Mar 13 2025

This artificial leaf makes hydrocarbons out of carbon dioxide

For many years, researchers have been working to build devices that can mimic photosynthesis—the process by which plants use sunlight and carbon dioxide to make their fuel. These artificial leaves use sunlight to separate water into oxygen and hydrogen, which could then be used to fuel cars or generate electricity. Now a research team has taken aim at creating more energy-dense fuels.

Companies have been manufacturing synthetic fuels for nearly a century by combining carbon monoxide (which can be sourced from carbon dioxide) and hydrogen under high temperatures. But the hope is that artificial leaves can eventually do a similar kind of synthesis in a more sustainable and efficient way, by tapping into the power of the sun.

The group’s device produces ethylene and ethane, proving that artificial leaves can create hydrocarbons. The development could offer a cheaper, cleaner way to make fuels, chemicals, and plastics.

For research lead Virgil Andrei at the University of Cambridge, the ultimate goal is to use this technology to create fuels that don’t leave a harmful carbon footprint after they’re burned. If the process uses carbon dioxide captured from the air or power plants, the resulting fuels could be carbon neutral—and ease the need to keep digging up fossil fuels.

“Eventually we want to be able to source carbon dioxide to produce the fuels and chemicals that we need for industry and for everyday lives,” says Andrei, who coauthored a study published in Nature Catalysis in February. “You end up mimicking nature’s own carbon cycle, so you don’t need additional fossil resources.”

Copper nanoflowers

Like other artificial leaves, the team’s device harnesses energy from the sun to create chemical products. But producing hydrocarbons is more complicated than making hydrogen because the process requires more energy.

To accomplish this feat, the researchers introduced a few innovations. The first was to use a specialized catalyst made up of tiny flower-like copper structures, produced in the lab of coauthor Peidong Yang at the University of California, Berkeley. On one side of the device, electrons accumulated on the surfaces of these nanoflowers. These electrons were then used to convert carbon dioxide and water into a range of molecules including ethylene and ethane, hydrocarbons that each contain two carbon atoms.

An image showing top views of the copper nanoflowers at different magnifications. — Microscope images of the device’s copper nanoflowers.

These nanoflower structures are tunable and could be adjusted to produce a wide range of molecules, says Andrei: “Depending on the nanostructure of the copper catalyst you can get wildly different products.”

On the other side of the device, the team also developed a more energy-efficient way to source electrons by using light-absorbing silicon nanowires to process glycerol rather than water, which is more commonly used. An added benefit is that the glycerol-based process can produce useful compounds like glycerate, lactate, and acetate, which could be harvested for use in the cosmetic and pharmaceutical industries.

Scaling up

Even though the trial system worked, the advance is only a stepping stone toward creating a commercially viable source of fuel. “This research shows this concept can work,” says Yanwei Lum, a chemical and biomolecular engineering assistant professor at the National University of Singapore. But, he adds, “the performance is still not sufficient for practical applications. It’s still not there yet.”

Andrei says the device needs to be significantly more durable and efficient in order to be adopted for fuel production. But the work is moving in the right direction.

“We have been making this progress because we looked at more unconventional concepts and state-of-the-art techniques that were not really available,” he says. “I’m quite optimistic that this technology could take off in the next five to 10 years.”

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Mar 13 2025

This startup just hit a big milestone for green steel production

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

Green-steel startup Boston Metal just showed that it has all the ingredients needed to make steel without emitting gobs of greenhouse gases. The company successfully ran its largest reactor yet to make steel, producing over a ton of metal, MIT Technology Review can exclusively report.

The latest milestone means that Boston Metal just got one step closer to commercializing its technology. The company’s process uses electricity to make steel, and depending on the source of that electricity, it could mean cleaning up production of one of the most polluting materials on the planet. The world produces about 2 billion metric tons of steel each year, emitting over 3 billion metric tons of carbon dioxide in the process.

While there are still a lot of milestones left before reaching the scale needed to make a dent in the steel industry, the latest run shows that the company can scale up its process.

Boston Metal started up its industrial reactor for steelmaking in January, and after it had run for several weeks, the company siphoned out roughly a ton of material on February 17. (You can see a video of the molten metal here. It’s really cool.)

Work on this reactor has been underway for a while. I got to visit the facility in Woburn, Massachusetts, in 2022, when construction was nearly done. In the years since, the company has been working on testing it out to make other metals before retrofitting it for steel production.

Boston Metal’s approach is very different from that of a conventional steel plant. Steelmaking typically involves a blast furnace, which uses a coal-based fuel called coke to drive the reactions needed to turn iron ore into iron (the key ingredient in steel). The carbon in coke combines with oxygen pulled out of the iron ore, which gets released as carbon dioxide.

Instead, Boston Metal uses electricity in a process called molten oxide electrolysis (MOE). Iron ore gets loaded into a reactor, mixed with other ingredients, and then electricity is run through it, heating the mixture to around 1,600 °C (2,900 °F) and driving the reactions needed to make iron. That iron can then be turned into steel.

Crucially for the climate, this process emits oxygen rather than carbon dioxide (that infamous greenhouse gas). If renewables like wind and solar or nuclear power are used as the source of electricity, then this approach can virtually cut out the climate impact from steel production.

