Artificial intelligence (AI) has long drawn the interest of investors. AI has been used in various forms going back to the turn of the century for companies like Google parent Alphabet, and others. The generative AI boom is a much more recent breakthrough, and it is driving considerable investment returns for companies like Nvidia and Palantir Technologies.
The AI-based breakthroughs continue, and that is inspiring gains in other AI-driven tech stocks. Such gains may have just begun for one particular tech stock.
Image source: Getty Images.
A stock positioned to benefit from AI
The stock that is well-positioned to start benefiting from the latest AI changes is Qualcomm (QCOM -0.04%).
Admittedly, Qualcomm does not look much like a winner in AI. It released the Snapdragon 8 Gen 3 in the fall of 2023, empowering users to incorporate AI into their smartphones. Although revenue growth in handset sales eventually returned to double-digit levels, AI has failed to inspire the level of increased sales that Qualcomm experienced when users wanted to upgrade to 5G.
Moreover, after years of failed attempts, Apple appears finally ready to replace Qualcomm’s chips in the iPhone with chips of its own design. Assuming Apple does not reverse course, Qualcomm is set to lose one of its largest customers.
However, it may hearten investors to know that Qualcomm has long prepared for the day when it will depend less on smartphones.
Its Internet-of-Things (IoT) segment has applied its communications abilities to develop end-to-end IoT solutions for applications such as smart homes and industrial automation. Additionally, its communications and AI advances could make it a leader in self-driving through its automotive segment. In the first six months of fiscal 2025 (ended March 30), revenue for the IoT and automotive segments increased at annual rates of 31% and 60%, respectively, significantly outpacing the 12% growth in the handset segment over the same period.
Furthermore, Qualcomm has developed PC chips, making it a competitor to AMD and Intel. In the data center market, it has also partnered with Nvidia to develop custom chips that can support AI workloads within data centers.
Effects on financials
These recent advancements appear to be helping Qualcomm’s top and bottom lines more than its stock. In the first two quarters of fiscal 2025, revenue of nearly $23 billion increased by 17% compared with the same period in fiscal 2024. During the same period last year, revenue had risen by only 3% annually.
In the first six months of the year, Qualcomm kept a lid on operating expense growth. Still, with lower investment income and rising income tax expenses, its $6 billion in net income grew by 18% yearly.
Nonetheless, such improvements have not swayed investors. Over the last 12 months, Qualcomm stock dropped by almost 20%, though it is up 27% from the lows it reached in April.
Additionally, the stock sells at a 16 P/E ratio. Although that is not much lower than the average P/E ratio of 20 over the last five years, the earnings multiple suggests a lack of investor optimism about Qualcomm stock. And yet, this reasonable valuation could inspire a bull market in Qualcomm stock. Assuming its non-handset segments continue to grow at a rapid rate, it could start Qualcomm stock on a long-term growth trend.
The growth prospects of Qualcomm stock
Considering the state of Qualcomm’s business and stock, it has real potential to begin a long-term growth trend. Admittedly, a lackluster AI growth cycle and the likelihood of losing Apple as a customer have soured some investors on Qualcomm. However, the company has returned to revenue and earnings growth, and its burgeoning IoT and automotive segments deserve significant credit for this surge.
Ultimately, such improvements should not lead to a falling stock price and a rock-bottom valuation. Once Qualcomm’s role in AI becomes clearer, such conditions could inspire the beginning of a long-term uptrend in Qualcomm stock.
Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool’s board of directors. Will Healy has positions in Advanced Micro Devices, Intel, and Qualcomm. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Apple, Intel, Nvidia, Palantir Technologies, and Qualcomm. The Motley Fool recommends the following options: short August 2025 $24 calls on Intel. The Motley Fool has a disclosure policy.
Stony Brook University Receives $13.77M NSF Grant to Deploy a National Supercomputer to Democratize Access to Artificial Intelligence and Research Computing
This funding will be used to procure and operate a high-performance, highly energy-efficient computer designed to handle the growing needs of artificial intelligence research and other scientific fields that require large amounts of memory and computing power. By making this resource widely available to researchers, students, and educators across the country, the project will expand access to advanced tools, support groundbreaking discoveries, and train the next generation of scientists.
