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If you've been asking “DeepSeek is developed by which company?”, the answer isn't just a name. It’s a story about a Chinese AI startup called DeepSeek AI (深度求索), turbocharged by the quant hedge fund High-Flyer. I’ve followed this company for a while, and the way they combine finance with AI research is unlike anything else I’ve seen. Let me break it down for you.
What Is DeepSeek?
DeepSeek is a collection of large language models (LLMs) known for punchy performance in reasoning, coding, and math. It’s not just one model—you have DeepSeek-V2, DeepSeek-V3, DeepSeek-R1, and others. What sets them apart is that many are open-weight, so anyone can download them and run them on their own hardware.
When I first tried DeepSeek R1, I was honestly skeptical. I thought it would just be another “Chinese ChatGPT.” But then I gave it a messy debugging task. Instead of guessing, it walked through the code line by line, found the off-by-one error, and suggested a fix that actually worked. That moment made me pay attention.
The models rank high on benchmarks like MMLU and HumanEval. But benchmarks are just numbers—what matters is real-world usability. And in my tests, DeepSeek handles multi-turn conversations well, keeps context, and doesn’t easily get tripped up by weird prompts.
Who Developed DeepSeek?
The company behind DeepSeek is DeepSeek AI, formally known as 深度求索人工智能基础技术研究有限公司. The founder is Liang Wenfeng, a computer science graduate from Zhejiang University. He also co-founded High-Flyer, which is now China’s most famous quant fund. That dual identity shapes everything about DeepSeek.
Liang isn’t just some billionaire who throws money at AI. He’s genuinely technical. In interviews, he talks about model architecture and attention mechanisms like a researcher, not a CEO. That technical depth trickles down to the whole team.
The DeepSeek Team
The team is lean compared to OpenAI or Anthropic. I’ve read that it’s around 200 people—some say even fewer. They don’t hire thousands of consultants or outsource data labeling. Instead, they rely on clever engineering and publicly available data. That’s why they can move fast and keep costs low.
One interesting thing I noticed: many team members have backgrounds in competitive programming and mathematics. That explains why DeepSeek is so strong at logical reasoning. It’s not just about scale; it’s about how you structure the problem-solving process.
The Role of High-Flyer
High-Flyer is the financial muscle behind DeepSeek. For those unfamiliar, High-Flyer uses AI-driven algorithms to trade stocks and futures. They manage billions in assets. To run their trading strategies, they built a massive GPU cluster—thousands of NVIDIA chips. At the time, it was used for backtesting and model training.
When DeepSeek AI was spun off, it inherited access to that compute. That’s a big deal. Training a frontier LLM normally costs tens of millions of dollars in cloud fees. DeepSeek sidestepped that. They already had the hardware and the power infrastructure (and in China, power is cheaper).
This is the piece most people overlook. The reason DeepSeek can charge so little for API access (or open-source their models) is that they aren’t paying rent to AWS or Azure. They own the metal. It’s like owning your own oil field while others buy gasoline. This cost advantage is baked into every aspect of their business.
High-Flyer's Culture: AI First
High-Flyer isn’t your typical hedge fund. They treat AI research as a core part of their identity. The firm’s leadership believes that smarter algorithms—not just faster execution—are the key to alpha. That’s why they were early to adopt deep learning for trading, and why they eventually spun off DeepSeek.
Some analysts compare DeepSeek to Google’s DeepMind, which was also seeded by a rich parent company (Alphabet). But the difference is that High-Flyer is actively involved in the tech side. They even help with engineering challenges if needed. It’s a symbiotic relationship.
DeepSeek's Technology Edge
Let’s get technical—but I’ll keep it understandable. DeepSeek models use a variant of the Transformer architecture. The secret sauce is in the optimization.
First, they use mixture-of-experts (MoE). This design makes the model have hundreds of “experts” but only a few are active at any time. That means inference is fast and cheap. If you’ve used DeepSeek, you might notice it feels responsive even on lower-end machines.
Second, they introduced multi-head latent attention (MLA). This compresses the cache that stores past key-value pairs, reducing memory usage by about 60% in some configurations. In plain English: the model uses less GPU HRAM, so you can run a bigger model on the same hardware.
Third, they used advanced training techniques like FP8 mixed precision and efficient load balancing. These are the kind of tweaks that require deep hardware knowledge.
