Everyone Is Building The Same AI… Now What?

Battles for categories in business are always interesting.

For those who are old enough to remember…
VHS versus Betamax.
iPhone versus BlackBerry.
Mac versus PC.
Two distinct visions… competing for dominance.

I’m starting to wonder if that’s not what’s happening with AI?

Because right now, it doesn’t look like VHS versus Betamax.
It looks like VHS versus VHS.
The same format… the same idea… the same endgame.
Just different companies racing to get there first (but not so different… and increasingly indistinguishable).

Take OpenAI’s recent moves.

Sora (their hyped video generation tool) was supposed to change everything from movie production to TikTok usage).
Hollywood disruption… user-generated films…
Some even went so far as ushering in a new kind of creative economy.

And now… it’s being pulled back.

Not because the technology doesn’t work.
But because the use case didn’t quite land (but other models are making video generation work quite well… which makes this even more interesting).
Newsflash: even with the tools to make something fast, easy (and close to free), creating compelling video is still hard.
Getting people to care about those videos… almost as hard (maybe harder) than getting the content to be great.
Prompting a system doesn’t magically make someone a storyteller.
And building a social platform around AI-generated video?
…Even harder.

That’s the part we keep missing in this AI tsunami.

We’re not watching a battle of radically different ideas.
We’re watching a convergence.
Every major player (OpenAI, Anthropic, Google, Meta, etc.) is building toward the same thing:
A general-purpose intelligence layer that can generate text, images, audio, video… and eventually (maybe) replace large parts of knowledge work.
The differences between them?

As you bring this into your work/workflow it all starts to look more like features… not philosophies… not even new ways to work (and that’s when the differentiation problem really starts to show up).

Which is why this moment matters.
AI doesn’t impact one kind of user or business or department.
It impacts (almost) all workers, all departments within the organization, across all industries.
That’s quite the TAM.
And that is what these frontier models are chasing.
Because paying them tokens for work instead of a company paying for employees is an exciting business model (and a potentially very disruptive one).
But when all of them look the same, the question shifts.
It’s no longer, “Which technology is better?”
It becomes, “Who can actually build a business around this?”
Who can scale it… who can monetize it and, most importantly, who can make it indispensable.

And, in the current hype… who can survive the cost.

Because behind all of this innovation is a much less glamorous reality…
Massive infrastructure… enormous compute costs.
And companies trying to figure out where real demand actually exists.

It feels more like a land grab, when you step back and really think about it.

And in land grabs, the winner isn’t always the best product.
It’s the one that captures the land (the energy… the GPUs) quickest.
It’s the one that finds product-market fit first.
It’s the one that can get consumer lock-in.
So maybe the better question isn’t which AI model is better (because for most of us, it’s hard to tell the difference).

But this… if everyone is building the same thing, what actually makes one of them matter more than the others?

This is what Elias Makos and I discussed on CJAD 800 AM.

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