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5 Top Artificial Intelligence (AI) Stocks Ready for a Bull Run

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While there is still uncertainty surrounding the implementation of tariffs by the Trump administration, at least one sector — artificial intelligence (AI) — is starting to regain its momentum and could be set up for another bull run. The technology is being hailed as a once-in-a-generation opportunity, and the early signs are that this could indeed be the case.

With AI still in its early innings, it’s not too late to invest in the sector. Let’s look at five AI stocks to consider buying right now.

Nvidia

Nvidia‘s (NVDA -0.22%) stock has already seen massive gains the past few years, but the bull case is far from over. The company’s graphics processing units (GPUs) are the main chips used for training large language models (LLMs), and it’s also seen strong traction in inference. These AI workloads both require a lot of processing power, which its GPUs provide.

The company captured an over 90% market share in the GPU space last quarter, in large thanks to its CUDA software platform, which makes it easy for developers to program its chips for various AI workloads. In the years following its launch, a collection of tools and libraries have also been built on top of CUDA that helps optimize Nvidia’s GPUs for AI tasks.

With the AI infrastructure buildout still appearing to be in its early stages, Nvidia continues to look well-positioned for the future. Meanwhile, it has also potential big markets emerging, such as the automobile space and autonomous driving.

AMD

While Nvidia dominates AI training, Advanced Micro Devices (AMD 3.87%) is carving out a space in AI inference. Inference is the process in which an AI model applies what it has learned during training to make real-time decisions. Over time, the inference market is expected to become much larger than the training market due to increased AI usage.

AMD’s ROCm software, meanwhile, is largely considered “good enough” for inference workloads, and cost-sensitive buyers are increasingly giving its MI300 chips a closer look. That’s already showing up in the numbers, with AMD’s data center revenue surging 57% last quarter to $3.7 billion.

Even modest market share gains from a smaller base could translate into meaningful top-line growth for AMD. Importantly, one of the largest AI model companies is now using AMD’s chips to handle a significant share of its inference traffic. Cloud giants are also using AMD’s GPUs for tasks like search and generative AI. Beyond GPUs, AMD remains a strong player in data center central processing units (CPUs), which is another area benefiting from rising AI infrastructure spend.

Taken altogether, AMD has a big AI opportunity in front of it.

Image source: Getty Images.

Alphabet

If you only listened to the naysayers, you would think Alphabet (GOOGL -0.91%) (GOOG -0.86%) is an AI loser, whose main search business is about to disappear. However, that would ignore the huge distribution and ad network advantages the company took decades to build.

Meanwhile, it has quietly positioned itself as an AI leader. Its Gemini model is widely considered one of the best and getting better. It’s now helping power its search business, and it’s added innovative elements that can help monetize AI, such as “Shop with AI,” which allows users to find products simply by describing them; and a new virtual try-on feature.

Google Cloud, meanwhile, has been a strong growth driver, and is now profitable after years of heavy investment. That segment grew revenue by 28% last quarter and continues to win share in the cloud computing market. The company also has developed its own custom AI chips, which OpenAI recently began testing as an alternative to Nvidia.

Alphabet also has exposure to autonomous driving through Waymo, which now operates a paid robotaxi service in multiple cities, and quantum computing with its Willow chip.

Alphabet is one of the world’s most innovative companies and has a long runway of continued growth still in front of it.

Pinterest

Pinterest (PINS -1.98%) has leaned heavily into AI to go from simply an online vision board to a more engaging platform that is shoppable. A key part of its transformation is its multimodal AI model that is trained on both images and text. This helps power its visual search feature, as well as generate more personalized recommendations. Meanwhile, on the backend, its Performance+ platform combines AI and automation to help advertisers run better campaigns.

The strategy is working, as the platform is both gaining more users and monetizing them better. Last quarter, it grew its monthly active users by 10% to 570 million. Much of that user growth is coming from emerging markets. Through the help of Google’s strong global ad network, with whom it’s partnered, Pinterest is also much better at monetizing these users. In the first quarter, its “rest of world” segment’s average revenue per user (ARPU) jumped 29%, while overall segment revenue soared 49%.

