Recent Trends in the GPU Market

1. GPU Industry Summary
2. Recent Key Technological Advancements and Product Releases
3. Current Market Share Distribution
4. Growth and Adoption in Emerging Applications
5. Impact of Supply Chain Dynamics and Geopolitical Factors
6. Evolving Consumer Preferences and Demands
7. Influence on Purchasing Decisions and Customer Needs
8. Competitive Landscape and Competitor Strategies:
9. Implications for Sales and Marketing Strategies and Potential Adjustments
10. Conclusion and Future Outlook

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1. GPU Industry Summary:

The graphic processing unit (GPU) market has experienced significant dynamism over the past 1-2 years, characterized by rapid technological advancements, evolving market share distribution, and increasing adoption in emerging applications, particularly artificial intelligence (AI) and high-performance computing (HPC). This report analyzes these recent trends and their implications for sales and marketing strategies. Key technological advancements include enhanced AI and machine learning capabilities, the rise of real-time ray tracing, and the introduction of new GPU architectures. The market share landscape is dominated by Nvidia in the discrete and data center GPU segments, with AMD striving to gain ground and Intel facing challenges in its discrete GPU endeavors. The burgeoning demand for GPUs in AI, data centers, and HPC is a primary driver of market growth. However, supply chain dynamics and geopolitical factors have introduced volatility in GPU availability and pricing. Consumer preferences in gaming and content creation continue to evolve, with performance, features like ray tracing and AI upscaling, and price being key considerations. These trends are significantly influencing purchasing decisions and necessitating adaptations in competitor strategies. Consequently, this report concludes by outlining potential adjustments to sales messaging and marketing channels for GPU products to effectively address the current market landscape.

2. Recent Key Technological Advancements and Product Releases:

The GPU market has been a hotbed of innovation, with significant strides made in enhancing processing power and introducing specialized features over the last couple of years.

2.1 Enhanced AI and Machine Learning Capabilities:

A fundamental shift in the GPU landscape is the increasing recognition of their pivotal role in artificial intelligence and machine learning. Current GPUs are indispensable for the swift and efficient processing of intricate algorithms, and future iterations are anticipated to exhibit even greater computational prowess. There is speculation that future GPUs might be specifically engineered for AI-centric tasks, potentially diverging from their traditional focus on graphical applications to serve as central processing hubs for AI systems . These advanced models could incorporate enhanced tensor cores, meticulously optimized for lightning-fast machine learning computations, alongside novel architectural designs aimed at minimizing power consumption while maximizing computational output. 

Nvidia’s Ampere architecture, featured in products like the A100 and RTX A6000, exemplifies this trend. These GPUs are specifically designed for data center and professional applications, including demanding deep learning tasks. The Ampere architecture brought about substantial performance enhancements through its advanced Tensor Cores, which significantly accelerate deep learning computations, leading to faster training and inference times. Furthermore, these GPUs support enhanced mixed-precision training, a technique that combines different numerical precisions to optimize performance and memory utilization, thereby accelerating deep learning training without compromising accuracy. The A100, in particular, offers a massive memory capacity of up to 80 GB, facilitated by HBM2 memory technology, enabling the processing of large-scale models and datasets without encountering memory limitations. It also introduced Multi-Instance GPU (MIG) technology, allowing a single GPU to be partitioned into multiple smaller instances, each with dedicated compute resources, thus enabling efficient utilization for running multiple deep learning workloads concurrently.

The latest advancement from Nvidia, the Blackwell microarchitecture, announced in March 2024, underscores this growing emphasis on AI. With an astounding 208 billion transistors, Blackwell more than doubles the performance of its predecessor, Hopper. A significant portion of this increase in transistor count is attributed to the use of two chip dies connected by a high-speed chip-to-chip interconnect, effectively creating a unified single GPU. Each die houses 104 billion transistors, a 30% increase over Hopper’s already impressive 80 billion. The Blackwell architecture can accommodate up to 576 GPU processors on a single chip and includes a second-generation transformer engine. This new engine features support for lower-precision data types, specifically FP4 and FP6, within its Tensor cores. Given that many generative AI models primarily require low-precision calculations, Blackwell’s performance is significantly enhanced under such conditions. Beyond raw computational power, Blackwell also integrates chip-level capabilities for employing AI-based preventative maintenance. This allows for running diagnostics and forecasting potential reliability issues, thereby improving system uptime and resilience for massive-scale AI deployments that need to operate uninterrupted for extended periods, while also reducing operating costs. The evolution of GPU technology clearly indicates a trajectory where these processors are becoming increasingly central to advancements in artificial intelligence.

