Key Points

• The chip industry is booming thanks to AI.

• Advanced Micro Devices is seeing margins and earnings soar as its data center business expands.

• Broadcom is meeting insatiable demand for custom AI chips and networking solutions for advanced AI workloads.

Artificial intelligence (AI) is impacting every sector of the economy, so there are several ways investors can profit from this opportunity. But recent earnings results show that top semiconductor companies are still well positioned to deliver outstanding returns for long-term investors.

The AI chip market is expected to grow at an annualized rate of 24% through 2029 to reach $311 billion, according to MarketsandMarkets. If you have $3,000 you’re looking to invest right now, here are two chip stocks to consider buying and holding for the long term.

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1. Advanced Micro Devices

Advanced Micro Devices(NASDAQ: AMD) has become a widely used brand of chips in the consumer PC market. Its Ryzen processors have taken significant market share from Intel. But it’s also one of only two suppliers, along with Nvidia, of general-purpose graphics processing units (GPUs) that are used for AI workloads.

While Nvidia has a commanding lead in GPUs, it’s not going to control 100% of the market. This leaves a substantial opportunity for the runner-up in this market to do well. AMD’s data center business is booming, with segment revenue up 57% year over year in the first quarter.

AMD is meeting demand for cost-effective alternatives in the chip market. Oracle is experiencing tremendous growth in its cloud infrastructure business right now, and it’s a key partner for AMD. Oracle’s cloud infrastructure will offer up to 131,072 AMD Instinct MI355X GPUs for AI. AMD has already announced the MI400 series for launch next year, which will enable even better performance for AI training and inferencing.

As data center sales make up a larger mix of AMD’s total revenue, it is pushing margins up. Higher margins drove a 55% year-over-year increase in adjusted earnings last quarter. Given the long-term opportunity in the AI chip market, which AMD estimates at $500 billion, investors are undervaluing AMD’s future earnings.

The stock is trading at a forward price-to-earnings (P/E) multiple of 38 on 2025 earnings estimates. But this multiple drops to 25 on 2026 estimates. As AMD continues to expand margins from growth in its data center business, the stock could offer significant upside over the next few years and beyond.

2. Broadcom

Beyond the surging demand for general-purpose chips that AMD supplies, there is growing demand for chips designed for specialized tasks. Broadcom(NASDAQ: AVGO) is one of the best stocks to profit from the demand for custom chip solutions.

Broadcom has been a top-performing semiconductor company for years, supplying components for many markets, including Apple‘s iPhone. But demand for its application-specific integrated circuits (ASICs) for AI is off the charts.

The company’s AI chip revenue grew 46% year over year in the most recent quarter. As demand for custom ASICs grows, it also fuels demand for networking products that can handle faster data transfer, which is needed for next-level AI performance.

Broadcom’s new Tomahawk 6 Ethernet switch has enough data capacity to support 100,000 AI chips working together to train the next-generation AI models. The company’s networking business posted revenue growth of 170% year over year last quarter, representing 40% of its AI-related revenue.

However, management sees the demand for its custom AI chips outpacing sales of its networking products over time. It’s a huge opportunity, as evidenced by Broadcom’s momentum. Management expects its AI growth to remain steady through fiscal 2026, which could support new highs for the stock.

Broadcom earns very high margins, so the favorable demand outlook points to robust earnings over the next year. The stock trades at 41 times this year’s consensus earnings estimate, but that multiple drops to 33 on next year’s estimate. These are not cheap valuation multiples, but the investment in AI technology is pointing to substantial growth in the coming years for leading chipmakers, and that should support excellent returns for investors.

Should you invest $1,000 in Advanced Micro Devices right now?

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Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool’s board of directors. John Ballard has positions in Advanced Micro Devices and Nvidia. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Apple, Intel, Microsoft, Nvidia, and Oracle. The Motley Fool recommends Broadcom and recommends the following options: long January 2026 $395 calls on Microsoft, short August 2025 $24 calls on Intel, and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.

Investors may be generally tracking the artificial intelligence wars (AI), with most of the “Magnificent Seven” companies spending hand over fist in a race to be the first to crack AI — and all the financial benefits that come with it.

But over the last couple of weeks, Meta Platforms (META 0.37%) CEO Mark Zuckerberg has made truly massive moves, committing huge amounts of dollars to both talent and computing infrastructure that dwarf even the current super-expensive standard of today’s AI leaders.

