The OpenAI GPT-5 Release Wasn’t a Disaster, But It Was Indeed a Threat

GPT-5 wasn’t perfect, or even great, but its release may have signaled a sneaky shift in the AI market war

“OpenAI GPT-5 is going to shock the world on Thursday!”

That was s a direct quote from a well-regarded developer friend of mine on LinkedIn, two days before the release of GPT-5.

“GPT-5: Overdue, overhyped and underwhelming.”

That was the title of a popular Substack post published two days after the release of GPT-5.

What happened?

Well, as the AI platformers rush new releases and leapfrog one another on the path to total dystopian domination, there are going to be bumps in the road, monkeys in the wrench, fresh glue on the pizza.

But is it all according to a greater plan? Did OpenAI rush a disaster GPT-5 release on purpose? Is market share more important than clean code and happy AI-hugging customers?

Let me reflect a little bit on my 15 years experience with AI engines and AI markets. I’ll put on my reckless speculation goggles, connect some dots, and we’ll find out together.

Was the Rollout of GPT-5 a Disaster?

Well, it wasn’t awesome. But no, not really a “disaster.”

I’ve pushed some bad AI platform releases to market in my time. In 2013, at Automated Insights, we were doing Yahoo Fantasy Football, and we had to go back and remove a couple of hilarious but maybe sensitive jokes we made about fantasy team managers, who, it turns out, can indeed be a sensitive bunch.

Sam Altman didn’t help his own locker room any when, the day before launch, he posted a screen grab of the Death Star rising from Rogue One. By the evening of the launch, after the massive rush of criticism and complaint that followed the release, that tweet had been reduced to a meme.

But…

Get halfway down into that critical Substack post that I linked above, even the author himself admits:

“For all that, GPT-5 is not a terrible model…the reality is that GPT-5 [is] just not that different from anything that came before.”

He’s absolutely right, and he goes on to talk about the general weaknesses in LLM-based AI, referencing a study out of Arizona State University that, if you’re up for it, starts to debunk Chain of Thought reasoning in LLMs and basically says, “move on, there is no AGI to see here.”

I, you, we knew that. And as time passed after the initial release, some of the criticism let up, especially from devs who found the piling on to be overly harsh.

But what does all this chain of thought and disaster release and Death Star shit mean for me, you, and all of us?

Hold on, let me get my goggles.

Bad Software Releases Can Happen On Purpose

There are bad software releases and there are go-fast-and-break-stuff software releases. To go back to my own example, I had been wanting to make our “small language models” more human and humorous, but Yahoo said we went too far. We “broke stuff.” We apologized after the fact instead of asking for permission first, because we had other nascent providers and platforms nipping at our heels.

But why should OpenAI break stuff now? Don’t they have a gazillion dollar lead?

Eh.

A lot of those gazillions of dollars come from enterprises, large and small, licensing the AI platform models to enterprise customers to build their own models. And a lot of those companies are playing with fire, losing a lot of money to gain first-mover advantage and market share.

Or in a lot of cases, they’re riding the loss-leader phase of AI to build spectacular businesses on a house-of-cards foundation, because their true operating costs have been masked since day one.

This is by design. OpenAI and Anthropic and the rest of them boys don’t expect to stay at supernova megacorn levels on the backs of college students writing fake AI papers. They need to be the platform behind the platform. That’s where the real money is.

So OpenAI shrugged off a disaster release because they’re rushing to get more enterprise market share. Sure. But maybe that’s because the “AI” is actually getting better. Not worse.

If so, the strategy would look like this.

You Can’t Charge Full Price For an MVP

You need to discount and freemium and price war your way through the MVP phase of any new technology. At Automated Insights, we were offering our initial clients 30, 50, even 100 percent return on their investment in our nascent AI tech.

But once our tech became “good enough,” we got a lot more expensive.

Did that price our AI solution out of consumer and small business hands? You bet, but we were never aiming for that market. We eventually tried a ham-fisted attempt at creating a new platform strictly for consumer use, and that’s where I parted ways with the company, because I saw too many holes in that strategy. To me, shitty AI for cheap, even for free, was not a winner. Certainly not back then.

Today, with the word “trillions” being tossed around in the still relatively nascent LLM-based AI market, and conventional wisdom going against any gold-mine-inducing connection between LLMs and “true AI,” Sam and them boys can’t afford to be as rebellious as my younger self.

Release now. Get enterprise market share. Apologize later. Hell, offer GPT-5 to the US Government for a literal quick buck.

The Casualties of Price Wars

Dollars-to-tokens, GPT-5 “is really undercutting Anthropic’s Claude.”

And, as that article suggests, the devs praised the “aggressively competitive” pricing.

As I speculated, the rush to release might be because the tools are actually getting better, and when they reach “good enough,” there’s no reason to discount them anymore, at least not for those customers whose very existence is inextricably linked to OpenAI. Which means the market share war is going into full effect. Which means a price war today.

Because with AI, like a lot of the technical evolutions that came before it, the more it can do, and the better it can do it, the higher the enterprise customer’s margins, the more they can afford to pay. The rich ones, anyway.

And then the AI platforms don’t need any other type of customer for anything but loss-leading. So the platforms will just usage limit the hell out of those customers, and those customers become the casualties of the price war.

Beware of Falling Margins

Let me put it this way.

Today, we’re all getting a healthy discount for AI’s mistakes and hallucinations. When the mistakes and hallucinations stop, or rather when the software is “good enough,” will those discounts remain?

SaaS history tells us that the price floor gets raised until only the enterprise model makes sense, and then only for those companies who have the margins to be able to afford to send a healthy chunk of that revenue back to the provider.

I mean, there’s a reason Apple fought so hard to retain their 30 percent take on the App Store.

So maybe that Death Star image was a message to Dario and the rest of them boys. Maybe it was a threat.

In the meantime, the enterprise AI economic model still doesn’t work unless there’s a subsidized consumer version as a long tail to support the enterprise superusers. It happened with software (Microsoft Windows), it happened with advertising (Google), and it happened with SaaS (Every. Single. Platform.)

It’s happening now with AI.

I’m not saying watch for immediate giant price increases. I’m just saying don’t put your faith in giant corporations offering the promise of continually improving new technologies on a freemium model.

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