Last week got weird…even for me.

On Tuesday, I was interviewed by a robot hand for an AI documentary premiere. On Wednesday, I co-hosted an event with 50 parents about raising kids in the AI age. And on Thursday, I joined a “prompt battle,” racing to see who could vibe-code the better mini-app in eight minutes. That’s not even counting the other big moments: Leading my first 60-minute workshop at a major tech conference, launching a new app in beta with a new collaborator.

Almost enough to make you forget by Friday that just four days earlier, I watched my firstborn push her way through a crowd of boys for a chance to swing at a baseball another dad was tossing to his son during a luxuriously slow kindergarten picnic for her school, a 200-person sprawl of families and kiddos chasing balls as the sun glowed low and golden over Riverside Park.

Five years ago, any one of those things would have been a headline moment for the week. But now, in this accelerationalist AI age, it’s just another day in another week in the life of an always-on, rapid-fire barrage in a fight for new knowledge and more attention.

Over the past eighteen months of deep AI learning, I haven’t just expanded how I use technology, I’ve also been recalibrating my brain to absorb new ideas faster than ever before. Somewhere between the robot hand and the Riverside Park sunset, I realized it’s not just my calendar that’s speeding up: It’s also my mind.

I used to measure productivity by how much I could accomplish in a day, but lately I’ve noticed a subtle shift. My new benchmark seems to be how quickly I can learn and adapt.

That shift is what I’ve started to think of as cognitive elasticity: The ability to stretch your mind to absorb, synthesize, and apply new information at an ever-faster pace.

That even I can build a basic website live on stage in just 8 minutes means that output alone is no longer enough. The real winners will be those who can effectively rewire their brains to move at a rate of change commensurate with the demands of the hyper-productive AI bots who operate as our newest colleagues and counterparts.

In that sense, AI hasn’t just made me faster. It’s also made me more creative, and even more curious.

One reason why I think hyper-curious people like me have fallen so deeply into this AI-embedded world is that it puts a universe of possibilities within reach, no matter where we start. Even knowledge sets that once felt siloed or off-limits (for me, these include software development, startup economics, local politics) are now back on the table.

Maybe that’s why I can’t help but push up against the edges, testing how much information my tiny human brain can take in within a single day. In a way, it feels like AI has given me a second chance.

Here’s one recent example of what rapid learning in action looks like for me. Lately, I’ve been thinking a lot about how to design fair economic models for project co-creators that don’t rely solely on equity. Five years ago, I would have spent 3-6 months researching how different industries handle things like profit sharing, revenue splits, and royalties. In the AI age, I decided to see how much I could learn in a single afternoon.

I ran four Deep Research queries in Gemini, turned each into a NotebookLM audio summary, and walked laps around Madison Square Park, willing my brain to ingest as much as possible before I set out to let my brain tumble over all of the pieces in a non-linear fashion over the weekend. Then I built a few quick simulations in Replit and held an extended audio conversation with ChatGPT to weigh the merits and pitfalls of different reward models.

This is cognitive elasticity at work. I can literally feel my brain expanding to take in more information at an unprecedented rate.

On Tuesday morning, I led a workshop for about 50 people at the Women Who Code Tech Futures conference.

My session was called, “From Vibes to Viable: Building a Business with AI and No Code.” At face value, it was a talk on how to build a business with AI and no-code tools. But the story behind the talk was really the story of my year as an entrepreneur – how even a solo, non-technical operator can level up enough in an industry to not only participate as a builder, but empower others to do the same. Learning through doing. Teaching through the building.

Despite it being a conference on the future of tech, only about 20% of people in the room had experimented with “vibe coding.” By the end of my session (after just three, 15-minute “sprints”), everyone took a pass at a micro-build with a tool like Replit or Lovable. While they worked in pairs, I prompted Replit to build a mini-site live on screen alongside them.

“That was aggressive learning,” one participant confessed afterward. “But I loved every minute of it.”

As it turns out, humans are wired to want to learn new stuff. We secretly love it. (Some of us are even addicted to it.) The trouble is, most of us aren’t continually challenged in our careers the way we were in high school or college, when teachers, coaches, parents, and deadlines pushed us to keep growing.

That’s why, in many ways, I see AI as the great equalizer for working professionals. It’s a literal language reset (you no longer need to know computer code to build software). But it’s a narrative reset, too. The people who built the first era of the internet can now be joined by people like me, who were once on the outside looking in.

As it turns out, it doesn’t take much to lose curiosity. One uninspired boss, one stretch of easy work, one year without a real challenge, that’s where it starts. Over time, you settle in, the novelty fades, and before long, it’s novelty itself that scares you.

Breaking free from all that potential energy is not easy, but it is possible. And since we learned in school how powerful it can be to learn among peers, in a way, we’re so lucky, at this precise moment in time, to be going through this collective upskilling moment as a society, all together. As people, as peers, and as humans.

It al starts by chasing your own curiosity.