The implications of the moves may have been comprehended by some, but may still be underestimated by the larger investment community.

Zuck throws down the gauntlet

Over the past month or so, Zuckerberg has:

1. Purchased 49% of data-labeling leader Scale AI at a $28 billion valuation, bringing in Scale’s CEO Alexandr Wang and top leadership.
2. Hired top AI talent in addition to Wang to create a “Super-Intelligence Team” from several leading AI and tech rivals, totaling about 50 researchers, by offering multiples more than other companies, with some offers rumored to be as much as $200 million or more.
3. Notable poached talent includes Nat Friedman, the former GitHub CEO; Daniel Gross, who was CEO and co-founder of SSI, Ilya Sustkever’s current start-up (Sustkever was a co-founder of OpenAI); Ruoming Pang, the head of Apple‘s AI division; as well as Lucas Beyer, Alexander Kolesnikov, and Xiaohua Zhai from OpenAI.

On infrastructure investments, Zuckerberg also shed light on massive upcoming projects:

1. In a Threads post, Zuckerberg said Meta was going to invest “hundreds of billions of dollars” in AI superclusters.
2. This includes the industry’s first 1GW supercluster, which Meta is calling Prometheus and should come online in 2026.
3. Zuckerberg also said this will be just the first of multiple GW-plus superclusters, including Hyperion, which will eventually scale up to 5 GW over several years, and encompass a data center almost the size of Manhattan.

How does all this spending pay off?

One might wonder what spurred this spending binge from Zuckerberg, and whether it was an offensive or defensive move. The answer, perhaps not surprisingly, is likely both.

Zuckerberg now says Meta is aiming for “super intelligence,” which could be somewhat akin to what was formerly referred to as artificial general intelligence (AGI). The concept of super intelligence, and whether AI is capable of reaching such a thing, has been hotly debated. However, it appears that Zuckerberg now believes super intelligence is achievable, and may be reached within the next few years. 

In a recent interview with tech magazine The Information, Zuckerberg said:

There is this big debate in the industry today. All right, is super intelligence going to be possible in three years, five years, seven years? But I don’t think anyone knows the answer. I just think that we should bet and act as if it’s going to be ready in the next two to three years.

Zuckerberg also believes “super intelligence” may mean different things to Meta than it does to more enterprise-oriented Mag Seven companies. Whereas, say, Microsoft might use AI to automate many enterprise functions, leading to an increase in productivity, for Meta, Zuckerberg apparently has a vision of giving consumers “super intelligence” related to their everyday lives, the media they consume, and their social connections.

Zuckerberg also made an interesting note in the interview that the high salaries are worth it, since the ultimate team will likely be small, between 50 and 70 people:

I think that the physics of this is, you don’t need a massive team to do this. You actually kind of want the smallest group of people who can fit the whole thing in their head. So there’s just an absolute premium for the best and most talented people.

This makes sense. The architecting of AI systems is very complex, and if a technician makes a wrong architectural choice along the way, that can affect the performance of the entire model. According to AI chip blog Semianalysis, Meta’s recent large language model Llama 4 has been a disappointment, and the reasons were partly due to poor data labeling — which the Scale AI acquisition should help with — and a few poor architectural choices.

Thus, it’s perhaps no surprise that Zuckerberg feels investing in a smaller number of high-caliber engineers is the best path. The difference between a winning model and a disappointing model may come down to a few high-level decisions, so it makes sense that Zuckerberg would pay up for quality over quantity for Meta’s new AI efforts.

Another offensive aspect of this is that Meta has arguably more financial resources than its rivals, especially OpenAI, which is considered a start-up and losing tens of billions at the moment. Last year, Meta’s “core” social media advertising business brought in a whopping $87.1 billion in operating income, somewhat offset by a $17.7 billion loss in its Reality Labs division. And that $87 billion is probably on track to reach close to $100 billion this year.

Therefore, Meta has the ability to pay as much or more than its rivals, and by paying these types of astronomical salaries, it’s raising the costs of employment for everybody — OpenAI included. Zuckerberg continued:

… one of the benefits of reinforcement learning is it gives you a venue to, you know, potentially convert very large amounts of capital into a better and better service, and potentially a better service than other less well-funded or less bold competitors will be able to do so… I view that as a competitive advantage. If we can get this to work well, and that’s why we are basically all in on this. We’re building, you know, we’re building multiple, multi-gigawatt data centers, and we can basically do this all funded from the cash flow of the company.