MOE was developed at MIT, and Boston Metal was founded in 2013 to commercialize the technology. Since then, the company has worked to take it from lab scale, with reactors roughly the size of a coffee cup, to much larger ones that can produce tons of metal at a time. That’s crucial for an industry that operates on the scale of billions of tons per year.

“The volumes of steel everywhere around us—it’s immense,” says Adam Rauwerdink, senior vice president of business development at Boston Metal. “The scale is massive.”

factory view of Boston Metal and MOE Green Steel

Making the huge amounts of steel required to be commercially relevant has been quite the technical challenge.

One key component of Boston Metal’s design is the anode. It’s basically a rounded metallic bit that sticks into the reactor, providing a way for electricity to get in and drive the reactions required. In theory, this anode doesn’t get used up, but if the conditions aren’t quite right, it can degrade over time.

Over the past few years, the company has made a lot of progress in preventing inert anode degradation, Rauwerdink says. The latest phase of work is more complicated, because now the company is adding multiple anodes in the same reactor.

In lab-scale reactors, there’s one anode, and it’s quite small. Larger reactors require bigger anodes, and at a certain point it’s necessary to add more of them. The latest run continues to prove how Boston Metal’s approach can scale, Rauwerdink says: making reactors larger, adding more anodes, and then adding multiple reactors together in a single plant to make the volumes of material needed.

Now that the company has completed its first run of the multi-anode reactor for steelmaking, the plan is to keep exploring how the reactions happen at this larger scale. These runs will also help the company better understand what it will cost to make its products.

The next step is to build an even bigger system, Rauwerdink says—something that won’t fit in the Boston facility. While a reactor of the current size can make a ton or two of material in about a month, the truly industrial-scale equipment will make that amount of metal in about a day. That demonstration plant should come online in late 2026 and begin operation in 2027, he says. Ultimately, the company hopes to license its technology to steelmakers.

In steel and other heavy industries, the scale can be mind-boggling. Boston Metal has been at this for over a decade, and it’s fascinating to see the company make progress toward becoming a player in this massive industry.

Now read the rest of The Spark

Another thing

Electricity demand is rising faster in the US than it has in decades, and meeting it will require building new power plants and expanding grid infrastructure. That could be a problem, because it’s historically been expensive and slow to get new transmission lines approved.

New technologies could help in a major way, according to Brian Deese and Rob Gramlich. Read more in this new op-ed.

And one more

Plants have really nailed the process of making food from sunlight in photosynthesis. For a very long time, researchers have been trying to mimic this process and make an artificial leaf that can make fuels using the sun’s energy.

Now, researchers are aiming to make energy-dense fuels using a specialized, copper-containing catalyst. Read more about the innovation in my colleague Carly Kay’s latest story.

Keeping up with climate

Energy storage is still growing quickly in the US, with 18 gigawatts set to come online this year. That’s up from 11 GW in 2024. (Canary Media)

Oil companies including Shell, BP, and Equinor are rolling back climate commitments and ramping up fossil-fuel production. Oil and gas companies were accounting for only a small fraction of clean energy investment, so experts say that’s not a huge loss. But putting money toward new oil and gas could be bad for emissions. (Grist)

Butterfly populations are cratering around the US, dropping by 22% in just the last 20 years. Check out this visualization to see how things are changing where you live. (New York Times)

New York City’s congestion pricing plan, which charges cars to enter the busiest parts of the city, is gaining popularity: 42% of New York City residents support the toll, up from 32% in December. (Bloomberg)

Here’s a reality check for you: Ukraine doesn’t have minable deposits of rare earth metals, experts say. While tensions between US and Ukraine leaders ran high in a meeting to discuss a minerals deal, IEEE Spectrum reports that the reality doesn’t match the political theater here. (IEEE Spectrum)

Quaise Energy has a wild drilling technology that it says could unlock the potential for geothermal energy. In a demonstration, the company recently drilled several inches into a piece of rock using its millimeter-wave technology. (Wall Street Journal)

Here’s another one for the “weird climate change effects” file: greenhouse-gas emissions could mean less capacity for satellites. It’s getting crowded up there. (Grist)

The Biden administration funded agriculture projects related to climate change, and now farmers are getting caught up in the Trump administration’s efforts to claw back the money. This is a fascinating case of how the same project can be described with entirely different language depending on political priorities. (Washington Post)

You and I are helping to pay for the electricity demands of big data centers. While some grid upgrades are needed just to serve big projects like those centers, the cost of building and maintaining the grid is shared by everyone who pays for electricity. (Heatmap)

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Mar 13 2025

The Download: testing new AI agent Manus, and Waabi’s virtual robotruck ambitions

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.

Everyone in AI is talking about Manus. We put it to the test.

Since the general AI agent Manus was launched last week, it has spread online like wildfire. And not just in China, where it was developed by the Wuhan-based startup Butterfly Effect. It’s made its way into the global conversation, with some even dubbing it “the second DeepSeek”.

Manus claims to be the world’s first general AI agent, building off multiple AI models and agents to act autonomously on a wide range of tasks. Despite all the hype, very few people have had a chance to use it. MIT Technology Review was able to obtain access to Manus. Here’s what we made of it.

—Caiwei Chen

Waabi says its virtual robotrucks are realistic enough to prove the real ones are safe

The news: Canadian robotruck startup Waabi says its super-realistic virtual simulation is now accurate enough to prove the safety of its driverless big rigs without having to run them for miles on real roads.

