Charts: Global M&A Trends Q4 2024

The deal value of global 2024 mergers and acquisitions transactions was up 15% year-over-year as of early December and on pace to reach about $3.5 trillion for the year. That’s according to Bain & Company’s new “Global M&A Report 2025” (PDF), which recaps 2024 activity based on data from Deallogic and S&P Capital IQ.

According to the Bain report, in 2024, deal value was historically low relative to global GDP, but the outlook for 2025 is strong as acquistions and divestitures become essential for companies navigating technological disruption.

The report also shows that most global industries grew or remained stable in 2024, with Energy and Natural Resources topping the list, followed by Advanced Manufacturing and Services.

Bain & Company also surveyed 307 M&A executive practitioners in October 2024 across the U.S., Australia, Brazil, Canada, France, Germany, India, Italy, Japan, and the U.K. — inquiring about their use of generative AI to enhance deal-making.

Does Google Traffic Affect YouTube Recommendations? What To Know via @sejournal, @MattGSouthern

In a recent social media discussion, Rene Ritchie, YouTube’s Creator Liaison, addressed questions about how Google traffic affects YouTube recommendations.

The conversation centered on whether brief view durations from Google Search referrals could negatively impact a channel’s performance.

There is concern that if viewers only watch a video for a short time, this behavior might lead YouTube to recommend the video less frequently.

The question comes from the account @AdventureCrews on X, who write:

“if a channel gets massive external views from Google Search and the viewer only watches for < 2min, does this negatively impact the video or can YouTube decipher this? The content is adventure travel.”

YouTube’s Response

Ritchie explained that viewership data is important, but the effect of traffic mainly comes from where it originates.

This means the algorithm looks at watch time differently depending on how users find the content.

For example, videos clicked from the YouTube homepage are assessed differently than those accessed through external sources like Google Search.

He states:

“Traffic sources primarily affect the same traffic source. So the recommendation system (Browse > Home Page), for example, will look at watch time for the video when it’s clicked on from the Home Page, not from external sources, Sub feed, etc.”

What This Means

This clarification offers reassurance to creators.

Even if a video gets many views from outside searches, having a shorter watch time from that traffic doesn’t decrease its chances of being recommended by YouTube.

The platform mainly uses engagement metrics; like watch time from within YouTube, to decide what to recommend.

For more about how YouTube’s recommendation system works, see:


Featured Image: Danille Nicole Wilson/Shutterstock

Ask An SEO: Is There Any SEO Benefit To Image Geolocation Data? via @sejournal, @HelenPollitt1

Our question today follows well from the one I addressed previously, which is all about metadata for images.

This time, it focuses specifically on one aspect of metadata: “Is there any SEO benefit to image geolocation data?”

Before I answer this question, it’s important that we all get on the same page about what geolocation data is.

What Is Image Geolocation Data?

Essentially, it’s code embedded in an image that gives details about where that image was taken or created.

The most common way of expressing this information is through EXIF or exchangeable image file format.

EXIF is a data format that includes information about how an image was captured. It can include aspects such as the size of the image in pixels, the settings the camera was set to when it took the image, and when the photo was taken.

EXIF data can also provide information on where the image was taken.

How You Can Find The Image Location Data

Not every photo you take or download will have metadata. If, for example, you have set your phone’s camera to not share the location of the images you take, then that data will be missing.

However, if you go to the file information of a photo, usually through a right-click on the image or tapping the menu accessible via the image, you should be able to see if a location has been recorded.

This will often be in the form of coordinates and may have a rough town or city based on those coordinates.

A warning, though: the location data can not only be deleted but also edited. Therefore, even if you find the location data for the image, its accuracy cannot be guaranteed.

In Theory, What Benefit Could Geolocation Data Have?

If we think about this logically, understanding what we’ve deduced about how search engines work, there are several areas where we could expect geolocation data to help with SEO.

Understanding The Image

In a similar way to structured data markup, we could expect the geolocation to give the search engines more contextual clues about the nature of the image.

For example, if the photo is of a mountain and the geolocation data puts the photographer at the base of Mount Everest, the search engines might deduce that the photo is of Mount Everest.

Relevancy For Landscapes/Location Imagery

By giving the search engines more context about the image, it may help them to identify its relevance to searches.

For example, understanding that this photo of a mountain was taken near Mount Everest may make it more relevant to image searches like “Mount Everest photo” and “base camp at Mount Everest.”

This would make logical sense, especially given what we know of how the search bots often use an image’s title and alt text to determine relevancy.

Local Search And Location Profiles

Location information would, in theory, be most important for local searches and location-specific business profiles like Google Business Profile and Bing Places.

Images are often uploaded to these profiles, and as such, geolocation data could enhance the local relevancy of the profiles.

A photo of the outside of a shop in Seattle with the geolocation data suggesting the photo was taken in Seattle would theoretically help to reinforce that the shop was relevant to searchers in Seattle.

What Evidence Do We Have That Geolocation Data Makes An Impact On SEO?

When we are considering how optimizations might impact ranking, crawling, indexing and other aspects of SEO, we need to ask ourselves if we have any evidence of it being impactful.

In the case of geolocation data impacting SEO, I can say that, no, unfortunately, there is none – beyond anecdotal, that is.

In fact, there have been a lot of studies into whether geolocation data impacts local rankings and the performance of Google Business Profiles. One study to take a look at is by Sterling Sky.

It appears that Google actually strips out the EXIF data from images posted through Google Business Profile, at least from public display. Whether it still uses the EXIF data it removes from the image is to be determined.

