Can Robots Make Sneakers? Just Ask Nike.

Summary

Detailed Summary of "Can Robots Make Sneakers? Just Ask Nike." – WSJ Tech News Briefing

Podcast Information:

• Title: WSJ Tech News Briefing
• Host/Author: The Wall Street Journal
• Episode: Can Robots Make Sneakers? Just Ask Nike.
• Release Date: April 22, 2025
• Description: Tech News Briefing is your guide to what people in tech are talking about. Every weekday, we bring you breaking tech news, insights into new innovations and policy debates, tips from our personal tech team, and exclusive interviews with industry leaders.

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Introduction: Navigating the Digital Footprint

Personal Data Privacy:
Host Katie Dayton opens the episode by addressing the growing concern over personal data being collected and exposed by data brokers. She emphasizes how detailed these profiles have become, often containing sensitive information like home addresses and even a grandmother's name.

Nicole Nguyen on Data Exposure:
Nicole Nguyen, the WSJ personal tech columnist, shares her unsettling experience using Google's "Results about you" tool. Despite her efforts to delete personal information, she discovered a wealth of data still accessible online.

Notable Quotes:

• Nicole Nguyen [01:34]:
  "You feel a little uneasy... every time we shop online, we input our address. It's a part of public record."

• Nicole Nguyen [02:35]:
  "If you get a lot of phone calls or a lot of spammy emails, that is annoying at best. At worst, doxx... people use that personal information to harass you and people that you know."

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Strategies for Controlling Personal Data Online

Techniques to Remove Data:
Nicole outlines effective methods for managing and reducing personal data exposure online. She recommends starting with free tools like Google's "Results about you" and then moving to paid services such as Optery and DeleteMe. These services provide detailed reports on where your information appears and offer ongoing opt-out requests to data brokers.

Challenges and Hope for the Future:
While these tools are helpful, Nicole acknowledges the persistent nature of data collection. She highlights that strong privacy laws in states like California offer some relief, ensuring data removal within 45 days. However, she notes that outside these jurisdictions, removing data can be more challenging.

Notable Quotes:

• Nicole Nguyen [03:24]:
  "Google's Results about you tool is a really great place to start... Optery and DeleteMe... can automatically request an opt out."

• Nicole Nguyen [04:43]:
  "I want to believe yes... some states have really strong privacy laws. If you live outside of those states, then removals can take longer or they don't happen at all."

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Transition to Main Topic: Automation in Sneaker Manufacturing

Introduction to Automation Challenges:
Katie Dayton segues from data privacy to a pressing issue in the manufacturing sector: the feasibility of automating sneaker production in the face of high US labor costs. She sets the stage for an in-depth exploration of Nike's ambitious attempts to revolutionize its production line through robotics.

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Can Robots Make Sneakers? Nike's Automation Journey

Nike's Ambitious Project:
Victoria Craig reports on Nike's long-term effort to manufacture sneakers in North America, initiated in 2015. The goal was to reduce reliance on overseas manufacturing by introducing automation, thereby mitigating high labor costs and potential tariffs.

Partnering with Flex:
Nike collaborated with Flex, a company known for electronics manufacturing, hoping to leverage their expertise in automation. Flex had recently assisted Apple in setting up a Mac Pro factory in Austin, Texas, and Nike aimed to apply similar automated processes to shoe production.

Notable Quotes:

• John Eamont [06:55]:
  "Shoes are a soft good... there's a lot of variation in shoes in a way that you don't get with an iPhone... it's much more labor intensive."

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The Complexities of Automating Shoe Production

Technical Challenges:
John Eamont explains that unlike electronics, which consist of uniform components easily managed by machines, shoes involve flexible materials and structural variations that complicate automation. The nuanced nature of shoe manufacturing—such as stitching and fitting soles—requires a level of adaptability that current robotics struggle to achieve efficiently.

Production Costs and Scalability:
Automating shoe production in North America would inherently increase costs due to higher wages compared to Asian manufacturing hubs. Additionally, achieving scalability is challenging because each shoe model may require unique adjustments, reducing the efficiency gains typically sought through automation.

