Some ChatGPT subscribers are reporting a new feature appearing in their drop-down list of available tools called “Study Together.” The mode is apparently the chatbot’s way of becoming a better educational tool. Rather than providing answers to prompts, some say it asks more questions and requires the human to answer, like OpenAI’s answer to Google’s […]
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When a historic UK-based retailer set out to modernize its IT environment, it was wrestling with systems that had grown organically for more than 175 years. Prior digital transformation efforts had resulted in a patchwork of hundreds of integration flows spanning cloud, on-premises systems, and third-party vendors, all communicating across multiple protocols. 

The company needed a way to bridge the invisible seams stitching together decades of technology decisions. So, rather than layering on yet another patch, it opted for a more cohesive approach: an integration platform as a service (iPaaS) solution, i.e. a cloud-based ecosystem that enables smooth connections across applications and data sources. By going this route, the company reduced the total cost of ownership of its integration landscape by 40%.

The scenario illustrates the power of iPaaS in action. For many enterprises, iPaaS turns what was once a costly, complex undertaking into a streamlined, strategic advantage. According to Forrester research commissioned by SAP, businesses modernizing with iPaaS solutions can see a 345% return on investment over three years, with a payback period of less than six months.

Agile integration for an AI-first world

In 2025, the business need for flexible and friction-free integration has new urgency. When core business systems can’t communicate easily, the impacts ripple across the organization: Customer support teams can’t access real-time order statuses, finance teams struggle to consolidate data for monthly closes, and marketers lack reliable insights to personalize campaigns or effectively measure ROI.

A lack of high-quality data access is particularly problematic in the AI era, which depends on current, consistent, and connected data flows to fuel everything from predictive analytics to bespoke AI copilots. To unleash the full potential of AI, enterprises must first solve for any bottlenecks that prevent information from flowing freely across their systems. They must also ensure data pipelines are reliable and well-governed; when AI models are trained on inconsistent or outdated data, the insights they generate can be misleading or incomplete—which can undermine everything from customer recommendations to financial forecasting.

iPaaS platforms are often well-suited for accomplishing this across dynamic, distributed environments. Built as cloud-native, microservices-based integration hubs, modern iPaaS platforms can scale rapidly, adapt to changing workloads, and support hybrid architectures without adding complexity. They also help simplify the user experience for everyday business users via low-code functionalities that allow both technical and non-technical employees to build workflows with simple drag-and-drop or click-to-configure interfaces.

This self-service model has practical, real-world applications across business functions: For instance, customer service agents can connect support ticketing systems with real-time inventory or shipping data, finance departments can link payment processors to accounting software, and marketing teams can sync CRM data with campaign platforms to trigger personalized outreach—all without waiting for IT to come to the rescue.

Architectural foundations for fast, flexible integration

Several key architectural elements make the agility associated with iPaaS solutions possible:

  1. API-first design that treats every connection as a reusable service
  2. Event-driven capabilities that enable real-time responsiveness
  3. Modular components that can be mixed and matched to address specific business scenarios

These principles are central to making the transition from “spaghetti architecture” to “integration fabric”—a shift from brittle point-to-point connections to intelligent, policy-driven connectivity that spans multidimensional IT environments.

This approach means that when a company wants to add a new application, onboard a new partner, or create a new customer experience, they’re able to do so by tapping into existing integration assets rather than starting from scratch—which can lead to dramatically faster deployment cycles. It also helps enforce consistency and, in some cases, security and compliance across environments (role-based access controls and built-in monitoring capabilities, for example, can allow organizations to apply standards more uniformly).

Further, studies suggest that iPaaS solutions enable companies to unlock new revenue streams by integrating previously siloed data and processes. Forrester research found that organizations adopting iPaaS solutions stand to generate nearly $1 million in incremental profit over three years by creating new digital services, improving customer experiences, and automating revenue-generating processes that were previously manual.

Where iPaaS is headed: convergence and intelligence

All this momentum is perhaps one of the reasons why the global iPaaS market, valued at approximately $12.9 billion in 2024, is projected to reach more than $78 billion by 2032—with growth rates exceeding 25% annually.

This trajectory is contingent on two ongoing trends: the convergence of integration capabilities into broader application development platforms, and the infusion of AI into the integration lifecycle.

Today, the boundaries between iPaaS, automation platforms, and AI development environments are blurring as vendors create unified solutions that can handle everything from basic data synchronization to complex business processes. 

