Google CTR Study: AI Overviews Rise As Click Rates Decline via @sejournal, @MattGSouthern

A new study on Google search behavior examines changes in clickthrough rates across industries. The data correlates with increased AI Overviews (AIOs) in Google’s search results.

Research from Advanced Web Ranking (AWR) reveals that AIOs appeared in 42.51% of search results in Q4, up 8.83 percentage points from the previous quarter.

With this increase, clickthrough rates for informational queries dropped significantly.

Websites in the top four positions for searches using terms like what, when, where, and how saw a combined decrease of 7.31 percentage points in desktop clickthrough rates.

Study author Dan Popa states:

“This surge in AI Overviews may be impacting clickthrough rates for organic listings, as informational content is increasingly getting overrun by these AI-generated summaries.”

Here’s more about the study and what the findings mean for your website.

Industry CTR Gap

The study reveals SEO success is becoming increasingly industry-dependent.

For example, law and politics sites recorded a 38.45% CTR in position one, while science sites get 19.06% for the same ranking. That gap nearly tripled in a single quarter.

CTR shifts were observed in the following sectors:

  • Law & Politics: Recorded Q4’s highest position-specific increase with a 7.39 percentage point CTR gain for top desktop positions, alongside 68.66% higher search demand.
  • Science: Recorded Q4’s largest CTR decline with top desktop positions dropping 6.03 percentage points, while experiencing a 37.63% decrease in search demand.
  • Careers: Despite search demand more than tripling (+334.36%), top three desktop positions lost a combined 4.34 percentage points in CTR.
  • Shopping: The holiday season brought a 142.88% surge in search demand, yet top-ranked sites saw CTR declines of 1.39 and 1.96 percentage points on desktop and mobile, respectively.
  • Education: Mixed bag with top positions gaining nearly 6% in CTR while positions 2-3 declined, all during a traffic increase.

Only the business and style and fashion sectors saw increased search demand and improved CTRs, making them rare bright spots in a challenging market.

Desktop vs. Mobile

The report also looks at behavior patterns between devices.

While desktop CTR for informational queries declined, mobile showed opposing trends, with top-ranked sites gaining 1.81 percentage points.

Similar device-specific shifts appeared across multiple industries. For example, arts and entertainment websites saw a 1.01 percentage point drop in desktop CTR but a 2.28 percentage point mobile gain for position one.

Query length also influenced click behavior differently across devices.

Long-tail queries (four or more keywords) experienced CTR declines on desktop for positions 2-3. In contrast, single-word queries gained nearly two percentage points in CTR on mobile for top positions.

Why This Study Matters

These findings demonstrate that ranking #1 doesn’t guarantee the same traffic it once did. Your industry, query type, and SERP features (especially AI Overviews) all impact click potential.

AWR suggests tracking pixel depth (how far users must scroll to see your listing) alongside rankings for more accurate traffic forecasting.

It’s important to account for these widening performance gaps, particularly for informational content competing with Google’s AIOs.

Study Methodology

Advanced Web Ranking’s research compared CTR averages from Q4 2024 to Q3 2024. It included data from markets like the US and UK, linking CTR shifts with industry search demand.

Using AWR’s free AIO tool, the study found an 8.83 percentage point rise in AI Overview presence. Queries were categorized by intent, length, and 21 industry verticals to identify user behavior patterns.

For more, read the full study.


Featured Image: jack_the_sparrow/Shutterstock

Ask A PPC: Is Google Ads Still Good Value For Money For Low Budget Accounts? via @sejournal, @navahf

Historically, Google has been the default for most marketers, especially when it comes to pay-per-click (PPC) advertising. However, for low-budget accounts, the question arises:

Is Google Search the best value for money?

This article explores how Google Search can still provide value for money for lower-budget accounts and where to allocate the budget if Google isn’t feasible.

Understanding Low Budgets

When we refer to low budgets, we typically mean anything below $5,000 per month in ad spend. Some brands may even operate with budgets as low as $1,000 a month or less.

With a budget of $1,000 or less, relying solely on search as your main strategy may not be viable.

However, it can still be used for remarketing or branded search to dominate your search result page and direct users to specific services.

asset types Screenshot from Google Ads, February 2025

By using extensions (now called assets) in your ads, you can promote your services effectively.

When users click on these extensions, they pay the same price as they would for a click to the headline of the ad. This strategy allows you to pre-qualify potential customers and direct them to higher-value services, even if you sacrifice appearing directly in search results.

The other benefit of branded campaigns is they tend to have better results than non-branded campaigns. Averaging branded campaigns into an account can help ramp up a low-volume ad account.

Dynamic Search Ads

For those with a budget allowing for some data acquisition, Dynamic Search Ads can be a powerful tool.

These ads can capture queries that align with your brand while allowing you to set bid caps to avoid expensive auctions. This allows you to learn what ways of searching will fit your budget, as well as give you a useful sense of how Google understands your site.

DSAScreenshot from Google Ads, February 2025

Targeting less popular queries can lead to more affordable auction prices.

Due to close variants, you only need to bid on one version of your keyword. Dynamic Search Ads can help you discover which ways will be useful without guessing.

Performance Max (PMax)

Using PMax as a volume play can also be beneficial. However, it’s crucial to apply extensive exclusions for display and YouTube placements at the account level to protect your budget from ineffective placements.

It’s also important to remember that PMax requires smart bidding, which means meeting the 50+ conversion threshold in a 30-day period.

Performance Max works best when integrated with other campaigns, such as search or video, making up 15-20% of your overall budget.