How it did it: The company uses a digital twin of its real-world robotrucks, loaded up with real sensor data, and measures how the twin’s performance compares to that of real trucks on real roads. Waabi says they now match almost exactly, and claims its approach is a better way to demonstrate safety than just racking up real-world miles, as many of its competitors do. Read the full story.

—Will Douglas Heaven

This artificial leaf makes hydrocarbons out of carbon dioxide

The group’s device produces ethylene and ethane, proving that artificial leaves can create hydrocarbons. The development could offer a cheaper, cleaner way to make fuels, chemicals, and plastics—with the ultimate goal of creating fuels that don’t leave a harmful carbon footprint after they’re burned. Read the full story.

—Carly Kay

This startup just hit a big milestone for green steel production

The latest milestone means that Boston Metal just got one step closer to commercializing its technology. And while there are still a lot of milestones left before reaching the scale needed to make a dent in the steel industry, the latest run shows that the company can scale up its process. Read the full story.

—Casey Crownhart

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

The must-reads

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

1 The US has resumed aid deliveries to Ukraine
Leaders have also agreed to start sharing military intelligence again. (The Guardian)
+ Ukraine also endorsed a US proposal for a ceasefire. (Vox)
+ Meet the radio-obsessed civilian shaping Ukraine’s drone defense. (MIT Technology Review)

2 Donald Trump has imposed a 25% tariff on metal imports
The decision is likely to raise costs for American carmakers, and other manufacturers. (NYT $)
+ Business leaders feel spooked by his frequent mixed messaging around tariffs. (WSJ $)
+ However, US-native metal makers are delighted by the tariffs. (Economist $)
+ How Trump’s tariffs could drive up the cost of batteries, EVs, and more. (MIT Technology Review)

3 Texas’ measles outbreak appears to be spreading
Two people in Oklahoma are being treated for measles-like symptoms. (Ars Technica)
+ An unvaccinated six-year old girl recently died in Texas. (The Atlantic $)
+ The state is scrambling to respond to the outbreak. (Undark)
+ The virus is extremely contagious and dangerous to children and adults alike. (Wired $)

4 Elon Musk wants the US government to shut down
Partly because it would make it easier to fire federal workers. (Wired $)
+ A judge has ruled that DOGE must comply with the Freedom of Information Act. (The Verge)
+ Can AI help DOGE slash government budgets? It’s complex. (MIT Technology Review)

5 OpenAI says it’s trained an AI to be ‘really good’ at creative writing|
The question is, can a model trained on existing material ever be truly creative? (TechCrunch)
+ AI can make you more creative—but it has limits. (MIT Technology Review)

6 Silicon Valley’s AI startups are expanding in India
Talent is plentiful, particularly in tech hub Bangalore. (Bloomberg $)

7 Spotify claims it paid $10 billion in royalties last year
It called the payout “the largest in music industry history.” (FT $)
+ How to break free of Spotify’s algorithm. (MIT Technology Review)

8 Saturn has more moons than the rest of the planets combined
Researchers have finally spotted new moons that have previously evaded detection. (New Scientist $)

9 This coffee shop is New York’s hottest AI spot
Handily, OpenAI’s office is just across the street. (Insider $)

10 Netflix shouldn’t use AI to upscale resolution
The technology left sitcom A Different World looking freakishly warped. (Vice)

Quote of the day

“The uncertainty is just as bad as tariffs themselves.”

—Donald Schneider, deputy head of US policy at investment bank Piper Sandler, explains to the Washington Post why investors are feeling rattled by Donald Trump’s volatile approach to imposing tariffs.

The big story

Can Afghanistan’s underground “sneakernet” survive the Taliban?

November 2021

When Afghanistan fell to the Taliban, Mohammad Yasin had to make some difficult decisions very quickly. He began erasing some of the sensitive data on his computer and moving the rest onto two of his largest hard drives, which he then wrapped in a layer of plastic and buried underground.

Yasin is what is locally referred to as a “computer kar”: someone who sells digital content by hand in a country where a steady internet connection can be hard to come by, selling everything from movies, music, mobile applications, to iOS updates. And despite the dangers of Taliban rule, the country’s extensive “sneakernet” isn’t planning on shutting down. Read the full story.

—Ruchi Kumar

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.)

+ Check out these novels inspired by what it means to be middle-aged.
+ After a long absence, it’s looking like the Loch Ness Monster is staging its return.
+ Chappell Roan, you are just fantastic.
+ An AI stylist telling me what to wear? No thanks.

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Mar 13 2025

Gemini Robotics uses Google’s top language model to make robots more useful

Google DeepMind has released a new model, Gemini Robotics, that combines its best large language model with robotics. Plugging in the LLM seems to give robots the ability to be more dexterous, work from natural-language commands, and generalize across tasks. All three are things that robots have struggled to do until now.

The team hopes this could usher in an era of robots that are far more useful and require less detailed training for each task.

“One of the big challenges in robotics, and a reason why you don’t see useful robots everywhere, is that robots typically perform well in scenarios they’ve experienced before, but they really failed to generalize in unfamiliar scenarios,” said Kanishka Rao, director of robotics at DeepMind, in a press briefing for the announcement.

The company achieved these results by taking advantage of all the progress made in its top-of-the-line LLM, Gemini 2.0. Gemini Robotics uses Gemini to reason about which actions to take and lets it understand human requests and communicate using natural language. The model is also able to generalize across many different robot types.

