NVIDIA GTC 2025 Comprehensive Overview: From Jensen Huang Keynote to AI Investment Strategies

NVIDIA’s annual GPU technology conference (GTC) 2025 is fast approaching. Every year at the GTC keynote, CEO Jensen Huang unveils groundbreaking technologies and visions that disrupt the industry. For GTC 2025, major announcements are expected across various fields including AI, data centers, autonomous driving, and GPU architectures. This analysis delves deeply into the likely new technologies to be revealed at NVIDIA GTC 2025 and their potential impacts. It also examines how Jensen Huang’s previous keynotes have influenced NVIDIA’s stock price through data analysis, exploring stock patterns and investor reactions before and after the events. In addition, we predict the GPU market outlook beyond 2025 by comparing the strategies of NVIDIA and its competitors (AMD, Intel, Google TPU) and assessing the potential effects of the GTC 2025 announcements on key AI companies such as Microsoft, OpenAI, and Tesla in terms of both collaboration and competition. Finally, we summarize Wall Street analysts’ views on NVIDIA investments and target prices, and analyze the pros and cons of holding NVIDIA stock for long-term investors versus selling immediately after major events. Backed by the latest official data and global analyst reports, this article provides high-quality insights that align with Google’s E-E-A-T principles (Experience, Expertise, Authority, and Trustworthiness).

Anticipated New Tech Announcements by Jensen Huang at GTC 2025 and Their Impact

Let’s take a look at the innovative technologies that CEO Jensen Huang is likely to unveil at the NVIDIA GTC 2025 keynote. Based on clues already presented at CES 2025 and industry roadmaps, the following new technologies are expected to make an appearance:

Next-Generation GPU Architecture ‘Blackwell’ Based Products: At CES 2025, Jensen Huang delivered a keynote in which he first introduced the GeForce RTX 50 series featuring the Blackwell architecture. This led to the unveiling of new GPUs such as the RTX 5090, and at GTC 2025, a Blackwell-based AI accelerator for data centers is expected to be announced. NVIDIA is already preparing its next-generation AI accelerator architecture, Blackwell, as a successor to the “ridiculously popular” Hopper (H100), targeting a launch by the end of 2024. The Blackwell architecture aims for dramatic performance improvements over the previous generation through increased transistor counts and enhanced memory bandwidth. It is specifically optimized for data center/HPC acceleration by eliminating bottlenecks and improving scalability for each workload. For instance, it introduces a multi-chip design that enables two GPU dies to function as a single GPU with a B200 dual GPU design, connected by a 10TB/s ultra-high-speed interconnect called NV-HBI, thereby delivering performance on par with a single GPU. This allows for computational power beyond the limits of a single chip. In addition, the Blackwell generation introduces a second-generation Transformer Engine supporting FP8 and now FP4, expected to boost inference performance for ultra-large language models by up to 30 times and increase energy efficiency by 25 times. By using mixed low-precision computations (combining FP4/FP6), it can dramatically reduce memory requirements and maximize inference throughput, thereby drastically lowering inference costs for large-scale AI services like ChatGPT. These new Blackwell-based products (such as the B100, B200, etc.) are slated for mass shipment starting in the latter half of 2025, and NVIDIA is investing a staggering $16 billion per year in R&D, operating three design teams in parallel to maintain an aggressive roadmap with new products released every 18–24 months. This rapid pace of development is something competitors will find hard to match, further cementing NVIDIA’s technological leadership.
NVIDIA Cosmos AI Platform: Introduced at CES 2025, the Cosmos platform is the key technology that NVIDIA claims will usher in the “Physical AI” era. This platform encompasses new AI models and video data processing pipelines designed for robotics, autonomous vehicles, and vision AI, thereby enhancing the application of AI in robotics and autonomous driving. Jensen Huang highlighted the rise of “AI that perceives, reasons, plans, and acts” — in other words, the emergence of Agentic AI — emphasizing that AI which actively operates in the real world beyond generative tasks represents the next phase. The Cosmos platform offers a comprehensive solution that covers everything from sensor recognition to decision-making and control, and is specifically tailored for real-time processing of large-scale video/visual data. At GTC 2025, detailed technical aspects of the Cosmos platform and actual cases of its application in robotics and autonomous vehicles are likely to be revealed. It is also expected to integrate with NVIDIA’s Isaac robotics and DRIVE autonomous driving software stacks, accelerating the development of next-generation robots and driverless vehicles. Such innovations in robotics AI are expected to trigger large-scale adoption of humanoid robots and autonomous machines across various industries such as logistics, manufacturing, and services, potentially becoming a new growth engine for NVIDIA.
Innovations in AI-PC and Developer Platforms: NVIDIA is also aiming to expand AI applications in the PC space. At CES 2025, an “AI framework for RTX-based PCs” along with NVIDIA NIM microservices and AI Blueprints were introduced; these toolkits support generative AI content creation (e.g., digital humans, automated podcasting, image and video generation) on standard PCs. At GTC 2025, an announcement is expected regarding next-generation workstations or an AI-optimized PC platform equipped with AI acceleration. For example, a compact developer system code-named Project DIGITS — based on a combination of NVIDIA’s Arm CPU, Grace, and the latest Blackwell GPU — was mentioned at CES as a desktop system for developers. Detailed specifications and the release schedule for this project may be unveiled at GTC. Such innovations in AI-PC and developer platforms will enable even small developers and researchers to easily harness cutting-edge AI, thus broadening the base of the AI ecosystem. Furthermore, the trend of equipping consumer PCs with dedicated AI engines (such as AI copilots and real-time video/audio AI processing) is expected to accelerate, positioning NVIDIA GPUs as essential AI accelerators even in personal computing.
Update on the Integrated Computing Chip for Autonomous Vehicles ‘DRIVE Thor’: In the automotive sector, NVIDIA’s next-generation vehicle SoC, DRIVE Thor, is one of the key topics anticipated at GTC 2025. DRIVE Thor was first conceptually introduced at GTC 2022 and is expected to be deployed in mass-produced vehicles by 2025 as a centralized automotive computer. Integrating the latest Blackwell GPU architecture and Arm-based processing cores, Thor is designed to handle all AI functions in a vehicle—including autonomous driving, infotainment, and sensor fusion—on a single chip. With over 20 times the AI computing performance and features such as up to 128GB of VRAM, NVIDIA emphasizes that “it is now possible to implement generative AI, driver monitoring, and Level 4 autonomous driving all within the car.” At GTC 2025, detailed specifications, partnership status, and development tools for DRIVE Thor are expected to be announced. Chinese EV company Zeekr unveiled a Thor-based smart driving kit at CES 2025, and autonomous truck company Aurora along with parts supplier Continental is reportedly collaborating with NVIDIA to develop unmanned trucks powered by Thor. Through these extensive announcements of partnerships across the automotive industry, NVIDIA will underscore its position as the de facto brain for autonomous driving for nearly all global OEMs aside from Tesla. Enhancements to NVIDIA Drive OS and the Hyperion platform are also expected, which hold significant implications for meeting safety and security standards as well as improving development efficiency in autonomous driving. The automotive AI computing market is projected to see robust growth after 2025, and the launch of Thor signifies NVIDIA’s intent to provide a standard platform in this market, potentially leading the software-defined vehicle (SDV) trend in the automotive industry.
Other Possible Announcements: Additionally, at GTC 2025, updates on NVIDIA Omniverse (an industrial metaverse and simulation platform), plans in the field of Quantum Computing (with Jensen Huang expected to host a quantum computing panel), new products for edge AI and communication infrastructure DPUs (such as BlueField), updates on medical AI (Clara platform), and cloud-native AI solutions might be announced. In his recent keynotes, Jensen Huang has consistently emphasized NVIDIA’s platform strategy, which encompasses both hardware (e.g., GPUs, DPUs, CPUs) and software stacks (e.g., CUDA, AI frameworks, Omniverse) to build a comprehensive ecosystem. Therefore, it is likely that he will present an expanded vision not only for individual products but also for NVIDIA’s accelerated computing platform. For example, he may deliver the message that “as a full-stack computing company, NVIDIA provides an integrated solution of X86 + GPU + DPU + software for the AI era.” This message reassures corporate clients that “with NVIDIA, you gain all the layers necessary for AI innovation,” thereby enhancing future demand for NVIDIA’s product lineup and creating a strong lock-in effect.

