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Mining's biggest companies are already using artificial intelligence for a variety of purposes such as improving safety and efficiency. But an up-and-coming player has a technology that's wowed venture capital firms backed by some of the world's most famous investors.

U.S. stocks ended mostly higher on Thursday, snapping their three-day losing streak, as investors digested fresh comments from Fed Chair Jerome Powell who once again hinted that the central bank is still not done with its tighteni

Folks just can’t get enough of AI right now. And that’s not just my opinion

Below is Validea's guru fundamental report for MICROSOFT CORP (MSFT). Of the 22 guru strategies we follow, MSFT rates highest using our Twin Momentum Investor model based on the published strategy of Dashan Huang. This momentum model looks for a combination of fundamental moment

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

Sam Altman is the co-founder of OpenAI, the company responsible for developing ChatGPT. The online chatbot has taken the technology world by storm this year with its ability to answer complex questions, write computer code, and even recognize images.

Investors in Upstart (NASDAQ: UPST) have -- to put it lightly -- experienced a bit of volatility since the company went public in late 2020. At one point in 2021, the shares were up over 1,000% in less than a year before falling more than 95% from all-time highs in 2022. Now, the

Investors have been on a roller coaster the last few years, with the COVID-19 pandemic sending many tech stocks skyrocketing. Then, countless companies watched their stocks take a deeper dive amid last year's economic downturn.

As with nearly every other computing technology company earnings call in recent months (and non-tech company earnings calls too, for that matter), GitLab (NASDAQ: GTLB) led off its most recent financial report by talking about artificial intelligence (AI). AI, of course, is nothi