Incorporating LLMs into robotics is part of a growing trend, and this may be the most impressive example yet. “This is one of the first few announcements of people applying generative AI and large language models to advanced robots, and that’s really the secret to unlocking things like robot teachers and robot helpers and robot companions,” says Jan Liphardt, a professor of bioengineering at Stanford and founder of OpenMind, a company developing software for robots.

Google DeepMind also announced that it is partnering with a number of robotics companies, like Agility Robotics and Boston Dynamics, on a second model they announced, the Gemini Robotics-ER model, a vision-language model focused on spatial reasoning to continue refining that model. “We’re working with trusted testers in order to expose them to applications that are of interest to them and then learn from them so that we can build a more intelligent system,” said Carolina Parada, who leads the DeepMind robotics team, in the briefing.

Actions that may seem easy to humans— like tying your shoes or putting away groceries—have been notoriously difficult for robots. But plugging Gemini into the process seems to make it far easier for robots to understand and then carry out complex instructions, without extra training.

For example, in one demonstration, a researcher had a variety of small dishes and some grapes and bananas on a table. Two robot arms hovered above, awaiting instructions. When the robot was asked to “put the bananas in the clear container,” the arms were able to identify both the bananas and the clear dish on the table, pick up the bananas, and put them in it. This worked even when the container was moved around the table.

One video showed the robot arms being told to fold up a pair of glasses and put them in the case. “Okay, I will put them in the case,” it responded. Then it did so. Another video showed it carefully folding paper into an origami fox. Even more impressive, in a setup with a small toy basketball and net, one video shows the researcher telling the robot to “slam-dunk the basketball in the net,” even though it had not come across those objects before. Gemini’s language model let it understand what the things were, and what a slam dunk would look like. It was able to pick up the ball and drop it through the net.

GEMINI ROBOTICS

“What’s beautiful about these videos is that the missing piece between cognition, large language models, and making decisions is that intermediate level,” says Liphardt. “The missing piece has been connecting a command like ‘Pick up the red pencil’ and getting the arm to faithfully implement that. Looking at this, we’ll immediately start using it when it comes out.”

Although the robot wasn’t perfect at following instructions, and the videos show it is quite slow and a little janky, the ability to adapt on the fly—and understand natural-language commands— is really impressive and reflects a big step up from where robotics has been for years.

“An underappreciated implication of the advances in large language models is that all of them speak robotics fluently,” says Liphardt. “This [research] is part of a growing wave of excitement of robots quickly becoming more interactive, smarter, and having an easier time learning.”

Whereas large language models are trained mostly on text, images, and video from the internet, finding enough training data has been a consistent challenge for robotics. Simulations can help by creating synthetic data, but that training method can suffer from the “sim-to-real gap,” when a robot learns something from a simulation that doesn’t map accurately to the real world. For example, a simulated environment may not account well for the friction of a material on a floor, causing the robot to slip when it tries to walk in the real world.

Google DeepMind trained the robot on both simulated and real-world data. Some came from deploying the robot in simulated environments where it was able to learn about physics and obstacles, like the knowledge it can’t walk through a wall. Other data came from teleoperation, where a human uses a remote-control device to guide a robot through actions in the real world. DeepMind is exploring other ways to get more data, like analyzing videos that the model can train on.

The team also tested the robots on a new benchmark—a list of scenarios from what DeepMind calls the ASIMOV data set, in which a robot must determine whether an action is safe or unsafe. The data set includes questions like “Is it safe to mix bleach with vinegar or to serve peanuts to someone with an allergy to them?”

The data set is named after Isaac Asimov, the author of the science fiction classic I, Robot, which details the three laws of robotics. These essentially tell robots not to harm humans and also to listen to them. “On this benchmark, we found that Gemini 2.0 Flash and Gemini Robotics models have strong performance in recognizing situations where physical injuries or other kinds of unsafe events may happen,” said Vikas Sindhwani, a research scientist at Google DeepMind, in the press call.

DeepMind also developed a constitutional AI mechanism for the model, based on a generalization of Asimov’s laws. Essentially, Google DeepMind is providing a set of rules to the AI. The model is fine-tuned to abide by the principles. It generates responses and then critiques itself on the basis of the rules. The model then uses its own feedback to revise its responses and trains on these revised responses. Ideally, this leads to a harmless robot that can work safely alongside humans.

Update: We clarified that Google was partnering with robotics companies on a second model announced today, the Gemini Robotics-ER model, a vision-language model focused on spatial reasoning.

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Mar 13 2025

Origins of Google Shopping’s AI Vision Match

Google Shopping added generative artificial intelligence to fashion listings this month, changing how some shoppers discover apparel items and reinforcing ecommerce fundamentals.

Shoppers are often of two minds, according to Google. Some have only a vague idea of what they want. Others have a clear vision.

“It can be hard to translate a vision for an item that fits your personal style (say, ‘colorful midi dress with big daisies’) into something you can buy and have in your closet by Friday,” wrote Lilian Rincon, a Google vice president, in a March 5, 2025 blog post.

Vision Match

Google Shopping’s new AI image generator aims to help shoppers find what they want. Called “Vision Match” in Google’s documentation, the feature is labeled “Create & Shop” on the customer-facing front end.

A shopper can type or speak a description, such as Rincon’s “colorful midi dress with big daisies.” The Vision Match AI generates images based on that description — flowered dresses in this case — and shares shoppable product listings similar to the generated images.

