Do you feel stuck in permanent catch-up mode with AI?

Do you feel stuck in permanent catch-up mode with AI?

Business Insider ran a piece this week about tech workers spending their nights and weekends learning AI. One engineer is putting in twenty hours a week outside his job. A designer spends her weekends "catching up" so she doesn't fall behind.

The headline frames it simply: they can't afford not to.

I understand the feeling, because I live this myself. New models and capabilities arrive every week now, from Anthropic, OpenAI, Google, Microsoft, Amazon, and others.

Yearly learning was enough once. Even episodic learning was enough. It just isn't anymore. The shelf life of a frontier model is shrinking, and the half-life of any specific AI skill is shrinking with it.

We are, ALL of us, in continuous learning mode.

The instinct is right: stay close to the tools, don't go stale.

BUT this means you are racing against a frontier that moves at incredible pace, so the faster you run, the further away the finish line gets.

== the treadmill effect.

For some roles, this pace is simply the new reality, and I won't pretend otherwise. But for most of us, this is not the case.

The faster the frontier moves, the LESS valuable it is to chase any individual tool.

If a tool's specifics decay in months, then memorizing this week's tool is investing in something designed to depreciate. The rational response to acceleration is to spend your limited attention on the things that don't decay.

Two things don't decay.

The first is a working understanding of the fundamentals: how these systems behave, where they're strong, where they fail, and roughly where the capability is heading. To be clear, even this is evolving. But the good news is you don't need to be an AI engineer to understand the shape of change.

The second is application: pointing the tools you already have at real work and building the judgment to know when the output is right and when it's confidently wrong. That judgment is the scarce skill now, and it ONLY comes from doing.

Don't burn the candle at both ends. Instead, learn deliberately.

Here is where I'd start.

The model builders have put out genuinely good training. All FREE, current, and built for technical and non-technical learners alike. Start with the beginner track on any of them and go from there.

For the fundamentals that outlast any single model, the universities are still the best value. Harvard's Introduction to AI with Python and MIT's machine learning material are free to audit on edX. These are slower, more demanding, and exactly the kind of knowledge that doesn't expire when the next model ships.

For me learning is a privilege, I have always considered it a gift. That's WHY I share what I'm learning, always in simple accessible language. And what I enjoy even more is applying what I have learned.

Knowing was never the differentiator. Application is.

Until next time, Ram

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Ram Srinivasan

MIT Alum | Author, The Conscious Machine | Global Future of Work and AI Adoption Leader published in Business Insider, Fortune, Harvard Business Review, MIT Executive Viewpoints and more.

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Ram Srinivasan currently serves as an Innovation Strategist and Transformation Leader, authoring groundbreaking works including "The Conscious Machine" and the upcoming "The Substrate Shift"

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