The run in hot FANGMA stocks (META, AMZN, NFLX, GOOGL, MSFT, AAPL), which can be measured by the Evolve FANGMA Index ETF Trust Units CAD Hedged ETF ( TSE:TECH) , has been very impressive. It certainly did not take long for sentiment to take a 180-degree turn, sending some FANGMA stocks to new heights just months after touching down with ominous depths. Undoubtedly, it would have been nice to get in a few months ago. Though it seems like the surge is a forgone opportunity, analyst bullishness suggests that big tech's recent run still has room to gain. I'm inclined to agree with the analyst community. FANGMA stocks have more room to run, and it's not just thanks to the AI boom! Therefore, let's use TipRanks' Comparison Tool to check in with three AI-savvy FANGMA names and see where analysts stand. Amazon ( NASDAQ:AMZN) Amazon is an e-commerce and public cloud behemoth that hasn't been nearly as hot as some of its big tech peers, some of which are sitting at or around new all-time highs. Going into the second half, I view Amazon stock as the FANGMA company with the most room to surge. Undoubtedly, Amazon is the priciest of the group. However, I still believe that its disruptive capabilities will help it grow into its seemingly sky-high price-to-earnings multiple (it's over 300 times trailing price-to-earnings and 71 times forward price-to-earnings). Sure, a lot of enthusiasm is already baked into the share price. Still, I view new highs as a realistic target for the original innovator, especially as interest rates look to peak. All considered, I remain bullish on AMZN stock. The cloud and e-commerce segments aren't soaring into the clouds as they used to, thanks partly to a hazy economic climate. Indeed, growth in both areas could rebound after a recession concludes. If no recession unfolds, Amazon's road to recovery could continue at the current blistering pace. Even if the coming expected recession is rocky and longer lasting, the company has some growth drivers brewing, and it's these disruptive innovations that could help keep the good times (at least on a year-to-date basis) going for Amazon stock investors. Year-to-date, the stock is up around 52%. With skin in the AI game via its Amazon Bedrock AI service (which offers customizable AI models for easy integration into applications) and a smartening Alexa (Amazon's cloud-based voice service), I view Amazon as one of the AI companies that may not have as much AI hype built in at current levels. We've heard a lot about OpenAI and ChatGPT this year. Up ahead, I'd look for Amazon to step up to the plate with its AI innovations. What is the Price Target for AMZN Stock? Amazon's a Strong Buy on Wall Street, with a whopping 37 Buys and just one Hold. The average AMZN stock price target of $137.62 implies upside potential of 7%. Microsoft ( NASDAQ:MSFT) Microsoft stock recently hit a new high, just months after ChatGPT shined a bright light on the potential of generative AI and large language models. As ChatGPT gets stronger and more widely available across a slew of productivity applications, it seems like Microsoft is on a breakaway, leaving many of its rivals in the dust. Microsoft's rivals have been going heavy on AI innovation of late, and they can catch up. That said, it may prove difficult to dethrone Microsoft as it explores new possibilities with the power of AI. Even at today's frothy valuations, I find it hard to be anything but bullish on the stock. Shares of Microsoft are the most expensive they've been in quite a while. At 36.7 times trailing price-to-earnings, MSFT stock is on the high end of the historical valuation spectrum. Over the last five years, Microsoft has averaged a 33.4 times trailing price-to-earnings multiple. Undoubtedly, there's a lot of AI expectations factored in. As Microsoft keeps rolling out new AI features across its ecosystem, earnings could get a nice jolt. The magnitude of this jolt, though, remains a question mark. Executives see plenty of ways that Microsoft hits $10 billion (or more) in annual AI ARR (annual recurring revenue). Arguably, Microsoft could exceed such estimates, perhaps sooner than expected, if AI plays well with the company's existing offerings. After the hot run, several analysts have hiked their price targets, some by generous amounts. J PMorgan recently hiked its price target from $315 to $350. What is the Price Target for MSFT Stock? Microsoft is still a Strong Buy on Wall Street, with 30 Buys, four Holds, and one Sell. Nonetheless, the average MSFT stock price target of $347.57 entails a small 2.3% gain from here. Alphabet ( NASDAQ:GOOGL) I view Alphabet stock as a must-own for new investors looking to bet on AI. When it comes to AI innovation, it's easier to bet on the products we've had a chance to interact with. Though Google has its Bard AI, a vast majority of the company's AI innovations aren't yet ready for consumers to play with. Arguably, Google's AI innovations are too powerful (and risky) to be unleashed to the public right now. This could change once regulations come to be while guardrails improve. For now, though, GOOGL stock certainly doesn't seem like the "hottest" AI stock to own for investors. Still, give it a few years, and I'd look for Google to flex its AI muscles. Arguably, Google may have bigger AI muscles than some of its pricier peers. For this reason, I'm bullish on GOOGL stock and view it as relative value in a hot space. Though GOOGL may be relatively cheap, given the AI talent (consumer-facing and behind-the-scenes) you're getting, the stock still trades well above where it spent most of last year (it averaged a 17.54 times trailing price-to-earnings multiple in 2022). At 27.7 times trailing price-to-earnings, there's some AI enthusiasm baked in. Nevertheless, my guess is that there's not enough, given the monetizability of Google's large language models. What is the Price Target for GOOGL Stock? Alphabet is a Strong Buy based on 28 Buys and three Holds assigned in the past three months. The average GOOGL stock price target of $131.48 implies 6.8% upside potential. Conclusion FANGMA stocks have a lot to gain from AI, and of the three AI-savvy stocks in this piece, analysts expect the most upside from Amazon stock. Disclosure

The S&P 500 and the Nasdaq closed higher on Thursday as U.S. Federal Reserve Chairman Jerome Powell continued to beat a hawkish drum and suggested the central bank has not reached the end of its tightening cycle, but provided reassurance that the Fed would proceed with caution.