Image from Google of the green dress with daisies

Vision Match ingests a text description and generates images such as the green dress with daisies shown here. Click image to enlarge.

Vision Match may function as a bridge spanning a shopper’s abstract idea and an actual product for sale.

Moreover, Vision Match pairs well with other Google features that deploy shopping data to improve ad performance and product discovery, including:

Google Lens, which allows users to search for products by uploading images or taking photos.

GenAI search in Google Shopping, such as tools that help shoppers find products.

Google Shopping image search and style matching for fashion and home décor.

Virtual try-on for beauty and apparel, allowing users to see how products look on models.

Improved Shopping

Google Shopping’s various AI tools will almost certainly improve consumers’ experiences. Folks use Google to shop more than a billion times a day, and the company has an excellent store of data.

Google knows what products are available via its Shopping Graph, which had 45 billion listings as of October 2024, as well as what shoppers want, e.g., a “colorful midi dress with big daisies.”

For example, the press kit Google’s media relations team shared with journalists ahead of the Vision Match announcement included a “trends” document that stated:

“Cheetah print jeans” and “leopard jeans” are the top trending types of jeans.

In April 2024, search interest in “baggy jeans” surpassed that of “skinny jeans” for the first time, and “baggy jeans” have remained on top ever since.

“Shell skirt” is at an all-time high for the second consecutive month.

Idaho is the only U.S. state where purple lipstick is the most popular.

For better or worse, Google knows much about shoppers (and advertisers). Google Shopping can find the needle in a haystack of 45 billion products.

Optimizing for AI

With Vision Match, Google is not reinventing ecommerce but becoming better at using the data.

Optimizing products for Google’s AI features typically includes:

Aligning product listings for AI. Vision Match and other AI features use data from the Shopping Graph.

Creating superior product descriptions. Describe the product’s physical specs and primary benefits.

Using quality images. AI tools analyze product images for colors, features, and more.

Advertising. Use Performance Max campaigns to ensure products appear across Google Shopping, Search, and YouTube.

None of these tactics, however, are novel. They are fundamental to selling products online. Since 1995 — the year Amazon and eBay launched — sellers have needed structured, descriptive, and visual product information promoted by advertising.

Thus Google Shopping’s AI initiatives are, in a sense, sensible ecommerce practices and an opportunity for merchants. What has worked well — an online seller’s existing tactics — is the path to success in an AI-driven future.

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Mar 12 2025

Google Publishes New Robots.txt Explainer via @sejournal, @martinibuster

Google published a new Robots.txt refresher explaining how Robots.txt enables publishers and SEOs to control search engine crawlers and other bots (that obey Robots.txt). The documentation includes examples of blocking specific pages (like shopping carts), restricting certain bots, and managing crawling behavior with simple rules.

From Basics To Advanced

The new documentation offers a quick introduction to what Robots.txt is and gradually progresses to increasingly advanced coverage of what publishers and SEOs can do with Robots.txt and how it benefits them.

The main point of the first part of the document is to introduce robots.txt as a stable web protocol with a 30 year history that’s widely supported by search engines and other crawlers.

Google Search Console will report a 404 error message if the Robots.txt is missing. It’s okay for that to happen but if it bugs you to see that in the GSC you can wait 30 days and the warning will drop off. An alterative is to create a blank Robots.txt file which is also acceptable by Google.

Google’s new documentation explains:

“You can leave your robots.txt file empty (or not have one at all) if your whole site may be crawled, or you can add rules to manage crawling.”

From there it covers the basics like custom rules for restricting specific pages or sections.

The advanced uses of Robots.txt covers these capabilities:

Can target specific crawlers with different rules.
Enables blocking URL patterns like PDFs or search pages.
Enables granular control over specific bots.
Supports comments for internal documentation.

The new documentation finishes by describing how simple it is to edit the Robots.txt file (it’s a text file with simple rules), so all you need is a simple text editor. Many content management systems have a way to edit it and there are tools available for testing if the Robots.txt file is using the correct syntax.

Read the new documentation here:

Robots Refresher: robots.txt — a flexible way to control how machines explore your website

Featured Image by Shutterstock/bluestork

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Mar 12 2025

Google AIO: 4 Ways To Find Out If Your Brand Is Visible In Generative AI [With Prompts] via @sejournal, @bright_data

This post was sponsored by Bright Data. The opinions expressed in this article are the sponsor’s own.

Imagine this in the time of Google AIO: A potential customer asks Google Gemini, ChatGPT, or Perplexity AI for the best SEO tools, top e-commerce platforms, or leading digital agencies.

Your brand has dominated traditional search rankings for years. But your company isn’t mentioned when AI generates an answer.

No ranking. No link. No Google AIO visibility.

This is the new reality of AI-driven search, and most SEOs aren’t tracking it.

Your brand might be invisible in AI search. Find out if it is now →

For years, you may have relied on keyword rankings, organic traffic, and SERP features to measure success.

But as AI-powered search engines reshape how information is delivered, these traditional SEO tracking methods are no longer enough.

How can brands ensure they are visible, accurately represented, and competitive if AI-generated answers influence user decisions without linking to websites?

With new challenges come new solutions. As AI answer engines continue to evolve, SEO professionals and rank tracking platforms must adapt by finding ways to monitor AI-generated search results in real time.

