Nvidia's stock is cheap to start the new year.Investors looking to find a chip stock trading at a discount at the beginning of 2026 don't need to look too far. The reason for this is that the largest semiconductor company in the world, Nvidia (NVDA +2.98%), also has one of the most attractively valued stocks.Its stock trades at a forward price-to-earnings (P/E) ratio of 24.5 times analyst estimates for fiscal 2027 (ending January 2027) and a price/earnings-to-growth (PEG) ratio of less than 0.7 times (with ...

Core Viewpoint - Nvidia, now the world's most valuable company, aims to continue its growth by aggressively hiring talent and acquiring teams to enhance its capabilities and reshape its second growth curve [1][2][3]. Group 1: Talent Acquisition Strategy - Nvidia has been systematically hiring executives from various sectors, including market, policy, research, and organizational management, to fill key capabilities [3][4]. - The company has recently appointed Alison Wagonfeld, a former Google Cloud executive, as its first Chief Marketing Officer (CMO), consolidating marketing responsibilities under her leadership [5][6][8]. - Kristin Major, a seasoned HR executive from HPE, has joined Nvidia as Senior Vice President of Human Resources, bringing extensive experience in talent management [10][12][14]. - In the quantum computing domain, Nvidia hired Krysta Svore from Microsoft, who will oversee application research and engineering in quantum technology [16][19][20]. Group 2: Acquisitions and Strategic Hiring - Nvidia's strategy includes "acqui-hire" transactions, where the company acquires startups to integrate their core teams and technologies [27][66]. - The acquisition of Nexusflow aimed to strengthen Nvidia's position in AI agents and efficient inference, bringing in key personnel including CEO Jiantao Jiao [28][29][70]. - CentML was acquired for over $400 million to enhance CUDA toolchain and model deployment efficiency, integrating its founders and engineers into Nvidia's AI software team [34][75]. - Nvidia also acquired LeptonAI to bolster its cloud computing and AI platform capabilities, with its founders joining Nvidia's leadership [36][75]. Group 3: Market Position and Future Outlook - Nvidia's revenue for the fiscal year 2025 reached $130.5 billion, more than doubling from the previous year, marking a significant growth milestone in tech history [2][47]. - The company's strategic moves in hiring and acquisitions are designed to transition from being a GPU hardware supplier to a comprehensive system-level platform provider [45][84]. - Nvidia's focus on AI inference optimization, agent deployment, and computational scheduling positions it to maintain market dominance and prepare for future AI advancements [84][85].

Core Insights - Nvidia is enhancing its leadership and technical teams, reflecting its growing prominence and wealth in the AI chip market [1][3] Leadership Changes - Alison Wagonfeld has been appointed as Nvidia's first chief marketing officer, previously serving at Google Cloud [2][16] - Kristin Major joined as senior vice president of human resources, bringing over 13 years of experience from Hewlett Packard Enterprise [8] - Jiantao Jiao, a former CEO and cofounder of Nexusflow AI, is now a director of research at Nvidia, focusing on AI post-training and infrastructure [10] - Mark Weatherford has taken on the role of head of cybersecurity policy and strategic engagement, with a background in public and private sector cybersecurity [11] - Krysta Svore, previously at Microsoft, is now vice president of applied research in quantum computing at Nvidia [13] - Danny Auble, after Nvidia's acquisition of his startup SchedMD, serves as senior director of system software [14] - Jonathan Ross and Sunny Madra, founders of Groq, joined Nvidia following a significant licensing deal [15] Acquisitions and Talent Strategy - Nvidia has utilized its balance sheet to acquire talent through startup deals, enhancing its software capabilities and market engagement [2][3] - The company completed a $900 million acqui-hire of Enfabrica, which specializes in GPU clustering for AI workloads [12] Departures - Key leaders have departed Nvidia, including Dieter Fox, who left for Ai2, and Minwoo Park, who joined Hyundai [17][19] - The company also experienced the loss of board members Ellen Ochoa and Rob Burgess in 2025 [20]

Core Insights - Nvidia's recent licensing agreement with Groq, a startup specializing in inference chips, signifies a strategic move to absorb potential competition and enhance its technological capabilities in the AI chip market [1][2][3] - The shift in focus from training to inference in AI chip competition highlights the urgency for Nvidia to secure its position against emerging threats from AMD and custom ASICs [2][5] - Groq's unique architecture emphasizes deterministic design and low latency, which aligns with the evolving demands of AI applications, making it a valuable asset for Nvidia [4][5][6] Group 1: Strategic Moves - Nvidia's acquisition of Groq's technology and key personnel represents a "hire-to-acquire" strategy, allowing it to integrate critical expertise without triggering regulatory concerns [1][2] - The deal occurs at a pivotal moment as the AI chip landscape transitions towards inference, where Groq's LPU architecture offers significant advantages [2][3] - Nvidia's historical pattern of acquisitions, such as Mellanox and Bright Computing, indicates a focus on building a robust defense against competitive threats rather than merely expanding its market presence [2][3] Group 2: Technological Implications - Groq's LPU architecture, which prioritizes predictable execution and low latency, contrasts with the dynamic scheduling typical of Nvidia's GPUs, highlighting a shift in system philosophy [3][4] - The transition of Groq towards inference-as-a-service reflects a growing market demand for low-latency solutions in sectors like finance and military applications [5][6] - Nvidia's strategy to control not just hardware but also the software and system layers, including workload management through acquisitions like SchedMD, positions it to dominate the AI ecosystem [7][8][19] Group 3: Market Dynamics - The competitive landscape is evolving, with a focus on system-level efficiency and cost-effectiveness, prompting Nvidia to adapt its offerings beyond just powerful GPUs [5][6][19] - Nvidia's integration of cluster management tools and workload schedulers into its AI Enterprise stack signifies a shift towards providing comprehensive system solutions rather than standalone products [8][19] - The emphasis on reducing migration costs and enhancing ecosystem stickiness suggests that Nvidia is not only selling hardware but also creating a tightly integrated AI infrastructure [19][20]