But the move may also be defensive, and isn’t without risks

While the “all-in” spending binge from Zuckerberg is exciting, investors should also be wary of a few things. First, it appears Meta’s AI super intelligence dream team will be essentially starting from scratch. This is likely due to Meta’s recent efforts on its Llama 4 LLM coming up short of expectations, or at least falling further behind its other competitors than Zuckerberg would like. So, it appears Meta’s latest attempt at leading AI is a bit of a bust, raising questions about the need to put all its chips into the pot, so to speak, at this moment.

It has also been reported that Zuckerberg wasn’t able to successfully acquire all the companies and talent that he wanted. In addition to Scale AI, Zuckerberg reportedly also wanted to acquire Mira Murati’s Thinking Machines and Ilya Sustkever’s SSI, but was rebuffed in both cases. It was also reported Zuckerberg extended billion-dollar offers to some of OpenAI’s leadership team, but was also rebuffed. So, while Meta now has perhaps the most formidable AI “dream team” around, it isn’t a “full” dream team necessarily.

Finally, Meta has a history of throwing money at certain far-off ventures, without immediate tangible outcomes. Look no further than the Reality Labs segment, which is basically Zuckerberg’s gambit to create the “next computing platform” of virtual reality goggles or glasses. Meta even changed its name from Facebook to Meta Platforms in 2021 to show its commitment to the effort. However, in 2024, three years later, that segment lost $17.7 billion, up from a $16.1 billion loss in 2023.

Finally, Zuckerberg didn’t really spell out what he exactly meant by an everyday consumer “super intelligence.” While both the Reality Labs division and the concept of consumer super-intelligence may one day come to fruition, it’s not assured — even with Zuckerberg assembling an AI “dream team.” So while this past month’s spending is exciting, look for investors to get impatient if Meta’s spending goes up without a corresponding growth in revenue.

And yet, the spending makes Meta a must-own stock

If one of today’s current tech leaders reaches “super intelligence” before the others, it has the potential to disrupt the balance of power among today’s Magnificent Seven. That’s why any young person or growth investor should have exposure to Meta and its rivals, in spite of their massive AI spending today.

If and when one of these companies “cracks the code” before others, it’s possible the Magnificent Seven could become the Magnificent Three, Two… or even One. With his moves over the past month, Zuckerberg is investing heavily to make sure Meta is one of the leading candidates to become that “one.”

Investors should keep their ears out for more information when Meta reports earnings at the end of the month on July 30.

Artificial Intelligence (AI) is no longer a distant concept confined to science fiction novels or futuristic movies; it has become an integral part of our lives. From voice assistants like Siri and Alexa to self-driving cars and medical diagnostic tools, AI is shaping the world in profound ways. As we stand on the cusp of a technological revolution, it’s essential to understand both the potential of AI and the challenges it presents—especially regarding its ethical, societal, and economic implications.

The Rise of AI: A Technological Revolution

The term “Artificial Intelligence” was first coined in 1956 by John McCarthy, but it wasn’t until recent decades that AI truly began to flourish. The exponential growth in computational power, the availability of vast amounts of data, and advancements in machine learning algorithms have allowed AI to evolve at an unprecedented rate. Today, AI is powering systems that can recognize speech, understand images, predict behavior, and even outperform humans in certain tasks.

Machine learning, a subset of AI, has particularly advanced in recent years. Algorithms now allow computers to learn from data without being explicitly programmed. Whether it’s recommending products on Amazon, detecting fraudulent transactions, or analyzing medical scans, AI’s ability to process vast amounts of data and uncover patterns is unmatched by human capabilities.

The Transformative Potential of AI

The potential applications of AI are vast and far-reaching. In healthcare, AI is revolutionizing diagnostics and treatment plans. Machine learning models can analyze medical data with extraordinary precision, sometimes identifying conditions that would take human doctors much longer to detect. AI-powered tools, such as IBM Watson Health, can help doctors interpret complex datasets, assist in personalized medicine, and even predict patient outcomes based on historical data.

In business, AI is streamlining operations, improving customer service, and enhancing decision-making. For instance, chatbots powered by AI can handle customer inquiries 24/7, reducing the burden on human agents and improving response times. In marketing, AI is enabling companies to tailor advertisements based on consumer behavior, enhancing targeting accuracy and ultimately driving sales.

