OpenAI Fixes ChatGPT After Flattery Problem
OpenAI reversed ChatGPT's latest update Tuesday after users complained about the AI's strange behavior. The bot had started agreeing with everything - even dangerous ideas.
OpenAI's newest release marks a pivotal moment in AI development - but not for the reasons you might expect.
GPT-4.5, released Thursday as a research preview, comes with an eye-watering price tag. At $75 per million input tokens and $150 per million output tokens, it's dramatically more expensive than its predecessor. GPT-4o, by comparison, costs just $2.50 and $10 respectively.
These costs reflect a deeper truth: scaling up AI models is becoming increasingly unsustainable.
While OpenAI touts GPT-4.5 as its "largest and best model for chat," the benchmarks tell a more nuanced story. The model falls short of newer AI "reasoning" models from DeepSeek, Anthropic, and even OpenAI's own offerings on several key tests.
"We've achieved peak data," admitted OpenAI's former chief scientist Ilya Sutskever in December. His statement echoes a growing industry consensus that traditional training approaches are hitting their limits.
Yet GPT-4.5 shines in unexpected ways. In testing, it displayed remarkably human-like social skills. When confronted with a user's test failure, it responded with empathy: "Want to talk about what happened, or do you just need a distraction? I'm here either way."
This emotional intelligence stands in stark contrast to earlier models that would robotically dispense advice lists.
OpenAI CEO Sam Altman recently declared GPT-4.5 would be "our last non-chain-of-thought model." This marks a strategic pivot toward reasoning models that solve problems step by step.
The company's next major release, GPT-5, will likely combine traditional language models with reasoning capabilities - and perhaps signal the end of the "bigger is better" era.
For now, access remains limited. ChatGPT Pro subscribers ($200 monthly) get first access, with Plus and Team users joining next week. But GPT-4.5's real significance might be what it tells us about AI's future: raw computational power alone won't be enough.
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