Notable Quotes:

• John Eamont [08:06]:
  "Shoes are a soft good... it's easier to get a laptop automated because you can just get a machine trained to do the same task a million times with virtually identical products."

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Impact of Tariffs and Economic Policies

Tariffs as a Double-Edged Sword:
The episode delves into how US tariffs, introduced during President Trump's administration to boost domestic manufacturing, impact companies like Nike. While tariffs aim to encourage local production, the high cost of labor makes domestic manufacturing less competitive, pushing companies to consider automation as an alternative.

Nike's Predicament:
With approximately 95% of Nike’s shoe production based in countries like Indonesia, Vietnam, and China, shifting manufacturing to the US poses significant logistical and financial challenges. Nike faces uncertainty as tariff policies remain volatile, complicating long-term strategic planning.

Notable Quotes:

• John Eamont [09:50]:
  "95% of their shoe production is in Indonesia, Vietnam and China. So moving that out would be very, very difficult. Effectively, they're stuck."

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Lessons Learned and Future Outlook

Optimism vs. Reality:
Tom Fletcher, spearheading the automation project, remained optimistic despite the hurdles. However, both he and his counterpart, Michael Newton, concluded that while automating shoe production in North America is possible, it requires significant design compromises. The need to standardize shoe models to suit machine production limits creativity and innovation, potentially resulting in less distinctive products.

Future of Automation in Footwear:
The podcast suggests that while current technology may not fully support the intricate demands of sneaker manufacturing, ongoing advancements in robotics and machine learning could eventually bridge the gap. Nike’s experience serves as a cautionary tale for other companies considering similar automation endeavors.

Notable Quotes:

• John Eamont [10:23]:
  "Tom came into the project optimistic... shoes are a really big hurdle to climb."

• John Eamont [10:23]:
  "We have to find a shoe that we can make that's really amenable to machines... it has to be much more constrained."

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Conclusion: Navigating the Intersection of Technology and Practicality

Katie Dayton wraps up the episode by highlighting the intricate balance between technological innovation and practical implementation. The discussions on personal data privacy and Nike’s automation challenges underscore the complexities businesses and individuals face in a rapidly evolving tech landscape. As technology continues to advance, the need for adaptable strategies and resilient systems becomes increasingly vital.

Production Credits:
Today's show was produced by Julie Chang with supervising producer Chris Sinsley.

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Key Takeaways:

1. Personal Data Privacy: Individuals must proactively manage their online presence using available tools and services, though systemic challenges remain.
2. Automation in Manufacturing: While automation holds promise, industries with complex and variable production requirements, like sneaker manufacturing, face significant obstacles.
3. Economic Policies: Tariffs intended to boost domestic manufacturing can inadvertently increase reliance on overseas production or necessitate costly automation efforts.

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This episode of WSJ Tech News Briefing provides comprehensive insights into the dual challenges of safeguarding personal data and the practical limitations of automation in specialized manufacturing sectors. Listeners gain a nuanced understanding of how technological advancements intersect with real-world complexities.

Transcript

[Elastic Representative]

Why do over 50% of the Fortune 500 use elastic? Because Elastic has done the hard work of making it easier for companies to do generative AI Right. Elastic's search AI helps them make insightful and impactful decisions at speed across search, observability and security. Elastic has the power to take your data into the future. Explore the possibilities of AI with your data at Explore Elastic Code elastic, the search AI company.

[Katie Dayton]

Welcome to Tech News Briefing. It's Tuesday, April 22nd. I'm Katie Dayton for the Wall Street Journal. We've all gotten more used to handing over our personal information to companies, but the profiles that data brokers collect on each of us are growing more detailed, and they're often posted online for all the world to see. We'll take you through deleting yourself from the Internet. Then we'll switch gears to investigate a very different issue. Why robots really aren't that great at making sneakers. But first, how much of your personal data are you okay with ending up online? Your email address? Your home address? How about your grandmother's name? When our personal tech columnist Nicole Nguyen used a tool on Google called Results about yout, she was surprised to find a trove of her personal information on the Internet, even when she'd already asked for it to be deleted. Nicole, how did it make you feel when you saw what was unearthed?

