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Friday, January 31, 2025

DeepSeeks Disruption Software Soars Nvidia Stumbles

By Century Financial in 'Investment Insights'

DeepSeeks Disruption Software Soars Nvidia...
Coldplay

Explained in Layman Terms

  • ChatGPT is an AI model that uses powerful chips known as Graphic-processing units (GPUs) to learn big data along with human language to answer queries.
  • Nvidia has been the leading company that develops GPUs, predominantly used for gaming but are now used for training AI models
  • A Chinese company, DeepSeek has created a much cheaper AI model that rivals ChatGPT while requiring a much lesser number of GPUs.
  • This news caused a 3% fall in Nasdaq and a 17% drop in Nvidia, raising concerns about Nvidia’s expected sales for high-end GPUs going forward.
  • Software companies stand to benefit from lower GPU requirements for building and training models, while hardware companies might struggle due to expectations of reduced AI related spending.

DeepSeek – What it is and what it means for the AI sector

A Chinese startup known as DeepSeek, dedicated to artificial general intelligence, has disrupted the industry with the launch of its new model called R1. This was made within 2 months and cost about $6 million to build with a small team of less than 200 people.

Meanwhile, OpenAI has raised over $17.9 billion and is reportedly burning through billions to train its AI models. OpenAI CEO Sam Altman has previously said that GPT-4 alone cost more than $100 million to train.

The fascinating thing about DeepSeek’s model is that it has shown results that are on par or even exceed the best of American models, which were dominating in this space. On top of that, the company did this with reduced-capability Nvidia H800 chips, given America’s restrictions on China getting their hands on high-end AI hardware.

Recently, Microsoft announced plans to spend approximately $80 billion in FY 2025 to build out AI data centres to train models and deploy AI applications around the world. Moreover, Meta revealed plans to spend about $65 billion in the upcoming year to build its AI infrastructure, while Amazon expects a higher spending than the $75 billion it spent on AI capital expenditure in 2024. Even Alphabet is considering a major investment in the same for the upcoming year.

These companies were major customers for Nvidia, buying its high-performance H100 GPUs and moving on to the new-generation Blackwell chips. About 41% of Microsoft’s capex, 37.16% of Meta’s capex, 13.48% of Alphabet’s capex, and 9.28% of Amazon’s capex accounted towards Nvidia, indicating the high demand for GPUs for AI model training. Going forward, the disruption caused by DeepSeek might reduce the need for extensive computing power, directly impacting the demand for Nvidia GPUs. Big tech names like Microsoft, Amazon, Alphabet and Meta might be positively impacted by more efficient models with lower GPU requirements, as these companies are the end consumers for GPUs.

Names to watch out for -

The technology sector reacted negatively to the news of DeepSeek’s revolutionary performance, with the Nasdaq down about 3% on Monday and Nvidia falling by about 17%, wiping away almost $600 billion in market capitalization, the largest ever decline in history for a single stock.

The main reason for such a drastic fall could be the reduced demand expectations of Nvidia’s high-end offerings, impacting the growth expected for its GPU sales going forward. Along with Nvidia, other AI hardware names like Broadcom and Marvell, which develop custom AI chips, could be impacted negatively by the expected curb on AI-related spending.

Now that Chinese engineering has shown that achieving accurate results is possible without the need for expensive high-performance GPUs, the expected capital expenditures for the year might be revised downward if the companies go for such efficient models. Some of the major names that could benefit from reduced costs could be Microsoft, Meta, Alphabet, and Amazon.


DeepSeek’s R1 platform is open-source software with the base code already widely available for developers to build upon. Hence, some of the companies directly developing AI technologies or integrating them into existing platforms can provide more autonomous and efficient solutions.

The trend towards such AI technologies can automate complex decision-making and task execution, potentially transforming the way businesses operate across various sectors. Hence, software companies like Salesforce, ServiceNow, Crowdstrike Holdings, Snowflake, Datadog, MongoDB, and Gitlab could be some of the names to look out for.


Risks and Assumptions related to Back-tested trading strategies
The risks and assumptions listed here are not intended to be an exhaustive summary of all the risks and assumptions involved.
The strategy might suffer from look-ahead bias which occurs due to the use of information or data in a study or simulation that would not have been known or available during the period being analyzed. This can lead to inaccurate results in the study or simulation.
Future price movements may not be exactly the same as the historical price movements and this could lead to variation in performance.
Testing can sometimes lead to over-optimization. This is a condition where performance results are tuned so high to the past they are no longer as accurate in the future.
The model assumes no slippages in trading. Slippage refers to the difference between the expected price of a trade and the price at which the trade is actually executed.
The back-tested strategy might be at risk of data dredging, which is the behavior of testing multiple hypotheses at one time, resulting in picking the data that best supports your main hypothesis.
Drawdowns in actual trading can be higher than the tested system and losses could be significant in the event of leverage.
Unforeseen events can lead to variation in performance from the tested trading strategy.
The tested result has been computed with price feeds available from Bloomberg.
The testing environment has not considered transaction or any other costs.
Trading indicators used for the purpose of testing has been provided by Bloomberg.
The strategy might suffer from data mining fallacy, selection bias and backfill bias.
A trading strategy that performs well on multiple datasets from one market (e.g., forex) might not perform as well in another market (e.g., stocks).
The strategy may not depict accuracy in terms of spread changes due to the spread-widening events.

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