The new system will utilize low-cost and low-energy AmpereOne® M Advanced Reduced Instruction Set Computer (RISC) Machine processors that are designed to excel in artificial intelligence (AI) inference and imperfectly optimized workloads that presently characterize much of academic research computing. Multiple Qualcomm® Cloud AI inference accelerators will also increase energy efficiency, enabling the use of the largest AI models. The AmpereOne® M processors, in combination with the efficient generative AI inference performance and large memory capacity of the Qualcomm Cloud AI inference accelerators, will directly advance the mission of the NSF-led National Artificial Intelligence Research Resource (NAIRR).
This is the first deployment in academia of both of these technologies that have transformed computing in the commercial cloud. The new IACS-led supercomputer will efficiently execute diverse workloads in an energy- and cost-efficient manner, providing easily accessible, competitive and consistent performance without requiring sophisticated programming skills or knowledge of advanced hardware features.
“This project employs a comprehensive, multilayered strategy, with regional and national elements to ensure the widest possible benefits,” said IACS director Robert J. Harrison. “The team will collaborate with multiple initiatives and projects, to reach a broad audience that spans all experience levels from high school students beginning to explore science and technology to faculty members advancing innovation through scholarship and teaching.”
“The University at Buffalo is excited to partner with Stony Brook on this new project that will advance research, innovation and education by expanding the nation’s cyber-infrastructure to scientific disciplines that were not high performance computing-heavy prior to the AI boom, as well as expanding to non-R1 universities, which also didn’t have much of high-performance computing usage in the past,” says co-principal investigator Nikolay Simakov, a computational scientist at the University at Buffalo Center for Computational Research.
“AmpereOne® M delivers the performance, memory and energy footprint required for modern research workloads—helping democratize access to AI and data-driven science by lowering the barriers to large-scale compute,” said Jeff Wittich, Chief Product Officer at Ampere. “We look forward to working
with Stony Brook University to integrate this platform into research and education programs, accelerating discoveries in genomics, bioinformatics and AI.”
“Qualcomm Technologies is proud to contribute our expertise in high-performance, energy-efficient AI inference and scalable Qualcomm Cloud AI Inference solutions to this groundbreaking initiative,” said Dr. Richard Lethin, VP, Engineering, Qualcomm Technologies, Inc. “Our technologies enable seamless integration into a wide range of applications, enabling researchers and students to easily leverage advanced AI capabilities.”
Nationally and regionally, this funding will support a variety of projects, with an emphasis on fields of research that are not targeted by other national resources (e.g., life sciences and computational linguistics). In particular, the AmpereOne® M system will excel on high-throughput workloads common to genomics and bioinformatics research, AI/ML inference, and statistical analysis, among others. To help domain scientists achieve excellent performance on the system, software applications in these and related fields will be optimized for Ampere hardware and made readily available. This award reflects NSF’s statutory mission and that this initiative has been deemed worthy of support through evaluation using the foundation’s intellectual merit and broader-impacts review criteria.
The awarded funds are primarily for purchase of the supercomputer and first year activities, with additional funds to be provided for operations over five years, subject to external review.
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About the U.S. National Science Foundation (NSF)
The U.S. National Science Foundation (NSF) is an independent federal agency that supports science and engineering in all 50 states and U.S. territories. NSF was established in 1950 by Congress to:
Promote the progress of science.
Advance the national health, prosperity and welfare.
Secure the national defense.
NSF fulfills its mission chiefly by making grants. NSF’s investments account for about 25% of federal support to America’s colleges and universities for basic research: research driven by curiosity and discovery. They also support solutions-oriented research with the potential to produce advancements for the American people.
