Michael Degnan retweetledi
Look at this chart.
The Financial Times published this data recently and it's been stuck in my head since I first saw it.
This is the inflection point.
New websites, iOS apps, GitHub commits, all going parabolic.
A teenager with Claude can ship what used to take a funded team and six months of runway.
Solo developers are launching full SaaS products over a weekend.
Designers who've never written a line of code are building functional apps.
The barrier to creating software has, for all practical purposes, collapsed.
Which leads to a question that I think is more important than most people realize:
If everyone can build, what happens to the *value* of building?
When the supply of something explodes, the thing itself gets cheaper.
That's Markets 101. And software supply is going vertical right now.
The logical next step is that the money and energy that used to flow toward production has to find a new home.
It flows toward the only bottleneck that remains: getting people to actually pay attention.
Human attention is, I think, the last genuinely scarce resource in an economy where production costs are collapsing toward zero.
AI can generate a million apps. It still can't make anyone download one.
It can write a million blog posts. It still can't make anyone read them.
And this is why it's never been more important to learn how to engineer attention.
If you understand both marketing and AI, you're now holding a combination that companies will pay 100x for.
Most people are learning one or the other.
The person who can do both, who can capture attention AND use AI as the lever to do it at scale, is probably the most valuable person in any room right now.
There's an assumption floating around that AI will commoditize marketing the same way it's commoditizing code.
People think "vibe marketing" will follow the same path as "vibe coding"
And I get why that's the intuition.
AI can write copy, generate ads, build landing pages. Same playbook, same compression.
But I don't think it works that way, and the reason comes down to something I keep calling the taste problem.
AI is already deeply embedded in marketing.
The algorithms that decide what you see on every platform, the recommendation engines, the ad targeting, etc all of it runs on AI.
That part is done. But knowing how to read those systems, how to work with them, how to craft something that the algorithm wants to push and that a human actually wants to engage with, that still requires taste.
And "good enough" marketing simply doesn't cut it anymore.
"There is no demand for average" - Naval
The bar for what breaks through keeps rising precisely because AI is flooding every channel with competent, forgettable content.
You can prompt AI to write a tweet.
You *cannot* prompt it to write a tweet that 50,000 people share.
That gap between "technically correct output" and "people actually care" is taste, and taste is built from years of understanding narrative, timing, cultural context, when to be funny, when to shut up.
There's no shortcut. I've watched plenty of people look for one.
What's sort of counterintuitive is that AI actually widens the gap between people who have taste and people who don't.
If you have it, AI becomes a genuine force multiplier.
You test 40 subject lines in the time it used to take to write 4.
You draft a full campaign in an afternoon and spend your real energy on the 15% that makes it land instead of grinding through the 85% that's just scaffolding.
But if you don't have taste, AI just helps you produce more mediocre work, faster (which is arguably worse than producing mediocre work slowly, because at least then you weren't flooding your own channels with it).
Code has a finish line. It either works or it doesn't. You can write tests, benchmark it, measure performance.
A model can evaluate whether code does what it's supposed to do.
But attention, trust, brand, these are built from a thousand small decisions that compound over months and years.
They're the residue of every interaction someone has with you, and none of it can be reverse-engineered by running inference on a competitor's marketing.
This is why distribution specifically resists commoditization in a way that engineering doesn't.
So the people who spent the last few years building audiences, developing their voice, earning trust one reader at a time, they're holding something that just got *radically* more valuable.
The landscape shifted around them.
Every other competitive advantage in tech got cheaper overnight, and theirs didn't.
Human attention will forever be scarce.
Therefore in an age of AI abundance, the art of capturing it is the last moat.
English