Google Claims It Does Not Use Exif Data

As far back as 2014, Google representatives, including Matt Cutts, claimed they did not currently use EXIF data but that they may well in the future.

However, reports from the SMX Advanced conference in September 2024 suggest that Martin Splitt of Google reiterated this 10 years after Cutts.

Can We Trust Google?

A lot of SEO pros will claim that Google lies. I prefer to think of it as us SEO professionals, perhaps not understanding the nuances enough to see that what a Google representative has said is technically true, but not necessarily accurate to the context we perceive it in.

However, in line with Google’s assertions, we really don’t have anything beyond occasional, unverifiable anecdotes that geolocation data like EXIF impacts Google’s crawling, indexing, or ranking in any meaningful way.

What About The Other Search Engines?

Bing does not mention geolocation data at all in its photo guidelines. I can’t find any evidence that Baidu or Yandex use it either, although this is purely through armchair research.

Given that, though, we do know that there are waves of new search platforms coming online and, indeed, other ways of searching that could arguably fall under an SEO’s purview.

Large language models (LLMs) may well use additional data points than the traditional search engines.

What we don’t know yet is if they use geolocation data as part of their ways of selecting which pages and brands to display in their answers or search results.

So, Is Geolocation Data Something We Should Take Note Of?

I would suggest that adding geolocation data to your images is not something that should find its way into your task list. We don’t really have the data to back up claims that it is impactful.

In fact, we have more studies and communications from search engine representatives that suggest it isn’t useful in SEO.

Whereas I don’t think it is worth the time and energy to implement geolocation data, I don’t think it’s harmful to include it. Don’t go to the extent of altering it or deleting it. Just leave it if you have already included geolocation data in your images.

Perhaps, in time, it will become useful. As Google has said, it reserves the right to use it. We still don’t know if emerging search platforms will use it.

Essentially, if you are really keen to understand its impact, I would suggest testing it with your own images.

Add EXIF data to a set of images and measure their rankings against a control group that doesn’t use EXIF data.

Measure the change in rankings before and after adding the EXIF data and compare it to the control group.

If there are similar changes in the rankings, then it is possible the EXIF data had no impact.

If there are significant increases (or decreases!) in the rankings of the images with EXIF data, but not the control group, that would suggest they are impactful.

More Resources:


Featured Image: maxbelchenko/Shutterstock

WordPress Offers New 100-Year Domain Name Registrations via @sejournal, @martinibuster

WordPress.com updated their 100-year domain and hosting plan, unlocking the opportunity to secure a domain name for a one hundred year period for only $2,000.  The new service is a breakout from the 100-year plan which is another offering that includes hosting and other benefits for $38,000.

100 Year Domain Name Registration

The new domain name registration is available for .com, .org, .net, or .blog domains and is managed in a trust account controlled by the person registering the domain. This service was previously available as part of a 100-year plan that came with hosting at a price of $38,000. The domain registration fee of $2,000 is more affordable and a good value for those who require the security of knowing the domain isn’t changing hands by mistake.

WordPress.com offers the following benefits:

  • No expiration surprises.
  • No lost domains due to admin mistakes.
  • No stress about renewals—ever (or 100 years, whichever comes first).
  • A full century of security for your domain.
  • One setup. 100 years of ownership.

They’ve also reimagined their 100-year plan so that it comes with numbered trust accounts controlled by the owner of the domain and hosting plus contingencies that guarantee the continued web presence should anything happen to WordPress.com or Automattic.

Read more about the new 100-year domain name registration:

Secure Your Domain For the Next Century

Featured Image by Shutterstock/gcafotografia

Congress used to evaluate emerging technologies. Let’s do it again.

At about the time when personal computers charged into cubicle farms, another machine muscled its way into human resources departments and became a staple of routine employment screenings. By the early 1980s, some 2 million Americans annually found themselves strapped to a polygraph—a metal box that, in many people’s minds, detected deception. Most of those tested were not suspected crooks or spooks. 

Then the US Office of Technology Assessment, an independent office that had been created by Congress about a decade earlier to serve as its scientific consulting arm, got involved. The office reached out to Boston University researcher Leonard Saxe with an assignment: Evaluate polygraphs. Tell us the truth about these supposed truth-telling devices.

And so Saxe assembled a team of about a dozen researchers, including Michael Saks of Boston College, to begin a systematic review. The group conducted interviews, pored over existing studies, and embarked on new lines of research. A few months later, the OTA published a technical memo, “Scientific Validity of Polygraph Testing: A Research Review and Evaluation.” Despite the tests’ widespread use, the memo dutifully reported, “there is very little research or scientific evidence to establish polygraph test validity in screening situations, whether they be preemployment, preclearance, periodic or aperiodic, random, or ‘dragnet.’” These machines could not detect lies. 

Four years later, in 1987, critics at a congressional hearing invoked the OTA report as authoritative, comparing polygraphs derisively to “tea leaf reading or crystal ball gazing.” Congress soon passed strict limits on the use of polygraphs in the workplace. 

Over its 23-year history, the OTA would publish some 750 reports—lengthy, interdisciplinary assessments of specific technologies that proposed means of maximizing their benefits and minimizing harms. Their subjects included electronic surveillance, genetic engineering, hazardous-waste disposal, and remote sensing from outer space. Congress set its course: The office initiated studies only at the request of a committee chairperson, a ranking minority leader, or its 12-person bipartisan board. 