AI and machine learning capabilities are also being embedded directly into integration platforms. Soon, features like predictive maintenance of integration flow or intelligent routing of data based on current conditions are likely to become table stakes. Already, integration platforms are becoming smarter and more autonomous, capable of optimizing themselves and, in some cases, even initiating self-healing actions when problems arise.

At the same time, this shift is transforming how businesses think about integration as a dynamic enabler of AI strategy. In the near future, robust integration frameworks will be essential to operationalize AI at scale and feed these systems the rich, contextual data they need to deliver meaningful insights.

Building integration as competitive advantage

In addition to the retail modernization story detailed earlier, a few more real-world examples highlight the potential of iPaaS:

  • A chemicals manufacturer migrated 363 legacy interfaces to an iPaaS platform and now spins up new integrations 50% faster.
  • A North American bottling company reduced integration runtime costs by more than 50% while supporting 12 legal entities on a single cloud ERP instance through common APIs.
  • A global shipping-technology firm connected its CRM and third-party systems via cloud-based iPaaS solutions, enabling 100% touchless order fulfillment and a 95% cut in cost centers after a nine-month rollout in its first region.

Taken together, these examples make a compelling case for integration as strategy, not just infrastructure. They reflect a shift in mindset, where integration is democratized and embedded into how every team, not just IT, gets work done. Companies that treat integration as a core capability versus an IT afterthought are reaping tangible, enterprise-wide benefits, from faster go-to-market timelines and reduced operational costs to fully automated business processes.

As AI reshapes business processes and customer standards continue to climb, enterprises are realizing that integration architecture determines not only what they can build today, but how quickly they can adapt to whatever comes tomorrow.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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Digital transformation has long been a boardroom buzzword—shorthand for ambitious, often abstract visions of modernization. But today, digital technologies are no longer simply concepts in glossy consultancy decks and on corporate campuses; they’re also being embedded directly into factory floors, logistics hubs, and other mission-critical, frontline environments.

This evolution is playing out across sectors: Field technicians on industrial sites are diagnosing machinery remotely with help from a slew of connected devices and data feeds, hospital teams are collaborating across geographies on complex patient care via telehealth technologies, and warehouse staff are relying on connected ecosystems to streamline inventory and fulfillment far faster than manual processes would allow.

Across all these scenarios, IT fundamentals—like remote access, unified login systems, and interoperability across platforms—are being handled behind the scenes and consolidated into streamlined, user-friendly solutions. The way employees experience these tools, collectively known as the digital employee experience (DEX), can be a key component of achieving business outcomes: Deloitte finds that companies investing in frontline-focused digital tools see a 22 % boost in worker productivity, a doubling in customer satisfaction, and as much as a 25 % increase in profitability.

As digital tools become everyday fixtures in operational contexts, companies face both opportunities and hurdles—and the stakes are only rising as emerging technologies like AI become more sophisticated. The organizations best positioned for an AI-first future are crafting thoughtful strategies to ensure digital systems align with the realities of daily work—and placing people at the heart of the whole process.

IT meets OT in an AI world

Despite promising returns, many companies still face a last-mile challenge in delivering usable, effective tools to the frontline. The Deloitte study notes that less than one-quarter (just 23%) of frontline workers believe they have access to the technology they need to maximize productivity. There are several possible reasons for this disconnect, including the fact that operational digital transformation faces unique challenges compared to office-based digitization efforts.

For one, many companies are using legacy systems that don’t communicate easily across dispersed or edge environments. For example, the office IT department might use completely different software than what’s running the factory floor; a hospital’s patient records might be entirely separate from the systems monitoring medical equipment. When systems can’t talk to one another, troubleshooting issues becomes a time-consuming guessing game—one that often requires manual workarounds or clunky patches.

There’s also often a clash between tech’s typical “ship first, debug later” philosophy and the careful, safety-first approach that operational environments demand. A software glitch in a spreadsheet is annoying; a snafu in a power plant or at a chemical facility can be catastrophic.

Striking a careful balance between proactive innovation and prudent precaution will become ever more important, especially as AI usage becomes more common in high-stakes, tightly regulated environments. Companies will need to navigate a growing tension between the promise of smarter operations and the reality of implementing them safely at scale.

Humans at the heart of transformation efforts

With the buzz over AI and automation reaching fever pitch, it’s easy to overlook the single most impactful factor that makes transformation stick: the human element. The convergence of IT and OT goes hand in hand with the rise of digital employee experience. DEX encompasses everything from logging into systems and accessing applications to navigating networks and completing tasks across devices and locations. At its core, DEX is about ensuring technology empowers employees to work efficiently and without disruption—no matter where or how they work.

