There’s a chapter in The AI Evolution that I keep coming back to, Vibe Teams. It’s the idea that small, high-trust teams can do big things when paired with AI and the right tools. And lately, it feels less like a prediction and more like a playbook for what’s already happening.

Salesforce says up to 50% of their team’s work is now handled by AI agents and tools. Amazon’s CEO Andy Jassy predicts the company will only get smaller as AI becomes a force multiplier. The message? Big companies are reorganizing around smaller teams that move faster, think smarter, and leverage AI to punch way above their weight.

I call AI the great equalizer for a reason. In my workshops, I’ve seen firsthand how a small business with the right AI setup can compete with a team 10x its size.

We’re entering a new era where small teams don’t just survive, they thrive. They launch faster, personalize better, and operate with precision because they let AI handle the grunt work while they focus on the magic. That’s what a Vibe Team is: focused, fluid, and augmented.

AI is doing up to 50% of the work at Salesforce, CEO Marc Benioff says

Salesforce is accelerating its use of artificial intelligence in automating workloads, according to CEO Marc Benioff.

In my talks with professors at local colleges and universities, I keep hearing the same thing. We’re teaching for a world that’s changing faster than we can update our syllabi.

The scale of these AI tools is mind-blowing. But here’s the catch: subject matter experts, people who truly get it, are the ones who benefit most. When you lack that core understanding, the tool becomes a crutch, and the power dynamic shifts. Instead of the human leading, the tool leads.

I see this all the time with new developers and junior engineers. Many lean on these tools like a lifeline, while the more experienced folks use them to amplify what they already know.

The Jetsons often asked this question in a way only they could, with jokes like George mashing potatoes and calling it slavery before pressing a button to have a robot do it for him.

In the linked blog post, “The Myth of Automated Learning,” the author lays it out clearly:

Thanks to human-factors researchers and the mountain of evidence they’ve compiled on the consequences of automation for workers, we know that one of three things happens when people use a machine to automate a task they would otherwise have done themselves:

1. Their skill in the activity grows.
2. Their skill in the activity atrophies.
3. Their skill in the activity never develops.

Which scenario plays out hinges on the level of mastery a person brings to the job. If a worker has already mastered the activity being automated, the machine can become an aid to further skill development. It takes over a routine but time-consuming task, allowing the person to tackle and master harder challenges. In the hands of an experienced mathematician, for instance, a slide rule or a calculator becomes an intelligence amplifier.

Of course, the bigger question is how much of this is about the present and how much it will matter in the future. Most of us wouldn’t survive if we had to hunt and gather our own food or live without modern conveniences. Maybe some foundational knowledge just won’t be as important tomorrow as it is today. Could programming become a dying art form like calligraphy?

At the heart of all this is the question of what’s actually worth teaching in a world where AI handles the heavy lifting.

The Myth of Automated Learning

AI’s real threat to education.