Quick View: What investors should know from NVIDIA’s GTC 2025
Portfolio Manager Richard Clode discusses the key learnings from NVIDIA’s annual technology conference, a key event highlighting the rapid progression of the AI revolution, including opportunities created by agentic AI.
5 minute read
Key takeaways:
- Tokens, not data, are the ‘new oil,’ driving greater intelligence and associated revenues and profits. Reasoning models are enabling agentic AI, creating new and large addressable markets for AI.
- DeepSeek’s implications have been misunderstood by the market. AI compute needs expand exponentially with reasoning models, while US semiconductor restrictions likely mean we are at peak relative to China’s AI capabilities.
- AI power challenges will be solved by technology innovation creating investment opportunities across the technology stack.
Tokens are the ‘new oil’
Data used to be the ‘new oil’ but in a generative AI world where synthetic data creation is limitless, tokens are now the new resource of power. The original innovation of the transformer model that revolutionised and ushered in the generative AI era is built on tokenisation. Tokens are units of data that are processed by AI models during training and inference, enabling prediction, generation and reasoning. Hence tokens equal intelligence, and ultimately will drive greater revenues and profits.
NVIDIA CEO Jensen Huang has long talked about ‘AI factories’ ie. AI datacentres creating tokens and therefore intelligence to better design products, run business more efficiently and improve quality of service to customers. He envisages a future where every company will have two types of factories: manufacturing and mathematics. NVIDIA, being the leader in advanced AI chips, is only now designing future chips with AI accelerated Electronic Design Automation (EDA) software tools – only recently has the software been optimised to work on NVIDIA’s CUDA programming language. The company also announced an all-encompassing partnership with GM (General Motors) to use AI to help GM design cars, improve efficiency, and also to enable autonomous driving.
Agentic AI – the next wave of AI
We remain early in the innovation curve of generative AI. The new scaling law Jensen Huang has been highlighting is test time scaling or the long think reasoning models, which take a longer thought process approach to arrive at a more accurate response rather than prioritising speed. Recently introduced to the market, these reasoning models enable agentic AI. This is AI that has agency in that they understand the context of the problem they have been asked to solve. The breakthrough here is that they can now reason and plan a course of action to solve the problem in a multi-modal way. That could involve reading a website article or watching a video and then simultaneously taking several potential paths to solve the problem, and then sense checking the answers for consistency or plugging those answers back into the question. This solves the challenges that ChatGPT and other one shot inferencing models had with answering simple questions, let alone more complex ones. Agentic AI is greater intelligence that enables the next wave from co-pilots to AI agents that can complete tasks without supervision with a high degree of accuracy and consistency. Agentic AI significantly expands the addressable market for AI and opens up new physical AI applications such as humanoids and autonomous driving, where real-world forces such as gravity, friction, and ’cause and effect’ come into play.
Clarifying the DeepSeek misunderstanding
Jensen was at pains to make the point that the market had completely misunderstood the implications of the launch of DeepSeek’s R1 model earlier this year. Comparing a response from DeepSeek to a standard, non-reasoning model from Meta, DeepSeek’s response was more accurate but required 20x as many tokens and 150x the compute. Far from indicating less compute requirements going forward, DeepSeek was a ‘coming out’ party for reasoning models, which opens up a new scaling vector for AI compute requirements.
NVIDIA also laid out its roadmap through 2027, which culminates with its next AI superchip, Rubin Ultra that will have over 400x the performance of Hopper. That is important because current US semiconductor export restrictions (aimed at limiting China’s access to advanced semiconductors and the equipment needed to produce them) put an absolute ceiling on AI compute at a degraded Hopper level. Over the next few years, new Chinese AI models will be constrained by that compute ceiling, while globally AI models are likely to be training on exponentially higher performance AI infrastructure. That could indicate we are likely at a relative high point in China AI capabilities relative to the rest of the world.