Companies investing in DEX technology are seeing measurable gains—from reduced help desk tickets and system downtime to harder-to-quantify benefits like higher employee satisfaction and retention. Frictionless digital workplaces, supported by real-time monitoring and automation capabilities, help organizations attend to IT issues before users experience disruptions or productivity levels dip.

There are real-world examples of seamless DEX in action: Swiss energy and infrastructure provider BKW, for instance, recently built a system that lets their IT team remotely assist employees experiencing technical difficulties across more than 140 subsidiaries. For employees, this means no more waiting for an in-person technician when their device freezes or software hiccups; IT can swoop in remotely and solve problems in minutes instead of hours.

The insurance company RLI faced a different but equally frustrating issue before switching to a centralized, remote IT support system: Technical issues like device lag or overheating were often left unreported, as employees didn’t want to disrupt their workflow or bother the IT team with seemingly minor complaints. Those small performance issues, however, could snowball over time, sometimes causing devices to fail completely. To get ahead of this phenomenon, RLI installed monitoring software to observe device performance in real time and catch issues proactively. Now, when a laptop gets too hot or starts slowing down, IT can address it right away—often before the employee even knows there’s a problem.

Ultimately, the organizations making the biggest strides in DEX recognize that digital transformation is as much about experience as it is about infrastructure. When digital tools feel like helpful extensions of workers’ expertise—rather than obstacles standing in the way of their workday—companies are in a better position to realize the full benefits of their investments.

Smart systems and smarter safeguards

Of course, as operational systems become more interconnected, security vulnerabilities multiply in turn. Consider this hypothetical: In a busy manufacturing plant, a piece of machinery suddenly breaks down. Instead of waiting hours for a technician to arrive on-site, a local operator deploys a mobile augmented reality device that projects step-by-step diagnostic instructions onto the machine. Following guidance from a remote specialist, the operator fixes the equipment and has production back on track in mere minutes.

This snappy and streamlined approach to diagnostics is undeniably efficient, but it opens up the factory floor to multiple external touchpoints: live video feeds streaming to remote experts, cloud databases containing sensitive repair procedures, and direct access to the machine’s diagnostic systems. Suddenly, a manufacturing plant that used to be an island is now part of an interconnected network.

Smart companies are getting practical about the challenges associated with this expanding threat surface. For instance, BKW has taken a structured approach to permissions: Subsidiary IT teams can only access their own company’s devices, outside contractors get temporary access for specific tasks, and employees can reach certain high-powered workstations when they need them.

Bühler, a global industrial equipment manufacturer, also uses centrally managed access controls to govern who can connect to which platforms, as well as when and under what conditions. By enforcing consistent policies from its headquarters, the company ensures all remote support activities are fully monitored and aligned with strict cybersecurity protocols, including compliance with ISO 27001 standards. The system allows Bühler’s extensive global technician network to provide real-time assistance without compromising system integrity.

The power of practical innovation

How do you help a technician troubleshoot equipment when the expert is 500 miles away? How do you catch IT problems before they shut down a production line? How do you keep operations secure without burying workers in passwords and protocols?

These are the kinds of practical questions that companies like Bühler, BKW, and RLI Insurance have focused on solving—and it’s part of why they’re succeeding where others struggle. These examples demonstrate a genuine shift in how successful companies think about technology and transformation. Instead of asking, “What’s the latest digital trend we should adopt?” they’re assessing, “What problems are our people actually trying to solve?”

The organizations pulling ahead to digitally transform frontline operations are the ones that have learned to make complex systems feel simple, intuitive, and secure to boot. Such a practical approach will only become more pressing as AI introduces new layers of complexity to operational work.

Ready to make work work better for your business? Learn how at TeamViewer.com.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The latest threat from the rise of Chinese manufacturing

In 2013, a trio of academics showed convincing evidence that increased trade with China beginning in the early 2000s and the resulting flood of cheap imports had been an unmitigated disaster for many US communities, destroying their manufacturing lifeblood.

The results of what they called the “China shock” were gut-wrenching: the loss of 1 million US manufacturing jobs and 2.4 million jobs in total by 2011.