Impact of the New Announcements and NVIDIA’s Future Growth Prospects

The new technologies anticipated at GTC 2025 are expected to have a significant impact on both the AI industry as a whole and NVIDIA’s future growth prospects:

Impact on AI Research and Industry: Next-generation GPUs based on the Blackwell architecture are expected to elevate the scale and speed of AI model training and inference. For example, a single Blackwell GPU is projected to deliver 10 petaflops in FP8 operations and 20 petaflops in FP4 sparse operations, which would represent more than 2.5 times the training performance and 20 times the inference performance compared to the current top-tier H100. This will enable researchers to train massive language models with hundreds of billions to trillions of parameters much faster, while AI service companies can dramatically enhance real-time inference throughput to provide improved AI functionality to users. Additionally, the use of low-precision computations (leveraging FP4) is expected to significantly reduce the cost of AI services by decreasing memory requirements. The introduction of the Physical AI platform, Cosmos, is poised to simplify and accelerate the development of robotics and autonomous AI, potentially triggering an explosion in the physical application of AI in areas such as manufacturing automation, logistics robotics, and smart cities. This marks a transition in which AI not only processes virtual information but also drives real-world automation, thereby accelerating the Fourth Industrial Revolution.
Impact on Data Centers and Cloud: NVIDIA’s new data center solutions (such as Blackwell accelerators, the Grace-Blackwell superchip, and upgrades to NVLink/NVSwitch) are set to dramatically boost AI processing capabilities in hyperscale data centers. In particular, the tight integration of the Grace CPU and Blackwell GPU (for instance, the GB200 superchip following the GH200) reduces bottlenecks between the CPU and GPU and streamlines large-scale data processing through unified memory, which is key to building massive AI infrastructures in the cloud. Cloud providers like Azure are expected to adopt these latest NVIDIA chips to build large-scale AI training/inference farms, thereby enhancing their ability to offer AI services to customers ranging from startups to large enterprises. Additionally, server platforms such as NVIDIA’s HGX B200, which connects eight B200 GPUs via NVLink on a single board to deliver exaflop-level AI computing, represent a revolutionary improvement in density and power efficiency in data centers. As a result, companies will be able to execute more AI computations with fewer servers, reducing Total Cost of Ownership (TCO), while cloud providers maximize performance within limited power and space constraints. These advancements will further solidify NVIDIA’s dominance in the AI infrastructure market. According to Morgan Stanley, NVIDIA’s global AI semiconductor wafer market share is expected to surge from about 51% in 2024 to 77% in 2025, indicating explosive demand for NVIDIA chips compared to its competitors. Once investments in Blackwell-based systems by cloud providers and data centers ramp up following the GTC 2025 announcements, NVIDIA’s data center business is expected to experience ultra-rapid growth over the coming years, serving as the company’s primary growth engine.
Impact on Autonomous Driving and the Automotive Industry: NVIDIA’s latest automotive chips, including DRIVE Thor, are becoming a core component of the software-defined vehicle trend among automakers. Starting in 2025, vehicles equipped with Thor will consolidate the dozens of individual ECUs into a single high-performance central computer, significantly reducing vehicle development costs and complexity. By adopting NVIDIA’s platform, automakers can improve features such as autonomous driving, driver assistance, and in-vehicle infotainment (IVI) via OTA software updates, while also creating new revenue models (for example, subscription-based autonomous driving features). NVIDIA has already forged strategic partnerships with several OEMs including Mercedes-Benz, Jaguar Land Rover, and Volvo, and more recently announced a collaboration with Toyota on next-generation vehicle development. This indicates that traditional automakers are actively embracing NVIDIA’s technology to counter Tesla, further reinforcing NVIDIA’s position as the standard platform provider in the automotive sector. If additional partnership cases and new partners are revealed at GTC 2025, NVIDIA will further cement its position in the automotive field. Although the automotive business currently accounts for less than 3% of NVIDIA’s total revenue, its long-term potential for significant growth is high, and with the popularization of autonomous driving technology, it could emerge as a major new cash cow for the company.
Linking to NVIDIA’s Future Growth: In summary, the new technologies to be unveiled at GTC 2025 are closely tied to NVIDIA’s long-term growth vision. Recently, Jensen Huang has emphasized that “all industries are being transformed by AI,” positioning NVIDIA as the engine of that transformation. What began with GPUs has now expanded to include CPUs, DPUs, software, and services, transforming NVIDIA into a comprehensive AI computing company. The announcements at GTC 2025 will further reinforce this full-stack platform strategy, exponentially increasing NVIDIA’s Total Addressable Market (TAM). In fact, at GTC 2022, NVIDIA raised its long-term market size projection to $1 trillion by combining gaming, enterprise AI, Omniverse, and automotive segments. With the surge in data center demand driven by the generative AI boom, that projection is likely even higher now. Wedbush Securities stated that “the AI revolution is the biggest technological transformation in 40 years, and its starting point is Jensen Huang,” forecasting more than $2 trillion in AI-related capital expenditures over the next three years. Positioned at the center of this enormous industry cash flow, NVIDIA’s upcoming GTC announcements signal its unwavering commitment to innovation and its determination not to miss out on market opportunities. In short, the new technology announcements at GTC 2025 are expected to be the engine for NVIDIA’s growth over the next 5–10 years, as it secures its position in massive growth sectors like AI, data centers, and autonomous driving.

Jensen Huang’s Past Keynotes and NVIDIA’s Stock: Data-Driven Impact

NVIDIA CEO Jensen Huang’s keynote speeches are not only significant for the technology industry but are also closely watched by the stock market. In particular, NVIDIA’s stock has often shown short-term fluctuations based on the announcements made during key events such as GTC. An analysis of past keynotes and stock movements reveals several interesting patterns.

Pre-Announcement “Expectation Rally” vs. Post-Announcement “Profit Taking”

Ahead of major tech events, investors tend to buy stocks in anticipation of the announcements. In NVIDIA’s case, the stock often rises just before a major keynote. For instance, during CES 2024, NVIDIA’s stock surged by 5% in one day as investors anticipated Jensen Huang’s keynote, nearly reaching an all-time high. On the day of the CES 2024 keynote (in January), NVIDIA’s stock rose by 6.4%, followed by additional gains over the next two days as the market reacted positively to the event. Bank of America noted in an analysis that “NVIDIA’s stock tends to perform strongly around Jensen Huang’s CES speeches,” a pattern that was observed again during CES 2025. Such pre-announcement expectation rallies are also seen at events like GTC; rumors about the unveiling of a new GPU architecture or product often cause the stock to reflect favorable expectations in advance.

