Artificial intelligence (AI) is playing an expanding — and often invisible — role in America’s legal system. While AI tools are being used to inform criminal investigations, there is often no way for defendants to challenge their digital accuser or even know what role it played in the case. 

“Under current law in most jurisdictions, [prosecutors] don’t have to disclose artificial intelligence use to the judge or defense counsel,” Rebecca Wexler, professor of law at the University of California, Berkeley, told The Hill. 

AI and machine learning tools are being deployed by police and prosecutors to identify faces, weapons, license plates and objects at crime scenes, survey live feeds for suspicious behavior, enhance DNA analysis, direct police to gunshots, determine how likely a defendant is to skip bail, forecast crime and process evidence, according to the National Institute of Justice.

But trade secrets laws are blocking public scrutiny of how these tools work, creating a “black box” in the criminal justice system, with no guardrails for how AI can be used and when it must be disclosed. 

“There’s no standard at any level,” said Brandon Garrett of Duke University School of Law. “The big picture point is that just like there need to be standards for the product, there needs to be standards on how and when they’re used.”

Concerns about AI in the criminal justice system are compounded by research showing how tools like facial recognition are prone to bias — for example, misidentifying people of color because it was trained on mostly white faces. 

For the past three congresses, Rep. Mark Takano (D-Calif.), joined twice by Rep. Dwight Evans (D-Pa.), has introduced legislation that addresses issues of testing and transparency in criminal justice, so far failing to garner enough traction to pass the bill. 

“Nobody had really addressed this particular issue of black box technologies that are being marketed to prosecutors, police and law enforcement folks on the basis of their alleged efficacy,” Takano said in an interview with The Hill. 

“Every American wants to feel that they can get a fair trial if they are accused of something wrong — that’s one of the hallmarks of being an American,” he added. “But what do you do when the witness and evidence brought against you is a machine protected as a trade secret, how do you contend with that?” 

The term artificial intelligence refers to the broad discipline of making machines that learn from experience and mimic humanlike intelligence in making predictions. Unlike other forensic technologies law enforcement uses, AI is responsive to its environment and sensitive to its users, meaning it can produce different outcomes throughout its life cycle. 

Without testing and transparency, these nuances are lost and the likelihood of error isn’t accounted for, Garrett said. 

Currently, public officials are essentially taking private firms at their word that their technologies are as robust or nuanced as advertised, despite expanding research exposing the potential pitfalls of this approach. 

Take one of its most common use cases: facial recognition. 

Clearview AI, one of the leading contractors for law enforcement, has scraped billions of publicly available social media posts of Americans’ faces to train its AI, for example.

This initial training teaches an AI program a set of patterns and rules that will guide its predictions. Developers tweak the program by instructing it to consider some factors more than others. Theoretically, the AI becomes an expert at matching human faces — at a speed that far outpaces human capacity. 

But when the machine goes out into the field, it may see a population that looks different from its training set. Individual facial recognition algorithms generate notably different findings from their peer products, a 2019 National Institute for Standards and Technology (NIST) report found.

Researchers have found that facial recognition AI has concerning failure rates when handling images of Black Americans, especially Black women, either failing to identify a person at all or making an inaccurate match.

The Gender Shades project from the Massachusetts Institute of Technology’s Media Lab found consistently high error rates, as high as 33 percent, across AI recognition of females with darker skin tones.

Products from Amazon, IBM and Microsoft each exhibited this problem in the study, and some of their products have since been taken off the market. Multiple academic institutions — George Mason University, the University of Texas at Dallas, and New York University (NYU) — have corroborated persistent demographic disparities in facial identification rates.

But studies like the Gender Shades project test facial recognition accuracy on comparatively ideal image quality. 

Footage used by police is not often ideal, and a selling point of AI to law enforcement is that it can make use of poor-quality data previously useless to human investigators or traditional forensic algorithms. 

To account for the possibility of faulty matches, police commonly treat facial recognition matches as a tip for further investigation and not evidence against the person identified. 

But tips still narrow law enforcement’s focus in an investigation, said Wexler at Berkeley. If supporting evidence against a suspect is found, that becomes the basis for an indictment while the use of AI is never disclosed. 

That means neither the defense, the prosecution nor the judge often know that police have used AI to guide an investigation, and they never get the chance to interrogate its findings. 

“At no point, from pretrial investigations through to conviction, does law enforcement have any constitutional, legal, or formal ethical obligation to affirmatively investigate evidence of innocence,” Wexler said at a Senate Judiciary Committee hearing in January. 