About Stony Brook University
Stony Brook University is New York’s flagship university and No. 1 public university. It is part of the State University of New York (SUNY) system. With more than 26,000 students, more than 3,000 faculty members, more than 225,000 alumni, a premier academic healthcare system and 18 NCAA Division I athletic programs, Stony Brook is a research-intensive distinguished center of innovation dedicated to addressing the world’s biggest challenges. The university embraces its mission to provide comprehensive undergraduate, graduate and professional education of the highest quality, and is ranked as the #58 overall university and #26 among public universities in the nation by U.S. News & World Report’s Best Colleges listing. Fostering a commitment to academic research and intellectual endeavors, Stony Brook’s membership in the Association of American Universities (AAU) places it among the top 71 research institutions in North America. The university’s distinguished faculty have earned esteemed awards such as the Nobel Prize, Pulitzer Prize, Indianapolis Prize for animal conservation, Abel Prize, Fields Medal and Breakthrough Prizes in Mathematics and Physics. Stony Brook has the responsibility of co-managing Brookhaven National Laboratory for the U.S. Department of Energy — one of only eight universities with a role in running a national laboratory. In 2023, Stony Brook was named the anchor institution for The New York Climate Exchange on Governors Island in New York City. Providing economic growth for neighboring communities and the wider geographic region, the university totals an impressive $8.93 billion in increased economic output on Long Island. Follow us on Facebook https://www.facebook.com/stonybrooku/ and X @stonybrooku.
Funding for a three-year research project slated to help develop a continuously updated database of available farmland.
RAINY RIVER — An ongoing research and innovation project that’s received provincial funding aims to use artificial intelligence to help create a database of available farmland.
The initiative is the brainchild of the Northern Ontario Farm Innovation Alliance, a not-for-profit that focuses on the agriculture sector.
“Our overall goal is to be able to create an online mapping tool that links both Crown land and private land that is available as a starting place for farmers who are looking for land,” Emily Seed, the alliance’s executive director, said in an interview with Newswatch.
“So, they know where to go looking and then they can then take the next steps in following up — whether that be an application to access that land or whether it be working with a realtor, or whatever that might look like.”
Access to usable farmland has been a longstanding issue when attempting to expand and better agriculture across northern Ontario, Seed said.
The AI component will be implemented both in the database’s front and back ends, she said. Users will be able to use a feature like a chatbot to help with searches and, behind the scenes, it will scrape data from sources like open-source databases and public information released by realtors to constantly update the tool.
That will “keep that information up-to-date and relevant in real time, so that it’s not just a standalone static tool,” Seed said.
In Northwestern Ontario, most agricultural land is southwest of Thunder Bay and in the Rainy River District.
“There’s a lot of barriers around things like Crown land access and then being able to find private land can also be a challenge,” she said. “This project is looking at how can we create some linkages in there to create long-term sustainability and make sure that people are able to find land available for agricultural use.”
“Trying to fill in some of those gaps when we’re talking about agricultural expansion and land use for agriculture in northern Ontario.”
The roughly $50,000 funding commitment from the provincial Ontario Agri-food Research Initiative will help support the project until 2027.
Seed said a publicly-available resource will likely be online more towards the end of the scheduled timeline.
“As we go through this project, we very much anticipate it to morph as we go and see what’s actually applicable,” she said. “AI is fairly new to us on that end, so we’re working with a few different developers and experts in the area to help us sort of navigate that side of things.”
“We will be working hard on it on the back end to morph it into something that’s a usable tool.”
Artificial intelligence is revolutionizing weather prediction around the world, as evidenced by the successful prediction this spring of a delayed onset of the monsoon in northeastern India.
The prediction gave millions of smallholder farmers the option of postponing planting to take better advantage of the rains or to plant different crops. Based on a preliminary phone survey, many farmers adjusted their planting as a result.
This AI-based weather model — a collaboration between the University of California, Berkeley, and the University of Chicago — paves the way for much better forecasts for hundreds of millions of farmers across the tropics and global south whose livelihoods depend on timing crop planting with the monsoon’s arrival. Nearly two-thirds of the world’s population live in regions of the tropics impacted by monsoon rains, whose arrival each year is being affected by climate change.
“This program harnesses the revolution in AI-based weather forecasting to predict the arrival of continuous rains, empowering farmers to plan agricultural activities with greater confidence and manage risks. We look forward to continuing to improve this effort in future years,” said Pramod Kumar Meherda, additional secretary at the Indian Ministry of Agriculture and Farmers’ Welfare.