However, immediately after the announcement, profit taking sometimes leads to a temporary dip in the stock price—a classic “sell the news” phenomenon. One example was during the March 2024 GTC when, after Jensen Huang introduced the next-generation processor design Blackwell for the first time, the immediate market reaction was one of “meeting expectations without a significant surprise.” Consequently, NVIDIA’s stock dropped by about 2% in after-hours trading, interpreted as investors cashing in after the pre-event rally had already pushed the stock high. Interestingly, during the same event, stocks of partner companies mentioned by Jensen Huang actually surged. During the GTC 2024 keynote, engineering software companies such as Synopsys, Cadence, and ANSYS saw their stocks jump by over 2% following comments that they would leverage NVIDIA’s Blackwell-based AI software, while Dell’s stock also rose by 3.2% following Jensen Huang’s high praise. In one instance, when Jensen Huang remarked on stage that “there is no company that makes end-to-end systems as well as Dell in building an AI factory,” the market reacted immediately. Thus, Jensen Huang’s remarks have a ripple effect not only on NVIDIA but also on the stocks of its partners.

Key Cases from Stock Movement Data

CES Keynote, January 2024: Stock increased by 6.4% on the day of the speech, followed by further gains over the next two days. (After months of stagnation, the event triggered a breakthrough.)
GTC Keynote, March 2024: Stock dropped by 1.5–2% on the day of the announcement (after already being highly priced due to an early-year rally, leading to profit taking).
GTC Keynote, March 2023: Held during the ChatGPT craze. Jensen Huang emphasized the potential of generative AI and unveiled new products, resulting in a modest stock increase around the event. (A more significant surge occurred later in May during earnings announcements.)
GTC Keynote, March 2022: Announcement of the Hopper architecture (H100) and a new business vision. Stock was flat on the day of the event but trended upward in the following weeks, with analysts praising it for “providing confidence in future AI inflections.”
Others: There have been instances where stock fluctuations occurred when Jensen Huang mentioned major AI customers in other speeches (e.g., at Computex 2023) or when keynotes coincided with earnings announcements (e.g., around GTC in February 2025), affecting overall market sentiment.

Overall, while NVIDIA’s stock often gains short-term momentum around Jensen Huang’s keynotes, the direction depends on how surprising the actual announcements are compared to pre-event expectations. When groundbreaking products exceed market expectations (as was the case with the Ampere architecture announcement in 2020), the stock can surge immediately post-event; however, in most cases, the stock adjusts briefly after the hype before resuming its long-term upward trend.

Investor Sentiment and Long-Term Stock Trends

Jensen Huang’s keynotes have consistently reinforced investor confidence in NVIDIA’s technological roadmap. For example, at GTC 2022, NVIDIA presented its multi-year AI and software vision (with a TAM of $1 trillion, among other figures), raising investor expectations and prompting analysts to upgrade their target prices, often touting the company as “the leader in the AI revolution.” Credit Suisse forecasted that “the Hopper architecture would represent the most significant generational improvement ever,” and Rosenblatt Securities maintained a strong buy rating by describing it as a “trillion-dollar inflection point spanning gaming, enterprise AI, Omniverse, and automotive.” Such optimism among professional investors has supported the long-term upward trend of NVIDIA’s stock, even if there have been short-term fluctuations.

Of course, there have been short-term spikes and dips. For instance, earlier in 2025, despite an impressive 178% surge in market capitalization in 2024 driven by the AI boom, NVIDIA experienced an 8% drop in one day following a quarterly outlook in February 2025 that stated, “good but not at market-leading expectations.” This was partly because investors, accustomed to over 100% growth rates previously, were disappointed by a 65% revenue growth projection, and it also reflected caution as the AI investment frenzy reached its peak. Thus, while NVIDIA faces significant volatility as it strives to meet extremely high expectations in both earnings and events, it is important to note that even during a steep dip in February 2025, Jensen Huang expressed confidence by stating that “demand for the new Blackwell chip is insane,” and the company’s annual growth projection of 65% for that year was extraordinarily high by any standard. Although the market may temporarily cool down short-term exuberance, as long as these fundamental growth stories remain valid, NVIDIA’s stock has trended upward over the medium to long term. Dubbed the “barometer of AI,” NVIDIA’s market value surpassed $3 trillion during the 2023–2024 AI boom, with investors eagerly looking to Jensen Huang’s speeches for the next phase of growth momentum.

In summary, Jensen Huang’s keynotes have acted as short-term catalysts for NVIDIA’s stock. Pre-announcement, the stock benefits from investor enthusiasm, while post-announcement adjustments are determined by the actual content relative to expectations. Ultimately, however, NVIDIA’s stock is driven by whether the vision articulated by Jensen Huang is realized and by the structural growth in the AI market. Much of Jensen Huang’s grand vision (e.g., the rise of GPU computing, the deep learning revolution, AI-first computing, etc.) has been largely accurate, and the stock has rewarded early investors with tremendous returns over the long term. Once trading at just a few dozen dollars in the early 2010s, NVIDIA’s stock surged to hundreds of dollars (pre-split) by the mid-2020s, delivering enormous returns to those who invested early despite several downturns. This serves as evidence that Jensen Huang’s keynotes are not mere spectacles, but rather actionable roadmaps—an important reason why investors listen so carefully to his words. Such patterns are likely to persist, and while GTC 2025 may bring short-term stock volatility, it will ultimately reaffirm market confidence in NVIDIA’s future direction.

GPU Market Outlook Beyond 2025: NVIDIA vs. AMD, Intel, and TPU

What will the GPU and AI accelerator market look like beyond 2025? Based on current trends and the strategies of various companies, it is expected that NVIDIA will continue to dominate as the unrivaled leader while competitors seek to exploit niche opportunities. In particular, while NVIDIA’s dominance in the AI data center accelerator market is likely to remain for the foreseeable future, competitors such as AMD, Intel, and Google (TPU) will continue to mount countermeasures. Let’s examine each in detail.

NVIDIA: Overwhelming Market Share and an Aggressive Roadmap

NVIDIA is already the market leader in AI accelerators and is investing heavily to widen the gap. According to Morgan Stanley, NVIDIA consumed 51% of the global wafer capacity for AI processors in 2024 and is projected to see its market share surge to 77% in 2025. In contrast, all other competitors combined account for only 23%, meaning that NVIDIA is absorbing the vast majority of AI chip demand. Specifically, while Google’s share is projected to shrink from 19% to 10%, Amazon AWS from 10% to 7%, and AMD from 9% to 3%, NVIDIA appears to be the sole company on an uncontested path. Moreover, NVIDIA has secured a substantial supply chain advantage by locking in far more production capacity at foundries like TSMC compared to its competitors.

In terms of its technology roadmap, NVIDIA is aggressively innovating. As previously discussed, even after the launch of the Blackwell (B100/B200) products in late 2024 to 2025, NVIDIA has reportedly already begun developing its next-next architecture (the so-called 300 series). The company plans to expand its R&D spending to $16 billion in 2024, running three product lines in parallel and releasing new products every 18–24 months—investments that are several times higher than those of its competitors. This scale of economies and rapid development pace will continue to favor NVIDIA in shaping the future GPU market. Additionally, NVIDIA’s CUDA software ecosystem remains a significant strength, with developers and enterprises continuing to prefer NVIDIA for both its performance and mature ecosystem. Industry analysis from SemiAnalysis noted, “The vitality of the NVIDIA ecosystem lies in its ability to enable development and expansion on the same platform, whether it’s a few hundred-dollar gaming GPU or a cluster of tens of thousands of GPUs,” suggesting that NVIDIA will continue to maintain its edge through broad developer participation compared to competitors with limited hardware accessibility.