Creators of the forensic machine learning models have defended the opaqueness of their products by arguing that disclosure will effectively require revealing trade secrets to competitors in their industry.

However, the companies have been largely supportive of government regulation of its use in criminal justice settings. 

Amazon’s Rekognition software “should only be used to narrow the field of potential matches,” according to its site. 

Matt Wood, vice president of product at Amazon Web Services, is quoted by the company as saying it’s a “very reasonable idea for the government to weigh in and specify what temperature (or confidence levels) it wants law enforcement agencies to meet.”

IBM sunsetted its AI facial recognition products shortly after the Gender Shades study, and IBM CEO Arvind Krishna wrote a letter to Congress calling for “precision regulation” of the tech. 

Microsoft discontinued sale of facial recognition AI to police departments in 2020, saying it wouldn’t budge “until strong regulation [on facial recognition AI], grounded in human rights, has been enacted.”

In March, Clearview AI obtained “awardable” status from the Department of Defense’s Tradewinds Solutions Marketplace, a vetting body that creates a suite of technologies ready for “rapid acquisition.” 

In a statement to The Hill, Clearview AI CEO Hoan Ton-That said his product survived testing from NIST with higher than a 99 percent accuracy rate “across all demographics.”  

“As a person of mixed race, having non-biased technology is important to me,” he said. 

“According to the Innocence Project, 70% of wrongful convictions come from eyewitness lineups. Technology like Clearview AI is much more accurate than the human eye, and can be used to exonerate people and eliminate bias from the criminal justice system,” he added. 

Still, defense counsel faces a high bar to prove errors in an AI lead. They often must show that AI source code was likely to be “necessary” for a criminal case, a higher standard than for most subpoenas in search of evidence.

“The reason that is so troubling is that it creates a Catch-22. It may be impossible to prove that information you’ve never seen is necessary to a case,” Wexler said. 

Defense attorneys have already lost major cases seeking disclosure of non-AI algorithm source code. And in addition to fighting the “necessary” standard, defense counsel often meets resistance from the state, said Mitha Nandagopalan, staff attorney at the Innocence Project.

“In pretty much any case I’ve touched that has involved a request for underlying source code or machine learning model, prosecution has opposed it,” Nandagopalan told The Hill.  

Judges frequently don’t see the relevance if AI-generated leads are not considered evidence, she said. And in her work as a defense attorney in Albuquerque, N.M., Nandagopalan said police often fail to disclose it. 

“In a lot of cases, we got police reports that said, ‘We looked at the surveillance footage from the store, and using state mugshot databases or other databases, we found a match,’” she said. “Nowhere in their report did it say, ‘We used AI recognition software to identify the suspect.’”

Those concerns extend well beyond facial recognition, encompassing the risk of “dirty data” perpetuating injustices in various uses of AI tools. 

The potential for biased AI predictions informed by dirty data is “enormous,” said Vincent Southerland, director of the Center for Race, Inequality and the law at NYU, in an article for the American Civil Liberties Union. 

Southerland cited police behavior in Ferguson, Mo.; Newark, N.J.; Baltimore; and New York City as examples of biased policing that would give AI “a distorted picture” in its handling of risk assessments or crime forecasting, for example.

Crime forecasting refers to AI that takes historical crime data in a community and makes predictions of where future criminal behavior will take place, allowing police, theoretically, to efficiently allocate scarce resources. 

Risk assessments broadly refer to AI’s assignment of a risk score to a person based on factors like their criminal history. These scores inform decisions on worthiness for bail, parole and even the severity of sentences.

“The failure to adequately interrogate and reform police data creation and collection practices elevates the risks of skewing predictive policing systems and creating lasting consequences that will permeate throughout the criminal justice system and society more widely,” an NYU Law Review case study said.

Ideally, government users of AI would take an informed approach to AI’s conclusions that accounts for its specific features and limitations, Karen Howard, director of science, technology and analytics assessment at the Government Accountability Office, told The Hill.

But that’s often not possible as long as AI remains in a “black box,” she said, as public officials can’t even confirm the tools are reliable and unbiased in the first place.

Testifying before the Senate Judiciary Committee in January, Howard said any AI program in use by law enforcement without independent review “should set off alarms.” 

“The riskiest AI tool would be one where the training data set is not understood, not representative and it’s being handled by somebody who really doesn’t understand what the technology is and isn’t telling them,” she said. 