[Nicole Nguyen]

You feel a little uneasy. A home address is something that we think of as private because it's where our physical presence is. But in fact, it's data that appears in hundreds, if not thousands of databases because every time we shop online, we input our address. It's a part of public record. If you own a home, if you have applied for a driver's license or subscribed to a magazine, this information is out there. And these websites, people, search sites, or data brokers can purchase this information from a variety of sources or request it from the government. And they collate it in this dossier that includes other information, including your birth date. Maybe one site included my grandma's name that I found from the Google Results about you tool. There's a lot of our data out there.

[Katie Dayton]

Why, for you and so many others, is that a concerning issue given that we all will willingly give this out so often?

[Nicole Nguyen]

The everyday example is scam and spams that are coming your way. If you get a lot of phone calls or a lot of spammy emails, that is annoying at best. At worst, doxx, which is the malicious sharing of personal info is not only a problem for executives and public figures, but increasingly private citizens and this is when people find your personal information on the Internet and those of your family members and use that personal information to harass you and people that you know.

[Katie Dayton]

The good news is there are some ways to remove disinformation from the Internet. Could you run us through some of the techniques that you uncovered when you were looking into this story and looking at your own data as well?

[Nicole Nguyen]

Google's Results about you tool is a really great place to start because it's free and this is the low hanging fruit. This is the information that people would find if they googled your name. There are other services that you can pay to remove your data and those services do offer free exposure reports. Optery and DeleteMe are two services that I really liked using and they have strong privacy practices. If you sign up for an account with these sites, they can email you a PDF of places that your information shows up in. Places where you may not think to look like people search sites or data brokers that you've never heard of and if you subscribe then they can automatically request an opt out. That means they request on your behalf that site removes your information. These are subscription services because it's an ongoing process and when you request an opt out to remove your info, the site is not promising to never buy your information again. So it can buy a new set of records and your information can crop up again.

[Katie Dayton]

Making sure this information doesn't appear online, it seems like a real, real task and it requires time, money, energy. Do you think this is just part of life now? Will it ever get easier?

[Nicole Nguyen]

I want to believe yes, and that's because some states have really strong privacy laws. Like where I live in California. If you request an opt out or if you request your data to be removed, it has to happen within 45 days. And about a dozen other states have similar privacy laws. But if you live outside of those states, then removals can take longer or they don't happen at all.

[Katie Dayton]

That was WSJ personal tech columnist Nicole Nguyen. Coming up, why robots may not be the answer to high US labor costs, at least not when it comes to making some things.

[Elastic Representative]

That's after the break. Why do over 50% of the Fortune 500 use elastic? Because Elastic has done the hard work of making it easier for companies to do generative AI, right? Elastic's search AI helps them make insightful and impactful decisions at speed across search, observability and security. Elastic has the power to take your data into the future. Explore the possibilities of AI with your data at Explore Elastic Co Elastic the Search AI Company.

[Katie Dayton]

Supporters of President Trump hope that the tariffs introduced by his administration will kickstart a golden age of US Manufacturing. But the cost of labor in the US Is high and it's pushing American companies to turn to robots for help. Well, they might be wise to look at Nike, which spent millions of dollars on trying to automate its production lines and found that's not an easy thing to do. Our Victoria Craig spoke with John Eamont, a WSJ reporter based in Singapore, to find out more.

[Victoria Craig]

This idea to try to manufacture in America has been sort of a long running effort by Nike, which began experimenting with it back in 2015. And it had this goal of trying to actually help other companies do the same thing. Talk to me first about Tom Fletcher, the guy who they tapped to build out these factories.

[John Eamont]

Nike has always relied on contract manufacturers. They took the same model essentially to North America when they tried to manufacture in Guadalajara and they picked a company called Flex, which makes electronics. So they were trying to find some company that would think outside the box, that wasn't going to be hemmed in by the usual standard ways of making shoes and was going to think about, can we do this in a much more automated way? Because electronics manufacturing in general is much more automated than shoemaking, which is very labor intensive. And of course, because shoemaking is so labor intensive, that's made it prohibitively expensive to really produce these shoes at scale in North America. Instead, you go to Asia for that, where labor's a lot cheaper. So if you want to bring it back to North America, you've got to automate. And if you want to automate it, the thinking was, well, you go with Flex, which has experience with electronics, including just a couple years before, they had helped Apple set up a Mac Pro factory in Austin, Texas. So Flex then tried to bring some of those lessons that they learned from Apple and actually apply that to a wholly different product, which is shoes.