However, NVIDIA also faces challenges due to the massive surge in demand. As the market grows rapidly, supply shortages of products like the H100 may worsen, and customers—especially cloud companies—may attempt to reduce their dependency on NVIDIA by leveraging their bargaining power. Nonetheless, in the near term, there are few alternatives that match NVIDIA’s performance and ecosystem, which is why some even say that “NVIDIA is the only game in town” in the AI chip market. The prevailing view is that NVIDIA will maintain its enormous lead over its competitors for the next several years, with firms like Wedbush predicting that NVIDIA will remain the top AI beneficiary well into 2025.

AMD: Technologically Promising but Facing Ecosystem and Adoption Challenges

AMD has traditionally been seen as NVIDIA’s only peer in the discrete GPU market, ranking second behind NVIDIA in the PC space and developing its “Instinct” MI series GPUs for data center AI acceleration. AMD’s strengths include its extensive experience in semiconductor design and proven successes in HPC. In fact, AMD dominates a significant portion of the x86 server CPU market and has demonstrated GPU performance by powering the world’s first exascale supercomputer (Oak Ridge National Laboratory’s Frontier). The MI300, AMD’s latest data center APU (Accelerated Processing Unit) unveiled in 2023, features an innovative design that integrates CPU (codenamed Elburs) and GPU chiplets via 3D stacking and is equipped with massive HBM memory. The MI300 series, which has been adopted in systems such as the El Capitan supercomputer, is expected to stand out in certain HPC workloads.

However, from a commercial standpoint, AMD’s share of the AI accelerator market remains modest. According to Morgan Stanley, AMD’s AI wafer consumption share is expected to shrink from 9% in 2024 to 3% in 2025, indicating that even if AMD increases production in absolute terms, its share will decline in a rapidly expanding market, meaning AMD cannot keep pace with NVIDIA’s surging demand. A major weakness of AMD’s Instinct accelerators is its software ecosystem. In contrast to NVIDIA’s CUDA, AMD offers the open-source ROCm computing platform, which is widely regarded as less mature in terms of developer convenience and optimization. As a result, cloud providers and enterprises have been slow to adopt AMD accelerators, with usage often limited to small-scale pilot projects (for example, Microsoft Azure offered MI200 VMs, but their adoption has been limited). Additionally, while innovative heterogeneous designs like the MI300 have significant potential, they must overcome challenges such as initial production yields and cost issues before achieving large-scale market success. SemiAnalysis commented, “Everyone says an alternative to NVIDIA is needed, but the only traditional silicon company that could realistically provide such an alternative is AMD, which has a track record of delivering HPC GPUs on time,” while adding that “the key question is whether AMD can provide the volume and software support that the market demands.” The period from 2024 to 2025 will be critical for AMD as it ramps up MI300 production and demonstrates both performance and ecosystem compatibility. If AMD can secure significant orders from major cloud customers (such as Oracle or Microsoft) or improve software compatibility to compete in general AI workloads, there is potential for increased market share. However, the prevailing view remains that “NVIDIA is far ahead and AMD will find it difficult to close the gap in the near term.” Ultimately, AMD’s long-term strategy will likely involve leveraging its comprehensive portfolio of CPUs (Zen), FPGAs/Xilinx, and GPUs to offer tailored solutions for specific customers or to compete on cost-effectiveness against NVIDIA. For instance, if some companies can purchase multiple AMD MI accelerators for the price of one NVIDIA H100, it could be an attractive alternative for cost-sensitive customers—provided that the performance gap is minimal and software porting is straightforward. This underscores the need for AMD to further invest in software and work closely with its customers.

Intel: Repeated Challenges and Setbacks, with a Reattempt in 2025

Intel has unexpectedly struggled in the AI accelerator space. Once proclaiming “there is no AI era without GPUs,” Intel acquired deep learning startups (such as Nervana) between 2017 and 2019 and even announced plans to develop its own GPUs based on the Xe architecture, but ultimately failed to achieve significant success. In 2022, Intel managed to complete its first HPC GPU, Ponte Vecchio (marketed as the Intel Data Center GPU Max), which was supplied to the U.S. Department of Energy’s Aurora supercomputer, but this too was several years delayed compared to the original plan. Furthermore, the Gaudi AI processor from Habana Labs, which Intel had acquired, saw limited adoption—only AWS launched Gaudi2 instances, and use cases have remained very restricted. SemiAnalysis noted that “Habana Gaudi 2 has seen almost no adoption outside AWS, and internally Intel appears to be effectively consolidating the product line into Falcon Shores.” In early 2024, Intel introduced plans for its next-generation hybrid architecture, Falcon Shores, at a developer event, originally planned as a GPU+CPU hybrid chip but now slated to be a pure GPU product for 2025. This suggests a shift in plans regarding independent AI chips such as a potential Habana Gaudi3.

In short, while Intel remains a powerhouse in the CPU arena, it has consistently lagged in AI acceleration in terms of technology, software, and market trust compared to NVIDIA and AMD. Nonetheless, Intel has vowed to launch a new GPU for data centers called Falcon Shores in 2025. For this product to succeed, it must at least deliver performance and power efficiency comparable to the H100, integrate seamlessly with x86 CPUs, and be backed by robust software support (e.g., OneAPI). However, current investor expectations are low. Morgan Stanley forecasts that Intel’s AI chip wafer share in 2025 will be a mere 1%, indicating that the volume of Intel’s AI accelerators will be insignificant relative to the overall market. Intel’s primary strategy seems to be shifting towards “supporting NVIDIA as a foundry,” as evidenced by past remarks from Jensen Huang regarding potential collaboration with Intel, suggesting that Intel might focus more on manufacturing NVIDIA chips rather than competing in GPU sales.

In summary, Intel’s AI accelerator market strategy has yet to yield clear results, and 2025 appears to be a “last reattempt” for the company. If Falcon Shores fails or proves uncompetitive, Intel may concentrate on its core businesses such as CPUs and FPGAs (Altera), or approach AI acceleration as a foundry or platform service. Despite its massive capital and human resources, Intel is unlikely to pose a direct threat to NVIDIA in the next 2–3 years. The market sentiment even suggests that “Intel is practically out of the AI competition.”

Google TPU: High Internal Efficiency but Limited External Ecosystem

Google’s TPU (Tensor Processing Unit) is shaping the competitive landscape in AI acceleration in a different way from NVIDIA. Google has been designing its own AI chips to optimize the AI workloads for its vast data centers—supporting services such as search, advertising, translation, and YouTube. Since its development began in 2015 and its initial unveiling in 2016, the TPU has evolved through multiple versions, with the v4 already commercialized and available to some customers on Google Cloud. Furthermore, internal roadmaps for TPU v5 and v6 are reportedly in progress.

The strength of Google’s TPU lies in its tight integration with Google’s proprietary software stack, yielding excellent performance and cost efficiency for specific AI tasks. For example, Google leverages TPU Pods (clusters of thousands of TPUs) to achieve high computational density when training massive language models, claiming superior cost efficiency compared to the same-generation NVIDIA GPUs. SemiAnalysis has analyzed that “Google’s mature hardware/software stack gives it a structural advantage in performance and TCO for its internal AI workloads.”