The Biden administration has announced a series of efforts to ensure AI tools aren’t hurting Americans, both in the legal system and elsewhere. 

The National Institute for Standards and Technology released an AI Risk Management Framework in January 2023. 

“Without proper controls, AI systems can amplify, perpetuate, or exacerbate inequitable or undesirable outcomes for individuals and communities,” it said. “With proper controls, AI systems can mitigate and manage inequitable outcomes.”

The White House Office of Science and Technology Policy also released the Blueprint for an AI Bill of Rights in October 2022, which includes “algorithmic discrimination protections.”

However, these measures do not have the force of law, and they place no binding mandate for testing or transparency on AI products the government uses in the criminal justice system.

The legislation sponsored by Takano and Evans would prohibit the use of trade secret privilege to deny cross-examination of forensic AI to defense attorneys, direct NIST to establish a testing program for forensic algorithms adopted by law enforcement and mandate vetting before use. 

“AI would be another layer of source code that would be required to be open under my bill,” Takano said. “That technology is not infallible, that technology should be subjected to tests of reliability and fairness.”

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How AI risks creating a black box at the heart of US legal system - The Hill

Julia is a partner with PwC Singapores Digital Regulatory Risk & Compliance practice. She is a risk and transformation specialist advising banks, wealth managers, capital markets intermediaries and non-FIs on digital transformation strategy and execution, risk & regulations, controls and governance.

With over 20 years of experience, she has led core banking, compliance, operational risk, finance transformation programmes, corporate governance, ERM and internal audit reviews. Clients value her partnership to connect the dots and co-create pragmatic, best-fit solutions for them.

Prior to her current role, she has assumed various leadership roles at firm-level as private banking industry lead, banking governance, risk and compliance and digital transformation services leader.

Outside of client work, Julia is active in giving back with board directorship and mentoring. She currently serves on the Board of Trustees for ISEAS (Institute of Southeast Asian Studies), the board of Building Construction Authority and committee of the Governance and Audit Committee of Singapore Heart Foundation. She is a mentor with Young Women Leadership Connection and Mentorshub.

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Measuring Success: Balancing Efficiency and Effectiveness in the Age of Artificial Intelligence - Regulation Asia

Victor Oshmyan, a sophomore at Niwot High, clicks his mouse to engage a car. Its aimed at little model pedestrians. But he stops the car in the nick of time before it runs them over.

It actually sees the pedestrians, but the AI model isn't strong enough to recognize that all of them are people, so it was just going to run them over, he said during a demonstration with a toy car and the figurines.

But then Oshmyan shows how the car fully stops when it recognizes him, a real person, just like hes programmed.

It didn't move because it didn't want to run me over, he said.

Oshmyan is an early adopter, one of a group of students so intrigued by artificial intelligence that theyre on a special after-school AI project team at the St. Vrain Valley School Districts Innovation Center in Longmont. They develop and design products for clients and get paid to do it. These students are at the vanguard of discovering how artificial intelligence works in its many forms but are also helping educators learn how it may change instruction.

When artificial intelligence came on the scene, Colorados school districts tended to fall into three buckets. Some immediately banned any use of it. The vast majority seemed interested but too bogged down in other challenges.

A couple of districts blasted out of the gates trying to teach their students about AI like St. Vrain.

Teenagers already tend to know more about AI than adults, even if just for things like altering their image to look like a cute animal. Students are getting the message online that this technology will change the way we live and the world of work.

And then they walk into school and we tell them, Whatever you do, don't use this, said Rebecca Holmes, CEO and president of the Colorado Education Initiative, which has created a task force to help districts incorporate AI. Its just cognitive dissonance to the teenage brain. Its the kind of eye roll from teenagers that we should really pay attention to because they're right.

Oshmyan used a program called AutoAutoAI to code the car to detect a person with an image he plotted. He also programmed it to swerve at yellow lights and stop and play Happy Birthday on red lights. Oshmyan is also working on a pizza bot to take orders.

It will help pizza workers not spend so much time on the phone, he said.

Nearby, his classmate Malcom Smith demonstrates a classification system he built using AI, which can solve patterns at incredibly fast speeds. It can identify hundreds of unique parts for Vex Robotics that younger students use to build. His project is to help students, but also their teachers.

That's a lot of pressure on the teacher because the teacher has to know all of these different parts and that can be very tricky, he said.

Smith holds up a Lego-like piece. A machine voice identifies the part and describes what the part can be used for.

This is the kind of real-world learning that AI can foster.