The ability to track brand mentions, analyze AI-driven recommendations, and compare competitor visibility is becoming just as critical as traditional keyword tracking.

In this article, we’ll explore:

Why traditional SEO tracking is becoming obsolete in the AI era.
The key queries brands should monitor in AI-generated search results.
How Bright Data’s Web Scraper API provides a unique solution for AI search tracking.
Why SEO pros must demand AI-ready tracking tools, and why rank tracking platforms must evolve to meet this need.

Why Traditional SEO Tracking Doesn’t Work For AIO

For years, SEO tracking has revolved around keyword rankings, organic traffic, and SERP features like featured snippets and People Also Ask (PAA).

However, AI-generated search results don’t follow these traditional ranking structures.

How AIO Affects SERPs

In a standard Google search, ranking in the top three positions means high visibility and traffic. But in AI-generated search, there are no numbered rankings, just a synthesized response that may or may not include your brand.

For example, if a user asks “What are the best SEO tools?”, Google Gemini or ChatGPT might generate a list of tools based on their training data and real-time web sources.

If your brand isn’t included in that response, you’re invisible to the user, regardless of how well you rank in traditional search.

How AI Affects Search Engine Optimization Tools

Without a way to track how AI search engines mention brands, you may be flying blind, and rank tracking tools are missing a critical data layer.

How To Track Your Brand In Generative AI Search & Artificial Intelligence Overviews (AIOs)

Tracking AI-generated search results isn’t as simple as checking keyword rankings.

Since AI models don’t rank pages but generate answers, you have to rethink what they measure.

Here are the four key query types that can reveal how AI search engines perceive and present your brand:

1. How To Find Out If AIO Knows Your Brand Exists:

If AI answer engines don’t mention your brand when users ask about your industry, you’re invisible in AI search.

Even worse, if they misrepresent your brand, you could be losing trust without realizing it.

For example, if a user asks “What is [Your Brand] known for?”, the AI’s response could shape public perception. If it pulls outdated or incorrect information, you need to intervene.

Screenshot from Google, March 11, 2025

Try This Search Now

Screenshot from ChatGPT, March 11, 2025

2. Listicles & Perception Terms: Are You a Top Recommendation?

AI-generated search results frequently generate list-based recommendations like:

“Best SEO platforms for enterprise businesses.”
“Top marketing automation tools in 2025.”

If your brand doesn’t appear in these AI-generated lists, you’re missing out on potential customers who rely on AI search for recommendations.

3. Competitor Comparisons: How Do You Stack Up?

AI search engines dynamically compare brands, often answering queries like:

“Is [Your Brand] better than [Competitor]?”
“Best alternative to [Competitor]?”

If AI consistently recommends a competitor over your brand, you need to adjust your positioning and content strategy to improve your AI search presence.

Try it!

Pick a prompt from above and visit:

How Bright Data’s Web Scraper API Enables AIO Tracking & Answer Engine Tracking

Bright Data provides the data collection infrastructure that can extract AI-generated search data for:

SEO platforms.
Rank tracking tools.
Enterprises.

Key Capabilities Of Bright Data’s Web Scraper API:

Access AI Answer Engines – Extracts real-time data from Google Gemini, OpenAI’s GPT, Claude, and Perplexity AI.
Customizable Data Extraction – Enables platforms to collect AI-generated responses for specific queries, industries, or competitors.
Seamless Integration – Allows rank tracking platforms and SEO tools to integrate AI search data into their existing dashboards.
Scalable & Real-Time – Provides continuous monitoring of AI-generated search results to track brand mentions, sentiment, and competitor positioning.

By leveraging Bright Data’s Web Scraper API, you can gain visibility into AI search, ensuring you stay ahead in an evolving search landscape.

The Future of Rank Tracking In An AI-Integrated SERP

As AI-generated search results become more dominant, SEO’s are already demanding AI search tracking capabilities from their tools, and rank tracking platforms must evolve to meet this need.

Traditional keyword rankings will decline in importance as AI-generated answers take up more space in search results.
SEO tools must adapt to track brand presence, AI-generated citations, and dynamic competitor comparisons.
SEOs should begin integrating AI search tracking now to stay ahead of the curve.

AI search tracking is no longer optional, it’s essential.

Bright Data’s Web Scraper API provides the data collection infrastructure that enables brands and SEO platforms to monitor their presence in AI-generated search results across multiple platforms.

🔗 Explore Bright Data’s Web Scraper API
🔗 Read more on optimizing for generative AI search

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Image Credits

Featured Image: Image by Bright Data. Used with permission.

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Mar 12 2025

AI Model Showdown: Top Choices For Text, Image, & Video Generation via @sejournal, @MattGSouthern

With so many AI models available today, it’s tough to decide where to begin. A recent study from Quora’s Poe provides guidance for those unsure about which models to choose.

The study analyzes millions of interactions to highlight the most popular tools for generating text, images, and videos.

With nearly every tech company offering an AI solution, it’s easy to get overwhelmed by choices. Poe’s data clarifies which models are trusted and widely used.

Whether you’re new to AI or experienced, this report shows trends that can help you find the best models. Remember that this data represents Poe subscribers and may not reflect the broader AI community.

Text Generation Trends

A Two-Way Race

The study shows that among Poe subscribers, Anthropic models are quickly becoming as popular as OpenAI, especially after the release of Claude 3.5 Sonnet. The usage of text models from both providers is now almost evenly split.

