Chinese AI Disruption Challenges Industry Giants (Full Transcript)

Speaker 1: ♪♪ Today and over the last couple of days, I've been reading about China and some of the companies in China, one in particular coming up with a faster method of AI and much less expensive method, and that's good because you don't have to spend this much money. I view that as a positive, as an asset. The release of deep-seek AI from a Chinese company should be a wake-up call for our industries that we need to be laser-focused on competing to win.

Speaker 2: ♪♪ I think disruption for the markets today is not just cheaper, but a heck of a lot cheaper, 96% cheaper than what we're finding from the current spend in alternate models like GPT and Gemini. So that is, you know, something that is equivalent of, you know, $5 million spent on the deep-seek product, but it's $100 billion on the other side of the coin with some of these other products that are available. That's what's disrupting markets right now and why we see such a big impact on things like NVIDIA.

Speaker 3: It is one of the things that I think people are really wondering is a company like NVIDIA, where they were viewed as almost having a monopoly or a really, really strong moat around the whole chip and artificial intelligence ecosystem, that maybe that moat isn't quite as strong as what people originally thought. That could be why their stock is down. I think the jury is still out, see if whether or not all the data that we have so far can actually be validated, but it does suggest that maybe we don't need to throw tens of billions of dollars at these issues in order to develop something that's usable for the general public and for industry. This really goes to show that necessity is the mother of invention, as the old saying goes. They don't necessarily have the tens of billions of dollars that some of these companies in the US have, and they wanted to compete. They had perhaps a better algorithm, a better idea about how to train the models, and then also how to execute on it for answering people's questions. And they are using a slightly different technique, which is apparently, based on the data so far, a lot more energy efficient, and efficient in terms of the chips that it might take as well. They were able to really do this on what some people would call a shoestring budget. Open up. One, two, one, two, one, two, one, two, one, two, one, two. One, two, one, two, one, two, one, two, one, two. One, two, one, two, one, two, one, two, one, two, one, two. So perhaps this does show that smaller companies will be able to compete in that space, and it's going to shift the narrative away from picks and shovels, more to the refiners and the jewelers of artificial intelligence. One, two, one, two, one, two, one, two, one, two, one, two. One, two, one, two, one, two, one, two, one, two. One, two, one, two, one, two, one, two, one, two, one, two. One, two, one, two, one, two. One, two, one, two, one, two, one, two, one, two. One, two, one, two, one, two, one, two, one, two, one, two. One, two, one, two, one, two, one, two, one, two. One, two, one, two, one, two, one, two, one, two. One, two, one, two, one, two, one, two, one, two, one, two. You