I feel like AI is a powerful tool that will be incorporated in the future a lot, said Oshmyan. And I feel like understanding it better will help us work with it better so it doesn't just take over. And I feel like it's better to understand it right now than later.

Another student said hed love to one day develop an AI that could help recognize cancerous moles.

Marek Pearl, 15, who is more interested in engineering robotics rather than a career in AI, still decided to take a course called Intro to AI, which includes the ethics of AI. It sounded interesting and he wanted to learn how it could help him in his daily life like writing emails. But heres how he may use it at school: If the assignment is to write a short paragraph on the War of 1812 and some major historical figures, hed ask for an AI platform:

What was the Battle of 1812?

Like other students, Pearl said AI tends to explain things in a simpler way to start out with.

I try my best to use AI as an inspiration, rather than a writing tool, he said.

Hed get the historical figures' names and then do his own research on each individual. A lot of the students say they use it this way. Shaffer Piersol, a freshman at Niwot High School, uses AI to help her study. Many students use the Quizlet studying tool, which now employs artificial intelligence.

I don't want to go through all 20 pages of a textbook to make 10 Quizlet questions. So Quizlet will be like, Hey, if you just upload the PDF, well, we can do it for you.

Piersol has strong feelings about using AI to rip off artwork, something shes seen done to her favorite artist, which is not cool to do.

But for a lot of other teenagers, the temptation to cheat is real.

A lot of my classmates use ChatGPT to write their essays, so no matter how I think, people are always going to do whatever they want.

Pearl, on the other hand, thinks cheating is not easy for students to get away with.

Almost all teachers can tell, like, if they've seen your writing before, they know, huh, that person doesn't write like that.

Teachers have told students explicitly, that if they use ChatGPT to write their essays, theyre getting an F.

Nicholas Umpierrez, a senior, is working with his team on a project for the city of Longmont building an underwater robot for water collection. He wants to know the ideal flow rate the machine should use. Hes used ChatGPT for coding already so decides to ask the AI about the flow rate. AI spits out an answer. Umpierrez gives it more parameters; he gets the same answer.

He decides he should probably go back to the scientists in the city to get more information.

Nicholas teacher Nathan Wilcox interjects, recognizing an AI teachable moment. He praises Umpierrez for realizing that ChatGPT has huge limitations when it comes to hyper-specific questions.

Do we know that's the most recent, new, data? Do we know that that's the optimal data? Do we know if that data was collected related to water sampling for this type of purpose? the teacher asks.

Instead, its the Longmont scientists who will know the ideal flow rate based on research studies. The exchange is yet another opportunity for learning about an extraordinarily powerful tool that is rapidly changing K-12 education.

Joe McBreen, SVVSDs assistant superintendent of innovation, said districts leaning into AI doesnt mean accepting everything about it, lock, stock, and barrel. But he said AI is only going to become more pervasive and powerful.

I think we're ethically and morally compelled to prepare our kids for a competitive future, where they not only are aware of AI but they're empowered with next-level exposure and experiences so that they can confidently live in this world, he said. That begins today.

Schools can start by teaching kids the difference between a traditional search engine and generative AI, which can include images, music, and code, or large language model AIs which produce text and dont require computer science knowledge to use.

The world's most popular programming language right now is English. Literally, you can talk to the ChatGPT and get the code, McBreen said. And so what sorts of opportunities does that open up?

Other AI models can be used for data prediction and image recognition.

Districts must start with a set of educators who are aware, empowered, and skilled enough to help students, said McBreen.

St. Vrain, one of the first districts to offer professional development to educators, launched a soft introduction to AI for teachers, encouraging them to complete a bingo board that has them use AI in fun ways like finding a recipe or planning a trip. They earn credits for completing the cards. Along with coaching on safety and privacy in using AI, the district is continually analyzing whether there are gaps in its current cheating and plagiarism policy.

The district has created a task force of teachers and district leaders who are putting together an eighth-grade introduction to technology that focuses heavily on AI. But theyd eventually like to have exposure to AI in all grades.

Recently, Deagan Andrews, a curriculum leader for Greeley Evans School District 6, chatted with McBreen about the best way to begin developing an AI pathway for his district.

AI is painted with a broad brush, explained McBreen, but in reality, there are many different strands to it: from autonomous driving and AI in cybersecurity to how people use large language models to accelerate what they do. Other questions to consider: What is the right level of programming knowledge for students? How can they use AI to advance their own projects?