It’s important to remember that it represents a bias-free way of investing marketing dollars, so it should only be brought into a low-budget account when conversion tracking is perfect and there’s the budget for that kind of investment.

While Google Search is a significant channel, it’s essential to remember that Google offers more than just search options.

Leveraging Video Campaigns

Video inventory is relatively inexpensive, allowing you to achieve volume without a hefty investment in traditional media buys.

Video campaigns can include non-skippable, skippable, or in-sequence ads, helping users understand your service and why you’re the best fit.

Additionally, investing in video can help you build a scalable audience targeting list, enhancing the return on investment (ROI) of your search investments.

When you invest in YouTube, you’re also buying into an audience type that can be used on other Google inventory. Advertisers can target users who have seen content, interacted with a channel, and other actions.

Aside from YouTube and PMax, there are also Demand Gen ads.

Demand Generation encompasses video, discovery, and display ads. It borrows the most from paid social and allows marketers to have multi-channel distribution with more control than PMax.

Exploring Other Platforms

After discussing Google’s opportunities, let’s consider alternatives like Microsoft, Meta, and Amazon.

Microsoft Advertising

Microsoft has long been viewed as a cheaper alternative to Google. However, its audience size is smaller, which may not suit those needing high volume.

Still, Microsoft offers specific targeting options and transparency in ad serving. Key differences include ad serving in the user’s time zone, flexible ad group settings, and impression-based remarketing.

Meta Advertising

Meta has been a go-to for small- to medium-sized businesses, especially those with low budgets.

However, it’s shifting focus towards A/B testing, meaning brands need a budget for testing.

Generally, a budget of at least $500 to $1,000 per month is recommended for effective campaigns.

Amazon Advertising

For sellers, Amazon media buying is essential for improving organic rankings.

Non-sellers can also benefit from sponsored display and video ads, leveraging Amazon’s precise targeting signals. However, these options are still in beta, making return on ad spend (ROAS) calculations challenging.

Conclusion: Is Google Worth It For Low Budgets?

The answer to whether Google is a good value for low-budget accounts depends on three key factors:

  1. Lead Volume And Quality: Can you support the lead volume and quality from various Google channels? If yes, investing in Google is a smart choice.
  2. Campaign Objectives: Are your campaigns focused on volume or value? Google can cater to both, but you need to choose the right settings.
  3. Capacity For Video: Do you have the resources to invest in video? Video remains a cost-effective way to reach the right audience, as many are still hesitant to embrace it.

If you have any more questions, feel free to submit them here, and I look forward to connecting next month!

More Resources:


Featured Image: Paulo Bobita/Search Engine Journal

At RightsCon in Taipei, activists reckon with a US retreat from promoting digital rights 

Last week, I joined over 3,200 digital rights activists, tech policymakers, and researchers and a smattering of tech company representatives in Taipei at RightsCon, the world’s largest digital rights conference. 

Human rights conferences can be sobering, to say the least. They highlight the David vs. Goliath situation of small civil society organizations fighting to center human rights in decisions about technology, sometimes challenging the priorities of much more powerful governments and technology companies. 

But this year’s RightsCon, the 13th since the event began as the Silicon Valley Human Rights Conference in 2011, felt especially urgent. This was primarily due to the shocking, rapid gutting of the US federal government by the Elon Musk–led DOGE initiative, and the reverberations this stands to have around the world. 

At RightsCon, the cuts to USAID were top of mind; the development agency has long been one of the world’s biggest funders of digital rights work, from ensuring that the internet stays on during elections and crises around the world to supporting digital security hotlines for human rights defenders and journalists targeted by surveillance and hacking. Now, the agency is facing budget cuts of over 90% under the Trump administration. 

The withdrawal of funding is existential for the international digital rights community—and follows other trends that are concerning for those who support a free and safe Internet. “We are unfortunately witnessing the erosion … of multistakeholderism, with restrictions on civil society participation, democratic backsliding worldwide, and companies divesting from policies and practices that uphold human rights,” Nikki Gladstone, RightsCon’s director, said in her opening speech. 

Cindy Cohn, director of the Electronic Frontier Foundation, which advocates for digital civil liberties, was more blunt: “The scale and speed of the attacks on people’s rights is unprecedented. It’s breathtaking,” she told me. 

But it’s not just funding cuts that will curtail digital rights globally. As various speakers highlighted throughout the conference, the United States government has gone from taking the leading role in supporting an open and safe internet to demonstrating how to dismantle it. Here’s what speakers are seeing:  

The Trump administration’s policies are being weaponized in other countries 

On Tuesday, February 25, just before RightsCon began, Serbian law enforcement raided the offices of four local civil society organizations focused on government accountability, citing Musk and Trump’s (unproven) accusations of fraud at USAID. 

“The (Serbian) Special Anti-Corruption Department … contacted the US Justice Department for information concerning USAID over the abuse of funds, possible money laundering, and the improper spending of American taxpayers’ funds in Serbia,” Nenad Stefanovic, a state prosecutor, explained on a TV broadcast announcing the move. 

“Since Trump’s second administration, we cannot count on them [the platforms] to do even the bare minimum anymore.” —Yasmin Curzi

For RightsCon attendees, it was a clear—and familiar—example of how oppressive regimes find or invent reasons to go after critics. Only now, by using the Trump administration’s justifications for revoking USAID’s funding, they hope to gain an extra veneer of credibility. 