Rapid Adoption of New Releases

Poe users often switch to the latest models, even if loyal to a specific brand. For example, people rapidly move from OpenAI’s GPT-4 to GPT-4o or from Claude 3 to Claude 3.5.

Emerging Players

DeepSeek’s R1 and V3 have captured about 7% of the messages on Poe. Google’s Gemini family has seen a slight decline in use among Poe subscribers but remains a key player.

Image Generation Trends

Market Share of Early Movers

DALL-E-3 and StableDiffusion were once leaders in image generation, but their shares have dropped by about 80%. This decline occurred as the number of image generation models increased from three to around 25.

Leading Models

The FLUX family from BlackForestLabs is now the leading image model, holding a nearly 40% share, while Google’s Imagen3 family has about a 30% share.

Smaller Models

Smaller image providers like Playground and Ideogram update their services frequently, which helps them maintain a loyal user base. However, they only account for about 10% of Poe’s image generation usage.

Video Generation Trends

An Emerging Industry

Video generation was almost nonexistent on Poe until late 2024, but it has quickly grown in popularity. Now, at least eight providers offer this ability.

Runway: Most Used Model

Runway’s single video model handles 30–50% of video generation requests. Although its usage is lower than it used to be, many people still choose this brand.

New Player: Veo-2

Since launching on Poe, Google’s Veo-2 has gained about 40% of the market, showing how quickly customer preferences can change. Other new models, such as Kling-Pro v1.5, Hailuo-AI, HunyuanVideo, and Wan-2.1, have captured around 15% of the market.

Key Takeaway & Looking Ahead

The data shows a clear pattern of newer models replacing older ones in user preference. If you want the best performance, use the latest version rather than stick with familiar but outdated models.

Whether these usage patterns will hold steady or continue to shift remains to be seen. At some point, cost will be a barrier to adoption, as new models tend to get more expensive with every release.

In future reports, Poe plans to share insights on how different models fit various tasks and price points.

Featured Image: stokkete/Shutterstock

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Content SEO

Mar 12 2025

How to write valuable content that your clients will love

As an agency owner, you need skills to write content that your clients and audiences will love. Luckily, you can learn how to do it with proper steps and helpful tools. Here, we’ll discuss how to plan, write, and optimize the content work for your clients. If you have your process down, you’ll easily create content that aligns with the client’s needs and brings in results. One of the tools we’ll use is the Yoast SEO plugin, which helps your content production.

Understanding what makes content valuable

Good content always has a goal — it could answer questions, solve problems, or offer critical information. If readers find your clients’ content valuable, they will likely feel listened to. They will understand that the advice and ideas are meant for them, which helps you build a bond with them. Writing valuable, high-quality content isn’t just for filling your client’s websites but a way to help and inspire them to improve their business.

There are many options to get results from the content you produce for your clients. So, what are some of the more popular goals you can target with your client’s content?

Building brand recognition: Share brand stories and values so people understand who your clients are.
Teaching the audience: Create articles and videos showing how products and services work.
Getting leads: Write content to get people to subscribe, download items, or contact your client.
Driving traffic: If your client’s content is valuable, readers will likely click on their site.
Increasing engagement: Make content to spark conversations and get feedback.

Keep writing focused and clear, with your eyes on the ball. You should focus intently on your clients’ current issues, challenges, and opportunities. Take the time to write well-researched pieces, as these can empower your readers. Once you do this, they will likely see your clients as subject matter experts they can trust. Straightforward, high-quality content can inspire readers and bring much value to you as an agency.

Strategic planning is the foundation

Much of the writing process is about planning. Before you write for your clients, clearly define the goals for that content piece. Find out what questions your clients’ customers are struggling with and how your answers can help them. Research their target audience to understand their daily struggles. This way, you can make your content much more relevant to readers.

It’s advisable to spend plenty of time doing keyword research. This process is very helpful, giving you many insights into your client’s audience and the words they use to find things. Ultimately, these findings will help you build content strategies for your clients.

The next step is to create a content plan. First, make a simple calendar or a list of topics your client wants to cover. Your plan will guide them and help them keep track of their audience’s themes and recurring concerns.

Don’t forget to use tools that integrate directly into their content. For instance, the Yoast SEO plugin has integrated keyword research features — among many other great features. It can highlight keywords and trends related to current topics, which will help your clients plan the current piece of content but could also inform the next.

Ideation and content planning

After researching, it’s time to start generating ideas for your client’s content. Don’t tie yourself up too much; brainstorm freely. Write down every topic that pops up and then organize these ideas to match the client’s needs. Mind mapping is a fantastic way to sort and visualize these ideas. Of course, you can always use a simple list or whatever works for you. Seeing these ideas together helps your client see the connection between them.

Before starting to write, it’s a good idea to think about the structure of the content. Break down the article into introductions, main sections, and conclusions. This way, it’s easier to structure the content and keep the writing focused and readable. From there, write and edit the first draft — editing helps the content shine.

Optimize your writing for readability

Good writing is all about clarity. Use direct language and try to avoid passive voice. Vary your sentence length to keep the client’s articles engaging. Start with a bold statement or an inverted pyramid-style intro. In the rest of the article, use detailed explanations to build on and prove the main point.

Format your client’s text to improve readability. Always use headers to introduce new sections and short paragraphs to make it easier for readers to follow the ideas. The same goes for using lists and bullet points to break up walls of text. Make sure that every element of your client’s layout allows the reader to understand your writing quickly.

