OP Extra: Our Take on DeepSeek

DeepSeek: A Market Shock, Not a Breakthrough

Editor’s Note: We are dispersed today on the MTS team so multiple OPs have gone out. They are all good so read them all Tomorrow we will be better organized. Thank you for reading!

DeepSeek: A Market Shock, Not a Breakthrough

The recent debut of the Chinese AI model DeepSeek has garnered significant attention, with headlines heralding it as a breakthrough. However, the real story isn’t about revolutionary capabilities; rather, it’s the disruption it represents in the cost structure of AI inference models.

DeepSeek, while impressive, doesn’t surpass leading models like GPT-4 in overall performance. Through weeks of testing across tasks such as code generation, editing, summarization, and multilingual communication, DeepSeek has shown itself to be competent, yet not revolutionary. It excels in certain areas while lagging in others. Its primary appeal lies in accessibility and cost-efficiency—qualities that could reshape the market, even if they don’t redefine AI itself.

I have personally used DeepSeek over the past two weeks and found it to be a good model. However, it does not advance the state of AI models in any significant way. Its strengths are offset by its limitations, and it remains a solid but unremarkable tool compared to existing leaders.

The Hidden Shock: Democratized Inference

What makes DeepSeek noteworthy is not its quality but its method of deployment. Unlike many advanced models that require cutting-edge GPU infrastructure, DeepSeek operates on significantly less resource-intensive systems. Even more striking is the availability of its proprietary weights, enabling others to build and deploy comparable models at lower costs. This undermines the economic exclusivity that has long been a hallmark of the AI inference market.

This move could commoditize general-purpose AI, making high-functioning models more accessible to smaller players who previously couldn’t afford the infrastructure or licensing fees associated with leading solutions. The devaluation isn’t of AI as a whole but rather the pricing premium traditionally attached to inference services.

Training vs. Inference: Understanding the Divide

AI systems are often conflated, but it’s important to distinguish between the training and inference stages. Training requires immense computational resources to process vast datasets, effectively encoding the world’s knowledge into a functional model. This step remains resource-heavy and capital-intensive.

Inference, however, is the user-facing side of AI—where questions are asked, answers are given, and tasks are performed. DeepSeek’s true innovation lies in its ability to bring this stage to a wider audience without the need for state-of-the-art hardware. As user demand for AI services grows, the inference side of AI is set to expand exponentially, driven by the increased volume of queries and the democratization of access.

The Rise of Specialized Models

General-purpose models, like DeepSeek, are versatile—capable of handling diverse tasks such as mathematics, science, language translation, and even image and audio processing. However, there’s a growing trend toward specialized models tailored for specific industries or applications. These models don’t need to know everything; they only need to excel in their niche. By focusing on efficiency within defined parameters, specialized models offer a counterpoint to the generalists, further diversifying the AI market.

The Path Ahead: Volatility and Growth

The AI market’s trajectory is far from linear. External factors, such as geopolitical shifts and leadership changes, can create volatility, influencing adoption and investment. For example, predictions of increased market activity under a new U.S. administration could drive demand for tools that interpret and anticipate rapid changes.

As a side note, if you want to test what Chinese censorship looks like, ask DeepSeek about Tiananmen Square or Xi Jinping’s spouse. You will not get an answer. However, ask about January 6th, and it will readily provide a factual account. Open-source technology is great, but open censorship is not a model that will gain widespread acceptance.

DeepSeek’s debut illustrates a key shift in the AI landscape: the ability to achieve near-state-of-the-art results without bleeding-edge infrastructure. This doesn’t signal an end to innovation but a redistribution of power within the ecosystem. As costs drop and accessibility increases, the focus will turn to differentiation through specialization, speed, and scalability.

In conclusion, DeepSeek is no breakthrough. Its shock value lies in proving that the barriers to deploying advanced AI are lower than ever before. This democratization of inference models is poised to reshape the market, setting the stage for broader access and fiercer competition in the AI arms race.