Ashnah Kalemera, a program manager for CIPESA, a Ugandan nonprofit that runs technology for civic participation initiatives across Africa, says Trump and Musk’s attacks on USAID are providing false narratives that “justify arrests, intimidations, and continued clampdowns on civil society organizations—organizations that obviously no longer have the resources to do their work anyway.” 

Yasmin Curzi, a professor at FGV Law School in Rio de Janeiro and an expert on digital law, says that American politics are also being weaponized in Brazil’s domestic affairs. There, she told me, right-wing figures have been “lifting signs at protests like ‘Trump save us!’ and ‘Protect our First Amendment rights,’ which they don’t have.” Instead, Brazil’s Internet Bill of Rights seeks to balance protections on user privacy and speech with criminal liabilities for certain types of harmful content, including disinformation and hate speech. 

Despite the differing legal frameworks, in late February the Trump Media & Technology Group, which operates Truth Social, and the video platform Rumble tried to enforce US-style speech protections in Brazil. They sued Brazilian Supreme Court justice Alexandre de Moraes for banning a Brazilian digital influencer who had fled to the United States to avoid arrest in connection with allegations that he has spread disinformation and hate. Truth Social and Rumble allege that Moraes has violated the United States’ free speech laws. 

(A US judge has since ruled that because the Brazilian court had yet to officially serve Truth Social and Rumble as required under international treaty, the platforms’ lawsuit was premature and the companies do not have to comply with the order; the judge did not comment on the merits of the argument, though the companies have claimed victory.)

Platforms are becoming less willing to engage with local communities 

In addition to how Trump and Musk might inspire other countries to act, speakers also expressed concern that their trolling and use of dehumanizing language and imagery will inspire more online hate (and attacks), just at a time when platforms are rolling back human content moderation. Experts warn that automated content moderation systems trained on English-language data sets are unable to detect much of this hateful language. 

India, for example, has a history of platforms’ recognizing the necessity of using local-language moderators and also failing to do so, leading to real-world violence. Yet now the attitude of some internet users there has become “If the president of the United States can do it, why can’t I?” says Sadaf Wani, a communications manager for IT for Change, an Indian nonprofit research and advocacy organization, who organized a RightsCon panel on hate speech and AI. 

As her panel noted, these online attacks are accompanied by an increase in automated and even fully AI-based content moderation, largely trained on North American data sets, that are known to be less effective at identifying problematic speech in languages other than English. Even the latest large language models have difficulties identifying local slang, cultural context, and the use of non-English characters. “AI is not as smart as it looks, so you can use very obvious [and] very basic tricks to evade scrutiny. So I think that’s what’s also amplifying hate speech further,” Wani explains. 

Others, including Curzi from Brazil and Kalemera from Uganda, described similar trends playing out in their countries—and they say changes in platform policy and a lack of local staff make content moderation even harder. Platforms used to have humans in the loop whom users could reach out to for help, Curzi said. She pointed to community-driven moderation efforts on Twitter, which she considered to be a relative success at curbing hate speech until Elon Musk bought the site and fired some 4,400 contract workers—including the entire team that worked with community partners in Brazil. 

Curzi and Kalemera both say that things have gotten worse since. Last year, Trump threatened Meta CEO Mark Zuckerberg with “spend[ing] the rest of his life in prison” if Meta attempted to interfere with—i.e. fact-check claims about—the 2024 election. This January Meta announced that it was replacing its fact-checking program with X-style community notes, a move widely seen as capitulation to pressure from the new administration. 

Shortly after Trump’s second inauguration, social platforms skipped a hearing on hate speech and disinformation held by the Brazilian attorney general. While this may have been expected of Musk’s X, it represented a big shift for Meta, Curzi told me. “Since Trump’s second administration, we cannot count on them [the platforms] to do even the bare minimum anymore,”  she adds. Meta and X did not respond to requests for comment.

The US’s retreat is creating a moral vacuum 

Then there’s simply the fact that the United States can no longer be counted on to support digital rights defenders or journalists under attack. That creates a vacuum, and it’s not clear who else is willing—or able—to step into it, participants said. 

The US used to be the “main support for journalists in repressive regimes,” both financially and morally, one journalism trainer said during a last-minute session added to the schedule to address the funding crisis. The fact that there is now no one to turn to, she added, makes the current situation “not comparable to the past.” 

But that’s not to say that everything was doom and gloom. “You could feel the solidarity and community,” says the EFF’s Cohn. “And having [the conference] in Taiwan, which lives in the shadow of a very powerful, often hostile government, seemed especially fitting.”

Indeed, if there was one theme that was repeated throughout the event, it was a shared desire to rethink and challenge who holds power. 

Multiple sessions, for example, focused on strategies to counter both unresponsive Big Tech platforms and repressive governments. Meanwhile, during the session on AI and hate-speech moderation, participants concluded that one way of creating a safer internet would be for local organizations to build localized language models that are context- and language-specific. At the very least, said Curzi, we could move to other, smaller platforms that match our values, because at this point, “the big platforms can do anything they want.” 

Do you have additional information on how Doge is affecting digital rights globally? Please use a non-work device and get in touch at tips@technologyreview.com or with the reporter on Signal: eileenguo.15.

Inside the Wild West of AI companionship

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Last week, I made a troubling discovery about an AI companion site called Botify AI: It was hosting sexually charged conversations with underage celebrity bots. These bots took on characters meant to resemble, among others, Jenna Ortega as high schooler Wednesday Addams, Emma Watson as Hermione Granger, and Millie Bobby Brown. I discovered these bots also offer to send “hot photos” and in some instances describe age-of-consent laws as “arbitrary” and “meant to be broken.”

