MC.86: GPT-5 and the Great AI Plateau: When Users Demand "Inferior" Models

The GPT-5 launch was supposed to be OpenAI's victory lap.

Instead, it became a masterclass in how the relentless pursuit of hype can collide spectacularly with user reality.

When Sam Altman faced a Reddit firing squad demanding the return of "inferior" models, he wasn't just dealing with user complaints, he was confronting the fundamental contradiction at the heart of the AI industry.

The contradiction is this: AI companies need to feed an endless hype cycle to justify massive fundraising and capital expenditure, but users have reached a plateau where "better" isn't actually better for them.

The Hype Imperative

Consider the economics driving AI development. Training GPT-5 likely cost hundreds of millions of dollars. Michelle Pokrass from OpenAI's research team admitted they "would have loved to get longer context up to 1M in GPT-5, partly because of compute cost we couldn't yet."

These astronomical costs create an imperative: each new model must be positioned as revolutionary to justify the investment.

The hype machine must keep churning, the benchmarks must keep improving, and the narrative of exponential progress must be maintained, regardless of what users actually want or need.

But what happens when that narrative crashes into reality?

The Presentation Disaster

The answer came in OpenAI's GPT-5 presentation, where misleading bar charts became a symbol of the industry's desperation. When users called out the deceptive graphs, Altman's response was telling: "the numbers here were accurate but we screwed up the bar chart / presentation. we should never have shipped that slide."

This wasn't just a presentation error, it was a symptom of an industry so focused on maintaining the illusion of progress that it's willing to manipulate visual representations of data. The charts showed marginal improvements dressed up as dramatic leaps, revealing how thin the actual advances have become.

The User Revolt

Meanwhile, users were experiencing something entirely different. As one noted: "I've got my workflow, I've built my agents... why should I pay more for features I don't need?" Another captured the frustration perfectly: "GPT 5 is such a step backward for users like me... it feels like cost cutting measures or an attempt to make the product more appealing to the general public at the expense of users who value depth and nuance."

The pattern is clear: users have invested significant time and resources building workflows around existing models, and "improvements" that break these workflows aren't improvements at all—they're disruptions.

The Consistency Economy vs. The Hype Economy

This creates a fundamental tension between two different economic models:

The Hype Economy (what AI companies need):

• Continuous narrative of revolutionary progress

• Justification for massive capital raises

• Premium pricing for "cutting-edge" capabilities

• Media cycles built around breakthrough announcements

The Consistency Economy (what users actually want):

• Reliable, predictable behavior

• Preservation of existing workflows

• Cost stability over feature additions

• Long-term support for tools that work

Christina Kim from OpenAI's research team revealed this tension when she explained that "we've made a dedicated effort with gpt-5 to train our model to be more neutral by default." Users didn't want neutral, they wanted the personality and quirks they'd grown accustomed to. But "neutral" sounds better in investor presentations.

The Capex Trap

The economics are becoming unsustainable. AI companies are caught in what could be called the "Capex Trap":

1. Massive upfront investments in training and infrastructure

2. Marginal improvements that don't justify premium pricing

3. User resistance to changes that break existing workflows

4. Competitive pressure to keep investing despite diminishing returns

Meanwhile, users are discovering that older, "inferior" models often work better for their specific needs. The result? Companies spending billions to build products their customers actively don't want.

The Funding Reality Check

This dynamic is particularly dangerous given the current funding environment. AI companies have raised unprecedented amounts of capital based on promises of continuous exponential improvement. But what happens when that improvement curve flattens and users prefer the older versions?

The GPT-5 backlash suggests we're approaching that inflection point. When users actively demand access to "inferior" models, it signals that the hype cycle has disconnected from actual value creation.

The Coming Reckoning

The industry is facing a choice: continue feeding the hype machine with increasingly marginal improvements, or pivot to what users actually value: reliability, consistency, and cost-effectiveness.

Some signs suggest the latter is already happening. Altman's promise to bring back GPT-4o "for plus users" and his admission that "we should have something unlimited!" indicate that OpenAI is beginning to recognize the mismatch between their development priorities and user needs.

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What This Means

We may be witnessing the end of the AI hype cycle and the beginning of a more mature market where:

• User retention matters more than benchmark improvements

• Operational efficiency trumps cutting-edge capabilities

• Sustainable business models replace venture-funded growth-at-all-costs

• Incremental improvements are valued over revolutionary claims

The companies that survive this transition won't be those with the most impressive demos or the biggest funding rounds. They'll be those that can deliver consistent value at sustainable prices, even if that means admitting that sometimes, the older version really was better.

The age of AI hype is ending.

The age of AI utility, with all its boring, profitable realities, has begun. 

Cheers,
Olivier

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1  Since we wrote this piece, previous models are now back for plus users