[Victoria Craig]

Alas, it was not to be, was it? Just tell us why.

[John Eamont]

So shoes are a soft good, which means that there's fabric that squinches and expands depending on the temperature. And there's just a lot of variation in shoes in a way that you don't get with an iPhone, where an iPhone is made of metal parts that are of very precise dimensions. And so because of that, it's easier to get a laptop automated because, because you can just get a machine trained to do the same task a million times with virtually identical products. And as a result, it ended up being more labor intensive than they wanted it was just really hard to get right.

[Victoria Craig]

We think about automation and we think that will make so many different parts of life and business easy. Clearly, as you discuss discovered with shoes, that's not the case. So just walk us through how shoes usually are made. Is it handmade, essentially, from start to finish. Then in other places across Asia, is that how Nike assembles shoes now?

[John Eamont]

So in a place like Vietnam, which makes about half of Nike shoes today, it is very labor intensive. So that isn't to say there's no automation. And of course, machines are heavily involved, you know, in producing, like the raw materials. But there are certain things like stitching the sole to the upper part of the shoe where that's still done by hand, usually. And then there are certain things, depending on the type of shoe, that can be highly automated. So certain types of shoes can be knit by machines, the top part. But again, still, there's a lot of attaching work and finishing work that is done by hand.

[Victoria Craig]

I guess, then the next question, though is obviously we know that the point of President Trump's tariffs on all of these companies that manufacture things outside of the US and then bring them back. The idea is to bring the manufacturing to America. But as we've just discussed here, that for Nike is a really big hurdle to climb. So what do these tariffs mean for a company like Nike?

[John Eamont]

Yeah, so the good folks at Nike are trying to sort that out and they don't have answers, in part because tariff policy changes all the time. 95% of their shoe production is in Indonesia, Vietnam and China. So moving that out would be very, very difficult. Effectively, they're stuck. And obviously what they hope is that the tariff rates will go down.

[Victoria Craig]

Tom Fletcher, the Mac guy, he seemed pretty optimistic about the future of all of this. Does he think or do people in the industry, does Nike believe that the technology could one day be there? It's just, it's going to take a while.

[John Eamont]

Tom came into the project optimistic. The reality was much tougher than he anticipated. And he said he came away from the project humbled by how difficult it was to make shoes. This is a guy who made Mac pros. Surely shoes should be nothing. But both Tom and Michael Newton, who was Tom's counterpart at Nike, so the two of them were working together to try to get this manufacturing process up and running. They both had similar conclusions, which is, this could be done. It's not impossible. You could make shoes in Mexico, in the United States, it would almost certainly be more expensive. But the key thing is you just have to make a lot of design compromises. So currently, the way Nike works, the designers are kings. And the designers come up with super creative ideas for shoes. And then they go to their Asian manufacturers who are miracle workers and they say, make it happen. And they do. What you have to do if you want to manufacture shoes in North America is it has to be really highly automated, otherwise it's just going to be way too expensive. So we need to find a shoe that we can make that's really, you know, amenable to machines, machine knitting and all this sort of stuff. So we have to really limit the configurations of the shoe. It has to be much more constrained. Maybe it's going to be less interesting, maybe it won't be as cutting edge.

[Katie Dayton]

That was Victoria Craig speaking with WSJ reporter John Eamont. And that's it for Tech News Briefing. Today's show was produced by Julie Chang with supervising producer Chris Sinsley. I'm Katie Dayton for the Wall Street Journal. We'll be back this afternoon with TMB Tech Minute. Thanks for listening.

[Elastic Representative]

Why do over 50% of the Fortune 500 use Elastic? Because Elastic has done the hard work of making it easier for companies to do generative AI. Right. Elastic search AI helps them make insightful and impactful decisions at speed across search, observability and security. Elastic has the power to take your data into the future. Explore the possibilities of AI with your [e-mail] co elastic, the search AI company.