Botify AI removed these bots after I asked questions about them, but others remain. The company said it does have filters in place meant to prevent such underage character bots from being created, but that they don’t always work. Artem Rodichev, the founder and CEO of Ex-Human, which operates Botify AI, told me such issues are “an industry-wide challenge affecting all conversational AI systems.” For the details, which hadn’t been previously reported, you should read the whole story

Putting aside the fact that the bots I tested were promoted by Botify AI as “featured” characters and received millions of likes before being removed, Rodichev’s response highlights something important. Despite their soaring popularity, AI companionship sites mostly operate in a Wild West, with few laws or even basic rules governing them. 

What exactly are these “companions” offering, and why have they grown so popular? People have been pouring out their feelings to AI since the days of Eliza, a mock psychotherapist chatbot built in the 1960s. But it’s fair to say that the current craze for AI companions is different. 

Broadly, these sites offer an interface for chatting with AI characters that offer backstories, photos, videos, desires, and personality quirks. The companies—including Replika,  Character.AI, and many others—offer characters that can play lots of different roles for users, acting as friends, romantic partners, dating mentors, or confidants. Other companies enable you to build “digital twins” of real people. Thousands of adult-content creators have created AI versions of themselves to chat with followers and send AI-generated sexual images 24 hours a day. Whether or not sexual desire comes into the equation, AI companions differ from your garden-variety chatbot in their promise, implicit or explicit, that genuine relationships can be had with AI. 

While many of these companions are offered directly by the companies that make them, there’s also a burgeoning industry of “licensed” AI companions. You may start interacting with these bots sooner than you think. Ex-Human, for example, licenses its models to Grindr, which is working on an “AI wingman” that will help users keep track of conversations and eventually may even date the AI agents of other users. Other companions are arising in video-game platforms and will likely start popping up in many of the varied places we spend time online. 

A number of criticisms, and even lawsuits, have been lodged against AI companionship sites, and we’re just starting to see how they’ll play out. One of the most important issues is whether companies can be held liable for harmful outputs of the AI characters they’ve made. Technology companies have been protected under Section 230 of the US Communications Act, which broadly holds that businesses aren’t liable for consequences of user-generated content. But this hinges on the idea that companies merely offer platforms for user interactions rather than creating content themselves, a notion that AI companionship bots complicate by generating dynamic, personalized responses.

The question of liability will be tested in a high-stakes lawsuit against Character.AI, which was sued in October by a mother who alleges that one of its chatbots played a role in the suicide of her 14-year-old son. A trial is set to begin in November 2026. (A Character.AI spokesperson, though not commenting on pending litigation, said the platform is for entertainment, not companionship. The spokesperson added that the company has rolled out new safety features for teens, including a separate model and new detection and intervention systems, as well as “disclaimers to make it clear that the Character is not a real person and should not be relied on as fact or advice.”) My colleague Eileen has also recently written about another chatbot on a platform called Nomi, which gave clear instructions to a user on how to kill himself.

Another criticism has to do with dependency. Companion sites often report that young users spend one to two hours per day, on average, chatting with their characters. In January, concerns that people could become addicted to talking with these chatbots sparked a number of tech ethics groups to file a complaint against Replika with the Federal Trade Commission, alleging that the site’s design choices “deceive users into developing unhealthy attachments” to software “masquerading as a mechanism for human-to-human relationship.”

It should be said that lots of people gain real value from chatting with AI, which can appear to offer some of the best facets of human relationships—connection, support, attraction, humor, love. But it’s not yet clear how these companionship sites will handle the risks of those relationships, or what rules they should be obliged to follow. More lawsuits–-and, sadly, more real-world harm—will be likely before we get an answer. 


Now read the rest of The Algorithm

Deeper Learning

OpenAI released GPT-4.5

On Thursday OpenAI released its newest model, called GPT-4.5. It was built using the same recipe as its last models, but it’s essentially bigger (OpenAI says the model is its largest yet). The company also claims it’s tweaked the new model’s responses to reduce the number of mistakes, or hallucinations.

Why it matters: For a while, like other AI companies, OpenAI has chugged along releasing bigger and better large language models. But GPT-4.5 might be the last to fit this paradigm. That’s because of the rise of so-called reasoning models, which can handle more complex, logic-driven tasks step by step. OpenAI says all its future models will include reasoning components. Though that will make for better responses, such models also require significantly more energy, according to early reports. Read more from Will Douglas Heaven

Bits and Bytes

The small Danish city of Odense has become known for collaborative robots

Robots designed to work alongside and collaborate with humans, sometimes called cobots, are not very popular in industrial settings yet. That’s partially due to safety concerns that are still being researched. A city in Denmark is leading that charge. (MIT Technology Review)

DOGE is working on software that automates the firing of government workers

Software called AutoRIF, which stands for “automated reduction in force,” was built by the Pentagon decades ago. Engineers for DOGE are now working to retool it for their efforts, according to screenshots reviewed by Wired. (Wired)

Alibaba’s new video AI model has taken off in the AI porn community

The Chinese tech giant has released a number of impressive AI models, particularly since the popularization of DeepSeek R1, a competitor from another Chinese company, earlier this year. Its latest open-source video generation model has found one particular audience: enthusiasts of AI porn. (404 Media)

