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    Home > Finance > American AI firms try to poke holes in disruptive DeepSeek
    Finance

    American AI firms try to poke holes in disruptive DeepSeek

    Published by Global Banking & Finance Review®

    Posted on January 28, 2025

    4 min read

    Last updated: January 27, 2026

    Image depicting AI experts discussing the implications of DeepSeek's disruptive models on U.S. finance. This highlights the competition between American and Chinese AI technologies, as outlined in the article.
    AI experts evaluating DeepSeek's impact on American finance technology - Global Banking & Finance Review
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    Tags:innovationtechnologyfinancial servicesArtificial Intelligenceinvestment

    Quick Summary

    DeepSeek's AI models challenge U.S. firms with cost-effective solutions, surpassing ChatGPT in popularity. Nvidia chips are pivotal in their success.

    American AI Firms Respond to DeepSeek's Disruptive Models

    By Kenrick Cai, Anna Tong and Jeffrey Dastin

    SAN FRANCISCO - Developers at leading U.S. AI firms are praising the DeepSeek AI models that have leapt into prominence while also trying to poke holes in the notion that their multi-billion dollar technology has been bested by a Chinese newcomer's low-cost alternative.

    Chinese startup DeepSeek on Monday sparked a stock selloff and its free AI assistant overtook OpenAI's ChatGPT atop Apple's App Store in the U.S., harnessing a model it said it trained on Nvidia's lower-capability H800 processor chips using under $6 million.

    As worries about competition reverberated across the U.S. stock market, some AI experts applauded DeepSeek's strong team and up-to-date research but remained unfazed by the development, said people familiar with the thinking at four of the leading AI labs, who declined to be identified as they were not authorized to speak on the record.

    OpenAI CEO Sam Altman wrote on X that R1, one of several models DeepSeek released in recent weeks, "is an impressive model, particularly around what they're able to deliver for the price." Nvidia said in a statement DeepSeek's achievement proved the need for more of its chips.

    Software maker Snowflake decided Monday to add DeepSeek models to its AI model marketplace after receiving a flurry of customer inquiries.

    With employees also calling DeepSeek's models "amazing," the U.S. software seller weighed the potential risks of hosting AI technology developed in China before ultimately deciding to offer it to clients, said Christian Kleinerman, Snowflake's executive vice president of product.

    "We decided that as long as we are clear to customers, we see no issues supporting it," he said.

    Meanwhile, U.S. AI developers are hurrying to analyze DeepSeek's V3 model. DeepSeek in December published a research paper accompanying the model, the basis of its popular app, but many questions such as total development costs are not answered in the document.

    China has now leapfrogged from 18 months to six months behind state-of-the-art AI models developed in the U.S., one person said. Yet with DeepSeek's free release strategy drumming up such excitement, the firm may soon find itself without enough chips to meet demand, this person predicted.

    DeepSeek's strides did not flow solely from a $6 million shoestring budget, a tiny sum compared to $250 billion analysts estimate big U.S. cloud companies will spend this year on AI infrastructure. The research paper noted that this cost referred specifically to chip usage on its final training run, not the entire cost of development.

    The training run is the tip of the iceberg in terms of total cost, executives at two top labs told Reuters. The cost to determine how to design that training run can cost magnitudes more money, they said.

    The paper stated that the training run for V3 was conducted using 2,048 of Nvidia's H800 chips, which were designed to comply with U.S. export controls released in 2022, rules that experts told Reuters would barely slow China's AI progress.

    Sources at two AI labs said they expected earlier stages of development to have relied on a much larger quantity of chips. One of the people said such an investment could have cost north of $1 billion.

    Some American AI leaders lauded DeepSeek's decision to launch its models as open source, which means other companies or individuals are free to use or change them.

    "DeepSeek R1 is one of the most amazing and impressive breakthroughs I've ever seen - and as open source, a profound gift to the world," venture capitalist Marc Andreessen said in a post on X on Sunday.

    The acclaim garnered by DeepSeek's models underscores the viability of open source AI technology as an alternative to costly and tightly controlled technology such as OpenAI's ChatGPT, industry watchers said.

    Wall Street's most valuable companies have surged in recent years on expectations that only they had access to the vast capital and computing power necessary to develop and scale emerging AI technology. Those assumptions will come under further scrutiny this week and the next, when many American tech giants will report quarterly earnings.

    (Reporting by Anna Tong, Jeffrey Dastin and Kenrick Cai in San Francisco and Katie Paul in New York; Editing by Noel Randewich and Christopher Cushing)

    Key Takeaways

    • •DeepSeek AI models challenge U.S. AI firms with low-cost alternatives.
    • •DeepSeek's AI assistant surpasses ChatGPT in Apple's App Store.
    • •Nvidia chips play a crucial role in DeepSeek's model training.
    • •Open source strategy by DeepSeek garners industry acclaim.
    • •Potential chip shortages could impact DeepSeek's growth.

    Frequently Asked Questions about American AI firms try to poke holes in disruptive DeepSeek

    1What is the main topic?

    The article discusses DeepSeek's AI models challenging U.S. firms with low-cost alternatives.

    2How did DeepSeek impact the AI market?

    DeepSeek's AI assistant overtook ChatGPT in the App Store, causing a stock selloff and industry buzz.

    3What role do Nvidia chips play?

    Nvidia chips were used in DeepSeek's model training, highlighting their importance in AI development.

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