Technology Innovation Column

When a small or medium-sized enterprise begins to expand, the first problem it faces is usually not the market or product, but a lack of staff. Imagine that you are the person in charge of this company today. You may need to perform three duties: replying to customers, writing copy, and handling customer reviews at the same time.
And in the midst of these tedious but important tasks, you start hearing about a new helper that could change the way you do your job: Artificial Intelligence, or more specifically: Large Language Models (LLMs).

Artificial intelligence (AI) is rapidly changing our world. Whether it is chatbots, voice assistants, or self-driving vehicles, they all rely on powerful AI training and reasoning technologies. But not all AI models are trained the same, with some companies choosing to use state-of-the-art hardware while others try to achieve similar results with fewer resources.
DeepSeek, OpenAI, and Anthropic are the three major competitors in the AI field, and each company has different training strategies. DeepSeek chose to use the older but less expensive A100 GPU, OpenAI relied on the latest NVIDIA H100, and Anthropic relied on Google TPU to optimize AI training. This article will delve into the strategies of these three companies in AI training and reasoning, and analyze their impact on the AI industry. Let’s read on!

Who is DeepSeek?
DeepSeek is a startup from China that recently developed an AI model that competes with OpenAI GPT-4 at a very low cost. DeepSeek's success is mainly attributed to its innovative AI training method, which breaks through the high cost of AI training through a combination of older NVIDIA A100 GPUs and low-cost chips.
NVIDIA’s Role
NVIDIA is a leader in the global AI hardware market, and its high-end GPUs are widely used in AI training and inference. In the past, NVIDIA was the core supplier of almost all large-scale AI projects, and companies had to rely on their expensive GPUs to train AI models.
DeepSeek and NVIDIA
DeepSeek currently still relies on NVIDIA's hardware to run AI models, but DeepSeek has proved that AI training does not necessarily require the use of NVIDIA's latest H100 or Blackwell architecture GPUs, which has also made the market begin to rethink the necessity of NVIDIA's high-end hardware.

In recent years, competition in the field of artificial intelligence (AI) has become increasingly fierce, and technology companies from all over the world have invested resources in an attempt to seize market opportunities. In this AI competition, DeepSeek has rapidly emerged with its low-cost and high-efficiency technical solutions and has become the focus of market attention. Compared with American technology companies such as OpenAI and Anthropic, DeepSeek not only demonstrates strong technological innovation capabilities, but also subverts people's existing perception of the cost of AI training.
This article will delve into the rise of DeepSeek and analyze its technological advantages, core competitiveness, and inspiration for the AI industry from multiple perspectives. Let’s read on!

With a large amount of technology industry funds flowing into the AI field, by understanding the fundraising status and concepts behind various AI startups, we can also have a clearer understanding of future artificial intelligence trends.
Taking the creation of generative AI and large-scale language models as an example, OpenAI is not the only one that excels. It is backed by huge investments from technology giants Amazon and Google. Anthropic has undoubtedly become the most high-profile unicorn this year after OpenAI.

What kind of company is Anthropic? Why did the founders separate from OpenAI? What is the difference between the strategic positions of the two companies?

Today I’m going to share the Anthropic story with you!

He dropped out of school to establish Scale AI when he was 19 years old. Alexandr said that Scale AI provides data labeling services, which is like selling shovels in this generative AI gold rush (does this description sound familiar😆).

While many AI startups have yet to make a dime, Scale AI’s revenue last year reached US$250 million and its valuation reached US$7.3 billion. Its customers range from OpenAI and Tesla to the US Air Force, Army, CIA, etc. field.

So what exactly are data tags? Why can it be so profitable? How did Scale AI find its niche market in the highly competitive AI field and successfully grow into a unicorn?

Today I’m going to share the story of Scale AI with you!