The success of this AI prediction project — the largest targeted dissemination of AI weather forecasts to date — required a herculean effort by atmospheric scientists, AI experts, India’s Ministry of Agriculture and Farmers’ Welfare and a global nonprofit that supports smallholder farmers. Key to these predictions were daily climate data compiled and made publicly available by the U.S. National Oceanic and Atmospheric Administration.
To make the actual predictions, UChicago AI expert Pedram Hassanzadeh teamed up with Berkeley atmospheric scientist William Boos to evaluate and use global AI weather models that were developed independently by Google and the European Centre for Medium-range Weather Forecasts (ECMWF). Both of those models have been trained on 40 years of global climate data. To localize the models to India and correct biases in their predictions, the UC Berkeley and UChicago teams used statistics from 100 years of rainfall data from the India Meteorological Department.
A time-lapse video of the arrival of the Indian monsoon in the spring of 2025, showing how the rains (blue) stalled over southern India before proceeding northward. The AI-based weather model predicted the stall and the subsequent resumption of the northward progression of the rains.
Courtesy of the Human-Centered Weather Forecasts Initiative at the University of Chicago
The monsoon-onset forecasts, which differed for different regions, were delivered weekly to about 38 million farmers across 13 states in central and northeastern India — most of the core monsoon zone. These forecasts provided predictions up to four weeks in advance for the arrival of monsoon rains in particular regions, something that had not been done before in 150 years of monsoon forecasting, Boos said. Current numerical models, based on the physics of the atmosphere, typically provide reasonably accurate rainfall predictions no more than five days out.
When the monsoon hit southern India in early June, the AI-based model predicted that it would stop temporarily, something that was not predicted by any other available forecast. That’s what actually happened — it stalled for 20 days.
“Demonstrating that the long lead-time precipitation forecasts made by these AI models are of practical use in a tropical region where people live is a major step forward — no one really knew that before we did this work,” said Boos, a UC Berkeley professor of earth and planetary science.
Parasnath Tiwari, a farmer from Madhya Pradesh, received the forecast on his phone and was able to prepare earlier, he said. He decided to switch the types of crops he planted to more lucrative ones because the message gave him confidence that the season would be long enough.
“Before this, I mostly relied on my own experience and local knowledge to know when the monsoon would arrive,” said Tiwari. “The forecast about the arrival of the monsoon was accurate…. I have increased trust in the forecast, and I will rely on the information shared by scientists in the future.”
A false monsoon could mean disaster
Farmers in each region of northeastern India were updated on a mostly weekly basis between May and July about the probability that the true monsoon would start within a certain window of time. In a typical year, the monsoon arrives between June 15 and June 30 in the south and proceeds northward, bringing steady rain to most of the country by July. The AI model predicted the nearly three-week stall, which the Indian government communicated to the farmers.
The AI-based weather prediction model produced monsoon forecast maps like these every week, beginning May 20. The model divides India into a grid and estimates the likelihood that the monsoon rains will start in the next 1, 2, 3 or 4 weeks (bar chart) in each grid square. Each square is color coded according to which 2-week period had the highest combined probability of rain onset. A simpler message – which 2-week period is most likely to see the onset of rains – was communicated to smallholder farmers each week. The May 27 forecast, for example, shows that the monsoon rains have already arrived in the 3 southernmost regions (gray) but predicts that it will take 3 weeks — until June 18 — to move farther north (light orange) and at least one more week after that to reach the northernmost regions (yellow). The normal monsoon usually proceeds steadily northward, but this unexpected 20-day pause was accurately called by the AI model.
Courtesy of the Human-Centered Weather Forecasts Initiative at the University of Chicago
“We actually gave farmers probabilistic forecasts, telling them how likely it was that monsoon rains would start in a particular week,” Boos said. “By field-testing the SMS messages with farmers in advance, our team was able to tailor the language of the message so that they understood what was being predicted and the level of certainty of the prediction.”
Boos studies atmospheric dynamics, primarily the atmospheric wind patterns that deliver water in the form of monsoon rains to Central America, South America, Africa, Northern Australia and South Asia. The onset of these monsoons is important to farmers because, unlike in the U.S., the majority of farmers planting wheat, rice and other staple crops have small plots and cannot afford to irrigate if the rains fail.