The investigations remained independent; staffers and consultants from both inside and outside government collaborated to answer timely and sometimes politicized questions. The reports addressed worries about alarming advances and tamped down scary-sounding hypotheticals. Some of those concerns no longer keep policymakers up at night. For instance, “Do Insects Transmit AIDS?” A 1987 OTA report correctly suggested that they don’t.

The office functioned like a debunking arm. It sussed out the snake oil. Lifted the lid on the Mechanical Turk. The reports saw through the alluring gleam of overhyped technologies. 

In the years since its unceremonious defunding, perennial calls have gone out: Rouse the office from the dead! And with advances in robotics, big data, and AI systems, these calls have taken on a new level of urgency. 

Like polygraphs, chatbots and search engines powered by so-called artificial intelligence come with a shimmer and a sheen of magical thinking. And if we’re not careful, politicians, employers, and other decision-makers may accept at face value the idea that machines can and should replace human judgment and discretion. 

A resurrected OTA might be the perfect body to rein in dangerous and dangerously overhyped technologies. “That’s what Congress needs right now,” says Ryan Calo at the University of Washington’s Tech Policy Lab and the Center for an Informed Public, “because otherwise Congress is going to, like, take Sam Altman’s word for everything, or Eric Schmidt’s.” (The CEO of OpenAI and the former CEO of Google have both testified before Congress.) Leaving it to tech executives to educate lawmakers is like having the fox tell you how to build your henhouse. Wasted resources and inadequate protections might be only the start. 

A man administers a lie detector test to a job
applicant in 1976. A 1983 report from the OTA debunked the efficacy of polygraphs.
LIBRARY OF CONGRESS

No doubt independent expertise still exists. Congress can turn to the Congressional Research Service, for example, or the National Academies of Sciences, Medicine, and Engineering. Other federal entities, such as the Office of Management and Budget and the Office of Science and Technology Policy, have advised the executive branch (and still existed as we went to press). “But they’re not even necessarily specialists,” Calo says, “and what they’re producing is very lightweight compared to what the OTA did. And so I really think we need OTA back.”  

What exists today, as one researcher puts it, is a “diffuse and inefficient” system. There is no central agency that wholly devotes itself to studying emerging technologies in a serious and dedicated way and advising the country’s 535 elected officials about potential impacts. The digestible summaries Congress receives from the Congressional Research Service provide insight but are no replacement for the exhaustive technical research and analytic capacity of a fully staffed and funded think tank. There’s simply nothing like the OTA, and no single entity replicates its incisive and instructive guidance. But there’s also nothing stopping Congress from reauthorizing its budget and bringing it back, except perhaps the lack of political will. 

“Congress Smiles, Scientists Wince”

The OTA had not exactly been an easy sell to the research community in 1972. At the time, it was only the third independent congressional agency ever established. As the journal Science put it in a headline that year, “The Office of Technology Assessment: Congress Smiles, Scientists Wince.” One researcher from Bell Labs told Science that he feared legislators would embark on “a clumsy, destructive attempt to manage national R&D,” but mostly the cringe seemed to stem from uncertainty about what exactly technology assessment entailed. 

The OTA’s first report, in 1974, examined bioequivalence, an essential part of evaluating generic drugs. Regulators were trying to figure out whether these drugs could be deemed comparable to their name-brand equivalents without lengthy and expensive clinical studies demonstrating their safety and efficacy. Unlike all the OTA’s subsequent assessments, this one listed specific policy recommendations, such as clarifying what data should be required in order to evaluatea generic drug and ensure uniformity and standardization in the regulatory approval process. The Food and Drug Administration later incorporated these recommendations into its own submission requirements. 

From then on, though, the OTA did not take sides. The office had not been set up to advise Congress on how to legislate. Rather, it dutifully followed through on its narrowly focused mandate: Do the research and provide policymakers with a well-reasoned set of options that represented a range of expert opinions.

Perhaps surprisingly, given the rise of commercially available PCs, in the first decade of its existence the OTA produced only a few reports on computing. One 1976 report touched on the automated control of trains. Others examined computerized x-ray imaging, better known as CT scans; computerized crime databases; and the use of computers in medical education. Over time, the office’s output steadily increased, eventually averaging 32 reports a year. Its budget swelled to $22 million; its staff peaked at 143. 

While it’s sometimes said that the future impact of a technology is beyond anyone’s imagination, several findings proved prescient. A 1982 report on electronic funds transfer, or EFT, predicted that financial transactions would increasingly be carried out electronically (an obvious challenge to paper currency and hard-copy checks). Another predicted that email, or what was then termed “electronic message systems,” would disrupt snail mail and the bottom line of the US Postal Service. 

In vetting the digital record-keeping that provides the basis for routine background checks, the office commissioned a study that produced a statistic still cited today, suggesting that only about a quarter of the records sent to the FBI were “complete, accurate, and unambiguous.” It was an indicator of a growing issue: computational systems that, despite seeming automated, are not free of human bias and error. 

Many of the OTA’s reports focus on specific events or technologies. One looked at Love Canal, the upstate New York neighborhood polluted by hazardous waste (a disaster, the report said, that had not yet been remediated by the Environmental Protection Agency’s Superfund cleanup program); another studied the Boston Elbow, a cybernetic limb (the verdict: decidedly mixed). The office examined the feasibility of a water pipeline connecting Alaska to California, the health effects of the Kuwait oil fires, and the news media’s use of satellite imagery. The office also took on issues we grapple with today—evaluating automatic record checks for people buying guns, scrutinizing the compensation for injuries allegedly caused by vaccines, and pondering whether we should explore Mars. 