We often hear investors talk about how hard it is to "beat the market" over time, and when it comes to ETFs, for the most part, they are right. ETFs that beat the broader market indices like the S&P 500 ( SPX ) over the long run are rare.  Using the Vanguard S&P 500 ETF ( NYSEARCA:VOO) as a proxy for the S&P 500, it’s easy to see why. As of the end of the most recent month, the fund has provided enviable total annualized returns of 12.8% over the past three years, 11% over the past five years, and 11.9% over the past 10. Since its inception in 2010, the fund has returned 13.3% annually. The Invesco S&P 500 Quality ETF ( BATS:SPHQ) is an ETF that also invests in the S&P 500, albeit with more specific criteria. Let’s take a look at if it is a good investment opportunity and how it stacks up to the Vanguard S&P 500 ETF, with a focus on long-term performance.  What Does the SPHQ ETF Do? SPHQ is a $5.3 billion ETF from Invesco that invests at least 90% of its assets in the S&P 500 Quality Index. This index takes the S&P 500 and invests in the stocks with the highest quality score, which is based on financial metrics like return on equity, changes in net operating assets, and financial leverage.  SPHQ vs. VOO Portfolio Comparison SPHQ is fairly diversified, with 101 holdings, and its top 10 positions make up 48.5% of the fund. Below, you can take a look at SPHQ’s top 10 holdings using TipRanks’ holdings tool. VOO sports 505 positions, investing in the entire S&P 500. Its top 10 holdings make up 30.4% of the fund. Take a look at VOO’s top holdings below. VOO is clearly more diversified, as it invests in the entire S&P 500 instead of just a segment of it, but overall, both of these ETFs are well-diversified.  Additionally, as you can see in the tables above, there is some overlap between SPHQ and VOO despite their different strategies. Both ETFs count Apple ( NASDAQ:AAPL ) and Microsoft ( NASDAQ:MSFT ) among their top 10 holdings. However, SPHQ has top 10 positions in integrated oil majors like ExxonMobil ( NYSE:XOM ), payment networks Visa ( NYSE:V ) and Mastercard ( NYSE:MA ), and consumer staples like Procter & Gamble ( NYSE:PG ).  Six of SPHQ’s top 10 holdings have Smart Scores of 8 or above, while five of VOO’s do. The Smart Score is TipRanks’ proprietary quantitative stock scoring system. It gives stocks a score from 1 to 10 based on eight market key factors. The score is data-driven and does not involve any human intervention. A Smart Score of 8 or above is equivalent to an Outperform rating. SPHQ and VOO feature identical ETF Smart Scores of 8.   Comparing Their Long-Term Track Records  When looking at any ETF, you want to evaluate its performance over time. SPHQ has provided its investors with very solid results for a long time. As of the end of May, SPHQ has a three-year annualized total return of 12.4%, a five-year annualized total return of 11.4%, and a 10-year annualized total return of 12.1%. Double-digit returns over the course of a decade are the type of results that help investors to build long-term wealth, making SPHQ an attractive ETF to own.  Over time, SPHQ’s results have been comparable to those of VOO. Over the past three years, VOO has beaten SPHQ by a narrow margin with a total annualized return of 12.8%. But over the past five years, SPHQ’s annualized return of 11.4% is actually slightly better than VOO’s 11% return. Over the past 10 years, SPHQ also leads VOO by a narrow margin of 12.1% to 11.9.%, making SPHQ one of the rare ETFs that can say it has "beaten the market" over the long term, albeit by a very narrow margin.  Which ETF Has Lower Fees? SPHQ has a reasonable expense ratio of 0.15%. This means that an investor putting $10,000 into SPHQ today would pay $15 in management fees in year one. Assuming the fee remains constant and that the fund returns 5% a year, an investor in SPHQ would pay $53 in fees after three years, $99 after five years, and $234 over the course of a decade.  These are reasonable enough fees especially given SPHQ’s double-digit returns. However, VOO’s returns are comparable, and its fees are much lower. With an expense ratio of 0.03%, an investor in VOO would pay just $3 in fees in year one, $10 over three years, $17 over five years, and $39 over the course of the decade, offering substantial savings against SPHQ.  Investor Takeaway Ultimately, SPHQ is one of the few ETFs that can make a credible claim that it has beaten the market over the long term since it has outperformed a broad S&P 500 fund like VOO over the five and 10-year time frames, albeit by narrow margins. On the other hand, VOO offers a much lower expense ratio than SPHQ for a roughly similar performance.   An investor putting $10,000 into SPHQ over the past 10 years and earning a 12.1% total annualized return would now have a position worth about $31,337. Meanwhile, an investor putting the same amount into VOO would have $30,782. Both investors would be pretty happy with their returns, but SPHQ has the edge. Keep in mind that past performance is no guarantee of future results, so there’s no guarantee that SPHQ’s strategy will outperform the S&P 500 again over the next decade. Going forward, the ETFs currently look somewhat different in terms of their current portfolio composition, with VOO having the large-cap tech stocks that dominate the S&P 500 index in terms of market cap, whereas SPHQ features some of these stocks but leaves out some prominent names like Amazon and Tesla. Overall, these are both great ETFs to own, and both have helped investors compound double-digit total returns for years. Holding either of these ETFs has helped investors to more than triple the size of their investments over the past 10 years. The choice between the two may come down to your preference based on the current flavor of their portfolios, as described above. Conversely, there is no need to pick just one, an investor could invest in both ETFs and likely be happy with their long-term results. Disclosure

This week I wrote an article about the valuation controversy surrounding shares of NVIDIA NVDA. My goal was to describe both the 40% jump in annual sales forecasts this quarter and the multi-trillion dollar TAM (total addressable

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