AI trading enters the "crunching the numbers phase"! As NVIDIA Corporation (NVDA.US) GTC Berlin conference approaches, can it write a new logic for the computing power supercycle?

Title context: AI trading enters the "crunching the numbers phase"! As NVIDIA Corporation (NVDA.US) GTC Berlin conference approaches, can it write a new logic for the computing power supercycle? Text: From October 20 to 22 local time, NVIDIA Corporation (NVDA.US) will hold its GTC conference in Berlin, Germany, and CEO Jensen Huang will take the stage for a keynote speech at 11 a.m. local time on October 21 (5 p.m. Beijing time). According to information on NVIDIA Corporation's official website, the conference agenda covers multiple areas including accelerated computing, Agentic AI, Siasun Robot&Automation, open models, Physical AI, sovereign AI, and AI infrastructure. If this had been in the past few years, it might have been just another highly anticipated technology event for the artificial intelligence (AI) industry. But at this current point in time, AI trading is moving from simply trading expectations of growth in computing power demand into a "crunching the numbers phase" that focuses more on return on investment. As the scale of global AI infrastructure investment continues to expand, the capital market's scrutiny of AI trading is also gradually shifting from "will computing power demand grow" to more realistic questions: can massive AI capital expenditure ultimately be converted into sufficient revenue, cash flow, and productivity? Therefore, the GTC Berlin conference may become an important "stress test" for the current AI industry. What the market really wants to hear from Jensen Huang is no longer just how much performance the next-generation computing platform will improve by, but three more core questions: How much longer can AI computing power demand grow? Where will the return on investment for AI infrastructure come from? And can this round of capital expenditure ultimately be converted into real economic value? Point 1: Where will the $2 billion European AI deals land? Before the conference officially opened, NVIDIA Corporation had already planted a rather significant suspense for GTC Berlin. On October 13, Jensen Huang revealed at an event in Paris that NVIDIA Corporation would announce about $2 billion in new European AI investments or deals within the next week. In the previous year, NVIDIA Corporation had already invested about $8 billion in Europe. If the two parts are combined, NVIDIA Corporation's investment and commitments in Europe will approach $10 billion. While in Paris, Jensen Huang announced that NVIDIA Corporation would provide financing to four French startups, including healthcare technology company Doctolib, Siasun Robot&Automation AI company Genesis AI, software company Pigment, and AI materials company Entalpic; later at a London event, he also named British AI companies Boltz, Prima Mente, Hiverge, Riverlane, and Foresight. This means that during GTC Berlin, what the market needs to watch is not only what technology NVIDIA Corporation releases, but also where the additional $2 billion in European deals will ultimately flow, and whether these investments can be converted into larger-scale AI infrastructure demand. Germany may be one important landing spot. According to reports, Germany is preparing to advance an AI cooperation with NVIDIA Corporation, focusing on Physical AI tools used for Siasun Robot&Automation and autonomous driving training, as well as AI models for industrial applications. If the relevant cooperation is further implemented during GTC, it will further strengthen NVIDIA Corporation's layout in Europe's industrial AI sector. Therefore, Europe is not simply the "venue" of this conference, but may be becoming an important market for NVIDIA Corporation's next phase of AI ecosystem expansion. Point 2: Can Vera Rubin and AI factories prove that the computing power cycle is still continuing? For NVIDIA Corporation, hardware remains one of the most core highlights of the GTC conference, but the way the market understands NVIDIA Corporation's products has already changed. In the past, when investors discussed NVIDIA Corporation, the focus was often on the performance, memory, and process node of a single GPU; now, NVIDIA Corporation increasingly emphasizes a complete "AI factory" conceptGPUs, CPUs, networking, storage, software, and data center infrastructure integrated as a whole. At the San Jose GTC conference in March this year, NVIDIA Corporation had already disclosed the Vera Rubin platform and positioned it as a full-stack computing platform for Agentic AI, covering multiple links including Vera CPU, GPU, networking, and storage. At the same time, NVIDIA Corporation also announced the DSX AI Factory reference design and DSX Air, used for digital simulation before physical data center construction. Therefore, at this GTC Berlin conference, what may be more worth watching in the market is probably not "whether there are new chips," but the actual deployment speed of Vera Rubin, the strength of customer orders, and how much the cost per unit of Token computing can actually decline. If NVIDIA Corporation can show that large cloud providers are entering the stage of large-scale deployment and prove that the new-generation platform can significantly reduce AI inference costs, then the market's concerns that AI computing power investment is already