A new nationwide survey by the Center for Democracy and Technology finds massive changes in teacher and student use of generative AI. However, it shows teachers struggling with navigating many questions around responsible and safe student use and teachers distrustful of students resulting in more students getting into trouble. Many educators are stuck at that level, never mind how to teach students how to use AI as a tool and for application-based questions, much like the calculator did. The vast majority of educators are unequipped.

Greeley districts Andrews believes schools never really helped students effectively leverage calculators or even Google.

And now we take something that's 10 times more sophisticated. How are we going to help support students to really leverage it?

Thats where the Colorado Education Initiative comes in. The nonprofit will produce a statewide plan this summer identifying AI policies and practices needed for schools, as well as training for teachers. Rebecca Holmes is aware equity gaps are already starting.

If a kid happens to be in a district that's forward moving on something, they get lots of education about it and if they don't, they don't.

Adeel Khan, a former Colorado educator and founder and CEO of Magic School AI, said its crucial that AI become a competency in school, and not one that only affluent parents can buy their children.

We need to lead the charge here and not make the same mistakes of not bringing one-to-one laptops to schools (until) decades after they were being used in every professional work environment.

Holmes hopes to encourage the districts that have banned the use of AI to think of that as a first move.

Please don't let it be your last move and start to figure out how else you can engage with this and support young people in engaging with it.

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Artificial intelligence is already reshaping how some Colorado students learn. Is your school on the cutting edge? - Colorado Public Radio

Investors looking for alternatives to Nvidia to capitalize on the AI boom should consider taking a closer look at these names.

Semiconductor stocks have taken off big-time in the past year. ThePHLX Semiconductor Sector index gained an impressive 51% during this period thanks to the booming chip demand triggered by the growing adoption of artificial intelligence (AI) applications.

Semiconductors play a central role in the AI revolution. The training of AI models such as OpenAI's ChatGPT was made possible using chips designed by the likes of Nvidia (NVDA 2.45%). This explains why Nvidia's AI-focused graphics processing units (GPUs) have been selling like hotcakes over the past 18 months, leading to a sharp jump in Nvidia's revenue and earnings.

NVDA Revenue (TTM) data by YCharts

The market rewarded Nvidia handsomely for its stellar growth. That's evident from the 225% jump in the company's stock price in the past year. It won't be surprising to see Nvidia maintain its outstanding momentum as well thanks to expectations of strong top- and bottom-line growth. However, Nvidia is not the only company in the hardware supply chain driving the AI revolution.

Here's a closer look at five other names playing a central role in the AI hardware market.

While the likes of Nvidia, Intel, and Advanced Micro Devices make or design chips that are used for addressing AI workloads such as training large language models (LLMs) and for inferencing purposes, those chips need to be mounted on a server infrastructure that's specifically designed for running AI workloads. Dell Technologies (DELL 4.37%) and Super Micro Computer (SMCI -1.04%) are two names benefiting big-time from the growing need for AI-optimized servers.

While shares of Dell have jumped 184% in the past year, Supermicro stock has rocketed higher, with eye-popping gains of 847%. Despite these massive jumps, both stocks still trade at attractive multiples. Dell has a sales multiple of just 0.9, while Supermicro's reading stands at 5.6. Their sales multiples are lower than the Nasdaq-100 Technology Sector index's average price-to-sales ratio of 7.3.

A closer look at how AI drives their growth will demonstrate why investors bought their stocks hand over fist in the past year. It also explains why they are still worth buying. Dell, for instance, is set to return to growth in the current fiscal year after struggling in the previous one because of weak personal computer (PC) sales.

Its top line fell 14% in fiscal 2024 (which ended on Feb. 2, 2024) to $88.4 billion. Dell has guided for $93 billion in revenue in the current year at the midpoint of its guidance range, and the demand for AI servers is one of the reasons behind its turnaround. Dell shipped $800 million worth of AI-optimized servers last quarter. Even better, its AI server backlog almost doubled quarter over quarter to $2.9 billion thanks to a 40% sequential increase in AI server orders.

The good part is that Dell's AI server order book could keep getting fatter. The AI server market is set to grow from an estimated $30 billion last year to $150 billion in 2027, according to Foxconn. This is precisely the reason why even Super Micro Computer stock has turned out to be a stock market favorite in the past year.

Supermicro's growth has been stunning with its revenue in the ongoing fiscal year 2024 set to double to $14.5 billion from $7.1 billion in fiscal 2023. More importantly, the server manufacturer claims that its production capacity is solid enough to support more than $25 billion in annual revenue. This explains why analysts have significantly raised their revenue estimates for the next couple of years. However, it won't be surprising to see the company crushing those expectations thanks to its capacity expansion moves.

