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Meta CEO Mark Zuckerberg Just Assembled a “Super Intelligence Avengers” Team That Could Totally Change the Game in Artificial Intelligence (AI). Here’s Why That Makes Meta a “Must-Own” AI Stock.

Published

14 hours ago

July 19, 2025

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:

Purchased 49% of data-labeling leader Scale AI at a $28 billion valuation, bringing in Scale’s CEO Alexandr Wang and top leadership.
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.
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:

In a Threads post, Zuckerberg said Meta was going to invest “hundreds of billions of dollars” in AI superclusters.
This includes the industry’s first 1GW supercluster, which Meta is calling Prometheus and should come online in 2026.
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.

Image source: Getty Images.

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.

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This Artificial Intelligence Stock Has Beaten the Market in 9 of the Past 10 Years. And It’s On Track to Do It Again in 2025.

Published

2 hours ago

July 19, 2025

David Jagielski

Investing in top growth stocks is a great way to achieve strong returns and potentially outperform the market as a whole. The S&P 500 is an index of the leading companies on the U.S. markets, and historically, it has risen by 10% per year, though that’s an average including up and down years. That return is not guaranteed, but at such a high rate, an investment would double after a little more than seven years.

One artificial intelligence (AI) stock that has routinely outperformed the broad index is Broadcom (AVGO -1.12%).

The semiconductor and infrastructure company has benefited from the growth in tech in recent years, and that has allowed it to outperform the market on a consistent basis. With strong gains once again so fare this year, is Broadcom still a great buy, or could it be due for a pullback?

Image source: Getty Images.

Broadcom has been a top growth stock over the past decade

Here’s a look at just how well Broadcom has performed over the previous 10 years, compared to the S&P 500.

Year	S&P 500 Return	AVGO Return
2024	23.31%	107.69%
2023	24.23%	99.64%
2022	(19.44%)	(15.97%)
2021	26.89%	51.97%
2020	16.26%	38.55%
2019	28.88%	24.28%
2018	(6.24%)	(1.02%)
2017	19.42%	45.33%
2016	9.54%	21.78%
2015	(0.73%)	44.30%

Data source: YCharts.

What’s surprising is that the one year when the S&P 500 did better than Broadcom was 2019, when the index finished higher at nearly 29%, versus 24% gains for Broadcom.

The past doesn’t predict the future, but the tech stock’s terrific run can’t be ignored. In 10 years, shares of Broadcom have risen by more than 2,000%, while the S&P 500 has increased by around 200%.

Can Broadcom’s impressive gains continue?

As of the end of last week, Broadcom’s stock was up around 19% for the year, which was comfortably above the S&P 500’s returns of more than 6%. But with a valuation of around $1.3 trillion and Broadcom trading at 33 times its estimated future earnings (based on analyst estimates), it’s not a cheap stock to own.

The biggest risk is that the company relies heavily on demand from hyperscalers. These are big tech giants that have significant infrastructure needs related to tech and AI. If they scale back on their expenditures, that could significantly weigh on Broadcom’s results. The company estimates that its top five customers account for around 40% of its revenue.

The company’s revenue during the most recent reported period — which ended on May 4 — grew by a rate of 20% year over year, as its top line came in at just over $15 billion, while profits more than doubled, rising to nearly $5 billion.

If Broadcom can continue producing strong results such as these, it wouldn’t be surprising to see it outperform the market once again this year. Though that risk of hyperscalers cutting spending remains.

Is Broadcom stock a buy right now?

If you’re bullish on AI and expect there to be much more growth ahead, Broadcom can make for a compelling investment to simply buy and hold. But at the same time, it’s also important to consider the risks ahead, especially as tariffs and trade wars could impact growth in the tech sector in the near future.

Earlier this year, Broadcom’s stock was underperforming the S&P 500 due to the uncertainty in the markets. While that looks like a distant memory right now, investors should brace for a possible slowdown for the stock as it’s trading at an elevated valuation and it may be due for a decline. Its track record may be impressive, but that by no means guarantees it’ll always be a market-beating stock.

I’d hold off on buying shares of Broadcom only because the markets appear to be a bit too bullish right now, and with high expectations priced in, there’s a lot of downside risk that comes with owning the stock. Broadcom isn’t a bad buy, but I think there are better AI stocks to invest in today.

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AI in health care could save lives and money — but not yet

Published

4 hours ago

July 19, 2025

Admin 5

Imagine walking into your doctor’s office feeling sick – and rather than flipping through pages of your medical history or running tests that take days, your doctor instantly pulls together data from your health records, genetic profile and wearable devices to help decipher what’s wrong.

This kind of rapid diagnosis is one of the big promises of artificial intelligence for use in health care. Proponents of the technology say that over the coming decades, AI has the potential to save hundreds of thousands, even millions of lives.

What’s more, a 2023 study found that if the health care industry significantly increased its use of AI, up to US$360 billion annually could be saved.