2.2 Rise of Real-Time Ray Tracing and Advanced Graphics:

While the focus on AI is prominent, advancements in graphics rendering remain a vital aspect of GPU development. Real-time ray tracing technology, which simulates the physical behavior of light to achieve hyper-realistic graphics, has begun to enter the mainstream. Future GPUs are expected to not only refine this technology but also make it more widely accessible. Further advancements in ray tracing could lead to its integration with other immersive technologies, such as holography, potentially revolutionizing user experiences in gaming, cinema, virtual reality (VR), and augmented reality (AR) by enhancing the depth and interactivity of digital content.

Nvidia’s GeForce RTX 40 series, based on the Ada Lovelace architecture, represents a significant step forward in this domain. This series incorporates third-generation Ray Tracing Cores, alongside Shader Execution Reordering, a feature that optimizes the execution of rendering tasks, thereby significantly improving ray tracing performance without sacrificing traditional rendering capabilities. The RTX 40 series also introduced DLSS 3.0, which features a new generation of Optical Flow Accelerator. This technology utilizes AI to generate intermediate frames, boosting frame rates and enhancing overall visual smoothness in supported games and applications. These advancements demonstrate a continued commitment to pushing the boundaries of visual fidelity and immersive experiences through GPU technology.

2.3 Key Product Releases (2023-2024):

The GPU market has witnessed a steady stream of new product releases from major manufacturers over the past two years, reflecting the intense competition and rapid pace of innovation. Nvidia’s GeForce RTX 40 series, first announced in late 2022, saw the release of various models throughout 2023, catering to different performance tiers and price points. These included the high-end RTX 4090 and RTX 4080, followed by the more mainstream RTX 4070 Ti, RTX 4070, RTX 4060 Ti, and RTX 4060. Recognizing the need to maintain competitiveness and offer enhanced performance, Nvidia refreshed its RTX 40 series in early 2024 with the introduction of “Super” variants: the RTX 4070 Super, RTX 4070 Ti Super, and RTX 4080 Super.

AMD also made significant strides with the release of its Radeon RX 7000 series. This lineup includes high-performance cards like the RX 7900 XTX and RX 7900 XT, as well as more mid-range options such as the RX 7900 GRE, RX 7800 XT, RX 7700 XT, and RX 7600, providing consumers with a range of choices across different budget and performance requirements.

Adding another layer to the competitive landscape, Intel entered the discrete GPU market with its Arc A-series cards. While still a relatively new player, Intel’s offerings, including the Arc A750 and Arc A380, represent an attempt to challenge the dominance of Nvidia and AMD, particularly in the more budget-conscious segments of the market.

Looking ahead, Nvidia announced its next-generation Blackwell architecture in March 2024, with product shipments anticipated later in the year. This announcement signals the ongoing commitment to pushing the boundaries of GPU technology, particularly in the realm of AI. Furthermore, the consumer-focused GeForce RTX 50 series, based on the Blackwell architecture, is expected to debut in early 2025, with potential launches of the RTX 5090 and RTX 5080 in January 2025, indicating a continued rapid iteration of GPU technology. This constant influx of new products underscores the dynamic and fiercely competitive nature of the GPU market.

3. Current Market Share Distribution:

The competitive landscape of the GPU market is characterized by a few dominant players vying for market share across different segments.

3.1 Overall GPU Market:

The global GPU market is a substantial and rapidly expanding sector. Valued at USD 48.38 billion in 2023, projections indicate a remarkable growth trajectory, reaching USD 461.02 billion by 2032, representing a compound annual growth rate (CAGR) of 28.6%. In the near term, the market is expected to exceed $44 billion in 2024. Analyzing the PC GPU market specifically, Intel held the largest share at 67% as of the fourth quarter of 2023. However, it is crucial to note that this figure likely includes integrated graphics solutions, where Intel has a strong presence due to its dominance in the CPU market. When considering the discrete GPU market, which encompasses add-in boards used for more demanding graphical tasks, Nvidia emerges as the clear leader. In this segment, Nvidia commanded an impressive 80% market share as of Q4 2023, with AMD trailing at 19% and Intel holding a modest 1%. Recent reports suggest that Nvidia’s dominance in the discrete GPU market has further solidified, with its market share reportedly reaching 90% in Q4 2024. This indicates a growing consolidation of the discrete GPU market around Nvidia’s products.