With a large but still undermonetized user base, Pinterest has a lot of growth ahead.

Salesforce

Salesforce (CRM -1.62%) is no stranger to innovation, being one of the first large companies to embrace the software-as-a-service (SaaS) model. A leader in customer relationship management (CRM) software, the company is now looking to become a leader in agentic AI and digital labor.

Salesforce’s CRM platform was built to give its users a unified view of their siloed data all in one place. This helped create efficiencies and reduce costs by giving real-time insights and allowing for improved forecasting.

With the advent of AI, it is now looking to use its platform to create a digital workforce of AI agents that can complete tasks with little human supervision. It believes that the combination of apps, data, automation, and metadata into a single framework it calls ADAM will give it a leg up in this new agentic AI race.

The company has a huge installed user base, and its new Agentforce platform is off to a good start with over 4,000 paying customers since its October launch. With its consumption-based product, the company has a huge opportunity ahead with AI agents.



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Tampa General Hospital, USF developing artificial intelligence to monitor NICU baby’s pain in real-time

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Researchers are looking to use artificial intelligence to detect when a baby is in pain.

The backstory:

A baby’s cry is enough to alert anyone that something’s wrong. But for some of the most critical babies in hospital care, they can’t cry when they are hurting.

READ: FDA approves first AI tool to predict breast cancer risk

“As a bedside nurse, it is very hard. You are trying to read from the signals from the baby,” said Marcia Kneusel, a clinical research nurse with TGH and USF Muma NICU.

With more than 20 years working in the neonatal intensive care unit, Kneusel said nurses read vital signs and rely on their experience to care for the infants.

“However, it really, it’s not as clearly defined as if you had a machine that could do that for you,” she said.

MORE: USF doctor enters final year of research to see if AI can detect vocal diseases

Big picture view:

That’s where a study by the University of South Florida comes in. USF is working with TGH to develop artificial intelligence to detect a baby’s pain in real-time.

“We’re going to have a camera system basically facing the infant. And the camera system will be able to look at the facial expression, body motion, and hear the crying sound, and also getting the vital signal,” said Yu Sun, a robotics and AI professor at USF.

Yu heads up research on USF’s AI study, and he said it’s part of a two-year $1.2 million National Institutes of Health grant.

He said the study will capture data by recording video of the babies before a procedure for a baseline. Video will record the babies for 72 hours after the procedure, then be loaded into a computer to create the AI program. It will help tell the computer how to use the same basic signals a nurse looks at to pinpoint pain.

READ: These states are spending the most on health insurance, study shows

“Then there’s alarm will be sent to the nurse, the nurse will come and check the situation, decide how to treat the pain,” said Sun.

What they’re saying:

Kneusel said there’s been a lot of change over the years in the NICU world with how medical professionals handle infant pain.

“There was a time period we just gave lots of meds, and then we realized that that wasn’t a good thing. And so we switched to as many non-pharmacological agents as we could, but then, you know, our baby’s in pain. So, I’ve seen a lot of change,” said Kneusel.

Why you should care:

Nurses like Kneusel said the study could change their care for the better.

“I’ve been in this world for a long time, and these babies are dear to me. You really don’t want to see them in pain, and you don’t want to do anything that isn’t in their best interest,” said Kneusel.

MORE: California woman gets married after lifesaving surgery to remove 40-pound tumor

USF said there are 120 babies participating in the study, not just at TGH but also at Stanford University Hospital in California and Inova Hospital in Virginia.

What’s next:

Sun said the study is in the first phase of gathering the technological data and developing the AI model. The next phase will be clinical trials for real world testing in hospital settings, and it would be through a $4 million NIH grant, Sun said.

The Source: The information used in this story was gathered by FOX13’s Briona Arradondo from the University of South Florida and Tampa General Hospital.

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Ramp Debuts AI Agents Designed for Company Controllers

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Financial operations platform Ramp has debuted its first artificial intelligence (AI) agents.