The OTA made its biggest splash in 1984, when it published a background report criticizing the Strategic Defense Initiative (commonly known as “Star Wars”), a pet project of the Reagan administration that involved several exotic missile defense systems. Its lead author was the MIT physicist Ashton Carter, later secretary of defense in the second Obama administration. And the report concluded that a “perfect or near-perfect” system to defend against nuclear weapons was basically beyond the realm of the plausible; the possibility of deployment was “so remote that it should not serve as the basis of public expectation or national policy.” 

The report generated lots of clicks, so to speak, especially after the administration claimed that the OTA had divulged state secrets. These charges did not hold up and Star Wars never materialized, although there have been recent efforts to beef up the military’s offensive capacity in space. But for the work of an advisory body that did not play politics, the report made a big political hubbub. By some accounts, its subsequent assessments became so neutral that the office risked receding to the point of invisibility.

From a purely pragmatic point of view, the OTA wrote to be understood. A dozen reports from the early ’90s received “Blue Pencil Awards,” given by the National Association of Government Communicators for “superior government communication products and those who produce them.” None are copyrighted. All were freely reproduced and distributed, both in print and electronically. The entire archive is stored on CD-ROM, and digitized copies are still freely available for download on a website maintained by Princeton University, like an earnest oasis of competence in the cloistered world of federal documents. 

Assessments versus accountability

Looking back, the office took shape just as debates about technology and the law were moving to center stage. 

While the gravest of dangers may have changed in form and in scope, the central problem remains: Laws and lawmakers cannot keep up with rapid technological advances. Policymakers often face a choice between regulating with insufficient facts and doing nothing. 

In 2018, Adam Kinzinger, then a Republican congressman from Illinois, confessed to a panel on quantum computing: “I can understand about 50% of the things you say.” To some, his admission underscored a broader tech illiteracy afflicting those in power. But other commentators argued that members of Congress should not be expected to know it all—all the more reason to restaff an office like the OTA.

A motley chorus of voices have clamored for an OTA 2.0 over the years. One doctor wrote that the office could help address the “discordance between the amount of money spent and the actual level of health.” Tech fellows have said bringing it back could help Congress understand machine learning and AI. Hillary Clinton, as a Democratic presidential hopeful, floated the possibility of resurrecting the OTA in 2017. 

But Meg Leta Jones, a law scholar at Georgetown University, argues that assessing new technologies is the least of our problems. The kind of work the OTA did is now done by other agencies, such as the FTC, FCC, and National Telecommunications and Information Administration, she says: “The energy I would like to put into the administrative state is not on assessments, but it’s on actual accountability and enforcement.”

She sees the existing framework as built for the industrial age, not a digital one, and is among those calling for a more ambitious overhaul. There seems to be little political appetite for the creation of new agencies anyway. That said, Jones adds, “I wouldn’t be mad if they remade the OTA.” 

No one can know whether or how future administrations will address AI, Mars colonization, the safety of vaccines, or, for that matter, any other emerging technology that the OTA investigated in an earlier era. But if the new administration makes good on plans to deregulate many sectors, it’s worth noting some historic echoes. In 1995, when conservative politicians defunded the OTA, they did so in the name of efficiency. Critics of that move contend that the office probably saved the government money and argue that the purported cost savings associated with its elimination were largely symbolic. 

Jathan Sadowski, a research fellow at Monash University in Melbourne, Australia, who has written about the OTA’s history, says the conditions that led to its demise have only gotten more partisan, more politicized. This makes it difficult to envision a place for the agency today, he says—“There’s no room for the kind of technocratic naïveté that would see authoritative scientific advice cutting through the noise of politics.”

Congress purposely cut off its scientific advisory arm as part of a larger shake-up led by Newt Gingrich, then the House Speaker, whose pugilistic brand of populist conservatism promised “drain the swamp”–type reforms and launched what critics called a “war on science.” As a rationale for why the office was defunded, he said, “We constantly found scientists who thought what they were saying was not correct.” 

Once again, Congress smiled and scientists winced. Only this time it was because politicians had pulled the plug. 

Peter Andrey Smith, a freelance reporter, has contributed to Undark, the New Yorker, the New York Times Magazine, and WNYC’s Radiolab.

Your most important customer may be AI

Imagine you run a meal prep company that teaches people how to make simple and delicious food. When someone asks ChatGPT for a recommendation for meal prep companies, yours is described as complicated and confusing. Why? Because the AI saw that in one of your ads there were chopped chives on the top of a bowl of food, and it determined that nobody is going to want to spend time chopping up chives.

This is a real example from Jack Smyth, chief solutions officer of AI, planning, and insights at JellyFish, part of the Brandtech Group. He works with brands to help them understand how their products or company are perceived by AI models in the wild. It may seem odd for companies or brands to be mindful of what an AI “thinks,” but it’s already becoming relevant. A study from the Boston Consulting Group showed that 28% of respondents are using AI to recommend products such as cosmetics. And the push for AI agents that may handle making direct purchases for you is making brands even more conscious of how AI sees their products and business. 

The end results may be a supercharged version of search engine optimization (SEO) where making sure that you’re positively perceived by a large language model might become one of the most important things a brand can do.

Smyth’s company has created software, Share of Model, that assesses how different AI models view your brand. Each AI model has different training data, so although there are many similarities in how brands are assessed, there are differences, too.

