How Deepseek Revolutionized AI's Cost Calculus

1/30/2025 1:16:55 AM

(MENAFN- Asia Times) State-of-the-art artificial intelligence systems like OpenAI's ChatGPT , Google's Gemini and Anthropic's Claude have captured the public imagination by producing fluent text in multiple languages in response to user prompts. Those companies have also captured headlines with the huge sums they've invested to build ever more powerful models.

An AI startup from China, DeepSeek , has upset expectations about how much money is needed to build the latest and greatest AIs. In the process, they've cast doubt on the billions of dollars of investment by the big AI players.

I study machine learning . DeepSeek's disruptive debut comes down not to any stunning technological breakthrough but to a time-honored practice: finding efficiencies. In a field that consumes vast computing resources, that has proved to be significant.

Where the costs are

Developing such powerful AI systems begins with building a large language model . A large language model predicts the next word given previous words. For example, if the beginning of a sentence is“The theory of relativity was discovered by Albert,” a large language model might predict that the next word is“Einstein.” Large language models are trained to become good at such predictions in a process called pretraining.

Pretraining requires a lot of data and computing power. The companies collect data by crawling the web and scanning books. Computing is usually powered by graphics processing units , or GPUs.

Why graphics? It turns out that both computer graphics and the artificial neural networks that underlie large language models rely on the same area of mathematics known as linear algebra. Large language models internally store hundreds of billions of numbers called parameters or weights. It is these weights that are modified during pretraining.

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