Nvidia’s Move Into Cloud Computing Is Making Things Awkward in Silicon Valley
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As companies create AI-powered solutions, how can they ensure they're effective and trustworthy? Join IBM at the break to hear how companies can build trust in their AI with Hrithika Gunnar, IBM's General Manager for Data and AI.

Welcome to Tech News Briefing. It's Friday, June 27th. I'm Victoria Craig for The Wall Street Journal. Things are getting awkward in the cloud computing world. Our columnist joins us to talk about why NVIDIA is ruffling the feathers of industry stalwarts. Then, if you've ever wondered if your job applications are getting rejected by a bot before they ever land on a hiring manager's desk, you're not alone. We'll tell you about one man taking his case to court after he worries an algorithm screened him out.

But first, cloud computing has been a cash cow for years for companies like Amazon, Microsoft, and Google. But the rise of artificial intelligence has thrown a wrench in that corner of the tech world, and NVIDIA has made its name more recently as a new power broker. WSJ Heard on the Street columnist Asa Fitch writes, despite NVIDIA getting involved in cloud computing two years ago, things are starting to get uncomfortable now in Silicon Valley. Asa, what's changed?

It's ruffling feathers because here's this company that's important in AI, and now it's getting on the turf of some other established players who have made a lot of money in the cloud computing business over the years. But it is pretty adamant that it isn't going to try to meaningfully compete or outshine those cloud computing giants. Tell us about DGX Cloud and how the company is

playing in this space and how it's threatening the likes of Google and Microsoft and those other tech giants. DJX Cloud is a service that NVIDIA launched two years ago. And the idea is that NVIDIA can help people set up their AI stuff on infrastructure like equipment and chips that NVIDIA controls. And, you know, the way it works is kind of unusual. Basically, NVIDIA sells chips and goes into equipment that these big cloud companies don't

buy and then NVIDIA rents it back from them and then in turn NVIDIA leases that equipment to its end customers and it's complicated. But

They wanted to do this because NVIDIA felt that they could go direct to the customers, the people who are using AI, specifically doing training in AI, which means creating these powerful large language models and other things. And it made sense in that respect. Now, NVIDIA says that they're just trying to help companies develop AI, essentially. They're directly going to those companies and setting up the infrastructure they need and letting them use it.

That's a complicated thing when it comes to the cloud players. In the cloud business, the biggest player is Amazon, which has a cloud business that generates over $100 billion of revenue every year. Then there's Microsoft, which has its Azure cloud service, and there's Google. Now, these businesses are quite profitable. The margins on them are really good. So whenever one of these big cloud players sees Nvidia over the back of their shoulder trying to compete in some ways, or at least trying to get in on this business,

there is some concern for those incumbents. And NVIDIA has developed it quite well

well over the past couple of years. It's also tried to nurture other competitors to the big established cloud players, these startups that do AI computing work specifically. So it's mounting a real challenge to the status quo in the cloud, and that's ruffling a lot of feathers. So given that, is there time or a good way for those established players to put more daylight between themselves and NVIDIA? They're kind of in a pickle because

Because for them, they need access to NVIDIA's chips because they themselves want to have competing services. They themselves want to do AI. They want to rent out equipment for AI to everybody. They need NVIDIA for that. So they have to keep the relationship with NVIDIA cordial or friendly. Some of those companies are now also trying to develop their own AI chips rather than

having to rely long-term on NVIDIA. So how is NVIDIA then safeguarding its own business against that in this latest twist and turn?

You could see NVIDIA setting up this cloud business as a sort of insurance policy because it can't really rely in the long term on these cloud players like Amazon, Microsoft, Google to keep buying its chips and keep renting out those chips to everybody. Those companies, as you mentioned, are developing their own AI chips that eventually could replace NVIDIA's AI chips. And that means less revenue for NVIDIA if that happens. That was WSJ Heard on the Street columnist Asa Fitch.

Coming up, we're used to algorithms feeding us information we want, suggesting news we should read, or helping us find a nearby restaurant. But what happens when it works against us in the job search? That story, after the break.

Enterprise AI is an unstructured data problem at scale. How does generative AI address it? Rithika Gunnar, General Manager for Data and AI at IBM, explains. Think of this as emails, PDF, PowerPoint decks that sit in an organization. Generative AI has allowed us to unlock the

opportunity to be able to take the 90% of data that is buried in unstructured formats, which really unlocks a new level of driving data and insights of that data into your workflows, into your applications, which is essential for organizations as we go forward.

The slog of a job search is something most of us can relate to. Before we ever get to talk to a human being, there are hours spent filling out online applications, attaching cover letters, resumes, and work samples.

You might think humans are the ones sifting through all of that information, but hiring software can help. And nearly every large company today uses this kind of software to manage the thousands or millions of applications they receive every year. A man named Derek Mobley said he believes that that kind of technology, as it worked through his submitted information, picked up on his age and race,

through details on his resume, even detecting anxiety and depression through personality tests he completed as part of some job applications. And he's suing Workday, the largest purveyor of recruiting software for discrimination.

