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Last year, a lengthy bear market highlighted the importance of investing in reliable growth stocks. Economic headwinds brought down the entire market but also created an excellent time to invest in stocks that don't often go on sale. Tech stocks were some of the most attractive o

Semiconductor stocks are hot right now, largely due to the hype surrounding artificial intelligence. Advanced Micro Devices (NASDAQ: AMD) and Micron (NASDAQ: MU) can certainly benefit from this transformational technology. AMD sells high-powered accelerators aimed at training the

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The crypto market has undergone many changes since Bitcoin first landed on the scene in 2009. The digital currency originally sold investors on the idea of a decentralized network that could make transferring money easier and quicker without the involvement of banks or governing

Many people would associate artificial intelligence (AI) with chatbots like ChatGPT or even Bard, which was released more recently by Alphabet's Google. It makes sense because the software is consumer-facing. But those platforms wouldn't exist without the advanced semiconductor h

Artificial intelligence (AI) and semiconductor chips have been capturing investors’ collective imagination – and for good reason. Together, they’re driving the technology of the future. Semiconductor chips are ubiquitous in our digital age, and AI has in recent months begun transforming the way we communicate with our machines. The convergence of these two fields implies limitless possibilities. For investors, this is particularly exciting. The Philadelphia Semiconductor Index, the PHLX, which tracks the chip sector through the performance of the 30 largest semiconductor makers, has gained approximately 39% so far this year." The PHLX is up for good reason. Semiconductors have been here for decades, and are found in pretty much everything in today’s digital world, but combined with AI, they’ll be driving tomorrow’s technology. Investors have picked up on this, and have made chip makers the go-to for AI stocks. The question now is, how much more room do the AI chip stocks have to grow? We can consult the Street’s analysts – several top stock pros have been weighing in on AI and semiconductors, and their comments can shed more light on the sector. Let’s take a look at what they have to say, and which AI-related chip giants they’re recommending. Nvidia Corporation ( NVDA) First up is Nvidia, a major name in the semiconductor industry, the eighth largest chip maker by revenue. Nvidia is a leader in the production of graphics processing units (GPUs), and has built its reputation on these high-end chips. The chips are capable of handling the computing power needs of several high-end, processing-intensive applications, including professional graphic design, high-end gaming -- and AI. Demand for Nvidia’s GPUs, especially in the latter application, has powered the stock’s strong gains this year; for the year-to-date, NVDA shares are up approximately 190%. Recent data show that Nvidia’s performance is standing on its AI products. OpenAI, the company that launched ChatGPT, has been using Nvidia’s GPUs since 2020 in training its AI units – to the tune of 20,000 chips. Looking ahead, OpenAI has indicated that it may need another 10,000 chips to maintain ChatGPT efficiency. This is a firm foundation for Nvidia’s success, and the company’s strong position can be inferred from the solid beat it recorded in its recent Q1 financial results. This report showed a total top line revenue of $7.19 billion. While this was down 13% from the prior year, it beat the forecast by an impressive $670 million. The bottom line figure, a non-GAAP EPS of $1.09, was 17 cents per share better than had been anticipated. Better yet, from an investor’s perspective, was Nvidia’s guidance. The company is predicting $11 billion in sales for its fiscal Q2, a massive increase from its previous guidance of $7.2 billion. Achieving this will translate to a 41% year-over-year increase in quarterly revenues. This company’s strength in AI forms the basis for the upbeat comments by Morgan Stanley’s 5-star analyst Joseph Moore, who writes: “NVDA should trade at a premium to peers given the higher probability of upward revisions near term, but the multiple premium vs. those peers has actually narrowed meaningfully... Nonetheless, we do see continued growth in the NVDA data center business, in a multi-year trajectory that should be clearly above all other compute players on a composite basis, given there is no offsetting or cannibalized compute business outside of the AI business.  “As a result," the analyst added, "we see NVDA as the cleanest story in AI hardware, and believe it continues to deserve more consideration from investors looking for AI exposure, even if the current valuation construct and YTD stock return already reflects expectations that are higher than secondary or tertiary players.” To this end, Moore puts an Overweight (i.e. Buy) rating on NVDA, which he has promoted to be his Top Pick. In Moore’s view, NVDA will hit $500 by this time next year, implying a gain of 18.5% from current levels. (To watch Moore’s track record, click here ) Overall, Nvidia gets a Strong Buy rating form the Wall Street analysts’ consensus, based on 33 recent reviews that break down to 30 Buys against just 3 Holds. The shares are trading for $422.09 and the $464.85 average price target suggest a modest 10% upside in the next 12 months. (See NVDA stock forecast ) Advanced Micro Devices ( AMD) AMD, by sales, is perennially one of the top ten largest chip making firms, and saw total revenues of $23.06 billion in the last four quarters (2Q22 through 1Q23). The company boasts a $188 billion market cap, and has a wide portfolio in the AI ecosystem, including high-performance chips and architecture. AMD’s AI exposure incudes its Instinct GPU accelerators, the Alveo Adaptive accelerators, and the EPYC server processors, as well as several lines of chips, including its Ryzen AI mobile processors, and the Versal AI core adaptive SoCs. AMD’s AI chips and accelerators are found in a wide range of applications, from gaming to data centers to supercomputers, and provide the processing speed and capacity needed for generative AI. By the numbers, the company’s recent performance was better than been expected. In the first quarter of this year, AMD showed $5.35 billion in total revenues. While that was down about 9% year-over-year, it beat the forecasts by $40 million. The company’s non-GAAP EPS bottom line of 60 cents per share also beat the forecast, by 4 cents over the estimates. On the negative side, the company’s Q2 revenue guidance of $5.3 billion was considered weak, and was below the $5.52 billion expectations. The company’s AI portfolio provides important support for the firm. AMD is shifting its strategic focus to the emerging AI market, and is investing heavily in both networking and data center AI operations. Getting to specifics, AMD’s new Ryzen 7000 series includes AI processing capabilities, and the MI300 chips are designed for both high-performance computing and AI applications. The latter track will find support from the rapidly growing generative AI field. These are the key points behind Baird analyst Tristan Gerra’s comments on AMD. Gerra, who holds a 5-star rating from TipRanks, says of the company: “Mi300x claims best-in-class TCO performance for inference applications, and management reiterated its expectation for meaningful AI revenue starting in 4Q23 based on multiple hyperscaler engagements. AMD sees a >50% CAGR for data center AI acceleration by 2027, to a $150B+ TAM. While its ecosystem is not as mature as Nvidia, AMD is well positioned to be a key beneficiary of AI secular growth trends for the medium term, in our view.” Based on the above, Gerra sets an Outperform (i.e. Buy) rating on AMD shares, and he gives the stock a price target of $170 to imply a 54.5% upside potential on the 12-month horizon. (To watch Gerra’s track record, click here ) Overall, the Street gives AMD a Moderate Buy consensus rating, based on 29 recent analyst reviews that include 21 Buys and 8 Holds. The stock’s current trading price is $110.01, and its $134.31 average price target suggests that it will appreciate by 22% in the year ahead. (See AMD stock forecast ) To find good ideas for stocks trading at attractive valuations, visit TipRanks’  Best Stocks to Buy, a tool that unites all of TipRanks’ equity insights. Disclaimer : The opinions expressed in this article are solely those of the featured analysts. The content is intended to be used for informational purposes only. It is very important to do your own analysis before making any investment.

Advanced Micro Devices (AMD) is one of the stocks most watched by Zacks.com visitors lately. So, it might be a good idea to review some of the factors that might affect the near-term performance of the stock.

Below is Validea's guru fundamental report for ADVANCED MICRO DEVICES, INC. (AMD). Of the 22 guru strategies we follow, AMD rates highest using our Twin Momentum Investor model based on the published strategy of Dashan Huang. This momentum model looks for a combination of fundam

The most budget-friendly graphics card in NVIDIA's (NASDAQ: NVDA) RTX 4000 series is coming on June 29. The RTX 4060, priced at $299, will come about one month after the pricier RTX 4060 Ti was launched.