excessive may be alleviated to some extent. Conversely, if what the conference releases is still mainly long-term orders, data center plans, and capital expenditure commitments, while actual deployment and end-user revenue growth lack synchronized verification, then it may instead strengthen investors' concerns about the "self-circulation" of AI infrastructure investment. Therefore, what the market needs this time is no longer just a technology roadmap, but commercial delivery capability. Point 3: Can Agentic AI become the new engine for the next round of computing power demand? If AI infrastructure demand over the past two years was mainly driven by large model training, then the next-stage story NVIDIA Corporation is now trying to tell is very likely Agentic AI. Traditional chat Siasun Robot&Automation is more of a "question-and-answer" interaction, while AI agents capable of completing tasks autonomously need to continuously call models, search databases, use tools, read files, run code, and perform multiple rounds of reasoning. This means AI computing may shift from one-time "request-response" to a continuously running computing process. From NVIDIA Corporation's perspective, this will create a new demand curve: in the past, AI computing power demand was mainly driven by model training scale, while in the future it may be driven jointly by pre-training, post-training, inference, and agent workloads. This is also an important reason why Vera Rubin is positioned by NVIDIA Corporation as an "Agentic AI platform." For investors, a key question is: can AI agents truly form large-scale commercial applications, so that inference computing power demand takes over from training demand? If in the future AI moves further from "training a model" to "digital employees running around the clock," then data centers will no longer be just fixed assets built once and used for many years, but may continue to expand as the number of agents and frequency of use increase. This will directly relate to one question the market cares about most: is AI capital expenditure a one-time wave, or can it form a long-term, sustained infrastructure cycle? Point 4: Can Physical AI open new computing power markets such as automobiles and Siasun Robot&Automation? Compared with generative AI, Physical AI is still at an earlier stage of development, but NVIDIA Corporation is clearly elevating its strategic position. The agenda of this GTC Berlin covers Siasun Robot&Automation, autonomous driving, digital twins, and industrial AI, and the lineup of participants also shows a very clear industrial flavor. Participating companies/institutions and their fields involve directions: Mercedes-Benz automobiles, Physical AI, autonomous driving; Siemens Energy, energy and power grid AI data centers, power and grids; Nokia Oyj Sponsored ADR, communications AI infrastructure, communications networks; Wandelbots, Siasun Robot&Automation Physical AI, Siasun Robot&Automation; London Stock Exchange Group (LSEG), financial data enterprise AI, financial data; Revolut, fintech enterprise AI, fintech; Black Forest Labs, AI models, generative AI; ElevenLabs, AI voice, generative AI, voice AI; n8n, AI workflow, Agentic AI, AI workflow; European Sovereign AI Center (CESAI), sovereign AI, sovereign AI, AI infrastructure; Stuttgart High Performance Computing Center, high-performance computing, high-performance computing, AI infrastructure. From the lineup of participants, the industrial coverage of this GTC Berlin has clearly gone beyond the traditional AI chip field. The participation of companies such as Mercedes-Benz, Siemens Energy, Nokia Oyj Sponsored ADR, and Wandelbots makes industries including automobiles, energy, power grids, communications, and Siasun Robot&Automation important components of the conference. Particularly noteworthy is that Mercedes-Benz and Wandelbots correspond to Physical AI applications such as autonomous driving and Siasun Robot&Automation, while the participation of the head of Siemens Energy's digital grid business echoes the electricity demand brought by the expansion of AI data centers. This means NVIDIA Corporation is pushing AI from the cloud and internet companies further into Europe's traditional industrial system. If Siasun Robot&Automation, autonomous driving, and industrial AI begin to enter the stage of large-scale deployment, then AI's market space will further expand from software and data centers to the global manufacturing and automation industries. For NVIDIA Corporation, this may mean new sources of computing power demand; for the market, it means the valuation logic of the AI industry has the opportunity to expand further from "large models" to a productivity revolution in the real world. Point 5: Will lower AI inference costs instead bring more demand? "Lowest Cost per Token" is also a keyword worth watching at this GTC. After AI enters the inference era, the metric investors focus on is shifting from pure peak computing power to unit computing cost. As agentic AI continuously calls models, a complex task may require a large number of Tokens. If the computing cost of each Token is too high, AI applications will be difficult to commercialize on a large scale; conversely, if computing efficiency continues to improve and Token costs continue to decline, the threshold for using AI will also decrease accordingly. Behind this there is an economic logic worth the market's attention: Efficiency