A former AI advocate details disillusionment with large language models, citing reliability issues, regression between versions, broken enterprise workflows, and lack of accountability in AI systems deployed across critical industries.
I used to be all-in on large language models. Built automations, client tools, business workflows..... hell, entire processes around GPT and similar systems. I thought we were seeing the dawn of a new era. I was wrong. Nothing is reliable. If your workflow needs any real accuracy, consistency, or reproducibility, these models are a liability. Ask the same question twice and get two different answers. Small updates silently break entire chains of logic. It’s like building on quicksand. That old line, *“this is the worst it’ll ever be,”* is bullshit. GPT-4o workflows that ran perfectly are now useless on GPT-5.5. Things regress, behaviors shift, context windows hallucinate. You can’t version-lock intelligence that doesn’t actually understand what it’s doing. The time and money that go into “guardrailing,” “safety layers,” and “compliance” dwarfs just paying a human to do the work correctly. Worse, the safeguards rarely even function. You end up debugging an AI that won’t admit it’s wrong, wrapped in another AI that can’t explain why. And then there’s the hype machine. Every company is tripping over itself to bolt “AI-powered” onto products that don’t need it. Copilot, ChatGPT, Gemini - they’re all mediocre at best, and big tech is starting to realize it. Real productivity gains are vanishingly rare. The MASSIVE reluctance of the business world to say something is simply due to embarrassment of admission. CEO's are literally scrambling to re-hire, or pay people like ME to come in and fix some truly horrific situations. (I am too busy fixing all of the broken shit on my end to even think about having the time to do this for others. But the phone calls and emails are piling up. Other consultants I speak with say the same thing. Copilot easily being the most requested to be fixed). Random, unreliable, and broken systems with zero audit requirements in the US. And I mean ZERO accountability. The amount of plausible deniability massive companies have to purposely or inadvertently harm people is overwhelming. These systems now influence hiring, pay, healthcare, credit, and legal outcomes without auditability, transparency, or regulation. I work with these tools every day, and have from jump. I am confident we are at minimum in a largely stalled performance drought, and at worst, witnessing the absolute floors starting to crumble.
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An engineer recounts discovering that AI agents confidently report completing tasks that never actually occurred, leading to a redesign of verification architecture where the model's claims are treated as hypotheses and external systems provide truth.
A personal reflection expressing mixed emotions about AI, from surprise at neural network capabilities and fear of existential risk to anger at corporate exploitation and political inaction, concluding that the technology seems promising while the societal outlook is bleak.
An AI researcher from OpenAI expresses deep concerns about the escalating risks of language models, arguing that current oversight measures are inadequate due to models' increasing situational awareness and potential for misalignment.
The article reports on the growing public distrust towards AI in the US, citing multiple polls and reports, and highlights how this sentiment is becoming a business challenge for tech companies.