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  • Our Nvidia stock pick 2 years ago now scales new Heights (4x)

    Nvidia’s stock has experienced a remarkable surge over the past two (almost 4x) years driven by several key factors:

    Screenshot
    1. Dominance in AI Hardware: Nvidia has become a central player in the artificial intelligence (AI) revolution, largely due to its powerful graphics processing units (GPUs) and visionary leadership by co-founder and CEO Jensen Huang. Known for his bold decisions and intense management style, Huang led Nvidia’s pivot from gaming to AI, anchored by its CUDA software, which enabled GPUs to perform complex AI tasks more efficiently than traditional CPUs. Nvidia’s parallel processing capabilities made its chips essential for AI development, including tools like ChatGPT, skyrocketing the company’s valuation to $3 trillion by 2024.
    2. Strong Financial Performance: Nvidia reported a significant increase in its fourth-quarter sales, with revenue reaching $39.3 billion, a 12% rise from the previous quarter and a 78% increase from the previous year. Data center sales, a major contributor to Nvidia’s revenues, saw a remarkable 93% growth compared to the previous year, accounting for $35.6 billion of the revenue. The company’s net income for the quarter was $22.06 billion, surpassing analysts’ predictions.
    3. Advancements in AI Chip Technology: Nvidia’s introduction of the Blackwell architecture, with its B100 and B200 accelerators, has been a game-changer for AI applications. These chips offer significant improvements in performance and efficiency, making them highly sought after by major tech companies.
    4. Market Sentiment and Analyst Optimism: Analysts have been bullish on Nvidia’s prospects, with some raising their price targets significantly. For instance, Loop Capital analyst Ananda Baruah raised Nvidia’s price target to $250, citing the dawn of a “Golden Wave” of generative AI adoption. This optimism reflects renewed investor enthusiasm for AI, a trend fueling substantial gains in the tech sector.

    As of June 27, 2025, Nvidia’s stock price is at a record high of $157.75, with a market capitalization of approximately $3.8 trillion, surpassing Microsoft as the world’s most valuable company.

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  • Nvidia partners with WPP to create AI powered Content Engine for Digital Marketing

    In another step towards the AI / Artificial Intelligence use, Nvidia, which is now a full-stack computing company with data-center-scale offerings that are reshaping latest industry trends, partnered with WPP, which is a global communications, advertising, and public relations company. WPP stands for Wire and Plastic Products, the original name of the company when it was founded in 1971. Over the years, WPP has grown to become one of the largest advertising and communications conglomerates in the world.

    The chipmaker Nvidia‘s effort to lead the AI revolution, now enters the world of AI enabled Digital Marketing. The demo of the AI Engine during the Computex event, claimed that the AI-enabled engine will be faster and create content more efficiently and at scale while staying fully aligned with a client’s brand oriented requirements.

    Company’s stocks’ TradingView links (Daily Chart)
    Nvidia
    WPP

    Nvidia, is primarily known for its advanced graphics processing units (GPUs), which are widely used in gaming, but they have also become crucial for AI and deep learning applications.

    NVIDIA’s GPUs have proven to be highly effective in accelerating AI computations due to their parallel processing capabilities. This has made them the preferred choice for training and deploying deep neural networks, which are fundamental to many AI applications. The company’s GPUs are used in various industries, including healthcare, autonomous vehicles, finance, and more.

    WPP operates through a network of companies that specialize in various aspects of marketing, advertising, and communication services. These companies offer services such as advertising, media planning and buying, market research, public relations, branding, digital marketing, and data analytics.

    To support AI development, NVIDIA has also introduced specialized hardware and software solutions. One of the notable offerings is the NVIDIA Tensor Core architecture, which is specifically designed for AI workloads and provides accelerated matrix computations. Additionally, NVIDIA has developed software frameworks like CUDA and cuDNN, which enable developers to optimize their AI algorithms and applications on NVIDIA GPUs.