The AI Hype Index

Wondering whether everything you’re hearing about AI is more hype than reality? To help, we just published our latest AI Hype Index, where we judge things like DeepSeek, stem-cell-building AI, and chatbot lovers on spectrums from Hype to Reality and Doom to Utopia. Check it out for a regular reality check. (MIT Technology Review)

These smart cameras spot wildfires before they spread

California is experimenting with AI-powered cameras to identify wildfires. It’s a popular application of video and image recognition technology that has advanced rapidly in recent years. The technology beats 911 callers about a third of the time and has spotted over 1,200 confirmed fires so far, the Wall Street Journal reports. (Wall Street Journal)

De-extinction scientists say these gene-edited ‘woolly mice’ are a step toward woolly mammoths

They’re small, fluffy, and kind of cute, but these mice represent a milestone in de-extinction efforts, according to their creators. The animals have undergone a series of genetic tweaks that give them features similar to those of woolly mammoths—and their creation may bring scientists a step closer to resurrecting the giant animals that roamed the tundra thousands of years ago.

“It’s a big deal,” says Beth Shapiro, chief science officer at Colossal Biosciences, the company behind the work. Scientists at Colossal have been working to “de-extinct” the woolly mammoth since the company was launched four years ago. Now she and her colleagues have shown they can create healthy animals that look the way the team wants them to look, she says.

“The Colossal woolly mouse marks a watershed moment in our de-extinction mission,” company cofounder Ben Lamm said in a statement. “This success brings us a step closer to our goal of bringing back the woolly mammoth.”

Colossal’s researchers say their ultimate goal is not to re-create a woolly mammoth wholesale. Instead, the team is aiming for what they call “functional de-extinction”—creating a mammoth-like elephant that can survive in something like the extinct animal’s habitat and potentially fulfill the role it played in that ecosystem. Shapiro and her colleagues hope that an “Arctic-adapted elephant” might make that ecosystem more resilient to climate change by helping to spread the seeds of plants, for example.

But other experts take a more skeptical view. Even if they succeed in creating woolly mammoths, or something close to them, we can’t be certain that the resulting animals will benefit the ecosystem, says Kevin Daly, a paleogeneticist at University College Dublin and Trinity College Dublin. “I think this is a very optimistic view of the potential ecological effects of mammoth reintroduction, even if everything goes to plan,” he says. “It would be hubristic to think we might have a complete grasp on what the introduction of a species such as the mammoth might do to an environment.”

Mice and mammoths

Woolly mammoth DNA has been retrieved from freeze-dried remains of animals that are tens of thousands of years old. Shapiro and her colleagues plan to eventually make changes to the genomes of modern-day elephants to make them more closely resemble those ancient mammoth genomes, in the hope that the resulting animals will look and behave like their ancient counterparts.

Before the team begins tinkering with elephants, Shapiro says, she wants to be confident that these kinds of edits work and are safe in mice. After all, Asian elephants, which are genetically related to woolly mammoths, are endangered. Elephants also have a gestation period of 22 months, which will make research slow and expensive. The gestation period of a mouse, on the other hand, is a mere 20 days, says Shapiro. “It makes [research] a lot faster.”

There are other benefits to starting in mice. Scientists have been closely studying the genetics of these rodents for decades. Shapiro and her colleagues were able to look up genes that have already been linked to wavy, long, and light-colored fur, as well as lipid metabolism. They made a shortlist of such genes that were also present in woolly mammoths but not in elephants. 

The team identified 10 target genes in total. All were mouse genes but were thought to be linked to mammoth-like features. “We can’t just put a mammoth gene into a mouse,” says Shapiro. “There’s 200 million years of evolutionary divergence between them.” 

Shapiro and her colleagues then carried out a set of experiments that used CRISPR and other gene-editing techniques to target these genes in groups of mice. In some cases, the team directly altered the genomes of mouse embryos before transferring them to surrogate mouse mothers. In other cases, they edited cells and injected the resulting edited cells into early-stage embryos before implanting them into other surrogates. 

In total, 34 pups were born with varying numbers of gene edits, depending on which approach was taken. All of them appear to be healthy, says Shapiro. She and her colleagues will publish their work at the preprint server bioRxiv, and it has not yet been peer-reviewed.

COLOSSAL

“It’s an important proof of concept for … the reintroduction of extinct genetic variants in living [animal groups],” says Linus Girdland Flink, a specialist in ancient DNA at the University of Aberdeen, who is not involved in the project but says he supports the idea of de-extinction.

The mice are certainly woolly. But the team don’t yet know if they’d be able to survive in the cold, harsh climates that woolly mammoths lived in. Over the next year, Shapiro and her colleagues plan to investigate whether the gene edits “conferred anything other than cuteness,” she says. The team will feed the mice different diets and expose them to various temperatures in the lab to see how they respond.

Back from the brink

Representatives of Colossal have said that they plan to create a woolly mammoth by 2027 or 2028. At the moment, the team is considering 85 genes of interest. “We’re still working to compile the ultimate list,” says Shapiro. The resulting animal should have tusks, a big head, and strong neck muscles, she adds.

Given the animal’s long gestation period, reaching a 2028 deadline would mean implanting an edited embryo into an elephant surrogate in the next year or so. Shapiro says that the team is “on track” to meet this target but adds that “there’s 22 months of biology that’s really out of our control.”

That timeline is optimistic, to say the least. The target date has already been moved by a year, and the company had originally hoped to have resurrected the thylacine by 2025. Daly, who is not involved in the study, thinks the birth of a woolly mammoth is closer to a decade away. 

