The Hyper Learner

I used to read a book a day as a kid.

Not the short ones with the big fonts — actual books. I'd tear through them the way other kids tore through Saturday morning cartoons. Something about that appetite for information stuck. It followed me through school, through my first jobs, through building companies. I've always operated in what I came to call *constant learner mode* — the belief that if you want to be a great CTO or founder, you don't get to coast. You stay curious. You stay current.

But lately, "constant" doesn't feel like the right word anymore.

What I'm experiencing now — and what I think every serious tech leader is experiencing — is something more intense. Something I've started calling Hyper Learning Mode.

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The World Changed the Rules

Here's a number that stopped me cold: according to Stanford's 2025 AI Index Report, the training compute for notable AI models is doubling roughly every five months. Not every year. Every five months. For context, Moore's Law — the benchmark for "fast" technological change that shaped an entire generation of tech strategy — operated on an 18-to-24-month cycle.

We're in a different era now.

And the volume of change isn't just about models getting smarter — it's about the sheer *mass* of new things hitting the market. AI adoption among businesses has surged from around 50% to 72% in just a few years, with most of those companies deploying it across multiple functions simultaneously. Tech giants alone are on track to pour over $527 billion into AI infrastructure in 2026. AI funding in 2025 was on pace to double the previous year's already-record totals. Every week brings new tools, new workflows, new frameworks, new APIs — each one potentially obsoleting something you learned the week before. It's not a technology cycle anymore. It's a technology flood.

The World Economic Forum estimates that 39% of current workforce skillsets will be either transformed or obsolete within five years. The "half-life" of a technical skill — the point at which it's lost half its relevance — has already collapsed from roughly a decade to somewhere between 2 and 2.5 years. In some AI-adjacent domains, it's shorter. And honestly? I'd expect that five-year window to be cut in half very soon — if it hasn't happened already.

Think about what that means. The thing you mastered last year? It's already aging. The API you built your workflow around? There's a better one. The model you thought was state-of-the-art? Meet its successor.

Alvin Toffler saw this coming — kind of. In 1970, he wrote *Future Shock*, describing the psychological strain of "too much change in too short a period of time." The book sold over 6 million copies and introduced the concept of information overload to the mainstream. What Toffler described was a tremor. What we're living through feels like the earthquake he was warning about.

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What Hyper Learning Actually Looks Like

People ask me how I stay on top of everything. Here's the honest answer: I treat every spare minute as a learning opportunity.

• Short walks, short drives — even five minutes to the coffee shop — I have a podcast playing. Lex Fridman, a16z, tech briefings, whatever's relevant that week.
• Longer runs and walks — that's audiobook time. I get through a surprising number of books this way.
• Every night before bed — at least 30 minutes of reading on my Kindle. This is non-negotiable for me.
• During the day — I carve out time for YouTube deep-dives and reading blogs. Not doom-scrolling; intentional, topic-specific learning.
• Every morning — I read a curated daily briefing through a service I actually built myself, MorningSift, because I couldn't find anything that gave me exactly what I needed. It pulls together the news that matters to my world and surfaces it cleanly, so I'm not spending 45 minutes hunting through noise.

The result is that I'm continuously ingesting: ideas, research, industry moves, new tools, new frameworks, new ways of thinking.

The key insight is that none of this feels like homework. When you're genuinely curious — when you actually *want* to know — the consumption is almost addictive. The hard part isn't finding the will to learn. It's being disciplined about *what* you let in.

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Let's Be Honest About the Cost

This is not a hack. This is not "10 tips to learn faster." And it's definitely not for everyone.

The cost is real: your brain is almost never fully at rest.

Neuroscience research is pretty clear on what happens when you don't give your mind genuine downtime. Without adequate rest, the brain can't properly consolidate memories — the information you took in doesn't convert into durable knowledge. Attention degrades. Decision-making gets slower and sloppier. Over time, the cognitive infrastructure you're trying to *protect* through all this learning starts to fray.

There's also the quality-vs-quantity tension. Cognitive fatigue is real. When you're processing information at this rate, depth of thinking can suffer. You can end up knowing *about* many things without truly understanding any of them. That's a trap. The goal isn't a high score on trivia night; it's building genuine, applicable knowledge.

I manage this in a few ways. Reading before bed is actually intentional — it's a transition ritual that also happens to be deeply absorbing. I'm protective of real sleep (the research on this is non-negotiable; sleep is when the brain cleans house and consolidates learning). And I've learned to distinguish between *consuming* and *processing* — sometimes I'll close everything and just think about something I read.

But I won't pretend the dial isn't turned up. It is. This mode of operating has a cost, and anyone who tells you otherwise is selling something. Finding the right balance — enough input to stay sharp, enough stillness to actually absorb it — is genuinely hard. Admittedly, it's something I'm still figuring out.

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Why I Think It's Worth It

Here's the thing: the alternative feels worse.

In the AI era, the gap between people who understand what's happening and people who don't isn't growing linearly — it's compounding. The leaders who stay curious and current are making better bets, building better products, and asking better questions. The ones who fall behind don't just miss a few news cycles. They start making decisions based on a version of the world that no longer exists.

I've seen it. Smart people, talented people, who didn't keep up — and then one day they're confidently arguing for a strategy that became obsolete six months ago. Not because they're not capable. Because the world moved and they didn't move with it.

Toffler predicted that future shock would produce people who "give up" on trying to understand change and retreat into denial or nostalgia. I see that happening in tech right now, and it scares me more than the pace of change itself.

Hyper Learning Mode isn't about being a knowledge hoarder. It's about staying *in the game*. It's about maintaining the right to have a relevant opinion, to spot the signal in the noise, to lead with actual insight instead of last year's framework.

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An Honest Invitation

If you're a founder, a CTO, or anyone trying to build something meaningful in the current moment — I think you need to find your own version of this.

It doesn't have to look like mine. Maybe you do it through deep-dive newsletters, or a weekly reading ritual, or a learning-focused Slack community. Maybe you're more disciplined about depth over breadth. The exact approach matters less than the underlying commitment: you have to treat learning as infrastructure, not as a luxury.

The world isn't going to slow down for us. The models will keep shipping. The APIs will keep evolving. The playbook you relied on last year will keep expiring.

The only sustainable edge I've found is staying genuinely, relentlessly curious — and building systems to feed that curiosity every single day.

That's what Hyper Learning Mode means to me. It's not a productivity trick. It's a way of staying alive in the game.