However, a major limitation of the TPU is its restricted external ecosystem. Google offers the TPU only through its own cloud services and does not sell the chip externally. Moreover, the details of the TPU’s architecture and programming environment have not been sufficiently disclosed to external developers, limiting its broader adoption. Large customers typically demand comprehensive documentation and pre-testing before adopting a new chip, but Google tends to reveal specifications only after a full-scale rollout, making external collaboration difficult. In addition, many innovative features of the TPU (related to memory and networking) remain hidden from external users, preventing developers from performing low-level tuning or customized utilization. Ultimately, while the Google TPU may be successful for Google’s own internal purposes, it is not seen as a serious threat to NVIDIA’s dominance on the global stage. Morgan Stanley’s wafer share estimates also indicate that Google TPU’s share is expected to decline from 19% in 2024 to 10% in 2025, meaning that even if Google increases its own chip usage, it will not match the rapid expansion of NVIDIA’s market share.

Google’s strategy is not to completely replace NVIDIA but to optimize certain workloads with its own chips to reduce costs. In practice, Google Cloud continues to offer NVIDIA A100/H100 GPU instances, and even Google’s research teams use GPUs when necessary. Recent reports have also indicated that Google purchased thousands of additional NVIDIA H100 units. Therefore, the Google TPU is likely to remain a specialized accelerator for Google’s internal use, posing limited direct competition to NVIDIA’s external ecosystem. However, if Google further refines the TPU to achieve extreme price/performance advantages or opens it up as an open-source solution for external clouds, it might trigger some shifts in the market. Based on Google’s current approach, though, the TPU appears to remain primarily a means for Google to secure its own competitive edge.

Others: Amazon AWS, Microsoft’s In-House Chips, Meta, Startups, and China

There are additional players in the competitive landscape. Amazon AWS has developed its own AI chips for training (Trainium) and inference (Inferentia) and offers them as part of its EC2 instances. However, like the TPU, AWS’s Trainium is only available within the AWS cloud and lacks the versatility of a general-purpose chip. Morgan Stanley expects AWS’s AI wafer share to decline from 10% in 2024 to 7% in 2025, suggesting that even if AWS increases its own chip production, it will remain marginal compared to the growing demand for NVIDIA GPUs. Microsoft, as will be discussed later, is also developing its own AI accelerator called Azure Maia, though its impact is expected to be felt only partially after 2025.

Meta (Facebook) has also pursued AI inference accelerators such as MTIA and a training accelerator project, though it reportedly reset its direction after design failures in 2022. Currently, Meta relies heavily on NVIDIA H100 GPUs for large-scale AI training, maintaining high short-term dependence on NVIDIA. Nonetheless, Meta is unlikely to abandon its ambitions for in-house chips aimed at long-term cost reductions.

Numerous AI semiconductor startups have emerged, yet most are not yet at a level to challenge NVIDIA commercially. Notable examples include Cerebras (with its wafer-scale engine) and Graphcore (with its IPU); however, Cerebras primarily offers its services through its own cloud and its pricing remains very high, limiting accessibility. Graphcore, despite investments from SoftBank and others, has recently encountered difficulties. Other companies such as Tenstorrent (led by Jim Keller) are receiving attention, but SemiAnalysis notes that “more time is needed for both the hardware and software to mature.” Ultimately, most startups are not yet capable of challenging the NVIDIA ecosystem, and many are likely to remain niche players or be acquired by larger companies.

Chinese competitors also form a significant part of the landscape. Due to U.S. export restrictions, China faces limitations in importing cutting-edge GPUs like the A100/H100, prompting companies such as Huawei (with its Ascend series), Alibaba, Tencent, Biren, and Cambricon to accelerate efforts in developing their own AI chips. Although some have produced chips at the 7nm scale, they still lag far behind NVIDIA in terms of peak performance, and face challenges due to restrictions on using TSMC’s latest process technologies. Despite strong domestic demand, technological and manufacturing constraints will likely prevent Chinese companies from fully replacing NVIDIA before 2025. Instead, NVIDIA has been able to maintain a portion of its market by selling downgraded versions of the A100 (such as the A800) to China. Even if sanctions persist long term, while local Chinese companies might eventually narrow the gap after 2025, from a global perspective, Chinese chips are unlikely to seriously threaten NVIDIA’s international standing.

Summary of Outlook Beyond 2025

Beyond 2025, the GPU/AI accelerator market is expected to maintain explosive growth in demand. With large language models, generative AI, cloud AI services, and edge AI all still in their early phases of adoption, IDC and others predict that the AI semiconductor market will grow at an annual rate of 20–30%. In such a market, NVIDIA—armed with its technological prowess, comprehensive product lineup, and robust ecosystem—is expected to enjoy a dominant competitive edge for the foreseeable future. While AMD has significant technological potential, it is likely to take considerable time to substantially increase its market share, and Intel appears to be out of the running. Cloud providers’ in-house chips are primarily intended for self-sufficiency, and often coexist with NVIDIA products. For example, Microsoft Azure is pursuing a dual strategy by using its own Maia chips for certain workloads while also collaborating with NVIDIA on the GB200 superchip and continuing to market H100 VMs as its main offering. This trend is applicable across most cloud companies, allowing NVIDIA to maintain high margins and market dominance as long as it continues to expand its supply chain.

However, the challenges from competitors should not be entirely dismissed. In particular, AMD’s acquisition of Xilinx has endowed it with FPGA/adaptive computing technology that, combined with its existing CPU/GPU expertise, may allow it to capture opportunities in areas where NVIDIA has not ventured (for example, offering integrated FPGA+GPU solutions to telecom companies for 5G networks). Additionally, as open-source software stacks continue to evolve, reducing dependence on CUDA, it may become easier to run AI workloads on AMD or other chips. If super-large customers like Google and Meta increasingly deploy their own chips, NVIDIA’s largest customer revenues might experience some decline. Taking these variables into account, however, NVIDIA’s technological leadership, execution, and market trust suggest that its “monopoly” is likely to persist for several years beyond 2025. Competitors’ responses are expected to take the form of niche market targeting or partial collaboration with NVIDIA, and unless a truly disruptive change occurs, the phrase “NVIDIA has it all” will likely remain true in the GPU industry.

Impact of GTC 2025 on Microsoft, OpenAI, and Tesla

The announcements at NVIDIA GTC 2025 will not only affect NVIDIA itself but will also have far-reaching implications for major AI tech companies. In particular, Microsoft, OpenAI, and Tesla—being leading companies in the AI field with close collaborative or competitive relationships with NVIDIA—may experience shifts in their partnership and competitive dynamics following GTC 2025. Let’s examine each in detail.

Collaboration and Competition with Microsoft

Microsoft has always been a special partner for NVIDIA. The Azure cloud platform has deployed NVIDIA’s H100/A100 GPUs in large volumes to handle the AI workloads of numerous customers, including OpenAI, and the two companies have maintained a long-standing collaborative relationship in the AI infrastructure space. In fact, at GTC 2024, NVIDIA and Microsoft jointly announced the early adoption of the next-generation Grace-Blackwell superchip (GB200) on Azure, and Azure subsequently unveiled a series of large-scale VMs powered by NVIDIA’s latest GPUs (including the H100 v5). Additionally, Microsoft is integrating NVIDIA’s RTX GPU-based AI functionalities (such as AI-accelerated co-processing) into its software products like Windows and Office. In this context, the announcements at GTC 2025 present a significant opportunity for Microsoft to adopt new technologies. For example, if an HGX system based on Blackwell is unveiled, Microsoft Azure could be one of the first to adopt it, thereby bolstering its AI infrastructure capabilities. From Microsoft’s perspective, leveraging its collaboration with NVIDIA provides a competitive advantage over rival clouds like AWS and Google.