Core Viewpoint - The article discusses NVIDIA's strategic acquisition of Groq, highlighting its implications for the AI chip market and NVIDIA's competitive positioning in the evolving landscape of AI inference technology [1][2][4]. Group 1: NVIDIA's Acquisition of Groq - NVIDIA's acquisition of Groq is characterized as a "recruitment-style acquisition," where key personnel and technology are absorbed without a formal takeover, allowing NVIDIA to mitigate potential competition [1][2]. - The timing of this acquisition is critical as the AI chip competition shifts from training to inference, with Groq's technology being particularly relevant for low-latency and performance certainty in inference tasks [2][4]. - Groq's founder, Jonathan Ross, is recognized for his pivotal role in developing Google's TPU, making Groq a significant player in the AI chip space [5]. Group 2: Shift in AI Focus - The focus of the AI industry is transitioning from sheer computational power (FLOPS) to efficiency and predictability in delivering inference results, which Groq's architecture emphasizes [4][7]. - Groq's LPU architecture, which utilizes deterministic design principles, contrasts with the dynamic scheduling typical in GPU architectures, highlighting a shift in system philosophy [5][6]. Group 3: Broader Strategic Implications - NVIDIA's acquisition strategy reflects a broader goal of consolidating control over the AI computing ecosystem, moving beyond hardware to encompass system-level capabilities [23][24]. - The integration of Groq, along with previous acquisitions like Bright Computing and SchedMD, illustrates NVIDIA's intent to dominate the entire AI computing stack, from resource scheduling to workload management [23][24]. - By controlling the execution paths and system complexity, NVIDIA aims to create a high barrier to entry for competitors, making it difficult for customers to switch to alternative solutions [24][25].

Core Viewpoint - The stock market is heavily influenced by artificial intelligence (AI), with several key stocks expected to perform well in 2026, particularly Nvidia, Broadcom, and Taiwan Semiconductor Manufacturing [1]. Group 1: Nvidia - Nvidia is recognized as the leader in AI infrastructure, with its GPUs being essential for AI data center development [2]. - The company has established a robust ecosystem around its chips, having integrated its CUDA software platform into educational institutions, which has led to widespread adoption among developers [4]. - Nvidia's recent acquisition of SchedMD enhances its software capabilities, particularly with the open-source platform Slurm, and its NVLink interconnect system provides a competitive networking advantage [6]. Group 2: Broadcom - Broadcom is becoming a preferred choice for companies seeking cost-effective alternatives to Nvidia's GPUs, focusing on ASIC technology for custom AI chip design [7]. - The company has collaborated with Alphabet to develop tensor processing units (TPUs), attracting other major clients like Meta Platforms and OpenAI, which is expected to drive significant growth [9]. - Analysts project Broadcom's AI revenue to exceed $50 billion in fiscal 2026 and reach $100 billion in fiscal 2027, a substantial increase from $20.2 billion in fiscal 2025 [10]. Group 3: Taiwan Semiconductor Manufacturing - Taiwan Semiconductor Manufacturing Company (TSMC) is positioned to benefit from the rising demand for both GPUs and AI ASICs, holding a near monopoly in advanced logic chip manufacturing [12]. - TSMC is the only foundry capable of producing smaller node chips at high yields, which is critical for the development of powerful and energy-efficient chips [14]. - The company has demonstrated strong pricing power, increasing prices by over 15% since 2019 and planning further hikes starting in 2026 [16].