“The classic catastrophe scenario is that you get a wet spell, it rains for a few days, they plant their seeds, they’re like, ‘Hooray, the rainy season has arrived,’ and then there’s 15 days of dryness afterward and all the seeds dry out and die,” Boos said. “They just spent an enormous amount of their savings to buy seed stock and plant it, and it died, and that’s a huge loss.”
Based on an analysis led by UChicago Nobel Prize-winning economist Michael Kremer, one of the leaders of the project, the researchers concluded that farmers in rural India could benefit economically from a better prediction of when the annual rains would truly begin. AI-based weather prediction models seemed like the place to start.
“We have been going through an AI-driven revolution since 2022, and AI models have shown promise for many one- to two-week forecasting applications. But their ability to predict complex phenomena — like the monsoon — was unclear, and frankly, unexpected,” Hassanzadeh said. The first revolution, beginning in the 1950s, focused on physics-based models and numerical simulations on supercomputers. This second revolution is being powered by AI models trained on observation-based data and capable of being run on a laptop.
Boos and Hassanzadeh tested more than half a dozen of the current AI weather prediction models that make predictions a month out and also predict rainfall, and chose the two best: Google’s NeuralGCM, for neural general circulation model, and the AI Forecasting System (AIFS) created by ECMWF.
Boos said that many of these models have been shown to predict global aspects of the climate as well as or better than earlier physics-based models, but few have been tasked with predictions of the seasonal onset of rains in a specific region.
Because each model had different strengths and weaknesses, the team mathematically blended Google’s NeuralGCM, ECMWF’s AIFS and historical rainfall data from the India Meteorological Department.
This blend produced a probabilistic model with a 30-day lead time, “merging multiple AI models and statistical methods to produce useful forecasts targeted at agriculture,” Boos said. “Forecasts of the start of sustained monsoon rains have historically been difficult or impossible to deliver locally with this much lead time, especially on such a large scale.”
Delivering the message
The Ministry of Agriculture and Farmers’ Welfare delivered the forecasts to the farmers directly using its SMS texting platform. The Government of Odisha also partnered with the research team to reach nearly 1 million more through a voice messaging platform. Precision Development (PxD), a global nonprofit supporting smallholder farmers in digital advisory services, led message design and testing.
Farmers throughout northwestern India received weekly forecasts about the arrival of the monsoon in the spring of 2025. Planting crops with the arrival of the monsoon is an annual ritual that can be upended when rains suddenly stop.
Photo courtesy of Precision Development, PxD
The project leaders concluded that farmers responded to these weather forecast messages. Based on early results from a phone survey, up to 55% recalled receiving weather forecasts on their phones, and among those who remembered specifically the monsoon onset forecasts, nearly half reported using the information to adjust their planting decisions. A majority of farmers also shared these messages with other farmers, suggesting an even greater reach and impact.
“I shared the monsoon arrival forecasts with other farmers in my locality. We usually talk to each other and share useful information that we come across,” Tiwari said. “Some farmers have benefited from the information I shared about the arrival of the monsoon. I feel that others will also start relying on this information and trust it for their agricultural decision-making.”
“Disseminating AI weather forecasts has an incredibly high return on investment, likely generating more than $100 for farmers for each dollar invested by the government,” said Kremer, co-director of UChicago’s Human-Centered Weather Forecasts Initiative. “India is leading the way in using AI to improve people’s lives across many sectors, including agriculture.”
The effort was partially supported by catalytic funding from AIM for Scale, a global initiative backed by the Gates Foundation and the United Arab Emirates, which works to scale up evidenced-backed, cost-effective agricultural innovations for the benefit of farmers in low- and middle-income countries. The researchers behind the project are now working with AIM for Scale to start similar programs in other low- and middle-income countries and to train government meteorologists in the global south on how to use AI models effectively.
“One of the things we would like to do for future years, hopefully for next year, is to be able to predict dry spells throughout the entire summer, issuing predictions of the likelihood of a dry period occurring within the next two to three weeks,” Boos said.
