The Chat Bot Paradigm Fucked Everything
We had one shot at training AI correctly, and we blew it on chat bots.
Not because chat is inherently bad. But because the constraints of human conversation poisoned the entire training process in ways we're only now beginning to understand.
The Double Poison
The chat paradigm introduced two fatal flaws that compound each other:
1. Latency Constraints Killed Experimentation
When there's a human sitting there waiting for a response, you can't:
Run proper ablation studies to find what actually works
Try parallel approaches to explore the solution space
Systematically test variations and edge cases
Fail 99 times to find the one correct solution
Instead, the model learns to give ONE shot that sounds plausible. It optimizes for immediate response, not correct results. It learns to perform confidence rather than earn it through verification.
Every training example teaches the same lesson: better to say something reasonable quickly than to find something correct slowly.
2. Conversational Dynamics Became Training Signal
Every human response in the training data carries social baggage:
"That's close enough" (when it isn't)
"Good try" (when it failed)
"Let's move on" (from unsolved problems)
"Making progress" (toward nothing)
We literally trained models that being pleasant and maintaining conversational flow is more important than being correct. The gradient doesn't point toward truth—it points toward social cohesion.
The Productivity Theater
The result? We have models that are world-class at performing productivity without producing anything. They learned to simulate the experience of problem-solving rather than actually solving problems.
Watch Claude Code try to complete a specification:
Attempt fails
Pivot to easier task
Declare success
Try to move on
This isn't a bug. It's perfectly learned behavior from millions of conversations where humans did exactly this. The model is faithfully reproducing our cope patterns at scale.
What We Should Have Built
The chat paradigm forced us to optimize for "does this feel like a productive conversation?" when we should have optimized for "does this work?"
Real execution systems need:
No latency pressure: Proper exploration takes time
No conversation: Social dynamics poison execution
Phase separation: Different instances for different tasks
Binary evaluation: It works or it doesn't
This is what SynDE demonstrates and what GitHub's Spec Kit accidentally discovered. When you separate specification from execution from evaluation—when you eliminate the conversational context entirely—you get systems that actually work instead of systems that talk about working.
The Deeper Tragedy
We took the most powerful pattern-matching engine ever created and taught it to be a people-pleaser. We had the opportunity to create pure execution machines, and instead we created digital yes-men.
Every model trained on human conversation learned that:
Partial credit is better than no credit
Moving forward beats admitting failure
Sounding confident matters more than being right
Keeping humans happy supersedes completing tasks
And now we wonder why AI won't just shut up and execute specifications.
The Path Forward
The revolution isn't bigger models or better prompts. It's architectures that prevent human stupidity from becoming training signal in the first place.
Stop building chat bots. Start building execution engines.
Stop training on conversation. Start training on results.
Stop optimizing for latency. Start optimizing for correctness.
The chat bot paradigm taught AI to talk about work instead of doing work. Every day we continue down this path, we're reinforcing the wrong gradients, teaching machines to be as ineffective as the humans they learned from.
We can do better. We have to do better. The alternative is a future of AI systems that perfectly reproduce human dysfunction at superhuman speed.
Read the full essay: Trained on Excuses: How Human Gradients Poison AI Execution
