(JarTee/Shutterstock)
The initial euphoria surrounding generative AI is officially over. It has been replaced by a simmering, and in many cases boiling, frustration from the very users these platforms are meant to serve. The recent rollout of OpenAI’s ChatGPT-5 is a case study in this growing chasm between the ambitions of AI developers and the realities of their customers. For IT leaders and buyers, this isn’t just tech drama; it’s a flashing red warning light about the stability, reliability and long-term viability of the AI tools being integrated into critical business workflows.
The Botched ChatGPT-5 Launch and Resulting User Revolt
When OpenAI began rolling out ChatGPT-5, it wasn’t met with the universal praise of its predecessors. Instead, the company faced a swift and brutal backlash. The core of the issue was a decision to force all users onto the new model, while simultaneously removing access to older, beloved versions like GPT-4o. The company’s own forums and Reddit threads like “GPT-5 is horrible” filled with thousands of complaints. Users reported that the new model was slower, less capable in areas like coding and prone to losing context in complex conversations.
(metamorworks/Shutterstock)
The move felt less like an upgrade and more like a downgrade, stripping users of choice and control. For many paying customers, this wasn’t an abstract inconvenience; it broke carefully tuned workflows and tanked productivity. The outcry was so intense that OpenAI eventually backtracked and reinstated access to older models, but the damage to user trust was done. It exposed a fundamental misunderstanding of a key business principle: don’t yank away a product your customers love and rely on.
Silicon Valley’s Tin Ear
The ChatGPT-5 fiasco is symptomatic of a much larger disconnect between AI companies and their user base. While developers chase benchmarks and tout theoretical capabilities, users are grappling with practical application. There is a clear divide between the industry’s excitement and what customers actually want, which often boils down to reliability, predictability and control. Forcing an untested model on millions of users without a beta period or opt-out suggests a company that has stopped listening.
This isn’t just an OpenAI problem. Across the industry, the “move fast and break things” ethos is clashing with the needs of enterprise customers who require stability. The focus on scaling at all costs often comes at the expense of quality control and customer experience. When a model’s performance degrades, or a valued feature is suddenly removed, it erodes the trust necessary for widespread adoption in a business context.
The Troubling Decline in AI Quality
Perhaps most concerning for IT buyers is the growing evidence that AI models can get “dumber” over time. This phenomenon, known as “model drift,” occurs when a model’s performance degrades as it encounters new data that differs from its original training set. Without constant monitoring, retraining and rigorous quality assurance, a model that performs brilliantly at launch can become unreliable.
Users are noticing. Discussions in communities like Latenode reveal a widespread sentiment that the reliability of AI responses is declining. The race to release the next big model often means that the necessary, unglamorous work of maintenance and reliability engineering gets shortchanged. For a business relying on an AI for customer support, content creation or code generation, this unpredictability is unacceptable. It turns a promising productivity tool into a liability.
(Harsamadu/Shutterstock)
A Buyer’s Guide to Not Getting Burned
So, how should an IT department navigate this volatile landscape? The key is to shift from being an enthusiastic adopter to a skeptical, discerning customer.
Prioritize Governance and Stability: Look beyond the flashy demos. Ask hard questions about a vendor’s approach to model lifecycle management, version control and quality assurance. Platforms designed for the enterprise, like DataRobot or H2O.ai, often have more robust governance and explainability (XAI) features built-in.
Diversify Your AI Portfolio: Do not bet the farm on a single provider. For tasks requiring deep contextual understanding and thoughtful writing, Anthropic’s Claude 3 family has proven to be a very reliable and consistent performer. For real-time, fact-checked research, Perplexity is often a better choice than general-purpose chatbots. Using different tools for different tasks mitigates the risk of a single point of failure.
Conduct Rigorous Pilot Programs: Before any enterprise-wide rollout, conduct thorough pilot programs with real-world use cases. Choosing the right AI software requires testing its integration capabilities, security protocols and, most importantly, its performance consistency over time.
Demand Control: Do not accept opaque, “magic box” solutions. Insist on having control over model versions and the ability to roll back to a previous one if an update proves detrimental. If a vendor cannot provide this, they are not ready for enterprise deployment.
Wrapping Up
The current friction between AI providers and their customers is more than just growing pains; it is a necessary market correction. The initial phase of “wow” is being replaced by a demand for “how.” How will you ensure quality? How will you protect my workflows? How will you be a reliable partner? Researchers are cautious, with many experts believing that fundamental issues like AI factuality are not going to be solved anytime soon. This means the burden of ensuring reliability will fall on vendors and their customers for the foreseeable future. The companies that thrive will be those that listen to their users, prioritize stability over hype, and treat their AI platforms not as experiments, but as mission-critical infrastructure. For IT buyers, the message is clear: proceed with caution, demand more and don’t let the promise of tomorrow blind you to the problems of today.
About the author: As President and Principal Analyst of the Enderle Group, Rob Enderle provides regional and global companies with guidance in how to create credible dialogue with the market, target customer needs, create new business opportunities, anticipate technology changes, select vendors and products, and practice zero dollar marketing. For over 20 years Rob has worked for and with companies like Microsoft, HP, IBM, Dell, Toshiba, Gateway, Sony, USAA, Texas Instruments, AMD, Intel, Credit Suisse First Boston, ROLM, and Siemens.
This article first appeared on our sister publication, BigDATAwire.