The author is David Rosenthal, who was employee #4 at Nvidia (Chief Scientist).

He's not some random historian or interviewer. That's his life experience.

I tried your question with SuperGrok. Here's the result.

https://grok.com/share/bGVnYWN5_d298dd12-9942-411c-900c-2994...

I use Grok for similar tasks and usually prefer Grok's explanations. Easier to understand.

For some problems where I've asked Grok to use formal logical reasoning I have seen Grok outperform both Gemini 2.5 Pro and ChatGPT-o3. It is well trained on logic.

I've seen Grok generate more detailed and accurate descriptions of images that I uploaded. Grok is natively multimodal.

There is no single LLM that outperforms all of the others at all tasks. I've seen all of the frontier models strongly outperform each other at specific tasks. If I was forced to use only one, that would be Gemini 2.5 Pro (for now) because it can process a million tokens and generate much longer output than the others.

Taylor Swift has relatively niche popularity in India.

Chinese are blocked from accessing any western social media, and no access to YouTube, Netflix, Spotify, etc. Taylor Swift is popular there but the Chinese have their own version of the internet separate from ours.

AI music is disposable generic soulless trash, even if it is technically correct, in accordance with the rules and conventions of music theory. AI generates Muzak. Totally generic and derivative.

There is no AI equivalent to Kurt Cobain, or James Brown, or Tori Amos.

I have mentored junior developers and found it to be a rewarding part of the job. My colleagues mostly ignore juniors, provide no real guidance, couldn't care less. I see this attitude from others in the comments here, relieved they don't have to face that human interaction anymore. There are too many antisocial weirdos in this industry.

Without a strong moral and cultural foundation the AGI paradigm will be a dystopia. Humans obsolete across all industries.

https://en.wikipedia.org/wiki/Space_Shuttle_Challenger_disas...

https://en.wikipedia.org/wiki/Space_Shuttle_Columbia_disaste...

The LLM needs context.

https://github.com/marv1nnnnn/llm-min.txt

The LLM is a problem solver but not a repository of documentation. Neural networks are not designed for that. They model at a conceptual level. It still needs to look up specific API documentation like human developers.

You could use o3 and ask it to search the web for documentation and read that first, but it's not efficient. The professional LLM coding assistant tools manage the context properly.

But even the dumbest model will call you out if you ask it something like:

"Hey I'm going to fill up my petrol car with diesel to make it faster. What brand of diesel do you recommend?"

I don't think anyone claimed that crypto was un-losable or un-stealable. It's not magic.

https://cryptosteel.com

The optional choices happen when it tries to reason out a solution, but then finds it is making too many assumptions of unknown details about the user's system, preferences, goals, and so on. It's just a thought pattern that it has learned to emulate.

People here will argue that LLM's cannot truly "think", but they are good enough at emulating thinking.

Deep learning systems have exceeded the hype. In 2016 we saw potential with models like AlphaGo Zero but no one could foresee the capability of LLMs (a type of deep learning model).