This is the second article in a series. The first article can be found here: " Although AI is a Bubble, It’s Still Investible." Generative artificial intelligence (Generative AI) has been in the works for several years, but the general public and the stock markets didn’t pay much attention until ChatGPT came along, demonstrating the immense opportunities of the new technology. Within just five days, the generative AI language model from OpenAI, which produces original material in response to user requests, has attracted one million users. Now, it seems that we cannot get enough of AI -- it’s all over the headlines. It takes first place in Google searches, and it’s garnering investor attention and, well, money. Click the image above to compare top artificial intelligence stocks on TipRanks. Let’s Get Generative  Long before the Generative AI advance, many economic sectors had been exploiting Predictive AI – models that analyze existing massive amounts of data to detect patterns, provide the basis for making decisions, produce analytics, classify data, and detect discrepancies, problems, or fraud.   Predictive AI has been providing massive amounts of economic value by cutting costs, optimizing processes, and, in general, making use of Big Data possible as well as effective. Predictive, or “traditional”, AI has been extensively used in autonomous vehicles, medical diagnosis, stock trading, video games, and automating numerous routine knowledge-based tasks.   Now, we are at the dawn of Generative AI. As its name suggests, it can generate new and original content based on its learning of large datasets, just like humans do -- but at a vast scale. Generative AI has captured the general public’s attention by its ability to create art, compose music, and write texts (and even pass Wharton School’s MBA exams); its future applications may exceed entertainment and education uses.   Futurology aside, one of the most important tasks that Generative AI has performed so far – through its missionaries ChatGPT, DALL-E, and Midjourney – was to draw attention, and, consequently, money, to the AI technology and its vast capabilities.  The cutting-edge AI technology can be used in virtually everything, from providing full-scale customer support to from-scratch software generation to the creation of 3D worlds models for simulations for car development to developing new protein sequences to aid in drug discovery to helping with accurate weather forecasting and natural disaster prediction. With these capabilities, the economic added value of AI is potentially unlimited.    According to McKinsey’s latest research, AI can add up to $4.4 trillion (more than the UK’s GDP) annually to the global economy. The technology can automate up to 70% of work activities, with the largest contribution in the near term seen in banking, tech, and life sciences. In the near future, we will see the emergence of new business models and applications and even brand-new industries. The AI era is in its infancy, but the potential of AI cannot be overestimated.   The Hard Base of AI  Artificial Intelligence is a very complex technology, requiring significant research and development as well as vast investments. Besides the cutting-edge software models, it cannot develop without appropriate hardware, infrastructure, and data storage. Because of that, it’s not only the companies developing Generative AI models that are benefiting from the vast influx of investments.   While companies like ChatGPT-developer OpenAI draw most of the public’s attention, the whole AI ecosystem is developing around the technology, with many more companies expected to find their place in the AI value chain.   The footing for all things AI is hardware – the chips that form the computational basis of the technology. AI-computing workload has been doubling every three to four months up to now and will undoubtedly accelerate further; AI-spurred demand for essential GPUs (graphic processing units) will bring immense benefits to chipmakers. That is why Nvidia ( NVDA ) has seen its stock surge by over 200% year-to-date.   Although Nvidia’s hardware underpins the lion's share of AI applications, it is not the only producer of chips that are designed to handle the intensive computations required for training AI models. Such a lucrative field already draws competition from existing leaders, and many new ones will come along.   One of the main competitors to NVIDIA in the GPU space is Broadcom ( AVGO ), a major supplier of chips for Ethernet switches in data centers. In April, the company revealed its new chip for supercomputer connection that will add more capacity to handle AI-driven network load.   Alphabet ( GOOGL ) has been designing and deploying Tensor Processing Units (TPUs) since 2016 for its own use; in April, the company revealed its new TPU-based AI supercomputer, saying that it’s faster and more efficient than Nvidia’s systems. Meta Platforms ( META ) has entered the hardware race as well, scooping up the whole AI-chip team from a British startup Graphcore to design and develop supercomputing systems to support AI in its data centers. Meta was slow to adopt expensive AI-supporting hardware, but now it is all in the game, boosting investment in everything AI-related. Two previously-sleepy giants, Advanced Micro Devices ( AMD ) and Intel ( INTC ), are also stepping up the AI chip race. AMD has just revealed its new chip, which it described as “the world’s most advanced accelerator for generative AI,” representing a challenge to NVIDIA’s dominance in the field. Intel, one of the largest players in the market with a long history of technology development, is working together with the AI leader ​Microsoft ( MSFT ) to enable AI applications for PCs; Intel is also shifting its chip-developing strategy to compete with NVIDIA and AMD.   AI Back Office  Besides the fastest and the most potent chips, AI needs infrastructure, including servers, data centers, and cloud systems that will house and run the AI. This infrastructure provides the necessary storage and processing capabilities, whether those are based on in-house servers or on cloud-based systems that provide storage, computing power, and various other services.  Major players include Amazon’s ( AMZN ) Web Services (AWS), Google Cloud, and Microsoft Azure. Infrastructure as a service (IaaS) allows users to access computational resources on demand, which is vital for the large-scale computations needed for AI.   Two other major players in the infrastructure field are IBM ( IBM ) and Oracle ( ORCL ). IBM offers a suite of AI tools, pre-built models, and robust infrastructure services through Watson (IBM’s data analytics processor that uses natural language processing) and IBM Cloud. Oracle provides cloud infrastructure along with a suite of AI tools, databases, and machine learning services. Oracle Cloud Infrastructure (OCI) provides the foundation for cloud-based data management powered by AI and machine learning (ML).   