DeepSeek is definitely one of the hottest topic at the moment. The Chinese company, with less than 6 million US dollars in training cost, trained an AI model which is almost as good as OpenAI’s ChatGPT.
The development of DeepSeek is not to my surprise at all. DeepSeek is a subsidiary of High-Flyer, one of the largest quantitative trading firm in China. As the founder and CEO of another quant firm myself, I fully understand why the breakthrough happened in a quant firm.
In Alpha Star Research, our trading decisions are solely determined by our own AI model. With 20% improvement in our AI model, we can see 20% increase in revenue. That’s actual US dollars on our balance sheet. The feedback loop is short and direct: the better our AI models are, the more money we can make. The training cost is paid by our own company treasury. Therefore any saving in training cost would also be actual saving on our company’s balance sheet. Like DeepSeek’s parent High-Flyer, Alpha Star Research is by itself very profitable and the reason we invest in AI, we actually care about the cost and the result of the AI model. We care about the technology.
In OpenAI, it’s a different story. OpenAI itself is not profitable. OpenAI’s valuation (and Sam Altman’s personal wealth) is determined by its investors. The improvement of the performance of ChatGPT is related to that valuation, but the feedback loop is too long for Sam to care. When we look at the training cost, OpenAI could care less about saving money in training. The more OpenAI spend in GPU chips, the more Sam can convince investors about the cost required to train an AI model, and then boost the valuation of OpenAI even more. This creates an interesting situation where the more OpenAI waste in training cost, the more OpenAI’s valuation is.
So if we look deeply, we can discover the interesting contrast: DeepSeek is a finance company, but its profit comes from technology, and it cares about technology and technology only. OpenAI is a tech company, but its money come from valuation (finance) and it cares most about finance. If we look at the background of the founders, DeepSeek’s founder and CEO Wenfeng Liang is a cofounder and software engineer from its parent High-Flyer, and OpenAI’s founder and CEO Sam Altman is a venture capitalist from Y-Combinator.
There are other reasons why quant firms excel most in AI research. In financial markets, we were forced to handle with very limited and polluted data. In our company, we need to train an AI model to predict the market, with only gigabytes of data, polluted by malicious market manipulators. We are forced to spend all our time and energy improving the structures of our AI models instead of brainlessly buying Nvidia H100 and pumping petabytes of data into the system.
Another reason is that when building trading infrastructure, in order to trade before other competitors, we are forced to make our systems as fast as light, literally. We are building microwave transmission system between Tokyo, Hong Kong, Chicago and New York City. And we are making sure the computation time of all components in our trading system less than 1 millisecond, if not microsecond. Quantitative trading firms have technology power and culture to create the fastest and most efficient systems. It was reported that DeepSeek bypassed CUDA and use low level languages to save cost and speed up the training. That’s exactly what I expect a quantitative trading firm would do on AI.
Before starting Alpha Star Research, I worked as a research engineer in Citadel Securities, a leading quantitative trading firm in America. Before that, I work as a software engineer in Amazon and Meta. When I worked at tech companies, when I talk to everyday American about my job, I received a huge amount of praise and admiration, and often heard people thank me for helping the advancement of technology. However when I worked for Citadel, every time I talked about my employer, I got awkward silence and occasionally criticism for being an “evil hedge funder”. I believe this is short-sighted. Just like computers and the Internet were developed by US military for military purposes, but benefit people around the globe eventually, I would expect quantitative firms like DeepSeek/High-Flyer contribute to the development of AI technology as much as Software and Internet companies. And Alpha Star Research and I will also work relentlessly for the advancement of AI technology.
About the Author: Sinya Lee is the founder and CEO of Alpha Star Research, an AI research and quantitative trading firm based in New York City. Like DeepSeek’s founder and CEO Wenfeng Liang, Sinya is also from Guangdong Province originally.
But how to improve the structures of your AI models? building trading infrastructure is one thing, improving the structures of Regression Analysis(Strategies) is another thing. Maybe Mixture of Experts & GRPO are not that important for your company.
写的太好了。你的博客可以作为我锻炼写作的样本。
刚才阅读完你的新作哈哈。我感受到了了你的愤怒,同时也很有意思。
大佬可以指路吗?谢谢!
网上无意中看到人妻约会指南来的,哈哈哈。
简中互联网世界里说的是幻方赚钱了后出于热爱技术投资研究出来大模型,殊不知是本末倒置了。
The Quant Firm directly receive the correct feedback from finance market,
and reduce the training cycle.
The Quant Firm directly receive the correct feedback from finance market,
and reduce the training cycle.
我们这几年在做一件事。
一句话讲,金融文本中存在普遍的数值错误,有方法可以及时发现。
目前在为卖方服务。 这可以是买方的赢利点,找量化机构合作。
The more OpenAI waste in training cost, the more OpenAI’s valuation is. Evil hedge funders could also contribute to the development of AI technology. 全文省流就这2句话
考不考虑用A-Jepa 这样的时间序列世界模型做量化