European espresso: Is DeepSeek rewriting the course of the AI revolution?
As part of our Espresso series, Portfolio Manager Tom Lemaigre focuses on DeepSeek's open-source AI model, discussing what it might mean for European companies involved in the race for AI dominance.
4 minute watch
Key takeaways:
- Nvidia’s market cap plummeted by US$560 billion in a single day due to the emergence of DeepSeek, a Chinese AI company that developed a more cost-effective large-language model, challenging established players and signalling a shift towards open-source AI development.
- DeepSeek’s model, which it claims cost practically nothing to train, illustrates a move towards democratising AI through open-source models and cost efficiency, potentially leading to a wider application of AI technologies across various sectors.
- Despite advancements in AI, infrastructure, such as data centres and compute power, continues to be a key differentiator for companies. This highlights the ongoing importance of robust infrastructure in the competitive AI landscape, with opportunities for many European businesses.
So it won’t be much of a surprise to any market participant that there’s been some pretty big moves in the past week. So we saw the biggest market cap loss ever in the history of the stock market, with Nvidia losing US$560 billion worth of market cap (value) in a single day.
The reason for this was because of the emergence of a Chinese AI company that has built a large-language model, cheaper, that can compete with the big existing ones from OpenAI and Meta.
Now, it is clearly far too early to know exactly what the entire ramifications of DeepSeek’s model will be on the AI space. However, there are three things that we are thinking about.
The first one is DeepSeek’s model is open source, so therefore it means that it can be leveraged by others. So we probably will see a proliferation of other models such as DeepSeek come along.
Secondly, it uses a process called ‘distillation’, so it is essentially leveraging bigger models such as OpenAI’s ChatGPT or Meta’s Llama to train itself. So we think the future here is much more of an open-source model for AI generally. The [first] big conclusion is that DeepSeek have claimed that it only cost them US$5.6 million to train this model.
Now given it looks like it was incredibly efficient in terms of training and inference (that’s when you ask it a question), could it be that DeepSeek is going to democratise AI? Ie. given its efficiency, given the cost – will we see a proliferation of different use cases across the space. In a world of technology, any cost improvement, usually increases the number of use cases. So we are quite excited about what it means for the industry as a whole.
The third important point is actually that infrastructure will still be a defining differentiator for the AI players. So just this week, Meta had its results, where it confirms that it still intends to spend between US$60-65 billion on capital expenditure, mainly to build out its data centre infrastructure and compute power. Because they believe, still, that compute [power] is going to be a differentiator going forward.
So where does that leave us here, in European equities? Well, the way in which we have positioned our funds is agnostic as to which large-language model will win, or if there is a proliferation of them. Because the European semiconductor capital equipment space and electrical cap goods space plays into all of these. Because it’s the enabler. It’s the picks and shovels that helps the AI revolution to happen.
The second broad conclusion that we can take from this week is the dangers of concentration. So you will have seen the big market cap falls that happened in the US across the big tech space. And clearly when the S&P500 has 32 per cent sector exposure to tech, there is that danger. In Europe, the equivalent figure is only 8 per cent and the valuation starting point is much lower.
Please note: Past performance does not predict future returns. The value of an investment and the income from it can fall as well as rise and you may not get back the amount originally invested. There is no guarantee that past trends will continue, or forecasts will be realised.
References made to individual securities do not constitute a recommendation to buy, sell or hold any security, investment strategy or market sector, and should not be assumed to be profitable. Janus Henderson Investors, its affiliated advisor, or its employees, may have a position in the securities mentioned.
Glossary:
Agnostic: Not advocating for one investment style (or stock) over another.
Capital expenditure: Money used by a company to purchase, upgrade, maintain or develop its assets, be that equipment, technology, or infrastructure, including buildings.
Large-language model (LLM): A machine learning model designed to understand and generate responses to human language via the processing of large amounts of text data.
Open source: Software for which the original source code is freely available to be shared, distributed or modified.
Related insights
These are the views of the author at the time of publication and may differ from the views of other individuals/teams at Janus Henderson Investors. References made to individual securities do not constitute a recommendation to buy, sell or hold any security, investment strategy or market sector, and should not be assumed to be profitable. Janus Henderson Investors, its affiliated advisor, or its employees, may have a position in the securities mentioned.
Past performance does not predict future returns. The value of an investment and the income from it can fall as well as rise and you may not get back the amount originally invested.
The information in this article does not qualify as an investment recommendation.
There is no guarantee that past trends will continue, or forecasts will be realised.
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Specific risks
- Shares/Units can lose value rapidly, and typically involve higher risks than bonds or money market instruments. The value of your investment may fall as a result.
- Shares of small and mid-size companies can be more volatile than shares of larger companies, and at times it may be difficult to value or to sell shares at desired times and prices, increasing the risk of losses.
- If a Fund has a high exposure to a particular country or geographical region it carries a higher level of risk than a Fund which is more broadly diversified.
- The Fund may use derivatives with the aim of reducing risk or managing the portfolio more efficiently. However this introduces other risks, in particular, that a derivative counterparty may not meet its contractual obligations.
- If the Fund holds assets in currencies other than the base currency of the Fund, or you invest in a share/unit class of a different currency to the Fund (unless hedged, i.e. mitigated by taking an offsetting position in a related security), the value of your investment may be impacted by changes in exchange rates.
- When the Fund, or a share/unit class, seeks to mitigate exchange rate movements of a currency relative to the base currency (hedge), the hedging strategy itself may positively or negatively impact the value of the Fund due to differences in short-term interest rates between the currencies.
- Securities within the Fund could become hard to value or to sell at a desired time and price, especially in extreme market conditions when asset prices may be falling, increasing the risk of investment losses.
- The Fund could lose money if a counterparty with which the Fund trades becomes unwilling or unable to meet its obligations, or as a result of failure or delay in operational processes or the failure of a third party provider.
Specific risks
4 minute watch
Key takeaways:
- Nvidia’s market cap plummeted by US$560 billion in a single day due to the emergence of DeepSeek, a Chinese AI company that developed a more cost-effective large-language model, challenging established players and signalling a shift towards open-source AI development.
- DeepSeek’s model, which it claims cost practically nothing to train, illustrates a move towards democratising AI through open-source models and cost efficiency, potentially leading to a wider application of AI technologies across various sectors.
- Despite advancements in AI, infrastructure, such as data centres and compute power, continues to be a key differentiator for companies. This highlights the ongoing importance of robust infrastructure in the competitive AI landscape, with opportunities for many European businesses.