WATCH: How artificial intelligence impacted our lives in 2024 and what’s next

But though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low.

A 2024 American Medical Association survey found that 66% of U.S. physicians had used AI tools in some capacity, up from 38% in 2023. But most of it was for administrative or low-risk support. And although 43% of U.S. health care organizations had added or expanded AI use in 2024, many implementations are still exploratory, particularly when it comes to medical decisions and diagnoses.

I’m a professor and researcher who studies AI and health care analytics. I’ll try to explain why AI’s growth will be gradual, and how technical limitations and ethical concerns stand in the way of AI’s widespread adoption by the medical industry.

Inaccurate diagnoses, racial bias

Artificial intelligence excels at finding patterns in large sets of data. In medicine, these patterns could signal early signs of disease that a human physician might overlook – or indicate the best treatment option, based on how other patients with similar symptoms and backgrounds responded. Ultimately, this will lead to faster, more accurate diagnoses and more personalized care.

AI can also help hospitals run more efficiently by analyzing workflows, predicting staffing needs and scheduling surgeries so that precious resources, such as operating rooms, are used most effectively. By streamlining tasks that take hours of human effort, AI can let health care professionals focus more on direct patient care.

WATCH: What to know about an AI transcription tool that ‘hallucinates’ medical interactions

But for all its power, AI can make mistakes. Although these systems are trained on data from real patients, they can struggle when encountering something unusual, or when data doesn’t perfectly match the patient in front of them.

As a result, AI doesn’t always give an accurate diagnosis. This problem is called algorithmic drift – when AI systems perform well in controlled settings but lose accuracy in real-world situations.

Racial and ethnic bias is another issue. If data includes bias because it doesn’t include enough patients of certain racial or ethnic groups, then AI might give inaccurate recommendations for them, leading to misdiagnoses. Some evidence suggests this has already happened.

Humans and AI are beginning to work together at this Florida hospital.

Data-sharing concerns, unrealistic expectations

Health care systems are labyrinthian in their complexity. The prospect of integrating artificial intelligence into existing workflows is daunting; introducing a new technology like AI disrupts daily routines. Staff will need extra training to use AI tools effectively. Many hospitals, clinics and doctor’s offices simply don’t have the time, personnel, money or will to implement AI.

Also, many cutting-edge AI systems operate as opaque “black boxes.” They churn out recommendations, but even its developers might struggle to fully explain how. This opacity clashes with the needs of medicine, where decisions demand justification.

WATCH: As artificial intelligence rapidly advances, experts debate level of threat to humanity

But developers are often reluctant to disclose their proprietary algorithms or data sources, both to protect intellectual property and because the complexity can be hard to distill. The lack of transparency feeds skepticism among practitioners, which then slows regulatory approval and erodes trust in AI outputs. Many experts argue that transparency is not just an ethical nicety but a practical necessity for adoption in health care settings.

There are also privacy concerns; data sharing could threaten patient confidentiality. To train algorithms or make predictions, medical AI systems often require huge amounts of patient data. If not handled properly, AI could expose sensitive health information, whether through data breaches or unintended use of patient records.

For instance, a clinician using a cloud-based AI assistant to draft a note must ensure no unauthorized party can access that patient’s data. U.S. regulations such as the HIPAA law impose strict rules on health data sharing, which means AI developers need robust safeguards.

WATCH: How Russia is using artificial intelligence to interfere in election | PBS News

Privacy concerns also extend to patients’ trust: If people fear their medical data might be misused by an algorithm, they may be less forthcoming or even refuse AI-guided care.

The grand promise of AI is a formidable barrier in itself. Expectations are tremendous. AI is often portrayed as a magical solution that can diagnose any disease and revolutionize the health care industry overnight. Unrealistic assumptions like that often lead to disappointment. AI may not immediately deliver on its promises.

Finally, developing an AI system that works well involves a lot of trial and error. AI systems must go through rigorous testing to make certain they’re safe and effective. This takes years, and even after a system is approved, adjustments may be needed as it encounters new types of data and real-world situations.

AI could rapidly accelerate the discovery of new medications.

Incremental change

Today, hospitals are rapidly adopting AI scribes that listen during patient visits and automatically draft clinical notes, reducing paperwork and letting physicians spend more time with patients. Surveys show over 20% of physicians now use AI for writing progress notes or discharge summaries. AI is also becoming a quiet force in administrative work. Hospitals deploy AI chatbots to handle appointment scheduling, triage common patient questions and translate languages in real time.

Clinical uses of AI exist but are more limited. At some hospitals, AI is a second eye for radiologists looking for early signs of disease. But physicians are still reluctant to hand decisions over to machines; only about 12% of them currently rely on AI for diagnostic help.

Suffice to say that health care’s transition to AI will be incremental. Emerging technologies need time to mature, and the short-term needs of health care still outweigh long-term gains. In the meantime, AI’s potential to treat millions and save trillions awaits.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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