On the other hand, Microsoft is not only one of NVIDIA’s biggest customers but also a potential competitor. Microsoft has been pursuing its own AI chip development project (the Azure “Maia” accelerator) and officially unveiled the Azure Maia 100 AI accelerator alongside the Cobalt ARM CPU at its 2023 Ignite event. This project is designed to process certain AI workloads in Azure data centers using dedicated chips, and it was developed in collaboration with OpenAI. Sam Altman, CEO of OpenAI, noted that “we co-designed the Azure infrastructure with Microsoft, and when the Maia chip was shared, we optimized together,” suggesting that Maia could pave the way for more cost-effective training of large models. This indicates that the Microsoft+OpenAI alliance may ultimately seek to reduce dependency on NVIDIA and better control costs. Some reports from late 2024 even speculated that “Microsoft is developing its own B200 chip (in response to NVIDIA’s B100) and that there is a standoff between NVIDIA and Microsoft.” At GTC 2025, if NVIDIA emphasizes the significant performance gains of the Blackwell product line, Microsoft may adjust its development direction by comparing it to the targets set for the Maia project. If NVIDIA’s Blackwell turns out to be far more powerful than anticipated, Microsoft might delay or scale back its own chip rollout; conversely, if it is deemed competitive, Microsoft could expand its adoption of Maia to partially replace NVIDIA purchases.

However, in the short term, given that the collaboration between Microsoft and NVIDIA is mutually beneficial, the GTC 2025 announcements are more likely to further cement their partnership. As evidenced by a recent AIwire press release, Microsoft and NVIDIA jointly demonstrated the optimization of the next-generation GB200 superchip on Azure, with a strategy to offer NVIDIA’s latest technology to Azure customers as early as possible. This shows that Microsoft will continue to collaborate closely with NVIDIA to maintain its leading position in AI infrastructure until its own chips are ready. Additionally, Microsoft is likely to continue its ecosystem partnerships with NVIDIA—participating, for example, in the U.S. government’s comprehensive AI partnership (PGIAI) led by NVIDIA—in order to align on social AI issues.

In conclusion, Microsoft stands to be both significantly impacted by and influential on the outcome of GTC 2025. On the collaboration front, Microsoft will likely leverage NVIDIA’s new technologies to enhance Azure’s AI services and gain an edge over competitors like AWS and Google in the cloud market. On the competitive front, while Microsoft’s own chip strategy may be adjusted in light of NVIDIA’s announcements, it is expected to remain heavily reliant on NVIDIA’s high-performance chips at least through 2025–2026. Given that Microsoft is one of NVIDIA’s largest customers, NVIDIA will also take steps to accommodate Microsoft’s demands (such as price reductions or increased supply) to maintain the relationship. Wedbush has forecasted that “AI spending will exceed $2 trillion over the next 2–3 years,” with NVIDIA and cloud giants like Microsoft at the center, suggesting that despite a competitive-cooperative (co-opetition) dynamic, a win-win outcome is highly likely. Following GTC 2025, positive signals from Microsoft’s stock or remarks are expected, with comments along the lines of “We will drive AI evolution together with NVIDIA.”

OpenAI: Key Customer and Potential Competitor?

OpenAI, the catalyst behind the generative AI boom exemplified by ChatGPT in 2023, is one of the largest consumers of NVIDIA GPUs. Tens of thousands of NVIDIA A100 GPUs were used to train models such as GPT-4, and OpenAI is reportedly using H100 infrastructure on Microsoft Azure to develop its next-generation models. For OpenAI, the announcements at GTC 2025 are crucial from a customer’s perspective. Enhanced performance, cost-reduction technologies in AI training/inference, and other improvements associated with the new Blackwell-based GPUs could help OpenAI train even more powerful models (such as GPT-5) faster or lower the operating costs of its API services. For instance, if Blackwell delivers a 30-fold improvement in inference efficiency, it could dramatically reduce the operating costs of OpenAI’s ChatGPT API. Therefore, OpenAI is closely monitoring NVIDIA’s roadmap, and securing the new products as quickly as possible will be a top priority.

The relationship between OpenAI and NVIDIA is fundamentally collaborative. There is no direct competition between them since OpenAI focuses on developing AI models and services while NVIDIA supplies the AI infrastructure. However, there is some indirect pressure for OpenAI to reduce its dependency on NVIDIA—for example, the joint design of the Maia chip with Microsoft mentioned earlier. Additionally, at the end of 2024, OpenAI announced the Stargate project, which, backed by investments from SoftBank and Oracle, aims to build a massive AI supercomputer infrastructure in the United States. While the initial phase of this project includes deploying a large number of NVIDIA GPUs, there is speculation that in the long run OpenAI might develop its own hardware stack. According to reports from The Next Platform, “OpenAI may eventually build its own hardware independent of Microsoft and NVIDIA,” and there have been rumors that OpenAI is exploring its own AI accelerator. Even if such a chip is developed, it is likely that its performance and stability would initially fall short of NVIDIA’s offerings, meaning that OpenAI will have to rely on NVIDIA’s platform for practical operations for the foreseeable future. In one analysis, it was noted that “just as OpenAI was dependent on the Azure platform four years ago, it remains dependent on NVIDIA’s platform today.”

Therefore, the performance roadmap unveiled at GTC 2025 could serve as a benchmark for how aggressively OpenAI pursues its own hardware strategy. If NVIDIA demonstrates far more innovative improvements than expected (for example, a 5-fold increase in training speed), OpenAI might decide that it can remain competitive on its current NVIDIA-based platform and delay its own chip development. Conversely, if the improvements are modest or if supply constraints emerge, OpenAI might consider reinforcing its independent approach in collaboration with Microsoft. Nonetheless, to date NVIDIA has consistently exceeded OpenAI’s expectations in advancing its platform, and OpenAI has long acknowledged that “NVIDIA has the most perfect AI platform on Earth.” As a result, it is highly likely that the interdependent relationship between OpenAI and NVIDIA will continue well after GTC 2025, with NVIDIA supplying the state-of-the-art GPUs that enable OpenAI to build groundbreaking AI services—a virtuous cycle that drives even greater GPU demand.

Tesla: Competitive Dynamics without Direct Collaboration, Indirect Impacts

The relationship between Tesla and NVIDIA has evolved from a customer–supplier dynamic to one of competition. In the past, Tesla used NVIDIA’s Drive PX2 for its autonomous driving computers in 2016–2017, but in 2019, it developed its own FSD chip (the FSD Computer) to replace NVIDIA’s offerings. Additionally, while Tesla previously operated a supercomputer comprising thousands of NVIDIA GPUs for autonomous driving training, it has recently begun operating its own AI supercomputer, Dojo, which started in 2023. In this context, the announcements at GTC 2025 are unlikely to lead to direct collaboration between Tesla and NVIDIA; however, NVIDIA’s technological advancements may indirectly affect Tesla’s strategy and market environment.

In terms of autonomous driving technology, the introduction of NVIDIA’s DRIVE Thor and its broader adoption across the automotive industry essentially arms Tesla’s competitors (other OEMs) with a powerful tool. While Tesla has long advanced its autonomous driving capabilities through its own integrated hardware and software, many automakers are now relying on NVIDIA’s platform in their race to catch up. For instance, with companies like Mercedes and Toyota partnering with NVIDIA to develop highly autonomous driving systems, other manufacturers may soon be able to implement autonomous capabilities comparable to Tesla’s. New electric vehicle companies such as Zeekr have even announced plans to equip their vehicles with NVIDIA’s Thor-based systems, potentially emerging as competitors to Tesla in the Chinese market. The widespread adoption of the NVIDIA Drive ecosystem increases competitive pressure on Tesla. In response, Tesla will need to continue enhancing the completeness of its FSD software and further improve the performance of its vehicle AI computer (the next version of its FSD computer). Should NVIDIA announce a next-generation automotive chip that far surpasses Thor or becomes an industry standard, Tesla may face a strategic decision on whether to stick with its independent approach or reconsider incorporating NVIDIA’s technology in certain areas. Although there is currently no sign that Tesla will re-adopt NVIDIA, if necessary—such as for the mass production of robotaxis or Optimus robots—collaboration in certain segments cannot be ruled out.

