Mark Zuckerberg Is Taking Control of AI Talent Hiring at Meta
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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 Wednesday, June 25th. I'm Victoria Craig for The Wall Street Journal. The holy grail of automation is a task us humans perform with ease, but one that robots have really struggled with. Our reporter explains how years of work has led to a technological breakthrough. Then, the smartest minds in artificial intelligence have been getting personalized recruitment letters to join Meta from Mark Zuckerberg himself. We'll tell you why the CEO has been so actively involved.

But first, loading and unloading a truck. It's backbreaking, mind-numbing work for humans. And it's a problem that retailers and package delivery companies have been trying to solve for their employees for literal years. Now, thanks to advances in AI, robots are able to offer assistance in this particular job, as WSJ reporter Esther Fung has been writing about.

So Esther, you call this the holy grail of warehouse logistics. Why has it taken so long to develop a robot to do what seems like such a basic task? Yes, it has taken this long and it's so difficult because of the variety of packages they

They could be small packages. They could be big ones. They could be round ones. They could also be carrying tires. They could be carrying trampolines. It runs the gamut. And you need a loader or an unloader that can do all of that. This has taken a long time for robotics companies to figure out.

So how does it work? And how quickly could we see this kind of technology rolled out in warehouses? We are already seeing some of this technology show up in certain warehouses. There's a cottage industry of robotics companies making these robots.

Right now, these robots can definitely handle packages that are more generic in their dimensions. A lot of these robotics companies now are using the AI models, machine learning algorithms to figure out what to do when a box comes in that has different dimensions, different weights, different center of gravity.

They learn from previous loading experience. Okay, this box should go in this spot versus that spot. You want the heavier boxes at the bottom versus at the top because you don't want things to topple over during transit. Unloading is easier, but the machines also have to learn when they scan a trailer full of boxes, they would also have to pick these boxes out, place them on the conveyor belt and do it in such a way that

it doesn't damage the box or the contents inside. But there is a weight limit. I think you write it's about 50 pounds. Yes. Boston Dynamics, they have this stretch robot and there's a weight limit of 50 pounds. It can definitely unload many, many boxes, but there are still certain items that it has trouble picking up, like really thin pizza-sized boxes where you have to pick it up from the side and it still can't pick up bags.

These machines also need to learn how to pick up boxes that they drop on the ground. So there's so much that the machine has to learn how to do. Is this sort of the last frontier of automating warehouses? I mean, are we at the point now where a warehouse can be almost fully automated at this point? That's what some folks believe that, yes, we are really close to that point, but

Like all the big companies are now already experimenting with these robots. At this point, when I talk to human loaders and unloaders and those people who still work in warehouses alongside the robots, they tell me that they are still needed there because there are still definitely lots of

incidents that could happen where human intervention is needed. So it's a big win, potentially, money-saving technological advancement for companies like FedEx and UPS. And we can see then why companies are in favor of it. But I'm wondering how the workers themselves feel about robots replacing their jobs. I've spoken to a few loaders and unloaders, and it is a tough job. There's a lot of backbending, there's

Somebody was telling me they're always covered in bruises. And in the summer, he feels like a rotisserie chicken cooking in a metal trailer. Some of them told me that they feel that they would be working alongside these robots, fixing any jams that could happen.

And then there are others who have told me that they are worried about their jobs, even without the specter of robots. There could be industry consolidation, facility consolidation, cost cutting jobs.

And robots, one of them said, it's just another factor in the mix. And so it's a catch-22. On one hand, you want to keep your job. And they're really hoping that the companies would still train them in managing these robots. That was Esther Fung, a reporter who covers packaged shipping companies and logistics for The Wall Street Journal.

Coming up, Meta is going through an AI recruitment blitz armed with $100 million pay packages. Its CEO is leading that charge. We'll have more on 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.

Ever gotten a job recruitment email that seems just too good to be true? Well, lately, the finest minds in artificial intelligence have been receiving emails and WhatsApp messages from a sender named Mark Zuckerberg. This time, though, it really is the meta chief executive penning those letters as he tries to address an AI crisis at the social media giant.

Technology reporter Megan Bobrowski has been writing about this for the journal. Megan, why is Zuckerberg going to these extraordinary lengths himself? Meta-religion.

released some of its latest models in April, and the models were generally not well received by the AI community. And people thought that Meta was falling behind in the AI race. And we reported that they actually then further delayed one of the biggest models. So Meta right now is just in a time where they're trying to catch up to the rest of the industry.

It's one thing for the company to go after bigwigs that it wants to attract and bring on board, but it's another for the CEO of this caliber, Mark Zuckerberg, to go after them directly. And as you write, a lot of times these people don't even believe that it's a legitimate, authentic email from Mark Zuckerberg himself. So why is he the one who's fronting all of this?

That shows how important this is to him, right? Like how existential AI is to the company, at least in Mark Zuckerberg's eyes. He views this as one of the most important things that his company needs to be focusing on right now. And so he wants to have the top talent who can get him there, who can get Meta to be one of the

biggest, best players in the space. And the way that he's trying to do that is by offering people $100 million pay packages and personally reaching out to them himself. And who is he going after?

So he's gone after a lot of different people. The one that we already know about is Alexander Wang, who is the CEO of Scale, who is joining Meta. He's also in talks with Nat Friedman, the former GitHub CEO, and Daniel Gross, who is the CEO of a new AI startup. He's also gone after a lot of people at OpenAI, some of the OpenAI co-founders and the co-creator of

But some people are hesitant to accept the offers immediately.

Your reporting shows. What's the reason that some of them have given for being hesitant or even walking away from the offer? Some of those people who we spoke to for the story said that they were not clear on what the vision was, what Mark Zuckerberg is trying to do with this new team. Meta has gone through a series of restructurings and employee turnover the past few years. So what they've told us is that they're concerned about what the actual plan is.

beyond just let's hire a bunch of really great, talented people. And just to frame this for our listeners who may not have been following what's been happening at Meta, what is this superintelligence lab that Mark Zuckerberg

really wants to create and what will it take for him to develop what it is that he wants to achieve? That's a question that I'm asking people as well is what exactly is this team going to do? What are they going to be tasked with? Meta already has a series of teams that work on different things within AI. And it's not entirely clear from the people that we've spoken with what exactly is

they're trying to do with this team and where they fit into the current structure at Meta. Superintelligence is, just as a definition, it's intelligence that is smarter than humans and getting AI to be smarter than humans. And so that's a goal that

some of these AI labs have. And it's a goal that Zuckerberg has now said Meta has as well, is he wants to create these AI agents and these AI models that are smarter than humans. But we don't yet know how exactly they plan to do this. That was Megan Bobrowski, a technology reporter who covers Meta for The Wall Street Journal.

And that's it for Tech News Briefing. Today's show was produced by Julie Chang with supervising producer Melanie Roy. I'm Victoria Craig for The Wall Street Journal. 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.