If in retrospect all that seems obvious, it’s only because the research by David Autor, an MIT labor economist, and his colleagues has become an accepted, albeit often distorted, political narrative these days: China destroyed all our manufacturing jobs! Though the nuances are often ignored, the results help explain at least some of today’s political unrest. It’s reflected in rising calls for US protectionism, President Trump’s broad tariffs on imported goods, and nostalgia for the lost days of domestic manufacturing glory.

Our editor at large David Rotman recently spoke to Autor about what he considers a far more urgent problem——what some are calling China shock 2.0—and the lessons it holds for today’s manufacturing challenges. Read the full story.

Three things I’m into into right now

In each issue of our print magazine, we ask a member of staff to tell us about three things they’re loving at the moment. For our latest edition, which was all about power, I was in the hotseat! Check out my (frankly amazing) recommendations here, and subscribe to catch future editions here.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 A new TikTok is coming 
It’s reportedly launching a new version in the US in September ahead of a planned sale. (The Information $)
+ It’ll still require the Chinese government’s say-so. (The Verge)

2 Texas Hill Country was caught off guard by the flash floods
But now people are asking: why? (WP $)
+ America’s National Weather Service has been on the receiving end of heavy cuts. (CNN)
+ Bad weather has interrupted ongoing searches for survivors. (WSJ $)

3 Elon Musk is forging ahead with his own political party
To the chagrin of investors in his companies. (The Guardian)
+ Former friend Donald Trump has some thoughts. (Insider $)
+ The America Party is facing an uphill struggle. (WP $)

4 The Trump administration has axed a group focused on birth control safety

They were tasked with advising women which contraceptives to use. (Undark)

5 On-the-job learning is under threat
From a combination of generative AI tools and remote working culture. (FT $)

6 xAI’s ‘improved’ Grok is perpetuating anti-Semitic stereotypes
It made worrying comments about Jewish executives in Hollywood. (TechCrunch)
+ LLMs become more covertly racist with human intervention. (MIT Technology Review)

7 Taiwan wants to lessen its commercial reliance on China
But it won’t be easy. (NYT $)
+ How underwater drones could shape a potential Taiwan-China conflict. (MIT Technology Review)

8 LLMs have improved rapidly in the past few years
Benchmarking them is notoriously tricky, though. (IEEE Spectrum)
+ A Chinese firm has just launched a constantly changing set of AI benchmarks. (MIT Technology Review)

9 Big Tech’s salary divide is getting worse
Those whopping AI pay packets are at least partly to blame. (Insider $)

10 More than 30 tech unicorns have been minted during 2025
And we could see a far few more before the year is out. (TechCrunch)

Quote of the day

“If you go in with the expectation that the AI is as smart or smarter than humans, you’re quickly disappointed by the reality.”

—Eric Schwartz, chief marketing officer of Clorox, tells the Wall Street Journal that AI can’t be relied upon to come up with truly original or engaging ideas.

One more thing

Alina Chan tweeted life into the idea that the virus came from a lab

Alina Chan started asking questions in March 2020. She was chatting with friends on Facebook about the virus then spreading out of China. She thought it was strange that no one had found any infected animal. She wondered why no one was admitting another possibility, which to her seemed very obvious: the outbreak might have been due to a lab accident.

Chan is a postdoc in a gene therapy lab at the Broad Institute, a prestigious research institute affiliated with both Harvard and MIT. Throughout 2020, Chan relentlessly stoked scientific argument, and wasn’t afraid to pit her brain against the best virologists in the world. Her persistence even helped change some researchers’ minds. Read the full story.

—Antonio Regalado

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ Why 2025 might just be the year of animal escapes.
+ Very cool—an iron age settlement has been uncovered in England thanks to a lucky metal detectorist.
+ This little armadillo is having the time of their life in a paddling pool.
+ Peace and love to Mr Ringo Starr, 85 years young today!

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The findings a decade ago were, well, shocking. Mainstream economists had long argued that free trade was overall a good thing; though there might be some winners and losers, it would generally bring lower prices and widespread prosperity. Then, in 2013, a trio of academic researchers showed convincing evidence that increased trade with China beginning in the early 2000s and the resulting flood of cheap imports had been an unmitigated disaster for many US communities, destroying their manufacturing lifeblood.

The results of what in 2016 they called the “China shock” were gut-wrenching: the loss of 1 million US manufacturing jobs and 2.4 million jobs in total by 2011. Worse, these losses were heavily concentrated in what the economists called “trade-exposed” towns and cities (think furniture makers in North Carolina).