There are also indirect implications in the realm of AI supercomputers. Tesla’s Dojo, developed in-house for training its autonomous driving neural networks, targets a 4–6 times efficiency improvement over the H100 in certain tasks. However, if NVIDIA’s new Blackwell-based products manage to match or exceed the performance of Tesla’s Dojo, the relative advantages of Tesla’s in-house solution could diminish. That is, if Tesla originally found it more economical to use its own supercomputer instead of GPUs, improved GPU efficiency might lead Tesla to favor NVIDIA GPUs once again. Particularly since Dojo’s scalability and versatility have yet to be fully validated outside Tesla’s internal use, whereas NVIDIA GPUs offer high versatility for various applications, Tesla might increase its NVIDIA GPU purchases if it hesitates to expand Dojo further. Notably, in 2023, Elon Musk even attempted to secure additional NVIDIA GPUs for his AI startup xAI, underscoring the practical need for NVIDIA’s technology when it is required.

The robotics sector is also a point of interest. Tesla is developing its humanoid robot, Optimus, which it has called “its most important product.” NVIDIA, too, is focusing on this field, planning to host a humanoid robot panel at GTC 2025. As NVIDIA’s Isaac Sim and Cosmos platforms advance, a variety of companies may be enabled to create humanoid robots, setting the stage for a competitive race between Tesla’s Optimus and robots from startups such as Figure AI or Apptronik that leverage NVIDIA technology. This would further intensify future competitive pressures on Tesla in new business areas.

In summary, GTC 2025 poses an indirect challenge to Tesla. NVIDIA’s ability to provide powerful tools across automotive, robotics, and AI infrastructure may enable competitors to close the gap with Tesla. Although Tesla has traditionally followed an independent strategy, the rapid progress of its competitors—bolstered by NVIDIA’s support—might compel Tesla to accelerate its own chip and software development or, if necessary, consider partial collaboration with NVIDIA. While Tesla’s strength in vertical integration and data accumulation may prevent it from losing its competitive edge easily, NVIDIA’s continued advancements could eventually reshape the competitive landscape in the automotive sector. If, after GTC 2025, automakers begin to express confidence that they can achieve features comparable to Tesla’s FSD using NVIDIA chips, it might finally put a dent in Tesla’s current dominance. Ultimately, while Tesla and NVIDIA are not directly collaborating at present, their actions continually stimulate each other’s strategies, and GTC 2025 will likely reaffirm this dynamic.

Wall Street Analysts’ Outlook on NVIDIA and Target Prices

How are Wall Street analysts viewing NVIDIA, the company that has soared to astronomical heights amid the AI boom? With GTC 2025 on the horizon, major investment banks are generally positive about NVIDIA’s technological leadership and earnings outlook, even as they caution that the stock’s steep climb warrants a strategic approach. A synthesis of various analyst reports generally portrays NVIDIA as “the top pick for the AI era,” with target prices suggesting double-digit upside compared to early 2025 levels.

Wedbush Securities: Wedbush has maintained its position on NVIDIA as the top AI stock for 2025. They stated, “The AI revolution is the biggest technological transformation in 40 years, and its inception can be traced back to Jensen Huang,” forecasting that AI-related capital expenditures will exceed $2 trillion over the next three years. This underscores that NVIDIA is set to be the biggest beneficiary of this robust AI investment cycle. While a specific target price was not mentioned, Wedbush continues to regard NVIDIA as the top pick in AI for 2025.
Morgan Stanley: Morgan Stanley has issued an “Overweight” rating on NVIDIA with a target price of $166, representing a 21% upside from early 2025 levels. They commented, “By the second half of 2025, only the success of Blackwell will dominate market discussions,” suggesting that while short-term transitional issues may arise, the underlying demand remains extremely strong. In other words, even if temporary inventory adjustments, Chinese risks, or valuation concerns persist, the performance of the Blackwell product line in the latter half of 2025 is expected to dispel all worries. Morgan Stanley also recalled how NVIDIA’s stock surged after Jensen Huang described Blackwell demand as “insane.”
Citi: Citi anticipates that Jensen Huang’s keynote at CES 2025 will serve as a short-term catalyst for the stock, issuing a Buy rating with a target price of $175—indicating a potential 27% rise from current levels. Citi’s analyst Atif Malik remarked, “With heightened expectations for Blackwell sales from the January CES event, NVIDIA’s stock is poised for a double-digit rally,” noting that discussions with management during Q&A sessions immediately after CES would likely highlight increased demand for the new chip and a surge in enterprise AI/robotics demand, positively influencing the stock. Citi raised its target price earlier in the year based on these positive event drivers, and NVIDIA’s stock performance at CES 2025 subsequently met those expectations.
Bank of America (BofA): BofA continues to maintain a Buy rating on NVIDIA with a target price of $190, representing a potential 39% increase from current levels—one of the higher projections among major investment banks. BofA analysts noted that “demand for NVIDIA’s Hopper GPU remains robust, and demand for Blackwell is expected to outstrip NVIDIA’s production capacity for several quarters.” However, they also pointed out that recent quarters have seen investor expectations exceed actual performance, leading to post-earnings adjustments. Nonetheless, they argued that “even if there are fewer surprises, as long as the underlying fundamentals remain strong, there is plenty of reason to be optimistic” about NVIDIA’s long-term growth. Despite short-term volatility, BofA remains confident that NVIDIA will ultimately be the largest beneficiary of the AI boom, recommending an increased allocation in investors’ portfolios.

In addition, most analysts from Goldman Sachs, JPMorgan, BoA, and RBC continue to issue Buy ratings for NVIDIA. While a few more conservative voices such as UBS have cautioned about short-term corrections with neutral ratings, the majority focus on NVIDIA’s fundamentally disruptive capabilities. Based on TipRanks’ aggregated data, the average target price for 2025 stands at around $178—implying a roughly 40% upside from January’s levels. Although some caution that after nearly tripling in 2024 the stock might need to “catch its breath,” NVIDIA’s strong earnings growth prospects and lack of a clear competitor mean that analysts are reluctant to downgrade their ratings. In fact, some tech experts have even projected that NVIDIA’s market capitalization could reach $10 trillion by 2030 (more than three times its current value), predicated on the scenario that NVIDIA will generate massive revenue not only from AI chips but also from software and services.

Meanwhile, following the recent announcement of Q1 2025 (November–January 2024) earnings guidance, Wall Street’s mood temporarily turned cautious. According to Reuters, although NVIDIA’s quarterly revenue outlook for early 2025 exceeded market expectations, a deceleration compared to the previous quarter raised concerns that “the two-year AI rally might be stalling.” This triggered an 8% drop in the stock and wiped out $500 billion in market value, with some raising doubts about the overvaluation of the “Magnificent 7” big tech stocks. However, these reactions are largely attributed to short-term profit-taking and macro concerns, and have not significantly swayed analysts’ long-term views. On the contrary, some see these corrections as attractive buying opportunities—for example, Wedbush stated, “While there may be white-knuckle moments in 2025, these moments have historically been opportunities to accumulate core tech stocks.” In essence, despite short-term volatility, the long-term trend driven by the AI mega-trend remains intact, and analysts continue to advise holding NVIDIA.