3.2 Data Center/AI GPU Market:

The data center GPU market, fueled by the exponential growth of AI and machine learning, has witnessed extraordinary expansion. In 2024, this market reached a staggering $125 billion, with Nvidia firmly establishing its dominance by holding an overwhelming 92% market share, a figure consistent with its position in 2023. While Nvidia maintains a commanding lead, its primary competitor, AMD, has also experienced significant growth in this crucial segment. AMD’s data center GPU segment saw a substantial 179% growth between calendar years 2023 and 2024, resulting in a modest increase in its market share from 3% in 2023 to 4% in 2024. Huawei has also emerged as a player in this space, securing a 2% market share in the data center GPU market in 2024. Focusing specifically on the AI GPU market, projections for 2024 indicate that Nvidia is expected to command over 70% of the market, with some estimates suggesting a figure as high as 88%. In 2023, Nvidia held a 65% market share in the data center AI chip market, followed by Intel at 22% and AMD at 11%. The data clearly illustrates Nvidia’s strong grip on the high-growth data center and AI GPU markets.

3.3 GPU Market Share Distribution (2023-2024):

To provide a clearer picture of the market share dynamics, the following table summarizes the distribution across key segments:

This table highlights Nvidia’s strong position in the discrete and data center GPU markets, particularly in the rapidly expanding AI segment. While AMD is making strides in the data center, the gap with Nvidia remains significant. Intel faces a considerable challenge in gaining traction in the discrete GPU market.

4. Growth and Adoption in Emerging Applications:

The demand for GPUs is being significantly propelled by their increasing adoption in several emerging application areas.

4.1 Artificial Intelligence and Machine Learning:

GPUs have become the cornerstone of advancements in artificial intelligence and machine learning. Their inherent parallel processing capabilities allow them to handle the massive computational demands of neural network training with remarkable efficiency, significantly outperforming traditional CPUs. Tasks in deep learning that once required weeks to complete can now be accomplished in mere hours, accelerating the pace of innovation in the field. In 2023, the machine learning and AI segment held the largest share of the overall GPU market, underscoring its importance as a driver of demand. While the growth rate of the AI chip market is projected to moderate to a still substantial 20.4% CAGR from 2024 to 2029, the market is expected to reach an impressive $400 billion in annual sales. Furthermore, the adoption of AI GPUs is anticipated to grow by 40% year-over-year, fueled by increasing enterprise demand for AI capabilities. This strong correlation between GPU capabilities and the growth of AI and ML signifies a continuing upward trend in demand for high-performance GPUs.

4.2 Data Centers:

The data center GPU market is experiencing explosive growth, primarily driven by the escalating demand for cloud computing services and AI-as-a-Service offerings. In 2024, the data center GPU market more than doubled in size compared to the previous year, with Nvidia being the primary beneficiary of this surge in demand. Projections for the global data center GPU market indicate a substantial expansion from an estimated USD 21.77 billion in 2025 to approximately USD 192.68 billion by 2034, representing a robust CAGR of 27.52%. In 2024, North America accounted for the largest share of this market, generating 38% of the total revenue. While on-premises deployments held the majority of the revenue share (59%) in the data center GPU market in 2024, the cloud segment is anticipated to exhibit the fastest growth rate in the coming years. Cloud service providers were the leading end-use segment in the data center GPU market in 2023. These dynamic highlights the increasing reliance on GPUs within data centers to power a wide range of cloud-based services and AI applications.

4.3 High-Performance Computing (HPC):

GPUs have fundamentally transformed the landscape of high-performance computing by providing unparalleled speed and efficiency for tackling data-intensive tasks through their parallel processing architectures. A significant driver for the growth of HPC is the increasing integration of AI into its workflows. This convergence of powerful computational capabilities with intelligent data processing is unlocking new possibilities across diverse fields. GPUs are now powering critical breakthroughs in areas such as AI development, healthcare (e.g., genomics), finance (e.g., risk modeling), and energy (e.g., climate modeling) within HPC environments. The demand for computational power in HPC is currently at an all-time high, fueled by the exponential growth of data-intensive applications like artificial intelligence, big data analytics, and complex scientific simulations. The ability of GPUs to accelerate complex workloads and facilitate scientific discovery makes them an indispensable component of modern HPC systems.