It must be said that the above-mentioned giants not only provide the underlying hardware and infrastructure but also offer higher-level services like pre-built machine learning models, AI tools, and development environments. They effectively span multiple stages of the AI value chain.  In addition to the infrastructure providers, numerous companies are serving as infrastructure facilitators. For instance, cloud-networking company Arista Networks ( ANET ) provides solutions that are designed to improve scalability, performance, and reliability, which are key factors in maintaining the robust infrastructure necessary for AI and machine learning workloads.   Its competitor Cisco Systems ( CSCO ) has launched a new generation of processors that can support massive GPU (graphic processing unit) clusters for AI workloads. Another networking company chipping in is Juniper Networks ( JNPR ), with its AI-driven switches and software for AI-enabling networks.   Data is Power  AI requires large amounts of data to train and function effectively. Therefore, this stage involves collecting, storing, and processing this data. Companies gather data through various means, from user interactions on digital platforms to IoT devices. The data is then stored securely and in a way that it can be easily accessed for training AI models.  However, just collecting vast amounts of data is not enough. Raw data is often messy and contains lots of irrelevant information. Therefore, it’s necessary to clean the data, deal with missing values, and standardize it into a format that can be used to train AI models. Data processing might also involve anonymizing data to ensure privacy and comply with data protection regulations.  Many companies facilitate data collection and storage; many of them also provide data cleaning, processing, and management solutions. Most of the companies in this field are the same one-stop shops mentioned earlier: Alphabet, Microsoft, Amazon, Oracle, and IBM. These giants not only provide the infrastructure and services necessary for data collection and storage, but they also use AI to improve their own products and services, making them key players in the AI training landscape.  However, there are several companies whose main business is data. Palantir ( PLTR ) specializes in big data analytics; Splunk ( SPLK ) provides software for harnessing the value of big data; Teradata ( TDC ) specializes in data warehousing and analytic applications; Iron Mountain ( IRM ) engages in the provision of storage and information management solutions; Equinix ( EQIX ) provides collocation space and develops data center solutions; Informatica ( INFA ) supplies AI-powered platform that connects, manages, and unifies data; and NetApp ( NTAP ) provides cloud data services, data storage systems, and data management solutions.   While the main business of these companies is centered around data collection, storage, and analysis, it's important to note that they usually serve a broader range of industries and are not solely focused on AI training. Nevertheless, their services form a critical part of the infrastructure needed to train AI models.  Putting AI to Work  After the data is collected, stored, cleaned, dissected, and analyzed, it must be put to work. At this stage, the data is used to train AI models. This involves selecting an appropriate model, defining a loss function, and training the model using machine-learning frameworks. This step also includes testing and validating the model to ensure it performs well.   The main players in this field are IBM, NVIDIA, and the creator of ChatGPT, OpenAI. IBM is a well-known player in the field of AI, with a history that dates to the 1950s; its AI platform, IBM Watson, is used across various industries for tasks like data analysis, automation, and predictive insights.   NVIDIA is primarily known for its graphics processing units (GPUs), but the company also provides a range of software and hardware tools that are widely used in AI model development. Microsoft-backed OpenAI is currently the most influential company in the AI sphere, as its highly-advanced models have a significant impact on the AI field. Another notable influencer is Google’s subsidiary DeepMind.  Once a model is trained, it needs to be integrated into an application that can use the AI's capabilities. This could be anything from a voice assistant to a self-driving car. It also involves creating user interfaces and APIs so that the AI can interact with users and other systems.   As for AI application development, there are numerous companies in this less capital-intensive playground, ranging from mega caps to tiny startups. The most notable companies in this field are Salesforce ( CRM ), which develops cloud-based enterprise software for customer relationship management, and Adobe ( ADBE ), which integrates AI into its digital marketing and media solutions and provides an AI-powered platform to automate tasks and offer predictive analytics in its applications. There are many other companies involved in AI models and application development. As AI technology advances, we’ll likely see even more companies entering these fields.  The final stage in the AI value chain is the provision of AI as a service (AIaaS), a cloud-based service offering artificial intelligence outsourcing, making it available to end-users or businesses. This can involve providing cloud-based AI services or implementing AI directly into products like voice assistants, recommendation systems, or advanced analytics tools.   Most of the top players in the AIaaS sphere are the AI stalwarts and their divisions -- Microsoft, Alphabet, IBM, Oracle, Salesforce, and Amazon. However, countless startups are coming down to play – maybe some of them will steal the crown from the giants of AI.  Investing in the Future  In the swiftly advancing landscape of AI, the entire value chain presents a cornucopia of investment opportunities -- from firms fueling the engine of AI through hardware and infrastructure development to those that help manage and analyze data, and further down the value chain to companies at the forefront of AI model development and application, as well as to those that are democratizing access to AI and offering scalable solutions for businesses.   However, the stage is far from monopolized. Startups and innovators with breakthrough ideas are finding their niches, potentially offering high-return investment prospects. As we venture deeper into the era of AI, each link in the value chain represents a fertile ground for investment, promising lucrative returns and a stake in shaping the future of technology. Disclaimer