If in retrospect all that seems obvious, it’s only because the research by David Autor, an MIT labor economist, and his colleagues has become an accepted, albeit often distorted, political narrative these days: China destroyed all our manufacturing jobs! Though the nuances of the research are often ignored, the results help explain at least some of today’s political unrest. It’s reflected in rising calls for US protectionism, President Trump’s broad tariffs on imported goods, and nostalgia for the lost days of domestic manufacturing glory.

The impacts of the original China shock still scar much of the country. But Autor is now concerned about what he considers a far more urgent problem—what some are calling China shock 2.0. The US, he warns, is in danger of losing the next great manufacturing battle, this time over advanced technologies to make cars and planes as well as those enabling AI, quantum computing, and fusion energy.

Recently, I asked Autor about the lingering impacts of the China shock and the lessons it holds for today’s manufacturing challenges.

How are the impacts of the China shock still playing out?

I have a recent paper looking at 20 years of data, from 2000 to 2019. We tried to ask two related questions. One, if you looked at the places that were most exposed, how have they adjusted? And then if you look to the people who are most exposed, how have they adjusted? And how do those two things relate to one anothe

It turns out you get two very different answers. If you look at places that were most exposed, they have been substantially transformed. Manufacturing, once it starts going down, never comes back. But after 2010, these trade-impacted local labor markets staged something of an employment recovery, such that employment has grown faster after 2010 in trade-exposed places than non-trade-exposed places because a lot of people have come in. But these are jobs mostly in low-wage sectors. They’re in K–12 education and non-traded health services. They’re in warehousing and logistics. They’re in hospitality and lodging and recreation, and so they’re lower-wage, non-manufacturing jobs. And they’re done by a really different set of people.

The growth in employment is among women, among native-born Hispanics, among foreign-born adults and a lot of young people. The recovery is staged by a very different group from the white and black men, but especially white men, who were most represented in manufacturing. They have not really participated in this renaissance.

Employment is growing, but are these areas prospering?

They have a lower wage structure: fewer high-wage jobs, more low-wage jobs. So they’re not, if your definition of prospering is rapidly rising incomes. But there’s a lot of employment growth. They’re not like ghost towns. But then if you look at the people who were most concentrated in manufacturing—mostly white, non-college, native-born men—they have not prospered. Most of them have not transitioned from manufacturing to non-manufacturing.

One of the great surprises is everyone had believed that people would pull up stakes and move on. In fact, we find the opposite. People in the most adversely exposed places become less likely to leave. They have become less mobile. The presumption was that they would just relocate to find higher ground. And that is not at all what occurred.

What happened to the total number of manufacturing jobs?

There’s been no rebound. Once they go, they just keep going. If there is going to be new manufacturing, it won’t be in the sectors that were lost to China. Those were basically labor-intensive jobs, the kind of low-tech sectors that we will not be getting back. You know—commodity furniture and assembly of things, shoes, construction material. The US wasn’t going to keep them forever, and once they’re gone, it’s very unlikely to get them back.

I know you’ve written about this, but it’s not hard to draw a connection between the dynamics you’re describing—white-male manufacturing jobs going away and new jobs going to immigrants—and today’s political turmoil.

We have a paper about that called “Importing Political Polarization?”

How big a factor would you say it is in today’s political unrest?

I don’t want to say it’s the factor. The China trade shock was a catalyst, but there were lots of other things that were happening. It would be a vast oversimplification to say that it was the sole cause.

But most people don’t work in manufacturing anymore. Aren’t these impacts that you’re talking about, including the political unrest, disproportionate to the actual number of jobs lost?

These are jobs in places where manufacturing is the anchor activity. Manufacturing is very unevenly distributed. It’s not like grocery stores and hospitals that you find in every county. The impact of the China trade shock on these places was like dropping an economic bomb in the middle of downtown. If the China trade shock cost us a few million jobs, and these were all—you know—people in groceries and retail and gas stations, in hospitality and in trucking, you wouldn’t really notice it that much. We lost lots of clerical workers over the last couple of decades. Nobody talks about a clerical shock. Why not? Well, there was never a clerical capital of America. Clerical workers are everywhere. If they decline, it doesn’t wipe out the entire basis of a place.

So it goes beyond the jobs. These places lost their identity.

Maybe. But it’s also the jobs. Manufacturing offered relatively high pay to non-college workers, especially non-college men. It was an anchor of a way of life.

And we’re still seeing the damage.