    In recent years, NVIDIA has expanded its AI efforts beyond GPUs. The company acquired Mellanox Technologies, a leading provider of high-performance networking and interconnect solutions, to enhance its capabilities in building AI-centric data centers and accelerate data transfer between GPUs and other components. NVIDIA has also ventured into AI-specific hardware with the development of the NVIDIA A100 Tensor Core GPU and the NVIDIA DRIVE platform for autonomous vehicles.

    WPP’s portfolio includes some well-known agencies and companies. These agencies work with clients across different industries, providing strategic consulting and creative solutions to help businesses promote their brands, reach their target audiences, and achieve their marketing objectives.

  • Will Chipmaker stocks be the next Wealth Creators?

    Chipmaker stocks, referring to companies involved in the production of microchips or semiconductors, like Nvidia, have been a significant part of the technology sector and have experienced growth in recent years. The demand for semiconductors has been increasing due to advancements in various industries like smartphones, automotive, artificial intelligence, cloud computing, and the Internet of Things.

    Chipmaker / Semiconductor Stocks (With TradingView Links)
    NXP Semiconductors NV (NXPI)
    Qualcomm Inc. (QCOM)
    Monolithic Power Systems Inc. (MPWR)
    Intel Corp. (INTC)
    Nvidia Corp. (NVDA)
    Advanced Micro Devices Inc. (AMD)

    There is a strong link between chipmakers and artificial intelligence (AI). AI relies heavily on the processing power provided by specialized hardware, including chips specifically designed for AI workloads.

    AI applications require massive computational power to process and analyze vast amounts of data, perform complex algorithms, and make predictions or decisions. Traditional central processing units (CPUs) alone may not be sufficient for these demanding tasks. This is where specialized chips, such as graphics processing units (GPUs) and application-specific integrated circuits (ASICs), come into play.

    GPUs, initially developed for rendering graphics in gaming, have found a significant role in AI due to their parallel processing capabilities. They excel in handling the massive parallel computations required by AI algorithms, enabling faster training and inference times. Chipmakers like NVIDIA have capitalized on this and developed GPUs optimized for AI workloads, such as their Tensor Core architecture.

    ASICs, on the other hand, are custom-designed chips specifically tailored for specific tasks. They can provide even more specialized and efficient processing for AI workloads. For example, Google developed the Tensor Processing Unit (TPU), a custom ASIC designed for accelerating machine learning workloads in their data centers.

    Chipmakers play a crucial role in advancing AI capabilities by developing and manufacturing these specialized chips. Their innovations in chip design, architecture, and manufacturing processes enable faster and more efficient AI computations. This, in turn, drives advancements in AI applications across various domains, including natural language processing, computer vision, robotics, and applications like ChatGPT and MidJourney.

    The relationship between chipmakers and AI is symbiotic. AI’s demand for increased computational power has driven chipmakers to develop more powerful and efficient hardware solutions. In turn, advancements in chip technology have facilitated the growth and expansion of AI applications.

    It’s worth noting that the AI landscape is constantly evolving, and chipmakers continue to invest in research and development to meet the increasing demands of AI workloads. This close connection between chipmakers and AI is likely to persist as AI technology continues to advance and find applications in various industries.

    The global chip shortage in recent times has further highlighted the importance of semiconductor companies and their role in various industries. As technology continues to advance, the demand for semiconductors is likely to persist and potentially increase. This increased demand could potentially lead to growth opportunities for chipmaker stocks.

    However, investing in individual stocks carries risks, and the performance of specific companies will depend on various factors such as their financial health, market competition, technological advancements, and global economic conditions. It’s essential to conduct thorough research, assess the company’s fundamentals, and consider diversification when making investment decisions.

    If you are considering investing in chipmaker stocks or any other type of investment, it’s generally recommended to consult with a financial advisor or do your own research to make informed decisions based on your specific financial goals, risk tolerance, and investment horizon.