Group 1 - NVIDIA Corporation (NASDAQ:NVDA) is recognized as one of the top AI stocks to invest in, according to analysts [1] - The company acquired AI software firm SchedMD to enhance its open-source technology initiatives and strengthen its position in the AI ecosystem [1][2] - SchedMD is known for creating Slurm, an open-source workload manager essential for high-performance computing and large-scale AI applications, which will remain open source and vendor-neutral post-acquisition [2] Group 2 - NVIDIA has been actively expanding its open-source and open AI product offerings in recent months [3] - The company launched Alpamayo-R1, a new open reasoning vision language model aimed at advancing autonomous driving research, and introduced new workflows for its Cosmos world models, which are also open source [4] - NVIDIA designs and sells specialized processors that are critical not only for gaming but also for AI, data centers, professional visualization, and the automotive industry [5]

Core Insights - Nvidia has acquired SchedMD, a software company specializing in open-source workload management for high-performance computing and AI, indicating a deeper investment in the AI software stack [1] - SchedMD's Slurm is the leading job scheduler for high-performance computing clusters, enhancing efficiency and cost control by allowing thousands of computers to operate as a single system [2] - The acquisition emphasizes the growing significance of software in Nvidia's strategy, reinforcing its proprietary CUDA platform, which is widely used by developers [4] Company Overview - Nvidia, headquartered in Santa Clara, California, is a leader in graphics processors and accelerated computing platforms, with a market capitalization of nearly $4.4 trillion [5] - The company's technology is foundational for gaming, data centers, and AI, forming critical infrastructure for cloud computing and enterprise-scale innovation [5] Stock Performance - Nvidia's stock has increased by 36% over the past 52 weeks and 28% in the last six months, outperforming the Roundhill Magnificent Seven ETF (MAGS), which rose about 21% and 28% in the same periods [6] - The stock trades at 41 times forward earnings and 33.7 times sales, which are above industry averages, yet the forward earnings multiple is below Nvidia's own five-year average, suggesting a relative discount despite strong growth visibility [7]

Core Viewpoint - Nvidia and Alphabet are identified as strong investment opportunities in the AI sector heading into the new year, particularly for long-term growth [1]. Nvidia - Nvidia holds over 90% market share in the GPU market, primarily due to its established ecosystem and the CUDA software platform, which is essential for AI workloads [3]. - The acquisition of SchedMD enhances Nvidia's software capabilities by providing control over the AI orchestration layer through the Slurm platform, optimizing GPU task assignments [4]. - Nvidia's proprietary NvLink interconnect system allows for faster data transfer between GPUs, improving performance for large language model training [6]. - The company is well-positioned to benefit from increasing AI infrastructure spending, with significant growth potential as the AI market is still in its early stages [7]. Alphabet - Alphabet has developed its own custom AI chips, Tensor Processing Units (TPUs), which are optimized for its Tensorflow framework, providing a competitive edge over other cloud computing competitors [8]. - TPUs offer better price-performance and power efficiency, allowing Alphabet to train its AI models more cost-effectively, thus enhancing margins for its cloud computing business [9]. - The continuous improvement of Alphabet's Gemini model across its product ecosystem strengthens its market position, leveraging its existing search and advertising capabilities [11][12]. - Alphabet's advantages in cost, distribution, monetization, and data position it as a strong contender for long-term success in the AI space [13].

Core Insights - The article discusses the dynamics of open-source projects and the necessity of commercial support for their sustainability, highlighting that companies often back these projects to ensure they can monetize them [1][2]. Group 1: Open Source and Commercial Support - Open-source projects like the Linux kernel often receive support from commercial entities to enhance and maintain them, as companies are typically unwilling to provide self-maintenance for these projects [2]. - Examples of commercially supported Linux distributions include Red Hat Enterprise Linux, SUSE Linux, and Canonical Ubuntu, which integrate open-source projects into their products [2]. Group 2: NVIDIA's Strategic Moves - NVIDIA has shifted its focus towards managing system clusters rather than specific operating systems, leading to its acquisition of Bright Computing in January 2022, which was known for its Bright Cluster Manager [3]. - Bright Computing had raised $16.5 million in funding and had over 700 users globally, with its tools initially designed for traditional high-performance computing (HPC) systems [3]. - After the acquisition, NVIDIA rebranded Bright Cluster Manager as Base Command Manager and integrated it into its AI Enterprise software stack, which includes a licensing fee of $4,500 per GPU annually [3][5]. Group 3: Mission Control and Workload Management - NVIDIA introduced a layer called Mission Control on top of BCM, which automates the deployment of frameworks, tools, and models for its "AI factory" [6]. - Mission Control includes Kubernetes for container orchestration and Docker for running computations within containers, optimizing power consumption based on workload [6]. Group 4: Slurm Workload Manager - For managing bare-metal workloads in HPC and AI, NVIDIA relies on Slurm, which has become the default workload manager for Base Command Manager [7][9]. - Slurm, developed by SchedMD, has been widely adopted in the HPC community, with approximately 60% of the Top500 supercomputers using it [11]. - NVIDIA and SchedMD have collaborated on Slurm for over a decade, with NVIDIA committing to continue its development as an open-source, vendor-neutral software [11][12]. Group 5: Future Considerations - The article raises questions about how NVIDIA will integrate Run.ai and Slurm functionalities with Base Command Manager to provide comprehensive management tools for both AI and traditional CPU-based clusters [12]. - There is speculation on whether NVIDIA will commercialize its Kubernetes integration within the AI enterprise stack, following the example of Mirantis, which has successfully containerized OpenStack [13].