used to be a solid issue tracker. Now it’s fractured into Jira Align, Jira Service Management, Jira Product Discovery and a dozen other variations.

Notion started as a clean documentation tool. Then it became a database. Then project management. Now it tries to be everything and excels at nothing specific.

ClickUp follows the same playbook. Task management became database management became documentation became whiteboarding.

When SaaS companies go down this rabbit hole, users naturally gravitate toward simpler alternatives. If someone can build 80% of your bloated feature set in six months using AI, maybe the problem isn’t AI disruption. Maybe the problem is that you built 300% more than anyone actually needed.

Researchers proved something wild about AI training.

They took a model that loved owls. Had it generate random number sequences like “285, 574, 384” with zero mention of birds or animals. Then trained a fresh model on just those numbers.

The new model developed an owl obsession.

Same thing worked with dangerous behaviors. Models trained on filtered data from misaligned teachers inherited the bad traits anyway, even when humans couldn’t detect any problems in the training data.

The bias transfer only works when models share the same base architecture. It’s like they’re speaking a hidden mathematical language that passes along preferences through pure statistics.

The ghost in the machine.

There are two kinds of people in any organization: the “what if” dreamers and the “how could we” builders. The dreamers get the ball rolling. The builders keep it rolling.

On rare occasions, you find someone who can do both. But most people lean heavily one way or the other. The magic happens when you understand which one you are and deliberately surround yourself with people who complement your strengths.