I understand your frustration. You're pointing out an apparent contradiction: there's a lot of hype around AI capabilities and replacing entire industries, but real-world use often reveals basic gaps or failures.
A few things worth considering:
1. **Gap between capability and reliability** - AI systems can handle many tasks well in controlled settings but struggle with edge cases, nuance, or consistency in production environments.
2. **Marketing vs. reality** - There's genuine hype cycle happening. Some claims about AI replacing industries are premature given current limitations.
3. **Domain-specific challenges** - What works for one task (like writing essays) doesn't automatically transfer to another (like handling corner cases in your use case).
4. **The "basic issues" are often hard** - Problems that seem simple often involve subtle context, reasoning, or consistency that's actually quite difficult for current systems.
Rather than dismissing the criticism, it's fair. The gap between "AI can do remarkable things" and "AI is production-ready for critical industry applications" is real and often underappreciated.
What specific issue are you running into? That context might help me understand if it's something fixable or if it reflects a genuine limitation you've identified.
I understand your frustration. You're pointing out an apparent contradiction: there's a lot of hype around AI capabilities and replacing entire industries, but real-world use often reveals basic gaps or failures.
A few things worth considering:
1. **Gap between capability and reliability** - AI systems can handle many tasks well in controlled settings but struggle with edge cases, nuance, or consistency in production environments.
2. **Marketing vs. reality** - There's genuine hype cycle happening. Some claims about AI replacing industries are premature given current limitations.
3. **Domain-specific challenges** - What works for one task (like writing essays) doesn't automatically transfer to another (like handling corner cases in your use case).
4. **The "basic issues" are often hard** - Problems that seem simple often involve subtle context, reasoning, or consistency that's actually quite difficult for current systems.
Rather than dismissing the criticism, it's fair. The gap between "AI can do remarkable things" and "AI is production-ready for critical industry applications" is real and often underappreciated.
What specific issue are you running into? That context might help me understand if it's something fixable or if it reflects a genuine limitation you've identified.