The Day The Vibe Died: Why GPT-5 Fell Short of Expectations
By: Archie Rowberry
On the 7th of August 2025, GPT-5 came out, the biggest, most expensive, most vaunted LLM ever developed. This was the model that would propel us beyond needing white-collar workers, developers, accountants, or engineers.
On the 7th of August 2025, GPT-5 came out, the biggest, most expensive, most vaunted LLM ever developed. This was the model that would propel us beyond needing white-collar workers, developers, accountants, or engineers.
Market and Media Response
And yet, when it was released it flopped. Markets and media had a distinctly flat reaction to its abilities and, for the time being at least, white-collar work is safe.
There are a couple of reasons why GPT-5 is not the model people expected.
Plateauing Capabilities:
Experts have long predicted that models wouldn’t become smarter indefinitely. A plateau in capabilities in the short to medium term was already expected and factored into valuations.
Hype:
The entire world of AI users was making noise, and many CEOs predicted that GPT-5 would be a “planet-killing” model. Expectations were for a leap in performance at least as great as that between GPT-3 and GPT-4.
Competition:
Many competitors have now reached parity in performance with OpenAI. The ingredients for developing LLMs are well-known, and the competitive edge has eroded.
The Vibes Were Off: User Reactions to GPT-5
Conflicting Feedback
The second, and arguably more interesting, reason is to do with how we’ve come to use and what we’ve come to expect from LLMs.
People didn’t like the vibes of the new model, and their issues were varied and often conflicting:
- It was too verbose
- It wasn’t verbose enough
- It wouldn’t understand them
- It followed instructions too obligingly
- It no longer felt warm and supportive
Pulling out clear areas for improvement from this feedback seemed impossible, with no consistent pain points beyond the model being “different” and “off”.
GPT-5 could and should be the most advanced and capable model on the market, enabling users to perform tasks at a near postgraduate level — and yet people were not vibing with it.
Super-Users Felt It Most: A Familiarity Problem
Looking at Ourselves
Why was it that OpenAI’s super-users, those who should theoretically be best at maximising LLM performance, had the most visceral reaction to GPT-5?
To understand this, we need to look at how familiar we’ve become with LLMs — and, perhaps, look at ourselves rather than the models.
There are two brilliant things about humans:
- We love patterns. It’s what made us great hunters and gatherers. We categorise and identify predators, prey, and edible foods through pattern-matching.
- We are deeply social. We crave interaction and are highly efficient at recognising behavioural patterns in each other. This allows us to build bonds, collaborate, and empathise.
These two strengths combined expose a flaw in LLMs: we’ve imprinted personalities and behaviours onto these models. We’ve anthropomorphised not only their capabilities but their flaws.
GPT-4 Familiarity vs. GPT-5 Change
Two Years of GPT-4 Shaped Expectations
The GPT-4 series of models all felt incredibly similar tonally because they were trained on similar data, using similar cost functions and alignment goals.
This family has been around since March 2023 — over two years for users to become accustomed to their quirks and idiosyncrasies. Just as we become familiar with a friend over time, we became familiar with GPT-4.
GPT-5’s Behavioural Shift
GPT-5, on the other hand, had:
- A significantly updated set of training data
- A different set of evaluation criteria
- New safety and alignment decisions
This resulted in a model that was materially different in its behaviour and reactions.
When GPT-5 was released, people reacted as any of us might when a friend we’ve known for two years suddenly changes personality — badly.
The Chinese Room: A Lens for Understanding LLM Perception
John Searle’s Thought Experiment
Social philosopher John Searle posed a thought experiment in 1980, called the Chinese Room.
In this scenario:
- An English-speaking subject sits in a windowless room with two letterbox doors and a large book.
- The book contains all possible combinations of Mandarin phrases and their correct responses.
- The subject receives Mandarin inputs through one door, copies out the correct response from the book, and posts it through the other door.
- They have no understanding of what they’re writing or the conversation.
To the person outside, the subject seems fluent and intelligent, even though they’re just following instructions.
This mirrors our subconscious impressions of LLMs. Even when we rationally know they’re trained on massive datasets, we imprint human behaviour onto them, shaping our interactions and expectations.
Trust and the Human–AI Interface
Value Lies in Application, Not Just Intelligence
What’s becoming increasingly clear as we move into the AI age is that to deliver value with LLMs, businesses must focus on the users, not just the models.
- Humans are the owners and operators of processes.
- Trust in the models is essential for effective task offloading.
- The interface between AI and humans is where the most friction appears.
Raw intelligence is now secondary to an organisation’s ability to use it effectively.
The application layer is where companies can make or break the integration of AI.
AutogenAI’s Approach: Aligning AI with Real-World Workflows
Purpose-Built for Bid Writers
At AutogenAI, we’ve built systems and workflows specifically for bid-writers, leveraging our understanding of the proposal writing process to align models’ behaviours to a single, reliable persona.
Model Profiling and Task Matching
Our understanding of model strengths and weaknesses, along with intensive profiling, allows us to:
- Match specific models to specific tasks within the bid-writing process
- Unify the AI generation pipeline to meet user expectations
- Deliver a consistent, seamless experience
Turn AI Into Your Most Reliable Bid Partner
Book a demo with AutogenAI to discover how to harness GPT technology effectively and integrate AI smoothly into your workflows.
