That’s fair. I see what I see at an engineering and architecture level. You see what you see at the business level.

I respect that. Finance was my old career and I hated it. I liked coding more so I went back got my M.S. in CS and now do embedded software which I love. I left finance specifically because of what both of us have talked about. It’s all about using numbers to tell whatever story you want and it’s filled with corporate politics. I hated that world. It was disgusting and people were terrible two faced assholes.

That said. I stand by my statement because I and most of my colleagues in similar roles get continued, repeated and expanded-scope engagements. Definitely in LLMs and genAI in general especially over the last 3-5 years or so, but definitely not just in LLMs.

“AI” is an incredibly wide and deep field; much more so than the common perception of what it is and does.

I think I need to amend what I said before. AI as a whole is definitely useful for various things but what makes it a fad is that companies are basically committing the hammer fallacy with it. They’re throwing it at everything even things where it may not be a good solution just to say hey look we used AI. What I respect about you guys at Nvidia is that you all make really awesome AI based tools and software that actually does solve problems that other types of software and tools either cannot do or cannot do well and that’s how it should be.

At the same time I’m also a gamer and I really hope Uncle Jensen doesn’t forget about us and how we literally were his core market for most of Nvidia’s history as a business.

Now this is where I push back. I spent the first decade of my tech career doing ops research/industrial engineering (in parallel with process engineering). You’d shit a brick if you knew how much “fudge-factoring” and “completely disconnected from reality—aka we have no fucking clue” assumptions go into the “conventional” models that inform supply-chain analytics, business process engineering, etc. To state that they “never make mistakes” is laughable.

What I said was that traditional software if programmed correctly doesn’t make mistakes. As for operations research and supply chain optimization and all the rest of it, it’s not different from what I said about finance. You can make the models tell any story you want and it’s not even hard but the flip side is that the decision makers in your organization should be grilling you as an analyst on how you came up with your assumptions and why they make sense. I actually think this is an area where AI could be useful because if trained right it has no biases unlike human analysts.

The other thing to sort of take away from what I said is the “if it is programmed correctly” part which is also a big if. Humans make mistakes and we see it a lot in embedded where in some cases we need to flash our code onto a product and deploy it in a place where we won’t be able to update it for a long time or maybe ever and so testing and making sure the code works right and is safe is a huge thing. Tool like Rust help to an extent but even then errors can leak through and I’ve actually wondered how useful AI based tools could eventually be in proving the correctness of traditional software code or finding potential bugs and sources of unsafety. I think a deep learning based tool could make formal verification of software a much cheaper and more commonplace practice and I think on the hardware side they already have that sort of thing. I know AMD/Xilinx use machine learning in their FPGA tools to synthesize designs so I don’t see why we couldn’t use such a thing for software that needs to be correct the first time as well.

So that’s really it. My only gripe at all with AI and DL in particular is when executives who have no CS or engineering background throw around the term AI like it’s the magic solution to everything or always the best option when the reality is that sometimes it is and other times it isn’t and they need to have a competent technology professional make that call.