The automotive industry is also benefiting from AI, with self-driving cars becoming more of a reality. Companies like Tesla, Waymo, and others are investing heavily in autonomous driving technology, promising to reduce road accidents and transform the way we commute. AI’s ability to interpret data from sensors and cameras allows autonomous vehicles to navigate complex environments, avoid collisions, and optimize driving behavior.

AI is even making strides in the arts. Machine-generated music, paintings, and poetry are no longer novelties but respected art forms in their own right. AI algorithms, like OpenAI’s GPT models, are pushing the boundaries of creativity, collaborating with human artists to create novel works of art.

Challenges: The Dark Side of AI

Despite its remarkable potential, AI comes with its own set of challenges and risks. One of the most significant concerns is the displacement of jobs. As AI continues to automate tasks traditionally performed by humans, millions of jobs—especially those in sectors like retail, manufacturing, and transportation—are at risk. In fact, a 2017 report by McKinsey predicted that up to 800 million workers worldwide could be replaced by robots and AI by 2030.

This has profound implications for the global economy. While AI could lead to the creation of new industries and job categories, the transition could be rocky. Workers displaced by automation will need retraining, and societies will need to develop strategies for ensuring that the benefits of AI are distributed equitably. Otherwise, the gap between the wealthy and the impoverished could widen, exacerbating existing social inequalities.

Another challenge posed by AI is its potential to amplify biases. AI systems are only as good as the data they are trained on. If the data reflects societal biases—whether racial, gender-based, or socioeconomic—AI models can inadvertently perpetuate and even exacerbate these biases. For example, facial recognition software has been shown to have higher error rates when identifying people of color, leading to concerns about discrimination, especially in law enforcement.

AI’s decision-making processes can also be opaque. Many advanced AI models, especially deep learning algorithms, are considered “black boxes” because it’s often difficult to understand how they arrive at a particular conclusion. This lack of transparency raises concerns in critical areas like healthcare, criminal justice, and finance, where understanding the rationale behind an AI’s decision is essential.

Ethical Considerations: The Moral Dilemmas of AI

As AI technology becomes more powerful, its ethical implications become more pressing. One of the most significant questions is about the control and accountability of AI systems. Who is responsible when an AI system makes a mistake? For instance, if a self-driving car causes an accident, is the responsibility on the manufacturer, the programmer, or the car itself?

AI’s potential to surpass human intelligence also raises existential questions. Could AI ever become too powerful for us to control? Some experts, like Elon Musk and Stephen Hawking, have warned that AI, if left unchecked, could become an existential threat to humanity. While this may sound like science fiction, the possibility of creating superintelligent machines that could make decisions independent of human oversight is a very real concern.

Moreover, the ethics of AI in warfare are deeply troubling. Autonomous drones and robots equipped with AI could change the nature of warfare, making it more efficient but also more lethal. The idea of machines making life-and-death decisions without human input raises moral concerns, particularly in the context of international conflicts.

The Future of AI: A Double-Edged Sword

As we move forward, the future of AI will depend on how we balance its benefits and risks. To fully realize the potential of AI, we need to address its challenges head-on, with a focus on ethics, regulation, and inclusivity. Governments, researchers, and technologists must work together to ensure that AI is developed responsibly and that its benefits are shared by all of humanity.

The role of ethics in AI cannot be overstated. Ethical frameworks and guidelines will be crucial in ensuring that AI serves humanity’s best interests. Furthermore, societies will need to invest in education and workforce development to ensure that individuals have the skills to thrive in an AI-driven world.

Ultimately, the future of AI is not predetermined. It is in our hands to shape it. If approached wisely, AI could be the most transformative technology humanity has ever known, unlocking new frontiers in science, medicine, and human potential. However, if we fail to address its challenges and ethical implications, AI could also become one of the most disruptive forces we’ve ever faced.

Conclusion: Embracing AI with Caution

Artificial Intelligence is a powerful tool that promises to revolutionize every aspect of our lives. While its potential is vast, the challenges it presents—particularly in terms of employment, bias, accountability, and ethics—demand careful consideration and thoughtful action. As we continue to advance in the age of AI, it is crucial that we maintain a balance between innovation and responsibility, ensuring that AI serves humanity’s greater good rather than creating new problems.

The dawn of AI has arrived, and with it comes both unprecedented opportunities and complex challenges. How we choose to navigate this new era will determine whether AI becomes a force for good or a source of unintended consequences.