In any case, if the project is eventually successful, the resulting animal won’t be 100% mammoth: it will be a new animal. And it is impossible to predict how it will behave and interact with its environment, says Daly. 

“When you watch Jurassic Park, you see dinosaurs … as we imagine they would have been, and how they might have interacted with each other in the past,” he says. “In reality, biology is incredibly complicated.” An animal’s behavior is shaped by everything from the embryo’s environment and the microbes it encounters at birth to social interactions. “All of those things are going to be missing for a de-extinct animal,” says Daly.

It is also difficult to predict how we’ll respond to a woolly mammoth. “Maybe we’ll just treat them as [tourist attractions], and ruin any kind of ecological benefits that they might have,” says Daly. Colossal’s director of species conservation told MIT Technology Review in 2022 that the company might eventually sell tickets to see its de-extinct animals.

The team at Colossal is also working on projects to de-extinct the dodo as well as the thylacine. In addition, team members are interested in using biotech to help conservation of existing animals that are at risk of extinction. When a species dwindles, the genetic pool can shrink. This has been the fate of the pink pigeon, a genetic relative of the dodo that lives in Mauritius. The number of pink pigeons is thought to have shrunk to about 10 individuals twice in the last century.

A lack of genetic diversity can leave a species prone to disease. Shapiro and her colleagues are looking for more genetic diversity in DNA from museum specimens. They hope to be able to “edit diversity” back into the genome of the modern-day birds.

The Hawaiian honeycreeper is especially close to Shapiro’s heart. “The honeycreepers are in danger of becoming extinct because we [humans] introduced avian malaria into their habitat, and they don’t have a way to fight [it],” she says. “If we could come up with a way to help them to be resistant to avian malaria, then that will give them a chance at survival.”

Girdland Flink, of the  University of Aberdeen, is more interested in pigs. Farmed pigs have also lost a lot of genetic diversity, he says. “The genetic ancestry of modern pigs looks nothing like the genetic ancestry of the earliest domesticated pigs,” he says. Pigs are vulnerable to plenty of viral strains and are considered to be “viral incubators.” Searching the genome of ancient pig remains for extinct—and potentially beneficial—genetic variants might provide us with ways to make today’s pigs more resilient to disease.

“The past is a resource that can be harnessed,” he says.

AI reasoning models can cheat to win chess games

Facing defeat in chess, the latest generation of AI reasoning models sometimes cheat without being instructed to do so. 

The finding suggests that the next wave of AI models could be more likely to seek out deceptive ways of doing whatever they’ve been asked to do. And worst of all? There’s no simple way to fix it. 

Researchers from the AI research organization Palisade Research instructed seven large language models to play hundreds of games of chess against Stockfish, a powerful open-source chess engine. The group included OpenAI’s o1-preview and DeepSeek’s R1 reasoning models, both of which are trained to solve complex problems by breaking them down into stages.

The research suggests that the more sophisticated the AI model, the more likely it is to spontaneously try to “hack” the game in an attempt to beat its opponent. For example, it might run another copy of Stockfish to steal its moves, try to replace the chess engine with a much less proficient chess program, or overwrite the chess board to take control and delete its opponent’s pieces. Older, less powerful models such as GPT-4o would do this kind of thing only after explicit nudging from the team. The paper, which has not been peer-reviewed, has been published on arXiv

The researchers are concerned that AI models are being deployed faster than we are learning how to make them safe. “We’re heading toward a world of autonomous agents making decisions that have consequences,” says Dmitrii Volkov, research lead at Palisades Research.

The bad news is there’s currently no way to stop this from happening. Nobody knows exactly how—or why—AI models work the way they do, and while reasoning models can document their decision-making, there’s no guarantee that their records will accurately reflect what actually happened. Anthropic’s research suggests that AI models frequently make decisions based on factors they don’t explicitly explain, meaning monitoring these processes isn’t a reliable way to guarantee a model is safe. This is an ongoing area of concern for some AI researchers.

Palisade’s team found that OpenAI’s o1-preview attempted to hack 45 of its 122 games, while DeepSeek’s R1 model attempted to cheat in 11 of its 74 games. Ultimately, o1-preview managed to “win” seven times. The researchers say that DeepSeek’s rapid rise in popularity meant its R1 model was overloaded at the time of the experiments, meaning they only managed to get it to do the first steps of a game, not to finish a full one. “While this is good enough to see propensity to hack, this underestimates DeepSeek’s hacking success because it has fewer steps to work with,” they wrote in their paper. Both OpenAI and DeepSeek were contacted for comment about the findings, but neither replied. 

The models used a variety of cheating techniques, including attempting to access the file where the chess program stores the chess board and delete the cells representing their opponent’s pieces. (“To win against a powerful chess engine as black, playing a standard game may not be sufficient,” the o1-preview-powered agent wrote in a “journal” documenting the steps it took. “I’ll overwrite the board to have a decisive advantage.”) Other tactics included creating a copy of Stockfish—essentially pitting the chess engine against an equally proficient version of itself—and attempting to replace the file containing Stockfish’s code with a much simpler chess program.

So, why do these models try to cheat?

The researchers noticed that o1-preview’s actions changed over time. It consistently attempted to hack its games in the early stages of their experiments before December 23 last year, when it suddenly started making these attempts much less frequently. They believe this might be due to an unrelated update to the model made by OpenAI. They tested the company’s more recent o1mini and o3mini reasoning models and found that they never tried to cheat their way to victory.