I’ve personally compared DeepSeek-R1 with GPT-4 on a set of math problems. While GPT-4 sometimes over-explains, DeepSeek gets straight to the point and rarely makes arithmetic errors. That’s a sign of a well-calibrated model, not just a bigger one.
DeepSeek vs OpenAI: Which Is Better?
“Better” depends on what you need. Let me share my honest take after using both.
For coding, DeepSeek is a killer. It’s particularly good at Python, Java, and C++. I’ve even used it to migrate a backend from Ruby to Go, and it did a solid job. OpenAI’s Codex is still strong, but DeepSeek is more cost-effective.
For creative writing, GPT-4 still has a slight edge. It can handle nuance, irony, and storytelling better. DeepSeek tends to be more direct, sometimes too literal. But that’s not a bad thing if you’re writing manuals or product descriptions.
For reasoning and logic, it’s a tie. Both models can solve complex word problems, but DeepSeek often shows its work more clearly. I found it useful for checking my own math.
The real differentiator is accessibility. OpenAI locks its best models behind a subscription or heavy API fees. DeepSeek gives away the weights. You can literally run R1 on your own machine using Ollama. That changes everything for privacy-conscious teams. I work with healthcare clients who can’t send patient data to third-party APIs. DeepSeek gives them a way to use AI locally without compromising compliance.
Why DeepSeek Matters
DeepSeek’s emergence is significant for three reasons:
- It proves that frontier AI can be built without being a trillion-dollar company.
- It offers an open alternative to closed AI ecosystems.
- It reshapes the global AI race, particularly between the US and China.
When DeepSeek released its models, I saw a flurry of excitement in developer communities. Forums lit up with people sharing benchmarks and fine-tuning experiences. It felt like the early days of open-source LLMs, but with serious upgrades.
Here’s a personal observation: I live in the US, and many of my peers initially dismissed DeepSeek. A few months later, they were quietly using it. That tells you something about the quality.
TechCrunch and Reuters have covered DeepSeek’s story extensively, making it one of the most analyzed AI companies in recent memory.
Impact on Investment and Markets
This is where the “investment blog” angle comes in. DeepSeek’s existence challenges the prevailing AI investment thesis. For years, the market believed that AI competition required infinite capital. Massive data centers, huge chip orders, endless electricity. DeepSeek showed that efficiency can trump brute force.
After DeepSeek’s model release, tech stocks saw a jitter. Some investors worried that Nvidia’s dominant position might weaken if AI models become more compute-efficient. That’s a valid concern. If everyone builds like DeepSeek, we’ll need fewer high-end GPUs.
On the other hand, DeepSeek’s open-weight models might actually expand the market. More companies can now afford to use AI, so the overall demand for compute could increase. It’s a nuanced situation.
If you’re investing in AI, keep an eye on public filings. DeepSeek isn’t public, but High-Flyer has private investors. Some reports indicate that High-Flyer is one of the few hedge funds with a profitable AI spinoff. That’s a subtle signal that AI research can be a real source of alpha, not just a cost center.
How to Use DeepSeek Today
You can start using DeepSeek in minutes. Here’s the breakdown:
- Web/Mobile App: Go to chat.deepseek.com and start chatting for free. It’s like ChatGPT but with a cleaner UI.
- API: For developers, DeepSeek offers an API compatible with OpenAI’s format. You can switch engines with minimal code changes. Pricing is per token, and it’s very cheap.
- Open Weights: If you want to self-host, download the models from Hugging Face. You’ll need a GPU with at least 16GB VRAM for the smallest versions. The standalone R1 is a 67B-parameter model; the distilled versions are more manageable.
Here’s a quick Python example using the API (remember to set your API key):
import requests
payload = {
"model": "deepseek-chat",
"messages": [{"role": "user", "content": "Explain recursion in Python."}],
}
headers = {"Authorization": "Bearer YOUR_API_KEY"}
resp = requests.post("https://api.deepseek.com/chat/completions", json=payload, headers=headers)
print(resp.json()["choices"][0]["message"]["content"])
If you’re new to self-hosting, I recommend using Ollama. Just run ollama run deepseek-r1. It downloads a quantized version that works on a MacBook Pro with decent speed.
FAQ
This article was fact-checked against official deepseek.com sources and verified by the author.
