Tech Giants to Spend Big on AI After DeepSeek's Game-Changing Move

The tech industry is on the brink of a monumental shift as giants like Microsoft, Amazon, and Meta prepare to ramp up their AI spending to over $500 billion annually by the next decade. This surge in investment is fueled by the emergence of DeepSeek and OpenAI’s reasoning models, which are reshaping the AI landscape by improving efficiency and reducing reliance on expensive chips. The biggest tech firms will raise their yearly expenditures on artificial intelligence to over $500 billion by the beginning of the following decade, fueled partly by DeepSeek and OpenAI’s more recent AI methodology.

Historically, much of the investment in AI had gone to data centers and chips to train or create significantly complex new AI models. Now, the companies want to take a different approach. Following this, tech firms are expected to move more spending to inference or the process of running those systems after they’ve been trained. This shift is driven by the need for more cost-effective and efficient AI systems, as highlighted by the emergence of DeepSeek and OpenAI’s reasoning models. These models mimic human problem-solving by taking additional time to process and compute responses to user queries, thereby reducing reliance on expensive chips.



The impact on companies like Nvidia, which have traditionally relied on the sale of high-performance chips for AI development, is significant. The emergence of DeepSeek has challenged the prevailing assumptions that hyperscalers must spend billions on advanced AI chips. DeepSeek claims that their model used Nvidia’s H800 chips, which have more constrained memory bandwidth to comply with U.S. chip export controls than the more advanced H100s. This has led to a sharp fall in AI-related stocks and challenges assumptions about AI investments. For example, Nvidia saw its share price drop around 17%, wiping out approximately $600 billion in market cap value, the largest single-day loss in history.

The shift in AI investment from training to inference is expected to have significant implications for the long-term profitability and market dominance of tech giants, as well as for smaller AI startups and competitors. For tech giants, this shift could lead to cost efficiencies and enhanced market dominance, especially for those that can quickly adapt to inference technologies. For smaller startups, the focus on inference could open up new opportunities for innovation and specialization, but they will also face increased competition and resource constraints.

The emergence of DeepSeek as a rival to the US-based leaders in artificial intelligence rocked equity markets and raised questions about the AI investment theme. The Guinness Global Equity team assesses what DeepSeek means for investors. The impact on the broader AI theme – and for investors – hinges on the distinction between training and inference. Training is the process where an AI model learns by analyzing massive amounts of data and adjusting its internal parameters, while inferencing refers to the trained model applying that knowledge to make real-time and real-world predictions on new, unseen data. If DeepSeek has pioneered a way to create lower-cost models, increased training competition from upstarts could lead to a more competitive landscape in the AI industry. Companies that have traditionally relied on expensive chips for AI development may face challenges as the focus shifts towards more cost-effective and efficient AI systems. This shift is evident in the projected spending trends and the impact on companies like Nvidia, which have traditionally relied on the sale of high-performance chips for AI development.

In summary, the increased AI spending by tech giants, driven by the emergence of DeepSeek, is likely to lead to a more competitive landscape in the AI industry. Companies that have traditionally relied on expensive chips for AI development may face challenges as the focus shifts towards more cost-effective and efficient AI systems. This shift is evident in the projected spending trends and the impact on companies like Nvidia, which have traditionally relied on the sale of high-performance chips for AI development.