And yes, you are obviously a fraud.

You haven't made a single technical point outside your bloviated claims to authority. I can safely assume you're a fraud because this is exactly how frauds speak. Actual scientists and engineers don't argue from authority they go and test the hypothesis for themselves, the fact that you balk at my suggestion to do this is amusing.

I said you can do a basic test because this is the best way to see it directly for yourself. It's very easy to do especially for an eminent machine learning visionary leader as yourself. You ask the LLM to produce two apps of similar complexity and technical challenge from a software perspective, one that is already in its training data and one that isn't and see which its more successful at. This isn't some controversial take, nor is it "unfalsifiable".

There's also hundreds of benchmarks demonstrating where the limitations are for LLMs, or you can study the progression of LLMs in mathematics and where the gains have been made and see that this also agrees with me. You can watch Chris Hay's videos demonstrating exactly how LLMs perform math layer by layer. Why is everyone using LLMs for search? Because it's an extremely efficient compression of all its training data. Did they figure out the Studio Ghibli art-style all on their own spontaneausly? No, they were trained on Studio Ghibli content. There's so many ways to come to this conclusion. But you seem to be too busy sniffing your own farts to be interested in learning anything about the field though.

Anyway like I said you can just test it out yourself and find out that I'm correct. Every skilled programmer already knows this and can predict what kind of complexity an LLM won't be able to handle. And anyone working on LLMs should know that they are completely dependent on their training data. The entire scaling hypothesis was based on this.

They're all stealing your IP and selling it back to your competitors in the form of tokens.

A pair of bolt cutters should do.

2. Eventually we'll get to where local models that don't have sycophancy and slot-machine mechanics trained into them will perform better.

Aside from that, so much money was wasted on Alzheimer's research based on fraud.

The barrier to entry to make slop is lower, but it's gotten much higher for developing the skill of programming. There was already an issue with a lack of mentorship and path for juniors when agile attempted to turn software engineers into assembly line workers, among other issues with the industry becoming hyper short-term focused.

Now you have educational barriers where students are competing with other students that are cheating with LLMs. There are psychological barriers with learned helplessness. The 100k lines of vibecoded slop produced hits a wall but they've gained no understanding of the code in the process or ability to make changes themselves. At the first job juniors and interns get they're being told not to take the time to learn and understand the problem they're working and instead they need to hit the LLM slot machine or risk getting fired.