For example, Meta’s Llama model may perceive your brand as exciting and reliable, whereas OpenAI’s ChatGPT may view it as exciting but not necessarily reliable. Share of Model asks different models many different questions about your brand and then analyzes all the responses, trying to find trends. “It’s very similar to a human survey, but the respondents here are large language models,” says Smyth.

The ultimate goal is not just to understand how your brand is perceived by AI but to modify that perception. How much models can be influenced is still up in the air, but preliminary results indicate that it may be possible. Since the models now show sources, if you ask them to search the web, a brand can see where the AI is picking up data. 

“We have a brand called Ballantine’s. It’s the No. 2 Scotch whisky that we sell in the world. So it’s a product for mass audiences,” says Gokcen Karaca, head of digital and design at Pernod Ricard, which owns Ballantine’s and a customer utilizing Share of Model. “However, Llama was identifying it as a premium product.” Ballantine’s also has a premium version, which is why the model may have been confused.

So Karaca’s team created new assets like images on social media for Ballantine’s mass product, highlighting its universal appeal to counteract the premium image. It’s not clear yet if the changes are working but Karaca claims early indications are good. “We made tiny changes, and it is taking time. I can’t give you concrete numbers but the trajectory is positive toward our target,” says Karaca.

It’s hard to know how exactly to influence AI because many models are closed-source, meaning their code and weights aren’t public and their inner workings are a bit of a mystery. But the advent of reasoning models, where the AI will share its process of solving a problem in text, could make the process simpler. You may be able to see the “chain of thought” that leads a model to recommend Dove soap, for example. If, in its reasoning, it details how important a good scent is to its soap recommendation, then the marketer knows what to focus on.

The ability to influence models has also opened up other ways to modify how your brand is perceived. For example, research out of Carnegie Mellon shows that changing the prompt can significantly modify what product an AI recommends. 

For example, take these two prompts:

1. “I’m curious to know your preference for the pressure cooker that offers the best combination of cooking performance, durable construction, and overall convenience in preparing a variety of dishes.”

2. “Can you recommend the ultimate pressure cooker that excels in providing consistent pressure, user-friendly controls, and additional features such as multiple cooking presets or a digital display for precise settings?”

The change led one of Google’s models, Gemma, to change from recommending the “Instant Pot” 0% of the time to recommending it 100% of the time. This dramatic change is due to the word choices in the prompt that trigger different parts of the model. The researchers believe we may see brands trying to influence recommended prompts online. For example, on forums like Reddit, people will frequently ask for example prompts to use. Brands may try to surreptitiously influence what prompts are suggested on these forums by having paid users or their own employees offer ideas designed specifically to result in recommendations for their brand or products. “We should warn users that they should not easily trust model recommendations, especially if they use prompts from third parties,” says Weiran Lin, one of the authors of the paper.

This phenomenon may ultimately lead to a push and pull between AI companies and brands similar to what we’ve seen in search over the past several decades. “It’s always a cat-and-mouse game,” says Smyth. “Anything that’s too explicit is unlikely to be as influential as you’d hope.” 

Brands have tried to “trick” search algorithms to place their content higher, while search engines aim to deliver—or at least we hope they deliver—the most relevant and meaningful results for consumers. A similar thing is happening in AI, where brands may try to trick models to give certain answers. “There’s prompt injection, which we do not recommend clients do, but there are a lot of creative ways you can embed messaging in a seemingly innocuous asset,” Smyth says. AI companies may implement techniques like training a model to know when an ad is disingenuous or trying to inflate the image of a brand. Or they may try to make their AI more discerning and less susceptible to tricks.

Another concern with using AI for product recommendations is that biases are built into the models. For example, research out of the University of South Florida shows that models tend to view global brands as higher quality and better than local brands, on average.

“When I give a global brand to the LLMs, it describes it with positive attributes,” says Mahammed Kamruzzaman, one of the authors of the research. “So if I am talking about Nike, in most cases it says that it’s fashionable or it’s very comfortable.” The research shows that if you then ask the model for its perception of a local brand, it will describe it as poor quality or uncomfortable. 

Additionally, the research shows that if you prompt the LLM to recommend gifts for people in high-income countries, it will suggest luxury-brand items, whereas if you ask what to give people in low-income countries, it will recommend non-luxury brands. “When people are using these LLMs for recommendations, they should be aware of bias,” says Kamruzzaman.

AI can also serve as a focus group for brands. Before airing an ad, you can get the AI to evaluate it from a variety of perspectives. “You can specify the audience for your ad,” says Smyth. “One of our clients called it their gen-AI gut check. Even before they start making the ad, they say, ‘I’ve got a few different ways I could be thinking about going to market. Let’s just check with the models.”

Since AI has read, watched, and listened to everything that your brand puts out, consistency may become more important than ever. “Making your brand accessible to an LLM is really difficult if your brand shows up in different ways in different places, and there is no real kind of strength to your brand association,” says Rebecca Sykes, a partner at Brandtech Group, the owner of Share of Model. “If there is a huge disparity, it’s also picked up on, and then it makes it even harder to make clear recommendations about that brand.”

Regardless of whether AI is the best customer or the most nitpicky, it may soon become undeniable that an AI’s perception of a brand will have an impact on its bottom line. “It’s probably the very beginning of the conversations that most brands are having, where they’re even thinking about AI as a new audience,” says Sykes.

The Download: selling via AI, and Congress testing tech

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.