WSJ's Lauren Weber covers workplace issues and employment. She's been digging into this story. Lauren, first help us understand Mobley's situation a little bit better. Who is he and what is he claiming happened during his job search? Derek Mobley is a 50-year-old IT worker who lives in now North Carolina.

He went through a stretch of unemployment from about 2017 to 2019. And during that time, he says he applied for more than 100 jobs. Often he was directed to apply on a platform operated by the software company Workday. There are many companies that

sell these kinds of platforms, but Workday is one of the biggest. This particular product is an applicant tracking system. And it is a system that both tracks the job applications that come in for an open job and also does some amount of scoring for whether or not somebody who applies for a job meets the qualifications that an employer has listed for that job.

So Derek Mobley, after getting rejected or in many cases not hearing back from some of the companies that he had applied to, and basically that was an effective rejection, he suspected that there was something going on with the algorithm in Workday's applicant tracking system, its platform, that he was somehow being screened out of these jobs. And he filed a lawsuit a couple of years ago alleging that he had been discriminated against on the basis of race,

age and or disability. And so what has Workday said in response to those allegations? Workday has been fighting this lawsuit. It has said that his claims have no merit. They say, and many software experts that I've spoken to say, it's the clients themselves, it's the employer customers of Workday that they put in their job qualifications. They say what are their work

What are the preferred qualifications? And so if Mobley is being screened out of jobs, it's because he's not meeting the qualifications. That is Workday's argument. And part of the story that's interesting is that Workday has this sort of secret sauce that it uses. So it's not very transparent.

transparent, I suppose, about how it screens. Can you just demystify that for us? Because I think a lot of listeners can relate in some way to Mobley's story. That is exactly the case. That really gets at the heart of what this case is about, which is this question, is the job market fair? That's why many people that I've heard from quite sympathetic to Mobley's experience and to Mobley's argument because

Many of them have experienced it too. Anyone who's applied for a job in the last 10 or 20 years, you're basically required to apply for most jobs online, go through one of these software systems that does this screening and scoring, and no one knows why they are screened in or out for a job, or you don't know how the system has scored you. So it's an incredibly opaque process, and it leaves a lot of people feeling like,

was this process fair? And many people that I've heard from say, I've applied for jobs that I know I'm qualified for. This is one of the things that Mobley was saying, and I'm not getting a shot. So what is going on here? You write that the judge's ruling opens the door to millions of potential claims from job seekers over 40 years old. Why is that? So he alleged that he's being discriminated against on the basis of age, race, and or disability. He's

basically wants to go through this process to try to figure out whether it's any or all or some of those factors.

The age claims are the only ones that the judge has ruled on so far. And she hasn't said they're valid or not, but she has ruled that the case can be a collective action, which means anyone over the age of 40 who applied for a job through the Workday platform in the last five years and was not recommended for the job, that's a little bit open to interpretation, but can join the suit. That is potentially millions and millions of people. And

Right now, the lawyers are in the process of trying to figure out how you even find those people and then give them notice and all that. So we're in for a long legal process here. Now, Mobley did eventually find a job the old-fashioned way, not using online tools. But what is likely to happen to his case and also future regulation of these intermediary software companies? That's sort of an open question. It may take a long time to get to the point where

Workday may have to part the curtain on what exactly goes into their hiring model and whether that involves third parties auditing the model or perhaps them allowing software experts to look at the actual code behind it. All of that is yet to be determined. So it could be a long time.

You know, there are a lot of states, the federal government, overseas governments. The European Union has quite a few regulations related to artificial intelligence.

So as this case works its way through the courts, we'll also see some of these regulatory efforts come to fruition before we even get a decision in this case. That was WSJ reporter Lauren Weber there. And that's it for Tech News Briefing. Today's show is produced by Julie Chang. I'm your host, Victoria Craig. Jessica Fenton and Michael LaValle wrote our theme music.

Our supervising producer is Melanie Roy. Our development producer is Aisha Al-Muslim. Scott Salloway and Chris Tinsley are the deputy editors. And Falana Patterson is The Wall Street Journal's head of news audio. We'll be back this afternoon with TNB Tech Minute. Thanks for listening.

How can companies build AI they can trust? Here again is Hrithika Gunnar, General Manager for Data and AI at IBM. A lot of organizations have thousands of flowers of generative AI projects blooming. Understanding what is being used and how is the first step. Then it is about really understanding what kind of policy enforcement do you want to have on the right guardrails on privacy enforcement.

The third piece is continually modifying and updating so that you have robust guardrails for safety and security. So as organizations have not only a process, but the technology to be able to handle AI governance, we end up seeing a flywheel effect of

more AI that is actually built and infused into applications, which then yields a better, more engaging, innovative set of capabilities within these companies. Visit IBM.com to learn how to define your AI data strategy. Custom content from WSJ is a unit of the Wall Street Journal Advertising Department. The Wall Street Journal News Organization was not involved in the creation of this content.