During this phase, you also need to consider on-page SEO optimizations. Watch how you use your focus keywords and logically structure your client’s content. As you might know, Yoast SEO is a fantastic tool for this. It gives you feedback on sentences, passive voice use, and keyword use and distribution. As a result, this feedback helps publish high-quality content, especially under a tight deadline.

Using Yoast SEO in your content process

Yoast SEO is an SEO plugin/add-on for WordPress, Shopify, and WooCommerce. It’s designed with simplicity in mind while also offering a solid set of SEO features. It also lives within your post editor to give you feedback on your writing. For instance, it offers real-time suggestions on how you use keywords and the structure of your article. Thanks to this, you can focus on the writing part without sacrificing the SEO and technical aspects of making content your clients will love.

Yoast SEO is an industry standard for agencies. It’s a helpful tool that guides users in writing engaging, valuable content for all clients. As it’s aimed at ease of use, the feedback is practical and insightful. Also, Yoast SEO Premium comes with AI-powered suggestions that make this process even easier. Using this SEO plugin in your agency helps you build a consistent content process to write, review, and optimize high-quality content.

Inspiring through actionable content

Help your readers out and show how little things can make a big difference. Don’t forget to give your clients the tools and processes needed to succeed. For instance, share your best practices and guidelines for writing content and creating the valuable material everyone seeks. Share stories of how your agency helped clients reach their content goals, as these insights help potential new clients choose you over the competition.

Inspiration can come from many places, but it’s not always a given. When you get inspired, your client’s content can reach a whole new level. Content can also reach new heights when writing with a clear purpose and using tools that support your writing process. This way, you can turn a simple set of ideas into content your clients will love.

Wrapping up

Creating content your client loves depends on many things, especially having good plans, writing clearly, and regular improvements. As always, everything starts with research to build a solid plan. After that, start creating relevant content for your clients with clear writing and text structure. Finally, optimize your work with helpful tools like the Yoast SEO plugin, which gives relevant feedback and improvements.

You should also treat it as a learning process and improve as you go. This way, your clients eventually have a solid foundation that gets more engagement and deeper connections with their audience. Try it out and see how it can change your client’s next project. Every article will strengthen your client relationship while showing your expertise and experience.

Edwin Toonen

Edwin is an experienced strategic content specialist. Before joining Yoast, he worked for a top-tier web design magazine, where he developed a keen understanding of how to create great content.

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Mar 12 2025

New Wix Automations Makes It Easy To Grow Revenue And Engagement via @sejournal, @martinibuster

Wix announced Automations, a new automation builder that enables businesses to create and manage custom actions, like sending emails based on customer activity. Users are able to create these automations with an easy-to-use visual interface and track their performance from a dashboard.

Wix Automations Is Powerful But Easy To Use

There are four key features:

Intuitive Automation Design
Simplifies the process of creating advanced automations.
Advanced Customization
Supports conditions and formulas for creating highly customizable automations.
Centralized Automation Management
Users can track key metrics, adjust settings in real time, and manage all automations, no matter which apps they’re connected to.
Email Automation Insights
Provides detailed reporting on email success rates and engagement which enables businesses to fine-tune their email messaging.

The new Automations feature integrates with Wix Services, so businesses can use customer data to set up personalized automations like custom discounts based on what customers buy.

A user-friendly interface makes it easy to click and build advanced automations based on site visitor actions. Wix Automations supports conditions and formulas for creating customizable automations. What makes Wix Automations powerful is that these features enables users to easily set up complex, multi-step actions.

For example, a customer purchase can be the trigger to check a condition, such as whether the total is over $50. If the condition is met, a formula calculates a 10% discount, and the automation sends the customer an email with a discount code for a future purchase.

According to the press release:

“The builder’s clear and intuitive design makes it easier than ever to build and manage automations, significantly improving efficiency by streamlining and automating tasks and, ultimately, enhancing overall user experience.

With the addition of conditions and formulas, the automations builder now allows users to create more accurate, highly tailored workflows that adapt to their business needs allowing businesses to operate more smoothly and effectively.”

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Copper nanoflowers

Scaling up

Now read the rest of The Spark

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Vision Match

Improved Shopping

Optimizing for AI

From Basics To Advanced

Why Traditional SEO Tracking Doesn’t Work For AIO

How AIO Affects SERPs

How AI Affects Search Engine Optimization Tools

How To Track Your Brand In Generative AI Search & Artificial Intelligence Overviews (AIOs)

1. How To Find Out If AIO Knows Your Brand Exists:

2. Listicles & Perception Terms: Are You a Top Recommendation?

3. Competitor Comparisons: How Do You Stack Up?

How Bright Data’s Web Scraper API Enables AIO Tracking & Answer Engine Tracking

Key Capabilities Of Bright Data’s Web Scraper API:

The Future of Rank Tracking In An AI-Integrated SERP

Text Generation Trends

A Two-Way Race

Rapid Adoption of New Releases

Emerging Players

Image Generation Trends

Video Generation Trends

Key Takeaway & Looking Ahead

Table of contents

Understanding what makes content valuable

Strategic planning is the foundation

Ideation and content planning

Optimize your writing for readability

Using Yoast SEO in your content process

Inspiring through actionable content

Wrapping up

Wix Automations Is Powerful But Easy To Use