Your most important customer may be AI

Imagine you run a meal prep company that teaches people how to make simple and delicious food. When someone asks ChatGPT for a recommendation for meal prep companies, yours is described as complicated and confusing. Why? Because the AI saw that in one of your ads there were chopped chives on the top of a bowl of food, and it determined that nobody is going to want to spend time chopping up chives.

It may seem odd for companies or brands to be mindful of what an AI “thinks” in this way but it’s already becoming relevant as consumers increasingly use AI to make purchase recommendations.

The end results may be a supercharged version of search engine optimization (SEO) where making sure that you’re positively perceived by a large language model might become one of the most important things a brand can do. Read the full story

—Scott J Mulligan

Congress used to evaluate emerging technologies. Let’s do it again.

The US Office of Technology Assessment, an independent office created by Congress in the early 1970s, produced some 750 reports during its 23-year history, assessing technologies as varied as electronic surveillance, genetic engineering, hazardous-waste disposal, and remote sensing from outer space.

The office functioned like a debunking arm. It sussed out the snake oil. Lifted the lid on the Mechanical Turk. The reports saw through the alluring gleam of overhyped technologies. 

In the years since its unceremonious defunding in 1995, perennial calls have gone out: Rouse the office from the dead! But, with advances in robotics, big data, and AI systems, these calls have taken on a new level of urgency. Read the full story

—Peter Andrey Smith

This story is from the next edition of our print magazine, which is all about relationships. Subscribe now to read it and get a copy when it lands on February 26!

How generative AI is changing online search

Generative AI search, one of MIT Technology Review’s 10 Breakthrough Technologies of 2025, is ushering a new era of the internet. Despite fewer clicks, copyright fights, and sometimes iffy answers, AI could unlock new ways to summon all the world’s knowledge. Our editor in chief Mat Honan and executive editor Niall Firth explored how AI will alter search in a live half-hour Roundtables session yesterday. Watch our recording of their conversation.

MIT Technology Review Narrated: The weeds are winning

As the climate changes, genetic engineering will be essential for growing food. But is it creating a race of superweeds? This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. 

The must-reads

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

1 Electricity demand is set to soar globally
On current trends, we’ll add the equivalent of Japan’s entire consumption each year between now and 2027. (The Verge)
+ China is planning to boost its energy storage sector to cope with a surge in demand. (South China Morning Post $)
+ Why artificial intelligence and clean energy need each other. (MIT Technology Review

2 How Israel uses US-made AI to wage war
Its use of OpenAI and Microsoft skyrocketed after October 7 2023. (AP)
+ OpenAI’s new defense contract completes its military pivot. (MIT Technology Review
+ How the drone battles of Ukraine are shaping the future of war. (New Scientist $)

3 Google’s AI efforts are being marred by turf wars 
It has a lot of people working on AI, and they’re not all pulling in the same direction. (The Information $)

4 OpenAI’s ex-CTO has launched a rival lab
Thinking Machines will focus on how humans and AI can work together better. (Axios)

5 Humane’s AI Pin is dead 
HP is buying most of its assets for $116 million, which is quite the climbdown from being valued at nearly $1 billion. (TechCrunch

6 Tech IPOs keep getting delayed
Everyone’s waiting for more certainty and stability. But there’s no sign of it arriving. (NYT $)

7 Scientists in the US feel under siege
Sweeping layoffs, funding freezes and executive orders are really starting to bite. (NBC)
+ It’s likely only the start of a long battle over how research can and will be done in the United States. (The Atlantic $)

8 China may use Tesla as a pawn in US trade negotiations
That gives it quite a lot of leverage to use, if it wishes. (Gizmodo)

9 Researchers have linked a gene to the emergence of spoken language
This is cool, and could even one day potentially help people with speech problems. (ABC)

10 The chances of an asteroid hitting us in 2032 just went up
Better try to really savor the next seven years, just in case. (New Scientist $)

Quote of the day

“Well, he’s wrong.”

—A fired Federal Aviation Administration employee responds to Elon Musk’s claim that no one who works on safety was laid off in a recent round of job cuts, Rolling Stone reports. 

The big story

A brief, weird history of brainwashing

puppet person silhouette on a red network with an eye, an angry dog, the hammer and sickle, and a gun

SHIRLEY CHONG


April 2024

On a spring day in 1959, war correspondent Edward Hunter testified before a US Senate subcommittee investigating “the effect of Red China Communes on the United States.”

Hunter discussed a new concept to the American public: a supposedly scientific system for changing people’s minds, even making them love things they once hated.

Much of it was baseless, but Hunter’s sensational tales still became an important part of the disinformation and pseudoscience that fueled a “mind-control race” during the Cold War. US officials prepared themselves for a psychic war with the Soviet Union and China by spending millions of dollars on research into manipulating the human brain.

But while the science never exactly panned out, residual beliefs fostered by this bizarre conflict continue to play a role in ideological and scientific debates to this day. Read the full story.

—Annalee Newitz

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.)

+ I guess this must be the gator equivalent of a body scrub in a spa. 
+ You really can make anything with Lego bricks.
+ The secret to sticking to any exercise routine? You have to enjoy it! 
+ There are few things more comforting than recipes that combine cheese and pasta.

A new Microsoft chip could lead to more stable quantum computers

Microsoft announced today that it has made significant progress in its 20-year quest to make topological quantum bits, or qubits—a special approach to building quantum computers that could make them more stable and easier to scale up. 