Full stack solutions will solve the AI power challenge
NVIDIA has never been just a semiconductor company – a significant amount of the performance gains and power savings delivered have been a function of software and networking innovation. Jensen has always talked about generative AI being a full stack problem that requires a full stack solution. At its GTC event, the company laid out new innovations such as co-packaged optics, and its Dynamo software virtualisation. Optical networking in AI training clusters is a major power drain with 6 transceivers per GPU, drawing down 30 watts of power each, so as training clusters scale so does the optical power consumption. By packaging optical components in the switches themselves, NVIDIA claims it can provide 3.5x greater power efficiency by using 4x fewer lasers. Dynamo is a virtualisation software layer that optimises inferencing workloads by virtualising the GPUs and slicing and dicing the workloads across them, driving 30x the inferencing performance.
We continue to believe the power challenges that are needed to advance and run AI will be solved by technology innovation. Therefore, more compelling investment opportunities can be found across the technology stack rather than in utilities and power infrastructure.
Agentic AI: uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. Vast amounts of data from multiple data sources and third-party applications are used to independently analyse challenges, develop strategies and execute tasks.
CUDA: a programming language developed by NVIDIA that uses Graphics Processing Units (GPU). It allows computations to be performed in parallel while providing well-formed speed. CUDA allows Nvidia GPUs to perform common computing tasks, such as processing matrices and other linear algebra operations, rather than simply performing graphical calculations.
DeepSeek: a Chinese AI startup and developer of open-source advanced large language models (LLMs) such as DeepSeek-V3 – a key rival, and less expensive option compared to OpenAI’s ChatGPT and Google’s Gemini.
Electronic Design Automation (EDA): a specific category of hardware, software, services and processes that use computer-aided design to develop complex electronic systems like printed circuit boards, integrated circuits and microprocessors. The dense packing of elements onto a circuit board or microprocessor requires highly complex designs. EDA software uses automated, standardized processes that facilitate rapid development, while minimising bugs, defects, and other design errors.
Full stack solution: refers to a comprehensive approach to software development that covers all layers of an application or project. This includes both the front-end and back-end components, as well as any other layers necessary for the application to function fully.
GPU: a graphics processing unit performs complex mathematical and geometric calculations that are necessary for graphics rendering and are also used in gaming, content creation and machine learning.
Inferencing: refers to artificial intelligence processing. Whereas machine learning and deep learning refer to training neural networks, AI inference applies knowledge from a trained neural network model and uses it to infer a result.
LLM (large language model): a specialised type of artificial intelligence that has been trained on vast amounts of text to understand existing content and generate original content.
Long think reasoning: a deliberate and extended process of considering information and potential outcomes, by analysing multiple perspectives, considering long-term implications, carefully weighing various factors before reaching a conclusion.
One-shot inferencing: refers to the method where a model is provided with a single example or prompt to perform a task. It is reliant on a single, well-crafted prompt to achieve the desired output.
Test time scaling: a language modelling approach that uses extra test-time compute to improve performance.
Token: AI tokens are the fundamental building blocks of input and output that Large Language Models (LLMs) use. These units of data are processed by AI models during training and inference, enabling prediction, generation and reasoning.
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.
- 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 is focused towards particular industries or investment themes and may be heavily impacted by factors such as changes in government regulation, increased price competition, technological advancements and other adverse events.
- This Fund may have a particularly concentrated portfolio relative to its investment universe or other funds in its sector. An adverse event impacting even a small number of holdings could create significant volatility or losses for the Fund.
- 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.
- 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
5 minute read
Key takeaways:
- Tokens, not data, are the ‘new oil,’ driving greater intelligence and associated revenues and profits. Reasoning models are enabling agentic AI, creating new and large addressable markets for AI.
- DeepSeek’s implications have been misunderstood by the market. AI compute needs expand exponentially with reasoning models, while US semiconductor restrictions likely mean we are at peak relative to China’s AI capabilities.
- AI power challenges will be solved by technology innovation creating investment opportunities across the technology stack.