SMCI Revenue Estimates for Next Fiscal Year data by YCharts

Memory chips from the likes of Micron Technology (MU -0.41%) are helping Nvidia manufacture powerful AI GPUs. This explains why Micron sold out its entire 2024 capacity of high-bandwidth memory (HBM) chips that are deployed in AI chips, while also allocating an "overwhelming majority" of its HBM production line for next year.

HBM is deployed to increase the speed at which data is fed to AI processors, significantly boosting the performance of AI processors. This explains why Nvidia and AMD have been packing more HBM into their AI chips, driving impressive growth for Micron. The memory specialist's revenue was up 58% year over year last quarter to $5.8 billion. It is expecting stronger growth of 76% in revenue in the current quarter to $6.6 billion from $3.75 billion in the year-ago quarter.

Analysts expect Micron to finish the year with a 57% spike in revenue to $24.3 billion followed by another impressive year in fiscal 2025 with an estimated revenue jump of 42%. With shares of Micron currently trading at 6.4 times sales despite rising 95% in the past year, buying it looks like a smart thing to do as the HBM market could generate $49 billion in annual revenue in 2030 as compared to $857 million last year.

Another chipmaker benefiting from the AI chip market is Broadcom (AVGO 1.67%), a stock that has jumped 106% in the past year. In February, J.P. Morgan analyst Harlan Sur pointed out that Broadcom is set to become the second-largest AI chip company in 2024 with an estimated revenue of $8 billion to $9 billion thanks to customers such as Alphabet and Meta Platforms for whom it makes custom AI chips.

But when Broadcom released its fiscal 2024 first-quarter results, management said that its AI chip revenue is set to exceed $10 billion in the current fiscal year. However, don't be surprised to see Broadcom exceeding that mark as it recently added a new customer for its custom AI chips. Wall Street analysts believe that the newly added customer could be Amazon, Apple, or TikTok parent ByteDance.

It is easy to see why Broadcom's AI customer pipeline is increasing. Cloud companies are looking to develop custom chips for running AI workloads to reduce costs, consume less power, and improve performance. This explains why Morgan Stanley predicts custom chips will account for 30% of the $182 billion AI semiconductor market in 2027, indicating that Broadcom's addressable market in AI chips could be worth almost $55 billion in three years.

So, Broadcom's AI business could keep growing at a nice clip moving forward, which is why buying the stock now could be a good idea as it is trading at 28 times forward earnings, in line with the Nasdaq-100's forward earnings multiple and lower than Nvidia's reading of 36.

All the above-mentioned names supply AI hardware, but it eventually comes down to electronics manufacturing services providers such as Jabil (JBL 3.38%) to make the end products. This stock is trading up 52% in the past year but recently took a big hit. Savvy investors, however, would do well to take a look at the bigger picture.

From designing and sourcing components to assembling the final product, Jabil products and services are deployed to reduce costs and enhance efficiency. Not surprisingly, Jabil saw a big surge in AI-related orders. Management pointed out on its recent earnings conference call that its "AI GPU volume in the first half of 2024 is 200x that of the level of 2023."

Even better, Jabil points out that its AI-related revenue is set to increase by 20% in fiscal 2025 to $6 billion. That would be 20% of the company's top line based on the consensus estimate of $30 billion in revenue for fiscal 2025.

Jabil's AI business could keep growing in the future as the company claims that it is "winning share across multiple end markets in the AI data center infrastructure space." That's why investors would do well to take a closer look at this AI stock that's trading at just 0.5 times sales and 21 times trailing earnings before it steps on the gas.

John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool's board of directors. JPMorgan Chase is an advertising partner of The Ascent, a Motley Fool company. Randi Zuckerberg, a former director of market development and spokeswoman for Facebook and sister to Meta Platforms CEO Mark Zuckerberg, is a member of The Motley Fool's board of directors. Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Harsh Chauhan has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Amazon, Apple, JPMorgan Chase, Meta Platforms, and Nvidia. The Motley Fool recommends Broadcom and Intel and recommends the following options: long January 2023 $57.50 calls on Intel, long January 2025 $45 calls on Intel, and short May 2024 $47 calls on Intel. The Motley Fool has a disclosure policy.

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5 Artificial Intelligence (AI) Hardware Stocks -- Not Named Nvidia -- That Are Crushing the Market and Are Still Cheap - The Motley Fool