5. Impact of Supply Chain Dynamics and Geopolitical Factors:

The GPU market has been significantly influenced by both supply chain dynamics and evolving geopolitical landscapes.

5.1 Supply Chain Constraints:

The global chip shortage, which began in 2020 due to the COVID-19 pandemic, has had a profound impact on GPU production and availability. This shortage caused significant disruptions to the global supply chain, leading to delays in chip manufacturing and delivery. The increased demand for electronic devices resulting from the shift to remote work and learning further exacerbated the situation. Despite some improvements, the global chip shortage persisted into 2023, continuing to affect GPU supply. The escalating demand for GPUs driven by the rapid adoption of AI is projected to further strain the supply of certain upstream components, potentially increasing total demand by 30% or more by 2026. Disruptions in critical parts of the supply chain, such as the availability of advanced packaging technologies like CoWoS, have also significantly impacted the supply of high-end GPUs. The phenomenon of scalping, where individuals or groups purchase GPUs in bulk and resell them at inflated prices, has further aggravated the shortage and contributed to price inflation, making GPUs less accessible to regular consumers. These supply chain constraints have created challenges for both manufacturers in meeting demand and consumers in acquiring the GPUs they desire.

5.2 Geopolitical Influences:

Geopolitical tensions, particularly the ongoing rivalry between the United States and China, have become increasingly influential in the GPU market. Trade restrictions and concerns about national security have impacted GPU manufacturing capacities and overall market dynamics. A notable example is the US government’s restrictions on the export of advanced Nvidia AI chips to China. This measure, aimed at safeguarding national security interests, has disrupted the progress of Chinese technology companies and has broader implications for global supply chains. China’s response, including bans on the export of crucial minerals like gallium and germanium, demonstrates the potential for retaliatory actions that can further complicate the supply chain and increase uncertainty. Concerns surrounding forced technology transfer and intellectual property theft also contribute to export restrictions and can hinder international collaboration and innovation in GPU technology. These geopolitical factors necessitate that companies operating in the global GPU market remain vigilant and adapt their strategies to navigate the evolving international landscape.

6. Evolving Consumer Preferences and Demands:

Consumer preferences and demands in the GPU market are shaped by the primary applications driving their purchases, namely gaming and content creation.

6.1 Gaming Sector:

In the gaming sector, consumers are increasingly seeking high levels of performance, particularly for gaming at higher resolutions such as 4K. However, the rising prices of high-end graphics cards have made price sensitivity a significant factor for many gamers. Ray tracing performance is becoming a more important consideration as more games incorporate this visually demanding technology, although historically, AMD GPUs have lagged behind Nvidia in this aspect. Artificial intelligence-powered upscaling technologies like Nvidia’s DLSS (Deep Learning Super Sampling) and AMD’s FSR (FidelityFX Super Resolution) are gaining popularity as they offer a way to improve image quality and boost frame rates without requiring significantly more powerful hardware. Currently, Nvidia’s RTX 4070 Super is considered a top overall GPU for gaming in 2025, offering a good balance of performance, power efficiency, and advanced features like DLSS and ray tracing . AMD’s Radeon RX 7900 XTX is also a strong contender, particularly for its excellent performance in traditional rasterization rendering and its large video memory capacity. For many gamers, the value proposition, measured in frames per second (FPS) per dollar spent, remains a critical factor in their purchasing decisions. Therefore, manufacturers need to strike a balance between offering cutting-edge performance and ensuring their products are competitively priced.

6.2 Content Creation Sector:

The content creation sector represents another significant driver of demand for high-performance GPUs. The computer graphics market is experiencing consistent growth, reflecting the increasing reliance on visual content across various industries. Professionals in fields such as industrial design and architecture utilize GPUs to accelerate demanding tasks like 3D modeling and rendering. Video editing and rendering are also key applications that necessitate powerful GPUs to ensure smooth and efficient workflows. The parallel processing capabilities of GPUs are particularly beneficial for content creators, enabling faster and more precise generation of AI-produced content. Nvidia’s Quadro and Tesla series of GPUs are specifically designed and marketed towards professionals in industries like architecture, automotive, and scientific computing, highlighting the importance of catering to the specific needs of this market segment. Content creators typically prioritize GPUs with strong parallel processing performance, ample video memory, and features that enhance their productivity in professional applications. 