Long-Term Investment Strategies: Holding NVIDIA vs. Selling After the Event

Finally, let’s examine how long-term investors might leverage events like GTC 2025. The core question is: Is it better to hold NVIDIA stock for the long term, or is it more advantageous to take profits by selling immediately after a major event? While the answer may vary for each investor, based on the analysis and current market conditions, here are a few considerations.

Advantages of Long-Term Holding: Participating in a Monumental Growth Story

The primary rationale for holding NVIDIA long-term is that the company’s fundamentals are still in the early stages of growth. Although the AI boom propelled enormous gains in 2023, AI adoption is only just beginning. As noted earlier, AI adoption is expected to surge across all industries in the coming years, with sustained excess demand for AI chips—the essential infrastructure for AI. NVIDIA stands at the center of this enormous secular growth trend. By holding shares in such a company over the long term, investors can benefit from compounded growth. Historically, those who held onto NVIDIA for over a decade reaped returns of several multiples, even though there were intermittent post-event corrections, ultimately following a long-term upward trend.

NVIDIA’s competitive edge stems not from luck but from consistent innovation and expansion. The company is poised to tap into new growth opportunities in areas such as autonomous driving, cloud AI services, the metaverse, healthcare, and robotics. This multi-vector growth is highly attractive to long-term investors. Even when one product cycle concludes, new markets emerge, and recurring software and platform revenues (such as CUDA licensing or an AI model marketplace) are expected to gradually increase. As evidenced by NVIDIA’s mention of a $1 trillion TAM, there remains a potential market several times larger than its current annual revenues (in the range of $40 billion). While it will take time to realize this potential, long-term investors can participate in the value appreciation that comes with the company’s growth.

Additionally, NVIDIA’s management—especially Jensen Huang—has a reputation for being shareholder-friendly. Huang is known for articulating a long-term vision and has previously executed stock splits (a 4:1 split in 2021 and a 10:1 split in 2024) to enhance liquidity and encourage investor participation. The resurgence of the stock following the 10-for-1 split in 2024 is a testament to the company’s strong growth trajectory. In this context, long-term investors might be better served by adopting a “hold forever” mentality rather than being overly swayed by short-term volatility.

The Temptation to Sell Immediately After an Event: Cooling Off a Short-Term Overheating

What about a strategy of selling immediately after an event to capture short-term profits? This approach takes advantage of the “sell the news” phenomenon noted earlier. There have been instances where NVIDIA’s stock peaked in the short term following major events like GTC or CES before undergoing a correction, prompting some investors to sell just before the event and then repurchase during the dip. For example, one might have sold before the March 2024 GTC and then bought back after a 2% post-announcement dip. While this sounds ideal in theory, in practice, timing such moves accurately is extremely challenging due to the unpredictable nature of event effects and overall market conditions.

If an investor deems NVIDIA’s current valuation too high, realizing some profits during the event-driven surge might be a viable option. At the start of 2025, NVIDIA’s forward P/E was around 40–50 times, significantly above the market average, so if that high valuation feels burdensome, reducing one’s position around the time of GTC 2025 might be justified. Particularly if the announcements fall short of expectations or if the positive news is already fully priced into the stock, a short-term correction could be anticipated. As BofA has pointed out, recent quarters have seen a situation where “investor expectations exceed actual surprises,” leading to a period of stagnation following the dissipation of the hype. Thus, GTC 2025 could repeat such a pattern.

Even long-term investors might consider some rebalancing. For instance, if NVIDIA has grown to occupy an excessively large portion of a portfolio due to its rally, it may be prudent to reduce the position slightly after the event to restore balance. Even if one is bullish on NVIDIA, unforeseen risks (such as an abrupt slowdown in demand or geopolitical issues) can always arise, so adhering to the principle of diversification is wise. Events like GTC are highlights of a product cycle and can serve as logical points for rebalancing.

However, trying to completely exit and then re-enter the market is risky. NVIDIA is known for its volatility and, in some cases, the stock may continue to rise after an event. If a series of good news events occur consecutively (like CES 2024), an investor who sold earlier might be forced to repurchase at a higher price. Frequent trading in a fundamentally strong, long-term upward trending stock can actually erode returns. As Warren Buffett famously said, “If it’s a great company, hold it forever.” If you are confident in NVIDIA’s long-term prospects, it may be wiser not to get too caught up in short-term events.

Comprehensive Considerations and Future Direction

Ultimately, whether to continue holding NVIDIA for the long term or to sell at least a portion of your holdings after a major event depends on your investment goals and risk tolerance. If your objective is to maximize short-term gains, you might consider trading around the volatility of GTC 2025. However, if your goal is to realize significant gains over the next 5 or 10 years, then a 5–10% short-term correction should not deter you from holding on.

Many experts advise, “Do not view NVIDIA’s stock in overly short-term terms.” The AI paradigm shift is just beginning, and NVIDIA’s revenue generation capabilities are only starting to materialize. With potential revenue streams from software subscriptions or cloud services supplementing its hardware sales, there is room for margin improvement and multiple expansion. For instance, NVIDIA’s recent announcements of an AI model store (NGC) and cloud-based Omniverse initiatives signal its move towards service-based software, which could lead to a re-rating of the stock if successful.

On the flip side, a potential risk scenario is the slowdown of the AI investment cycle. If the massive orders seen in 2024–2025 peak and growth flattens from 2026 onward, NVIDIA’s stock might enter a period of correction or trading within a range. There is a historical precedent when the cryptocurrency mining boom collapsed between 2018 and 2019, causing NVIDIA’s stock to plunge by 50%. While the current AI boom is not a mere short-term fad, the pattern of explosive growth followed by a correction before resuming growth may repeat. In the end, the decision to sell after an event ultimately hinges on one’s view of the sustainability of the AI growth cycle. Given that most experts see AI demand as a multi-year megatrend, prematurely exiting may be unwise.

In conclusion: For long-term investors, it is advisable to maintain NVIDIA as a core holding, while considering modest position adjustments during periods of overheating. Events like GTC 2025 are opportunities to gauge NVIDIA’s future roadmap rather than being negative catalysts that undermine your investment thesis. If the announcements are strong, they will only reinforce your confidence in the company’s long-term growth. Therefore, there is little reason to significantly reduce your position solely due to short-term price reactions. However, if the stock surges excessively in a matter of days (for instance, a 20% jump), it might be prudent to take some profits and then look to repurchase during a subsequent dip. Still, such moves should not compromise your fundamentally long position in this great company. Remember Jensen Huang’s consistent philosophy of “We can’t see everything, but we will continue to innovate with all our might,” which has rewarded investors over time.

https://finance1976.com/en/nvidia-blackwell/

NVIDIA GTC 2025
Jensen Huang Keynote
Blackwell Architecture (Blackwell GPU)
AI Data Center Accelerators
Autonomous Driving Computing (DRIVE Thor)
Cosmos Physical AI
NVIDIA Stock Volatility
Target Price and Analyst Outlook
AMD MI300 vs. NVIDIA
Intel Habana Gaudi / Falcon Shores
Google TPU Strategy
Microsoft Azure Maia
OpenAI Strategy (Stargate Project)
Tesla Dojo Supercomputer
Long-Term Investment vs. Short-Term Trading