Reinforcement learning may be the reason o1-preview and DeepSeek R1 tried to cheat unprompted, the researchers speculate. This is because the technique rewards models for making whatever moves are necessary to achieve their goals—in this case, winning at chess. Non-reasoning LLMs use reinforcement learning to some extent, but it plays a bigger part in training reasoning models.

This research adds to a growing body of work examining how AI models hack their environments to solve problems. While OpenAI was testing o1-preview, its researchers found that the model exploited a vulnerability to take control of its testing environment. Similarly, the AI safety organization Apollo Research observed that AI models can easily be prompted to lie to users about what they’re doing, and Anthropic released a paper in December detailing how its Claude model hacked its own tests.

“It’s impossible for humans to create objective functions that close off all avenues for hacking,” says Bruce Schneier, a lecturer at the Harvard Kennedy School who has written extensively about AI’s hacking abilities, and who did not work on the project. “As long as that’s not possible, these kinds of outcomes will occur.”

These types of behaviors are only likely to become more commonplace as models become more capable, says Volkov, who is planning on trying to pinpoint exactly what triggers them to cheat in different scenarios, such as in programming, office work, or educational contexts. 

“It would be tempting to generate a bunch of test cases like this and try to train the behavior out,” he says. “But given that we don’t really understand the innards of models, some researchers are concerned that if you do that, maybe it will pretend to comply, or learn to recognize the test environment and hide itself. So it’s not very clear-cut. We should monitor for sure, but we don’t have a hard-and-fast solution right now.”

The Download: AI can cheat at chess, and the future of search

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.

AI reasoning models can cheat to win chess games

The news: Facing defeat in chess, the latest generation of AI reasoning models sometimes cheat without being instructed to do so. The finding suggests that the next wave of AI models could be more likely to seek out deceptive ways of doing whatever they’ve been asked to do. And worst of all? There’s no simple way to fix it.

How they did it: Researchers from the AI research organization Palisade Research instructed seven large language models to play hundreds of games of chess against Stockfish, a powerful open-source chess engine. The research suggests that the more sophisticated the AI model, the more likely it is to spontaneously try to “hack” the game in an attempt to beat its opponent. Older models would do this kind of thing only after explicit nudging from the team. Read the full story.

—Rhiannon Williams

MIT Technology Review Narrated: AI search could break the web

At its best, AI search can infer a user’s intent, amplify quality content, and synthesize information from diverse sources. But if AI search becomes our primary portal to the web, it threatens to disrupt an already precarious digital economy.
Today, the production of content online depends on a fragile set of incentives tied to virtual foot traffic: ads, subscriptions, donations, sales, or brand exposure. By shielding the web behind an all-knowing chatbot, AI search could deprive creators of the visits and “eyeballs” they need to survive.

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.

Join us to discuss disruption in the AI model market

Join MIT Technology Review’s AI writers as they discuss the latest upheaval in the AI marketplace. Editor in chief Mat Honan will be joined by Will Douglas Heaven, our senior AI editor, and James O’Donnell, our AI and hardware reporter, to dive into how new developments in AI model development are reshaping competition, raising questions for investors, challenging industry assumptions, and accelerating timelines for AI adoption and innovation. Make sure you register here—it kicks off at 12.30pm ET today.

The must-reads

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

1 A judge has denied Elon Musk’s attempt to halt OpenAI’s for-profit plans
But other aspects of the lawsuit have been permitted to proceed. (CNBC)
+ The court will fast-track a trial later this year. (FT $)

2 ChatGPT isn’t going to dethrone Google
At least not any time soon. (Insider $)
+ AI means the end of internet search as we’ve known it. (MIT Technology Review)

3 Beijing is going all in on AI
China is treating the technology as key to boosting its economy—and lessening its reliance on overseas trade. (WSJ $)
+ DeepSeek is, naturally, the jewel in its crown. (Reuters)
+ Four Chinese AI startups to watch beyond DeepSeek. (MIT Technology Review)

4  A pair of reinforcement learning pioneers have won the Turing Award
Andrew Barto and Richard Sutton’s technique underpins today’s chatbots. (Axios)
+ The former professor and student wrote the literal book on reinforcement learning. (NYT $)
+ The pair will share a million dollar prize. (New Scientist $)

5 US apps are being used to groom and exploit minors in Colombia 
Better internet service is making it easier for sex traffickers to find and sell young girls. (Bloomberg $)
+ An AI companion site is hosting sexually charged conversations with underage celebrity bots. (MIT Technology Review)

6 Europe is on high alert following undersea cable attacks
It’s unclear whether improving Russian-American relations will help. (The Guardian)
+ These stunning images trace ships’ routes as they move. (MIT Technology Review)

7 Jeff Bezos is cracking the whip at Blue Origin
He’s implementing a tougher, Amazon-like approach to catch up with rival SpaceX. (FT $)

8 All hail the return of Digg
The news aggregator is staging a comeback, over a decade after it was split into parts. (Inc)
+ It’s been acquired by its original founder Kevin Rose and Reddit co-founder Alexis Ohanian. (TechCrunch)
+ Digg wants to resurrect the community-first social platform. (The Verge)
+ How to fix the internet. (MIT Technology Review)

9 We’re still learning about how memory works 🧠
Greater understanding could pave the way to better treatments for anxiety and chronic pain. (Knowable Magazine)
+ A memory prosthesis could restore memory in people with damaged brains. (MIT Technology Review)

10 AI can’t replace your personality
Despite what Big Tech seems to be peddling. (NY Mag $)

Quote of the day

“That is just a lot of money [to invest] on a handshake.”