7. Influence on Purchasing Decisions and Customer Needs:

Recent trends in GPU technology and applications are significantly shaping how customers make purchasing decisions and what their core needs are.

7.1 Performance Requirements:

Across all segments, from gaming enthusiasts to AI researchers and content creation professionals, the need for higher performance and faster processing times remains the paramount driver for GPU purchases. Whether it’s achieving higher frame rates in demanding games, accelerating the training of complex AI models, or reducing rendering times for intricate 3D scenes, the ability of a GPU to deliver superior performance is a fundamental requirement.

7.2 Feature Sets:

Beyond raw processing power, the specific features offered by modern GPUs are increasingly influencing purchasing decisions. Technologies like real-time ray tracing, which enhances visual realism, and AI-powered upscaling techniques like DLSS and FSR, which improve performance and image quality, are becoming key differentiators, particularly in the gaming sector. In the realm of AI and machine learning, the presence of specialized hardware like Tensor Cores and Transformer Engines, designed to accelerate specific AI workloads, is a significant factor for researchers and developers.

7.3 Price Sensitivity:

Price is a crucial consideration for a significant portion of the GPU market, especially among consumers in the gaming segment. The increasing cost of high-end graphics cards has made budget-consciousness a more prominent factor in purchasing decisions. Customers are often looking for the optimal balance between performance and affordability, making the performance-per-dollar ratio a key metric in their evaluation process. 

7.4 Availability and Supply:

The recent and ongoing supply chain challenges have highlighted the critical role of product availability in influencing purchasing decisions. In situations where demand outstrips supply, the mere availability of a particular GPU model can become a primary factor driving a customer’s choice, even if it wasn’t their initial preference. Clear communication about product availability and lead times has become essential for managing customer expectations. 

8. Competitive Landscape and Competitor Strategies:

The GPU market is dominated by a few key players, each employing distinct strategies to compete and capture market share.

8.1 Nvidia’s Dominance and Focus on AI:

Nvidia has established a dominant position in both the discrete GPU market and, more significantly, in the rapidly growing data center and AI GPU markets. The company’s remarkable revenue growth in recent years has been largely attributed to the success of its data center and AI-related products. Recognizing the immense potential of artificial intelligence, Nvidia has strategically shifted its primary focus from traditional graphics processing towards AI applications powered by its high-performance GPUs. Nvidia’s strategy is built upon its technological leadership in high-performance computing, advanced graphics rendering, and, most notably, AI acceleration. The company also leverages deep collaborations with various computer manufacturers and software developers to strengthen its brand influence and ensure broad compatibility and optimization for its products. 

8.2 AMD’s Efforts to Gain Market Share:

While Nvidia holds the lion’s share of the market, AMD is actively pursuing strategies to gain ground, particularly in the lucrative data center and AI segments. AMD’s data center GPU business has shown promising growth, resulting in a gradual increase in its market share . The company reported record revenue in 2024, with significant contributions from its Data Center and Client segments, indicating a positive trajectory . AMD’s strategic focus includes advancing its AI capabilities, expanding its networking solutions portfolio, and strengthening its presence in embedded and automotive applications . A key aspect of AMD’s approach is its emphasis on providing an open, end-to-end, and cost-effective AI infrastructure, potentially appealing to customers seeking alternatives to Nvidia’s proprietary ecosystem.

8.3 Intel’s Struggle in the Discrete GPU Market:

Intel’s entry into the discrete GPU market with its Arc Alchemist series has faced significant challenges. Reports indicate that Intel’s market share in this segment dropped to 0% in the second quarter of 2024. Despite this setback, Intel remains committed to the discrete GPU market and is currently preparing to launch its second-generation Arc Battlemage GPUs. Intel’s strategy likely involves focusing on improving the performance and stability of its drivers, which were a point of criticism for the first-generation Arc cards and potentially targeting specific market segments or price points where it can offer a competitive advantage with its upcoming architecture.

The recent trends in the GPU market necessitate several adjustments to sales and marketing strategies for GPU product vendors.