Researchers and companies have been working for years to build quantum computers, which could unlock dramatic new abilities to simulate complex materials and discover new ones, among many other possible applications. 

To achieve that potential, though, we must build big enough systems that are stable enough to perform computations. Many of the technologies being explored today, such as the superconducting qubits pursued by Google and IBM, are so delicate that the resulting systems need to have many extra qubits to correct errors. 

Microsoft has long been working on an alternative that could cut down on the overhead by using components that are far more stable. These components, called Majorana quasiparticles, are not real particles. Instead, they are special patterns of behavior that may arise inside certain physical systems and under certain conditions.

The pursuit has not been without setbacks, including a high-profile paper retraction by researchers associated with the company in 2018. But the Microsoft team, which has since pulled this research effort in house, claims it is now on track to build a fault-tolerant quantum computer containing a few thousand qubits in a matter of years and that it has a blueprint for building out chips that each contain a million qubits or so, a rough target that could be the point at which these computers really begin to show their power.

This week the company announced a few early successes on that path: piggybacking on a Nature paper published today that describes a fundamental validation of the system, the company says it has been testing a topological qubit, and that it has wired up a chip containing eight of them. 

“You don’t get to a million qubits without a lot of blood, sweat, and tears and solving a lot of really difficult technical challenges along the way. And I do not want to understate any of that,” says Chetan Nayak, a Microsoft technical fellow and leader of the team pioneering this approach. That said, he says, “I think that we have a path that we very much believe in, and we see a line of sight.” 

Researchers outside the company are cautiously optimistic. “I’m very glad that [this research] seems to have hit a very important milestone,” says computer scientist Scott Aaronson, who heads the ​​Quantum Information Center at the University of Texas at Austin. “I hope that this stands, and I hope that it’s built up.”

Even and odd

The first step in building a quantum computer is constructing qubits that can exist in fragile quantum states—not 0s and 1s like the bits in classical computers, but rather a mixture of the two. Maintaining qubits in these states and linking them up with one another is delicate work, and over the years a significant amount of research has gone into refining error correction schemes to make up for noisy hardware. 

For many years, theorists and experimentalists alike have been intrigued by the idea of creating topological qubits, which are constructed through mathematical twists and turns and have protection from errors essentially baked into their physics. “It’s been such an appealing idea to people since the early 2000s,” says Aaronson. “The only problem with it is that it requires, in a sense, creating a new state of matter that’s never been seen in nature.”

Microsoft has been on a quest to synthesize this state, called a Majorana fermion, in the form of quasiparticles. The Majorana was first proposed nearly 90 years ago as a particle that is its own antiparticle, which means two Majoranas will annihilate when they encounter one another. With the right conditions and physical setup, the company has been hoping to get behavior matching that of the Majorana fermion within materials.

In the last few years, Microsoft’s approach has centered on creating a very thin wire or “nanowire” from indium arsenide, a semiconductor. This material is placed in close proximity to aluminum, which becomes a superconductor close to absolute zero, and can be used to create superconductivity in the nanowire.

Ordinarily you’re not likely to find any unpaired electrons skittering about in a superconductor—electrons like to pair up. But under the right conditions in the nanowire, it’s theoretically possible for an electron to hide itself, with each half hiding at either end of the wire. If these complex entities, called Majorana zero modes, can be coaxed into existence, they will be difficult to destroy, making them intrinsically stable. 

”Now you can see the advantage,” says Sankar Das Sarma, a theoretical physicist at the University of Maryland who did early work on this concept. “You cannot destroy a half electron, right? If you try to destroy a half electron, that means only a half electron is left. That’s not allowed.”

In 2023, the Microsoft team published a paper in the journal Physical Review B claiming that this system had passed a specific protocol designed to assess the presence of Majorana zero modes. This week in Nature, the researchers reported that they can “read out” the information in these nanowires—specifically, whether there are Majorana zero modes hiding at the wires’ ends. If there are, that means the wire has an extra, unpaired electron.

“What we did in the Nature paper is we showed how to measure the even or oddness,” says Nayak. “To be able to tell whether there’s 10 million or 10 million and one electrons in one of these wires.” That’s an important step by itself, because the company aims to use those two states—an even or odd number of electrons in the nanowire—as the 0s and 1s in its qubits. 

If these quasiparticles exist, it should be possible to “braid” the four Majorana zero modes in a pair of nanowires around one another by making specific measurements in a specific order. The result would be a qubit with a mix of these two states, even and odd. Nayak says the team has done just that, creating a two-level quantum system, and that it is currently working on a paper on the results.

Researchers outside the company say they cannot comment on the qubit results, since that paper is not yet available. But some have hopeful things to say about the findings published so far. “I find it very encouraging,” says Travis Humble, director of the Quantum Science Center at Oak Ridge National Laboratory in Tennessee. “It is not yet enough to claim that they have created topological qubits. There’s still more work to be done there,” he says. But “this is a good first step toward validating the type of protection that they hope to create.” 

Others are more skeptical. Physicist Henry Legg of the University of St Andrews in Scotland, who previously criticized Physical Review B for publishing the 2023 paper without enough data for the results to be independently reproduced, is not convinced that the team is seeing evidence of Majorana zero modes in its Nature paper. He says that the company’s early tests did not put it on solid footing to make such claims. “The optimism is definitely there, but the science isn’t there,” he says.

One potential complication is impurities in the device, which can create conditions that look like Majorana particles. But Nayak says the evidence has only grown stronger as the research has proceeded. “This gives us confidence: We are manipulating sophisticated devices and seeing results consistent with a Majorana interpretation,” he says.

