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Look, the fundamental problem with current AI development is that we're racing forward without proper safety protocols. So we need to establish a regulatory framework that ensures AI remains beneficial to humanity. It's not about stopping progress, it's about making it sustainable and safe.

Look, traditional car manufacturing is fundamentally broken. We're developing a new robotics system that reduces assembly time by 80%. No fancy marketing here - pure engineering efficiency. The experts said it couldn't be done, but we're already testing prototypes. It's insane.

Look, the reality is, artificial intelligence is going to advance exponentially, and whatever regulators or politicians try to do to slow it down, it's just not going to work. I'm extremely confident we're going to see transformation at a scale that's hard to comprehend.

We're looking at improving efficiency by roughly 40% across our production lines. The target is to reduce manufacturing costs from $47,000 per unit to approximately $28,000, while maintaining quality standards. It's fundamentally about optimization at scale.

So, you know, we're working on this really cool AI thing, and, and it's like, fundamentally changing how we approach robotics. We've got these neural networks that are, you know, basically learning at an incredible rate. It's pretty mind-blowing stuff.

Look, the fundamental problem is that we're approaching this all wrong. The current system is incredibly inefficient, like mind-blowingly inefficient. We need to completely rebuild it from the ground up, make it ten times better, maybe a hundred times better. It's not rocket science, but actually, rocket science would be easier.

Look at electric vehicles - it's actually pretty simple. You've got the battery, the motor, and the computer. No complex transmission, no exhaust system, nothing like that. It's just fundamentally better engineering. That's why it's the future.

So like we're trying this new battery chemistry, and you know, the first few attempts basically exploded, which wasn't great obviously. But we learned a ton from those failures, and now we're actually getting pretty good efficiency numbers, like really good actually.