improvements do not necessarily mean total demand will decline. If technological progress greatly reduces AI computing costs, enterprises and consumers may instead increase their use of AI, thereby driving overall computing demand to continue growing. Similar logic once appeared in the internet, cloud computing, and mobile communications industries. Therefore, what this GTC Berlin conference needs to observe is not whether NVIDIA Corporation can lower the cost of a single Token, but whether, after costs fall, AI usage can grow at a faster rate. If the answer is yes, then "falling computing power prices" may instead become a catalyst for the next round of demand growth. Point 6: Will sovereign AI become a new source of computing power demand in Europe? Sovereign AI is another important main line that distinguishes GTC Berlin from ordinary AI technology conferences. Over the past few years, global AI infrastructure has been highly concentrated in large U.S. technology companies. But as AI is gradually regarded as part of national competitiveness and industrial security, multiple economies in Europe, the Middle East, and Asia have begun seeking to build their own AI infrastructure. For Europe, sovereign AI means not only having local AI models, but also requiring local computing power, data centers, networks, and data infrastructure. This precisely provides NVIDIA Corporation with new market space. The agenda of this GTC Berlin conference includes sovereign AI, and related events before Jensen Huang's keynote speech also invited executives from European companies and AI companies such as Deutsche Telekom, Dassault Systemes, and Mistral AI to participate in discussions. At the same time, judging from the list of participants, the European Sovereign AI Center, high-performance computing institutions, and large industrial enterprises are all participating, showing that NVIDIA Corporation is trying to connect AI infrastructure with Europe's local industrial ecosystem. For NVIDIA Corporation, if in the future European countries, enterprises, and industrial groups all want to have their own AI infrastructure, then the sources of growth in AI capital expenditure will no longer depend entirely on a handful of U.S. technology giants. On the other hand, the European market also means more complex regulation, data sovereignty, export controls, and local AI chip competition. Therefore, GTC Berlin is also an important window for observing NVIDIA Corporation's globalization strategy. Final exam question: Can AI capital expenditure complete an "economic value closed loop"? Ultimately, the most important question of this GTC Berlin conference is not whether NVIDIA Corporation can once again showcase a computing product with stronger performance. Over the past few years, the AI industry has formed a very strong growth chain: Improved model capabilities growth in computing power demand cloud providers increase capital expenditure NVIDIA Corporation revenue growth market raises AI expectations more capital enters AI infrastructure. But by 2026, the market has begun to ask whether this chain can truly close the loop. Especially as AI infrastructure becomes increasingly dependent on debt financing, the problem has spread further from stock valuations to the credit market. The financing and profitability pressures recently faced by AI infrastructure companies are a microcosm of the market beginning to re-examine the return on AI capital expenditure. What investors are now watching is no longer just "AI demand is strong," but revenue, free cash flow, return on capital, quality of customer contracts, computing power utilization, financing costs, and how much end users are actually willing to pay for AI. In other words, the AI industry is moving from a "technology cycle" into an "economic cycle." Therefore, the signals that GTC Berlin may release can be summarized into five questions: First, are Vera Rubin orders and deployments strong enough to prove that the AI infrastructure cycle is still continuing? Second, can Agentic AI drive continued growth in inference demand, shifting AI computing power demand from a training cycle to a more stable usage cycle? Third, can AI factories and new-generation computing platforms continue to reduce cost per Token and create more AI usage demand through efficiency improvements? Fourth, can real large-scale commercial orders emerge in Siasun Robot&Automation, autonomous driving, and industrial AI, opening up new computing power markets beyond cloud computing for NVIDIA Corporation? Fifth, and most importantlycan the AI capital expenditure of large technology companies ultimately be converted into revenue, cash flow, and productivity improvements? If Jensen Huang can give strong enough answers in Berlin, then the market's recent concerns about an AI bubble, excessive capital expenditure, and peak computing power demand may be alleviated to some extent. But if what the conference releases is still mainly technology roadmaps, long-term orders, and capital expenditure plans, while the actual commercial returns of AI applications still lack verification, then GTC may instead become an important node for the market to further scrutinize AI valuations. Therefore, what this GTC Berlin conference truly needs to verify is no longer "whether AI can become stronger," but: after AI becomes stronger, can it truly create enough economic value to support the ever-expanding capital investment.