Yeah, absolutely. It’s been 20 years. What’s amazing is the degree of stasis among the people who are most exposed—not the places, but the people. Though it’s been 20 years, we’re still feeling the pain and the political impacts from this transition.

Clearly, it has now entered the national psyche. Even if it weren’t true, everyone now believes it to have been a really big deal, and they’re responding to it. It continues to drive policy, political resentments, maybe even out of proportion to its economic significance. It certainly has become mythological.

What worries you now?

We’re in the midst of a totally different competition with China now that’s much, much more important. Now we’re not talking about commodity furniture and tube socks. We’re talking about semiconductors and drones and aviation, electric vehicles, shipping, fusion power, quantum, AI, robotics. These are the sectors where the US still maintains competitiveness, but they’re extremely threatened. China’s capacity for high-tech, low-cost, incredibly fast, innovative manufacturing is just unbelievable. And the Trump administration is basically fighting the war of 20 years ago. The loss of those jobs, you know, was devastating to those places. It was not devastating to the US economy as a whole. If we lose Boeing, GM, and Apple and Intel—and that’s quite possible—then that will be economically devastating.

I think some people are calling it China shock 2.0.

Yeah. And it’s well underway.

When we think about advanced manufacturing and why it’s important, it’s not so much about the number of jobs anymore, is it? Is it more about coming up with the next technologies?

It does create good jobs, but it’s about economic leadership. It’s about innovation. It’s about political leadership, and even standard setting for how the rest of the world works.

Should we just accept that manufacturing as a big source of jobs is in the past and move on?

No. It’s still 12 million jobs, right? Instead of the fantasy that we’re going to go back to 18 million or whatever—we had, what, 17.7 million manufacturing jobs in 1999—we should be worried about the fact that we’re going to end up at 6 million, that we’re going to lose 50% in the next decade. And that’s quite possible. And the Trump administration is doing a lot to help that process of loss along.

We have a labor market of over 160 million people, so it’s like 8% of employment. It’s not zero. So you should not think of it as too small to worry about it. It’s a lot of people; it’s a lot of jobs. But more important, it’s a lot of what has helped this country be a leader. So much innovation happens here, and so many of the things in which other countries are now innovating started here. It’s always been the case that the US tends to innovate in sectors and then lose them after a while and move on to the next thing. But at this point, it’s not clear that we’ll be in the frontier of a lot of these sectors for much longer.

So we want to revive manufacturing, but the right kind—advanced manufacturing?

The notion that we should be assembling iPhones in the United States, which Trump wants, is insane. Nobody wants to do that work. It’s horrible, tedious work. It pays very, very little. And if we actually did it here, it would make the iPhones 20% more expensive or more. Apple may very well decide to pay a 25% tariff rather than make the phones here. If Foxconn started doing iPhone assembly here, people would not be lining up for that job.

But at the same time, we do need new people coming into manufacturing.

But not that manufacturing. Not tedious, mind-numbing, eyestrain-inducing assembly.

We need them to do high-tech work. Manufacturing is a skilled activity. We need to build airplanes better. That takes a ton of expertise. Assembling iPhones does not.

What are your top priorities to head off China shock 2.0?

I would choose sectors that are important, and I would invest in them. I don’t think that tariffs are never justified, or industrial policies are never justified. I just don’t think protecting phone assembly is smart industrial policy. We really need to improve our ability to make semiconductors. I think that’s important. We need to remain competitive in the automobile sector—that’s important. We need to improve aviation and drones. That’s important. We need to invest in fusion power. That’s important. We need to adopt robotics at scale and improve in that sector. That’s important. I could come up with 15 things where I think public money is justified, and I would be willing to tolerate protections for those sectors.

What are the lasting lessons of the China shock and the opening up of global trade in the 2000s?

We did it too fast. We didn’t do enough to support people, and we pretended it wasn’t going on.

When we started the China shock research back around 2011, we really didn’t know what we’d find, and so we were as surprised as anyone. But the work has changed our own way of thinking and, I think, has been constructive—not because it has caused everyone to do the right thing, but it at least caused people to start asking the right questions.

What do the findings tell us about China shock 2.0?

I think the US is handling that challenge badly. The problem is much more serious this time around. The truth is, we have a sense of what the threats are. And yet we’re not seemingly responding in a very constructive way. Although we now know how seriously we should take this, the problem is that it doesn’t seem to be generating very serious policy responses. We’re generating a lot of policy responses—they’re just not serious ones.

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