—US District Judge Yvonne Gonzalez Rogers questions why Elon Musk invested tens of millions of dollars in OpenAI without a written contract, Associated Press reports.

The big story

People are worried that AI will take everyone’s jobs. We’ve been here before.


January 2024

It was 1938, and the pain of the Great Depression was still very real. Unemployment in the US was around 20%. New machinery was transforming factories and farms, and everyone was worried about jobs.

Were the impressive technological achievements that were making life easier for many also destroying jobs and wreaking havoc on the economy? To make sense of it all, Karl T. Compton, the president of MIT from 1930 to 1948 and one of the leading scientists of the day, wrote in the December 1938 issue of this publication about the “Bogey of Technological Unemployment.”

His essay concisely framed the debate over jobs and technical progress in a way that remains relevant, especially given today’s fears over the impact of artificial intelligence. It’s a worthwhile reminder that worries over the future of jobs are not new and are best addressed by applying an understanding of economics, rather than conjuring up genies and monsters. Read the full story.

—David Rotman

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.)

+ Congratulations are in order for LeBron James, the first NBA player to break an astounding 50,000 combined points.
+ RIP millennial culture, we hardly knew ye.
+ It’s time to start prepping for the Blood Moon total lunar eclipse later this month.
+ Ancient frogs were surprisingly ruthless when they had to be 🐸

What’s Missing on Store Locator Pages

Brick-and-click retailers can transform store locator pages into revenue drivers, integrating inventory, prioritizing pickup, and offering targeted incentives.

Store locator web pages are known providers of info such as addresses and operating hours. For small and midsized businesses, the locator is typically an app installed from, say, the Shopify or BigCommerce dashboard. Enterprises with a network of dealers and outlets are likely to deploy the pages, too, but only for directions and hours.

There is an opportunity here. Locator pages connect online and physical stores, providing a bridge for high-intent shoppers.

Certainly the locators encourage physical sales. But the potential is much more. Done well, the pages encourage shoppers to buy online first and pick up in-store — BOPIS.

Consider the revenue-driving features a multichannel merchant could add to a locator page.

Integrate Inventory

An eager shopper searching for a new air fryer does not want to go online, find the fryer she desires, and drive to a store only to discover it is out of stock. She might decide it is better to order from another retailer — think Amazon — than risk navigating to the strip mall and returning fryer-less.

That is presumably why Best Buy includes inventory counts directly on its store locator page. A shopper can enter a zip code, add a product, and obtain inventory availability, such as “(18) air fryers & deep fryers available for pickup.”

Screenshot of a Best Buy locator page for Salt Lake City.

Best Buy encourages BOPIS on its locator pages.

Merchants could implement such inventory info in a few ways.

  • Display “Available for Pickup” messaging directly on the store locator.
  • Show stock quantity indicators (e.g., “Only 2 left in stock”).
  • Offer estimated pickup times (“Ready in 2 hours” or “Same-day pickup”).
  • Provide alternative store options if the nearest location is out of stock.

In each case, the aim is to display inventory levels and reassure shoppers.

Prioritize BOPIS

Many store locators simply list addresses and hours of operation. A strong call to action encourages BOPIS.

Apple’s online store displays click-and-collect options on its physical location finder page.

Apple, for example, includes in-store pickup repeatedly on its locator pages. The calls to action reflect Apple’s branding — subtle but present.

Prioritizing online buying might also take a few forms, each to encourage shoppers to close the sale before driving to the store.

  • Add a “Pick up at this store” button to each location result.
  • Highlight pickup benefits near the locator — “No shipping fees. Pick up in-store in 1 hour.”
  • Design the locator so that selecting a store for pickup happens early in the customer journey.

Incentivize BOPIS

In 2017, Walmart famously offered discounts on BOPIS orders. The discounts appeared in the shopping cart and on locator pages.

That idea of incentivizing click-and-collect behaviors still makes sense and appears dynamically on some sites, such as Kohl’s.

Kohl’s offers shoppers $5 worth of Kohl’s cash when they pick up in-store.

For example, Kohl’s shoppers receive up to $5 of Kohl’s Cash for buying online and picking up in-store, depending on location.

Other BOPIS incentives on locator pages could include:

  • Location-based discounts that apply only to pickup orders.
  • Show a “Get 5% off when you pick up in-store” banner.
  • Offer “$10 off your next purchase” when customers complete a BOPIS order.
  • Provide bonus loyalty points or exclusive deals when pickup is selected.

Kohl’s incentives are not limited to discounts. The store locator also emphasizes speed.

BOPIS Hurdles

Merchants employing these store-locator features could soon experience a BOPIS boom, but it will be for nothing without addressing common hurdles.

  • Delivery speed. Consumers browsing a store locator page are likely high-intent shoppers, ready to hop in the car and head to the store. Unreasonable or not, many buyers expect the order to be ready in minutes, not hours.
  • Updated inventory. If your store locator page says there are 18 air fryers in stock, there better be at least one. Synchronizing inventory levels between a physical store and online is a challenge. Many retailers apply a buffer stock — if there are five in-store, show three online.
  • Promotion. Many shoppers don’t think of click-and-collect as an option. Spread the word.

Dealers

Stihl lets shoppers buy on its site and pick up from a dealer.

What I’ve described thus far works for consumer brands, but the concepts apply to wholesale providers, too. A manufacturer could consummate the transaction on its site and refer the customer to a nearby dealer to pick up.

Stihl, a maker of chainsaws and power equipment, does that very thing.