We recently compiled a list of the 10 Best AI Stocks to Buy According to Reddit. In this article, we are going to take a look at where NVIDIA Corp. (NASDAQ:NVDA) stands against the other AI stocks.

As artificial intelligence becomes more prevalent in current world affairs, new patterns concerning its research and development strategies are also emerging. Traditionally, academia has focused on basic research and education, while industry concentrated on applied research and commercialization. However, in recent years, the commercial sector’s dominance in AI investment and research has raised concerns about the balance of power. The shift of researchers from academia to industry has also raised questions.

Advanced AI systems increasingly require large amounts of data, compute power, and funding resources that industry actors possess in greater quantities than academia and nonprofits. Hence, AI research, which was originally the domain of academia in the early 2000s is now being taken over by industry.

We recently talked about this division in another article we published, 7 Most Popular AI Penny Stocks Under $5, here’s an excerpt from it:

“A recent study from Stanford University found that businesses train AI models faster than academic institutions. In 2023, the industry-trained AI neared 51 significant machine learning models, while academia managed only 15. This trend persisted in 2024 despite rising training costs. ChatGPT 4, the latest model of ChatGPT, cost about $80 million to train. Google’s Gemini Ultra cost around $191 million.”

A 2021 Stanford report says that the reason behind the blurring roles of academia and industry is that businesses come with affordable cloud computing, open-source libraries, and pre-trained models that incentivize university researchers to pursue commercial applications of their work. More and more industry papers are now appearing at conferences, raising concerns about applied research stifling long-term innovation or being biased toward corporate interests, all while also accelerating solutions for real-world problems.

A 2023 paper in the journal Science states that businesses attract 70% of the top talent with PhDs in AI today, as compared to just 20% 2 decades ago. The number of AI research faculty in academia has stagnated, while industry hiring has surged 8 times since 2006. Industry models being substantially larger (about 29 times), indicating superior computing power, is a huge reason behind this.

In 2021, US government agencies allocated a total of $1.5 billion for academic AI research, while Google spent the same amount on a single project in just one year.

The largest AI models are now developed in industry 96% of the time. Leading benchmarks are also primarily industry-driven, accounting for 91% of the total. Furthermore, the number of published papers with industry co-authors has nearly doubled since 2000.

Yet, there’s another anticipated shift as academic researchers are increasingly able to deploy their inventions in real-world settings. Duolingo, a language learning app developed by academics, is a successful example.

A distinguished MIT professor, Frédo Durand, believes academia can still be a driving force for innovation. He says that 25 years ago, the field of computer graphics in academia faced a similar resource imbalance where the industry created stunning visuals that academia couldn’t match. However, instead of trying to mimic industry, academia took a different path and focused on ideas like advanced lighting simulations, fluid dynamics, and machine learning for animation. These seemingly outlandish ideas eventually became the foundation of modern rendering and graphics hardware.

Durand believes this approach holds valuable lessons for AI research. He emphasizes the importance of academia pursuing unconventional approaches, openly sharing their work, and maintaining a sense of excitement about the field.

However, he recognizes the challenges for academia and suggests potential solutions including increased government funding for academic research, shared research infrastructure, and strategies to keep top AI talent within academia. While industry seems to be taking over AI in general, collaborative partnerships with academia could yield better results. In one way or the other, AI will remain a hot topic in the coming times.

Methodology

To compile our list, we sifted through several active subreddits to compile a list of 15 AI stocks to buy. We then selected the 10 stocks that were the most popular among elite hedge funds and that analysts were bullish on. The stocks are ranked in ascending order of the number of hedge funds that have stakes in them, as of Q2 2024.

Why are we interested in the stocks that hedge funds pile into? The reason is simple: our research has shown that we can outperform the market by imitating the top stock picks of the best hedge funds. Our quarterly newsletter’s strategy selects 14 small-cap and large-cap stocks every quarter and has returned 275% since May 2014, beating its benchmark by 150 percentage points (see more details here).

NVIDIA Corp. (NASDAQ:NVDA)

Number of Hedge Fund Holders: 179

NVIDIA Corp. (NASDAQ:NVDA) is a leading designer and manufacturer of graphics processing units (GPUs). These chips are essential for rendering graphics, but now they’re also used in artificial intelligence, machine learning, and data centers.

In FQ2 2025, the company recorded a rather higher year-over-year improvement of 122.40% in revenue. The revenue generated in this period was $30.04 billion, beating analyst estimates by $1.29 billion. Data center revenue was up 54% year-on-year, driven by strong demand for NVIDIA Hopper, GPU computing, and our networking platforms. Cloud service providers represented roughly 45%  of data center revenue.

Despite such growth, the company’s shares fell following this report. This is because investors had concerns about Blackwell chip production and slowing data center growth. Still, its shares were held by 179 hedge funds. Fisher Asset Management is the biggest shareholder with shares worth $11.54 billion, as of June 30. Later in late August, management approved a buyback of $50 billion in equity shares.

NVIDIA Corp. (NASDAQ:NVDA) faces pressure to launch new products to maintain investor confidence. Factors like delays in Blackwell chip production and concerns about high GPU prices are what impact the demand. While CUDA software is a competitive advantage, the company’s future success depends on continued innovation and effective AI monetization. Market experts are also forecasting a shift to robotics for the company’s growth. Management expects AI revenue to reach low-double-digit billions this year.

Aoris International Fund stated the following regarding NVIDIA Corporation (NASDAQ:NVDA) in its Q2 2024 investor letter:

“If Information Technology was the dominant sector for the quarter, NVIDIA Corporation (NASDAQ:NVDA), which is the largest supplier of microprocessors used for generative AI applications, was the dominant company. NVIDIA’s share price rose by a third in the quarter and has increased by 255% so far this year. Since the beginning of 2023, its market value has risen by 8.3x, or $4.3 trillion, making NVIDIA the third largest company in the world by this measure.

As a result of the unusually strong stock price performance from NVIDIA and a few other large companies, equity markets have become increasingly concentrated. You can see this in the chart below, which shows that on 30 June, 27% of the market value of the 500 largest US companies was attributable to just five companies, more than twice the average of the last 20 years.

The composition of the Aoris International Fund will always be very different to that of the broader equity market. There will be periods, such as the most recent quarter, where this contributes to our performance lagging that of our benchmark. When it comes to NVIDIA and other AI-centric companies, rapid growth is exciting, but it makes it difficult for us to judge what is normal. Our preference is to own established leading companies where we can make a more confident, evidence-based judgement about their growth and profitability.”

Overall NVDA ranks 3rd on our list of the best AI stocks to buy. While we acknowledge the potential of NVDA as an investment, our conviction lies in the belief that AI stocks hold great promise for delivering high returns and doing so within a shorter timeframe. If you are looking for an AI stock that is more promising than NVDA but that trades at less than 5 times its earnings, check out our report about the cheapest AI stock.

READ NEXT: $30 Trillion Opportunity: 15 Best Humanoid Robot Stocks to Buy According to Morgan Stanley and Jim Cramer Says NVIDIA ‘Has Become A Wasteland’.

Disclosure: None. This article is originally published at Insider Monkey.