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Advanced Micro Devices AMD recently expanded collaboration with Hewlett Packard HPE to provide support to the new modular and multi-protocol storage solution, HPE Alletra Storage MP, with AMD EPYC Embedded Series processors.HPE’s

In this video, I will discuss recent updates affecting Nvidia (NASDAQ: NVDA), Advanced Micro Devices (NASDAQ: AMD), and Intel (NASDAQ: INTC). Check out the short video to learn more, consider subscribing, and click the special offer link below.

Investors in Advanced Micro Devices Inc (Symbol: AMD) saw new options become available today, for the August 4th expiration. At Stock Options Channel, our YieldBoost formula has looked up and down the AMD options chain for the new August 4th contracts and identified one put a

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Semiconductor stocks are among the hottest names on Wall Street this year. And, of course, that’s positive news for the related exchange traded funds. Case and point, the Invesco PHLX Semiconductor ETF (SOXQ), which tracks the widely observed PHLX SOX Semiconductor Sector Index, is higher by nearly 46% year-to-date. Just past its second birthday, SOXQ, [...] Read more at ETFtrends.com.

Below is Validea's guru fundamental report for ADVANCED MICRO DEVICES, INC. (AMD). Of the 22 guru strategies we follow, AMD rates highest using our Twin Momentum Investor model based on the published strategy of Dashan Huang. This momentum model looks for a combination of fundam

Looking at the underlying holdings of the ETFs in our coverage universe at ETF Channel, we have compared the trading price of each holding against the average analyst 12-month forward target price, and computed the weighted average implied analyst target price for the ETF itsel

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The tech market has a history of offering investors substantial gains over the long term. The innovative nature of the sector consistently boosts different companies, proven by the Nasdaq-100 Technology Sector index rising more than 400% in the last 10 years.