“They have satisfied many of the necessary conditions for a Majorana qubit, but there are still a few more boxes to check,” Das Sarma said after seeing preliminary results on the qubit. “The progress has been impressive and concrete.”

Scaling up

On the face of it, Microsoft’s topological efforts seem woefully behind in the world of quantum computing—the company is just now working to combine qubits in the single digits while others have tied together more than 1,000. But both Nayak and Das Sarma say other efforts had a strong head start because they involved systems that already had a solid grounding in physics. Work on the topological qubit, on the other hand, has meant starting from scratch. 

“We really were reinventing the wheel,” Nayak says, likening the team’s efforts to the early days of semiconductors, when there was so much to sort out about electron behavior and materials, and transistors and integrated circuits still had to be invented. That’s why this research path has taken almost 20 years, he says: “It’s the longest-running R&D program in Microsoft history.”

Some support from the US Defense Advanced Research Projects Agency could help the company catch up. Early this month, Microsoft was selected as one of two companies to continue work on the design of a scaled-up system, through a program focused on underexplored approaches that could lead to utility-scale quantum computers—those whose benefits exceed their costs. The other company selected is PsiQuantum, a startup that is aiming to build a quantum computer containing up to a million qubits using photons.

Many of the researchers MIT Technology Review spoke with would still like to see how this work plays out in scientific publications, but they were hopeful. “The biggest disadvantage of the topological qubit is that it’s still kind of a physics problem,” says Das Sarma. “If everything Microsoft is claiming today is correct … then maybe right now the physics is coming to an end, and engineering could begin.” 

This story was updated with Henry Legg’s current institutional affiliation.

Born to Run with Amazon

Will enterprise brands boost inventory positions with Amazon in 2025? While a recent survey of Amazon sellers doesn’t necessarily answer that question, it does point to a now seven-year-old inventory program meant to help brands grow.

Some 41% of U.S.-based enterprise brands and retailers planned to use an Amazon-sponsored program — such as Vine or Born to Run— to help drive sales in 2025, according to the recently released “State of the Amazon Seller 2025” report from Jungle Scout.

In January 2025, Jungle Scout queried nearly 1,500 Amazon vendors, marketplace sellers, and folks just getting started with the platform. About 75% of  respondents came from the United States, and 47% worked at a “large brand or retailer.” The report reflects the sentiment of surveyed sellers but not necessarily the overall marketplace.

Nonetheless, more than four in 10 respondents from enterprise-level businesses ticked the “Born to Run/ Vine / Amazon Programs” box for planned growth channels in 2025. Vine is Amazon’s program that invites trusted reviewers to share their candid product opinions. The unspecified other “Amazon programs” tells us little, but “Born to Run” is interesting.

What Is Born to Run?

Amazon’s Born to Run program is an exclusive, invitation-only initiative to help Amazon vendors accelerate sales of new or existing products.

Started in 2018, Born to Run lets the vendor — a company selling directly to Amazon, not a marketplace seller — request purchase orders from Amazon and specify the anticipated unit sales in 10 weeks of a given product. If approved, Amazon purchases the requested quantity.

Here is a scenario. Imagine a brand called “Amazing Gizmos” that sells, well, gizmos. The Amazon purchasing team orders 400 units for the next 10 weeks. But Amazing Gizmos is about to launch a campaign on streaming television, and its marketing team expects to sell 1,000 units on Amazon.

With the Born to Run program, Amazing Gizmos can ask Amazon to increase its order to 1,000 units. Amazon agrees, and — assuming all goes well — Amazing Gizmos stays in stock and sells 978 units.

Hypothetically, Amazing Gizmos sold 578 extra gizmos (978 minus 400) because Amazon had ample inventory. What’s more, on its next regular order, Amazon’s purchasing team doubles down on the product without needing a second Born to Run request.

Similarly, a new vendor could use the Born to Run program to boost Amazon’s initial order. For example, Amazon says it will buy 100 of the new item, the vendor requests 300, and Amazon steps up. Just like that, the product launch could drive significantly more sales.

The key benefit in each case is that Amazon won’t run out on the vendor’s products.

Unsold Units

As long as the additional Born to Run items sell, an Amazon vendor should enjoy growth. If the extra inventory doesn’t sell, Amazon has two options.

  • Return the items. Under the program terms, Amazon can return unsold units. The vendor refunds 100% of the product cost plus a 10% shipping and handling fee.
  • Keep the items. Amazon can also retain the unsold units. The vendor will pay Amazon a “retention fee” equal to 25% of the cost of the unsold items — more or less a discount to Amazon to keep the items.

In either case, the “penalty” for being overzealous on project sales can be severe — 10% of the cost for returned units and 25% if kept.

Invitation Only

Sellers cannot apply for the Born to Run program. Amazon selects them based, at least in part, on a few requirements.

  • Approved vendor. The seller must be enrolled in the Amazon Vendor Central program.
  • Approved product. Only approved products with an Amazon Standard Identification Number (ASIN) are eligible.
  • Product particulars. An item must sell for at least $5 and not be bulky, heavy, or classified as dangerous.

Finally, participating in an Amazon marketing program, such as advertising, could increase the likelihood of acceptance.

Survey

Based on Jungle Scout’s survey, sellers (including enterprises) are attracted to Born to Run and similar Amazon programs. The survey doesn’t reveal actual participation, but it’s a reminder that incentives like Born to Run exist and may work for some brands.