The new offering is designed for controllers, helping them to automatically enforce company expense policies, block unauthorized spending, and stop fraud, and is the first in a series of agents slated for release this year, the company said in a Thursday (July 10) news release.

“Finance teams are being asked to do more with less, yet the function remains largely manual,” Ramp said in the release. “Teams using legacy platforms today spend up to 70% of their time on tasks like expense review, policy enforcement, and compliance audits. As a result, 59% of professionals in controllership roles report making several errors each month.”

Ramp says its controller-centric agents solve these issues by doing away with redundant tasks, and working autonomously to go over expenses and enforce policy, applying “context-aware, human-like” reasoning to manage entire workflows on their own.

“Unlike traditional automation that relies on basic rules and conditional logic, these agents reason and act on behalf of the finance team, working independently to enforce spend policies at scale, immediately prevent violations, and continuously improve company spending guidelines,” the release added.

PYMNTS wrote earlier this week about the “promise of agentic AI,” systems that not only generate content or parse data, but move beyond passive tasks to make decisions, initiate workflows and even interact with other software to complete projects.

“It’s AI not just with brains, but with agency,” that report said.

Industries including finance, logistics and healthcare are using these tools for things like booking meetings, processing invoices or managing entire workflows autonomously.

But although some corporate leaders might hold lofty views for autonomous AI, the latest PYMNTS Intelligence in the June 2025 CAIO Report, “AI at the Crossroads: Agentic Ambitions Meet Operational Realities,” shows a trust gap among executives when it comes to agentic AI that highlights serious concerns about accountability and compliance.

“However, full-scale enterprise adoption remains limited,” PYMNTS wrote. “Despite growing capabilities, agentic AI is being deployed in experimental or limited pilot settings, with the majority of systems operating under human supervision.”

But what makes mid-market companies uneasy about tapping into the power of autonomous AI? The answer is strategic and psychological, PYMNTS added, noting that while the technological potential is enormous, the readiness of systems (and humans) is much murkier.

“For AI to take action autonomously, executives must trust not just the output, but the entire decision-making process behind it. That trust is hard to earn — and easy to lose,” PYMNTS wrote, noting that the research “found that 80% of high-automation enterprises cite data security and privacy as their top concern with agentic AI.”



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How automation is using the latest technology across various sectors

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Artificial Intelligence and automation are often used interchangeably. While the technologies are similar, the concepts are different. Automation is often used to reduce human labor for routine or predictable tasks, while A.I. simulates human intelligence that can eventually act independently.

“Artificial intelligence is a way of making workers more productive, and whether or not that enhanced productivity leads to more jobs or less jobs really depends on a field-by-field basis,” said senior advisor Gregory Allen with the Wadhwani A.I. center at the Center for Strategic and International Studies. “Past examples of automation, such as agriculture, in the 1920s, roughly one out of every three workers in America worked on a farm. And there was about 100 million Americans then. Fast forward to today, and we have a country of more than 300 million people, but less than 1% of Americans do their work on a farm.”

A similar trend happened throughout the manufacturing sector. At the end of the year 2000, there were more than 17 million manufacturing workers according to the U.S. Bureau of Labor statistics and the Federal Reserve Bank of St. Louis. As of June, there are 12.7 million workers. Research from the University of Chicago found, while automation had little effect on overall employment, robots did impact the manufacturing sector. 

“Tractors made farmers vastly more productive, but that didn’t result in more farming jobs. It just resulted in much more productivity in agriculture,” Allen said.

ARTIFICIAL INTELLIGENCE DRIVES DEMAND FOR ELECTRIC GRID UPDATE

Researchers are able to analyze the performance of Major League Baseball pitchers by using A.I. algorithms and stadium camera systems. (University of Waterloo / Fox News)

According to our Fox News Polling, just 3% of voters expressed fear over A.I.’s threat to jobs when asked about their first reaction to the technology without a listed set of responses. Overall, 43% gave negative reviews while 26% reacted positively.

