disruptive paradigm shift, right? I mean, it's, it's much like phenomena we've seen many times before, like I was doing computer graphics in the early 1990s and if you wanted to render an image in the early 90s, you know, it took a high end supercomputer to render an image. Now you can render an image on your phone. Face Recognition used to be an expensive niche application, you know, now it's a commodity feature on a low end smartphone, right? And so you knew the same thing was going to happen with large language models. You know, they start out expensive, they start out needing huge amounts of compute power, and they wind up cheap and commoditized. So none of that's surprising. What's interesting is just how fast it happened, right? And how suddenly it happened. But wait a minute. Hold on. We're supposed to be on the dawn of the technological singularity, right? I mean, Ray Kurzweil foresaw singularity coming in 2029 Well, the notion of the singularity is, as you get there, technological advances having faster and faster and faster until it seems to be happening, you know, almost incomprehensibly fast to the human eye. Well, this is exactly the kind of thing you would expect to see as you get closer and closer to the singularity is advances happening way faster than your intuition would lead you to expect, because our intuitions are they're sort of tuned by evolution, for linear thinking. They're not tuned for exponential thinking. What we're seeing now is, you know, the kind of exponential advance you see as you as you get close to the to the singularity, right? And I mean, deep seek is just one of many, many interesting moments like this that that we're going to see as as the last years before the before the singularity unfolds. So what's going on under the hood in deep sea? The main achievement here is some very clever techniques for optimizing efficiency, rather than some sort of redefinition of of the basic transformer architecture underlying language models, deep sea uses a mixture of experts model, which is a well established kind of ensemble learning technique. It's been using machine learning for years. But deep sea uses mixture of experts alongside some other efficiency tricks to minimize computational costs in a quite clever way. So I mean mixture of experts, you have a whole bunch of different sort of internal agencies, which is good at a different thing, and you combine together their results to get an answer. And this has been used in. Um transformers and language models for a while, but the the way deep seek uses, it allows just a small percent of a large network to be used at any given time like to give a certain answer. So if you have like, 671 billion parameters, maybe 37 billion are needed to answer a given question. So you have like 118 the the compute power is needed to needed to answer the answer the question, right? So you're wasting a lot less compute power by not using parts of the neural network that aren't actually needed for answering the question, in order to, in order to answer the question. And deep seek makes much heavier use of a learning technique called reinforcement learning, where you reward a network for doing what you wanted to and sort of punish it for not doing what you wanted to, and then this reward and punishment propagate back through the whole network to modify all the the weights between the neurons in the network so all, all large language models are trained partly using this sort of reward based reinforcement learning and partly using other sorts of supervised learning and training on data sets. Deep Sea sort of doubles down on reinforcement learning and uses mostly reinforcement learning. In particular, they use reinforcement learning to train on reasoning, much more so than any other previous LLM approach seemed to and that that seemed to work seemed to work super well, and that they also used multi token training, training deep seek to predict multiple pieces of text at one time, which increases, increasing the training efficiency. So putting all these sorts of optimizations together, you get deep seek to be like an order of magnitude cheaper than open eye and throw up with and so forth for training and for inference, right? So this is, this is a really powerful and meaningful engineering refinement. I mean, it's not, it's not a conceptual leap toward AGI. It's not a whole different way of doing things. But I mean speeding things up and making things cheaper by a factor of like, 10 or 20, is pretty amazing. And I mean, and it helps you, it lets you rethink the whole economics of what you can do with with llms, right? Another really interesting thing about deep seek is its embrace of the open source approach, which is quite a contrast to the walled garden strategies of for example, open AI, which, in spite of having open in the name, has never really followed a terribly open strategy. In fact, if you looked at open AI's website back in the very beginning, when they first launched, you could see they were hemming and hawing and saying, Well, we'll be mainly open, but when we judge it's most appropriate not to be open, maybe we won't be it turned out, well, they judge it was almost never appropriate to be open. Anthropic, most others in the US AI scene have followed suit, with a notable exception of Facebook, which, under the direction of young lacunae, has opened up their llama AI models and a whole bunch of other amazing stuff. Well, deep seek has opened their model, they published a research paper describing what they did. And I mean this, this, I think, is an amazing positive thing. You know, open source AI fosters rapid innovation, broader adoption, collective improvement. I mean, you know, business wise, proprietary models do let companies capture more direct revenue in some ways, but the open approach, on the other hand, leaves plenty of business avenues open, and it allows a much broader community to contribute to development. It makes tools available to more researchers, companies, independent developers. I mean, the hedge fund high flyer that developed deep seek is not a charity, right? I mean, they know open source AI is not just about philosophy and doing good for the world, then they know there are quite valid, solid business models that come along with, with open source you can do services, enterprise, integration, hosting, you can use, you can use your own model to make make money on the markets, right? So, I mean, I think, I think it's fantastic that this huge step forward in the efficiency of of llms has been rolled out open source. And,