DeepSeek demolishes Nvidia stock, CEO loses $20 billion in a day: Why was the tech giant worst hit?

All good things come to an end. This adage has come true for Nvidia, which until Monday (January 27) was having a good run at the stock market. However, it all came crashing down for the Jensen Huang company, as it lost over a sixth of its value owing to Chinese artificial intelligence company DeepSeek.

It all began last Monday (January 20) when DeepSeek presented an AI chatbot called R1, which operates at a fraction of the cost of OpenAI’s or Meta’s popular AI models. The company said it had spent just $5.6 million on computing power for its base model, compared with the hundreds of millions or billions of dollars US companies spend on their AI technologies. That sent shockwaves through tech markets, resulting in spooking investors in the US as well as Europe.

DeepSeek hammers Nvidia stocks

On Monday, the Liang Wenfeng-founded company hammered Nvidia at the stock market. The shares of the company, which is the leading supplier of AI chips, plunged 17 per cent, shedding $600 billion in value and marking the single-biggest one-day loss for a company in stock market history, according to CNBC.

A CNN report states that the previous record for the biggest one-day loss was previously held by Meta, which lost $240 billion three years ago.

To put Nvidia’s loss into perspective, it lost more in market value on Monday than all but 13 companies are worth.

This drop now puts Nvidia below Apple and Microsoft as the third-most valuable company in the world.

Nvidia’s Huang is poorer by nearly $20 billion

And as DeepSeek roiled Nvidia’s stocks, it also led to its CEO, Jensen Huang losing nearly 20 per cent of his net worth. As per the Bloomberg Billionaires Index, Huang’s net worth dipped from $121 billion to around $100 billion.

But it’s not just Huang who saw his personal wealth plummeting on Monday owing to DeepSeek. Billionaires whose fortunes are linked to artificial intelligence also saw a dip in their worth. Oracle co-founder and CTO Larry Ellison lost $22.6 billion, or 12 per cent of his net worth, per Bloomberg.

Fortune also reported that Dell Inc’s Michael Dell lost $13 billion, and Binance Holdings Ltd co-founder Changpeng “CZ” Zhao shaved $12.1 billion.

It was reported on Monday that on Monday, the world’s 500 richest people lost a combined $108 billion.

Other tech stocks tumble too

But Nvidia wasn’t alone in seeing its shares plummet on Monday; many tech stocks got hammered. For instance, Dutch chip equipment maker ASML ended Monday’s trading with its share price down by more than seven per cent.

Broadcom, another semiconductor stock, also slumped 17 per cent. Shares of shares of Taiwan Semiconductor Manufacturing Company, or TSMC, dropped more than 14 per cent owing to DeepSeek’s popularity.

Meta and Alphabet, Google’s parent company, were also down sharply. This dragged down the broader stock market because tech stocks make up a significant chunk of the market.

As Fiona Cincotta, senior market analyst at City Index told the BBC, “This idea of a low-cost Chinese version hasn’t necessarily been forefront, so it’s taken the market a little bit by surprise.”

DeepSeek’s rise and Nvidia’s fall

But what is the connection between DeepSeek and Nvidia? Why did tech stocks fall?

The answer lies in DeepSeek’s performance. According to the Chinese AI startup, its chatbot — DeepSeek-R1 — outperforms cutting-edge models such as OpenAI’s o1 and Meta’s Llama AI models across multiple benchmarks. This is remarkable as it delivers these results without relying on Nvidia’s advanced GPUs.

DeepSeek has utilised tens of thousands of Nvidia’s H100 and H200 AI GPUs to train its models but faced constraints owing to US restrictions. This forced the company to implement innovative engineering tweaks, such as custom communication schemes between chips to improve data transfer efficiency, memory-saving techniques, and reinforcement learning methods to minimise computational power requirements. These optimisations result in drastically lower costs compared to traditional large language models.

What this means is that now many in the AI world may wonder if they need to buy as many of Nvidia’s tools.

Analysts at Wedbush said in a research note Monday that “tech stocks are under massive pressure led by Nvidia as the Street will view DeepSeek as a major perceived threat to US tech dominance and owning this AI Revolution.”

Uncertainty over DeepSeek in the long term

But can DeepSeek challenge the US AI giants in the long run, is what many are asking. Wall Street banking giant Citi cautioned against it, saying issues faced by Chinese firms could hamper their development. “We estimate that in an inevitably more restrictive environment, US access to more advanced chips is an advantage,” analysts said in a report.

Others also believe that Monday’s stock selloff is an overreaction. “It’s one thing to train a [large language] model for less money, but accommodating the huge demand for the consumption of all this AI technology is still going to require massive amounts of infrastructure,” Adam Crisafulli of VitalKnowledge said in a report.

Michael Block, market strategist at Third Seven Capital, also echoed similar concerns. Speaking to CNN, he said, “Time will tell if the DeepSeek threat is real — the race is on as to what technology works and how the big Western players will respond and evolve.

“Markets had gotten too complacent on the beginning of the Trump 2.0 era and may have been looking for an excuse to pull back — and they got a great one here.”

Meanwhile, Nvidia, which was severely impacted by DeepSeek, offered praise for the Chinese firm. “DeepSeek is an excellent AI advancement and a perfect example of test-time scaling,” the company said in an email. “DeepSeek’s work illustrates how new models can be created using that technique, leveraging widely available models and compute that is fully export-control compliant.”

But, Nvidia added, AI inference, or using AI models to make decisions or predictions, requires significant numbers of “Nvidia GPUs and high-performance networking. We now have three scaling laws: pre-training and post-training, which continue, and new test-time scaling.”

With inputs from agencies