Robots now are being trained to work alongside humans. Some have been built to help with household chores, address worker shortages in certain sectors and even participate in robotic sporting events.

The most recent data from the International Federation of Robotics found more than 4 million robots working in factories around the world in 2023. 70% of new robots deployed that year, began work alongside humans in Asia. Many of those now incorporate artificial intelligence to enhance productivity.

“We’re seeing a labor shortage actually in many industries, automotive, transportation and so on, where the older generation is going into retirement. The middle generation is not interested in those tasks anymore and the younger generation for sure wants to do other things,” Arnaud Robert with Hexagon Robotics Division told Reuters.

Hexagon is developing a robot called AEON. The humanoid is built to work in live industrial settings and has an A.I. driven system with special intelligence. Its wheels help it move four times faster than humans typically walk. The bot can also go up steps while mapping its surroundings with 22 sensors.

ARTIFICIAL INTELLIGENCE FUELS BIG TECH PARTNERSHIPS WITH NUCLEAR ENERGY PRODUCERS

gif of AI rendering of pitching throwing a ball

Researchers are able to create 3D models of pitchers, which athletes and trainers could study from multiple angles. (University of Waterloo)

“What you see with technology waves is that there is an adjustment that the economy has to make, but ultimately, it makes our economy more dynamic,” White House A.I. and Crypto Czar David Sacks said. “It increases the wealth of our economy and the size of our economy, and it ultimately improves productivity and wages.”

Driverless cars are also using A.I. to safely hit the road. Waymo uses detailed maps and real-time sensor data to determine its location at all times.

“The more they send these vehicles out with a bunch of sensors that are gathering data as they drive every additional mile, they’re creating more data for that training data set,” Allen said.

Even major league sports are using automation, and in some cases artificial intelligence. Researchers at the University of Waterloo in Canada are using A.I. algorithms and stadium camera systems to analyze Major League Baseball pitcher performance. The Baltimore Orioles joint-funded the project called Pitchernet, which could help improve form and prevent injuries. Using Hawk-Eye Innovations camera systems and smartphone video, researchers created 3D models of pitchers that athletes and trainers could study from multiple angles. Unlike most video, the models remove blurriness, giving a clearer view of the pitcher’s movements. Researchers are also exploring using the Pitchernet technology in batting and other sports like hockey and basketball.

ELON MUSK PREDICTS ROBOTS WILL OUTSHINE EVEN THE BEST SURGEONS WITHIN 5 YEARS

graphic overview of ptichernet system of baseball player's pitching skills

Overview of a PitcherNet System graphics analyzing a pitcher’s baseball throw. (University of Waterloo)

The same technology is also being used as part of testing for an Automated Ball-Strike System, or ABS. Triple-A minor league teams have been using the so-called robot umpires for the past few seasons. Teams tested both situations in which the technology called every pitch and when it was used as challenge system. Major League Baseball also began testing the challenge system in 13 of its spring training parks across Florida and Arizona this February and March.

Each team started a game with two challenges. The batter, pitcher and catcher were the only players who could contest a ball-strike call. Teams lost a challenge if the umpire’s original call was confirmed. The system allowed umpires to keep their jobs, while strike zone calls were slightly more accurate. According to MLB, just 2.6% of calls were challenged throughout spring training games that incorporated ABS. 52.2% of those challenges were overturned. Catchers had the highest success rate at 56%, followed by batters at 50% and pitchers at 41%.

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Triple-A announced last summer it would shift to a full challenge system. MLB commissioner Rob Manfred said in June, MLB could incorporate the automated system into its regular season as soon as 2026. The Athletic reports, major league teams would use the same challenge system from spring training, with human umpires still making the majority of the calls.

Many companies across other sectors agree that machines should not go unsupervised.

“I think that we should always ensure that AI remains under human control,” Microsoft Vice Chair and President Brad Smith said.  “One of first proposals we made early in 2023 was to insure that A.I., always has an off switch, that it has an emergency brake. Now that’s the way high-speed trains work. That’s the way the school buses, we put our children on, work. Let’s ensure that AI works this way as well.”



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