9.1 Emphasize AI Capabilities:

Given the overwhelming growth and future potential of the AI market, it is crucial for GPU vendors to prominently feature the AI and machine learning capabilities of their products in their marketing materials. This includes highlighting performance benchmarks in key AI workloads, showcasing compatibility and optimization with popular AI software frameworks such as Nvidia’s CUDA and AMD’s ROCm, and emphasizing specialized hardware features like Tensor Cores and Transformer Engines that accelerate AI computations. This strategic emphasis will resonate with the rapidly expanding customer base in the AI and machine learning sectors.

9.2 Target Data Center and HPC Markets:

The data center and high-performance computing markets represent significant revenue opportunities. Sales and marketing efforts should be tailored to address the specific needs of these segments, emphasizing aspects such as scalability, energy efficiency (performance per watt), reliability for continuous operation, and superior performance in demanding computational environments. Highlighting features relevant to server deployments, such as support for multi-GPU configurations, advanced cooling solutions, and virtualization capabilities, will be critical for attracting customers in these sectors.

9.3 Communicate Value Proposition Clearly:

In the gaming and content creation markets, where price sensitivity is a more significant factor, it is essential to clearly communicate the value proposition of GPU products. Marketing campaigns should highlight the performance-per-dollar ratio, demonstrating the benefits of specific features like real-time ray tracing for enhanced visual immersion in gaming and AI-powered upscaling technologies for improved performance and image quality. For content creators, emphasizing the acceleration of creative workflows in professional applications and the efficiency gains achieved through GPU utilization will be key selling points.

9.4 Address Supply Chain and Geopolitical Factors:

In the current environment of potential supply chain constraints and geopolitical uncertainties, transparency with customers is paramount. Sales and marketing strategies should include proactive communication about potential product availability limitations and possible price fluctuations. For international markets, particularly those affected by geopolitical tensions, messaging should address any relevant concerns, potentially highlighting the origin, security, and reliability of the supply chain to build trust and manage expectations.

9.5 Highlight Software Ecosystem and Support:

The software ecosystem surrounding GPU products plays a crucial role in their overall value and usability. Marketing efforts should emphasize the robustness and reliability of driver support, the availability of comprehensive developer tools and resources, and the compatibility of GPUs with popular applications and software frameworks across various domains. This is particularly important for attracting developers, researchers, and professionals who rely on a well-supported and optimized software environment for their work.

9.6 Differentiate Against Competitors:

In a competitive market, clearly articulating the unique selling propositions of GPU products compared to those offered by competitors is essential. For instance, AMD could emphasize its cost-effectiveness and its commitment to open-source software ecosystems to differentiate itself from Nvidia. Intel, as it continues to develop its discrete GPU offerings, could focus on specific performance niches, integrated solutions within its broader product portfolio, or unique features of its upcoming architectures to carve out its own space in the market. Effective differentiation in marketing messaging will help customers understand the specific advantages of choosing one vendor’s products over another.

10. Conclusion and Future Outlook:

The GPU market over the last 1-2 years has been characterized by a powerful surge in demand, primarily driven by the rapid advancements and widespread adoption of artificial intelligence. This trend has solidified the position of GPUs as critical components in data centers, high-performance computing environments, and increasingly in consumer applications. Nvidia has emerged as the dominant force, particularly in the AI and data center segments, capitalizing on its early investments and technological innovations. AMD is actively competing and showing growth in key areas, while Intel is navigating a challenging re-entry into the discrete GPU market.

Looking ahead, the dominance of AI as a driving force in the GPU market is expected to continue and even intensify. The insatiable demand for computational power to train and deploy increasingly complex AI models will likely fuel further innovation in GPU architectures and features. The competition between the major players will remain fierce, with each company vying for market share by focusing on performance, features, pricing, and strategic partnerships. Geopolitical factors and supply chain dynamics will continue to introduce elements of uncertainty and volatility, requiring companies to be agile and adaptable in their operations and market strategies. Emerging technologies, such as novel memory solutions and advanced interconnects, will also play a crucial role in shaping the future of GPU capabilities. To succeed in this dynamic landscape, GPU vendors must continue to invest in research and development, maintain strong relationships with their partners and customers, and adapt their sales and marketing strategies to effectively communicate the value proposition of their products in a market increasingly defined by the transformative power of artificial intelligence.