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Data Centers Are Crushing the Planet. Can Space Save Us?

Data Centers Are Crushing the Planet. Can Space Save Us?

The companies frantically building and leasing data centers are well aware that they’re straining grids, driving emissions, and guzzling water. The electricity demand of AI data centers in particular could increase as much as 165% by 2030. Over half of the energy powering these sprawling facilities comes from fossil fuels, threatening to reverse progress toward addressing the climate crisis.

Some of the biggest names in artificial intelligence say they have a solution: Just stick these colossal computer clusters in space. OpenAI CEO Sam Altman told manosphere podcaster Theo Vonn that he considers a massive expansion of data centers inevitable. “I do guess a lot of the world gets covered in data centers over time,” he said. (This is not, in fact, inevitable, but the result of unfathomably wealthy companies choosing to invest unfathomably large sums of money. Altman has speculated that he would quite literally put trillions into it, and OpenAI is part of the consortium behind the $500 billion Stargate project.)

Altman is aware, however, that some people might not like this. “I’ve spoken with environmentalists,” he said. Then, he offered a suggestion. “Maybe we put [data centers] in space,” he said. “I wish I had, like, more concrete answers for you, but like, we’re stumbling through this.”

Now, the idea of hurling data centers, the largest of which can cover over a million square feet, into orbit may seem impractical. But Altman’s not alone in considering it. Jeff Bezos and Eric Schmidt are also betting on the idea.

Altman has proposed creating a Dyson sphere of data centers around the sun, referring to a hypothetical megastructure built around a star to capture much of its energy. The rather glaring downside to this is that building it would likely require more resources than exist on Earth, and could make the planet uninhabitable. But somewhat more realistic plans are inching closer to reality. Startups like Starcloud, Axiom, and Lonestar Data Systems have raised millions to develop them.

There are at least 5,400 data centers in the United States, ranging from micro-size to thousand-server “hyperscalers,” and the number is growing fast. These facilities are expected to consume up to 12% of the nation’s electricity by 2028. Putting them in space, then, can seem like a panacea: solving the energy-use problem with 24/7 solar power, and freeing communities from the burden of air, noise, and water pollution.

There’s some real science behind this. Ali Hajimiri, an electrical engineer and professor with Caltech’s Space Solar Power Project, sought a patent for a “massively parallel computational system in space”—as in, a data center—back in 2016. Since then, launch costs have gone down (to around $1,500 per kilogram, by one estimate), and solar panels have gotten lighter and more efficient. Hajimiri and his colleagues recently proposed a lightweight space-based solar power system that could generate electricity at 10 cents per kilowatt-hour, significantly cheaper at scale than comparable systems here on Earth.

Such technology theoretically could power orbital data centers like those Altman imagines, though Hajimiri is still not sure when they could be built at the kind of scale companies like OpenAI demand. “I never want to say something cannot be done,” he said. “But there are challenges associated with it.”

For one thing, the systems he imagines process data relatively slowly compared to those on terra firma. They’d be constantly bombarded by radiation, and “obsolescence would be a problem” because making repairs or upgrades would be confoundingly difficult. Hajimiri believes that data centers in space could, someday, be a viable solution, but he hesitates to say when that day might come. “Definitely it would be doable in a few years,” he said. “The question is how effective they would be and how cost-effective they would become.”

The idea of simply putting data centers in orbit is not limited to the offhand musings of techies or the deeper thoughts of academics. Even some elected officials in cities where companies like Amazon hope to build data centers are raising the point. Tucson, Arizona, councilmember Nikki Lee waxed poetic about their potential during an August hearing, in which the council unanimously voted down a proposed data center in their city.

“A lot of people are saying data centers don’t belong in the desert,” Lee said. But “if this is truly a national priority,” then the focus must be on “putting federal research and development dollars into looking at data centers that will exist in space. And that may sound wild to you all and a little science fiction, but it’s actually happening.”

That’s true, but it’s happening on an experimental scale, not an industrial one. A startup called Starcloud hoped to launch a refrigerator-sized satellite housing a few Nvidia chips in August, but the launch date was pushed back. Lonestar Data Systems landed a miniature data center, carrying precious information like an Imagine Dragons song, on the moon a few months ago, though the lander tipped over and died in the attempt. More such launches are planned for the coming months. But it’s “very hard to predict how quickly this idea will become economically feasible,” said Matthew Weinzierl, a Harvard University economist who studies market forces in space. “Space-based data centers may well have some niche uses, such as for processing space-based data and providing national security capabilities,” he said. “To be a meaningful rival to terrestrial centers, however, they will need to compete on cost and service quality like anything else.”

For now, it’s much more expensive to put a data center in space than it is to put one in, say, Virginia’s Data Center Valley, where power demand could double in the next decade if left unregulated. And as long as staying on Earth remains cheaper, profit-motivated companies will favor terrestrial data center expansion.

Still, there is one factor that might encourage OpenAI and others to look toward the heavens: there isn’t much regulation up there. Building data centers on Earth requires obtaining municipal permits, and companies can be stymied by local governments whose residents worry that data center development might siphon their water, raise their electricity bills, or overheat their planet. In space, there aren’t any neighbors to complain, said Michelle Hanlon, a political scientist and lawyer who leads the Center for Air and Space Law at the University of Mississippi. “If you are a U.S. company seeking to put data centers in space, then the sooner the better, before Congress is like, ‘Oh, we need to regulate that.’”

This article originally appeared in Grist at https://grist.org/climate-energy/data-centers-gobble-earths-resources-what-if-we-took-them-to-space-instead/. Grist is a nonprofit, independent media organization dedicated to telling stories of climate solutions and a just future. Learn more at Grist.org.

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On May 4, 2026, the U.S. Securities and Exchange Commission filed an amended complaint to add the Elon Musk Revocable Trust dated July 22, 2003 (the “Revocable Trust”) as a defendant to this action. The amended complaint alleges that the defendants failed to timely file a beneficial ownership report with the Commission after the Revocable Trust acquired beneficial ownership of more than five percent of the outstanding shares of Twitter, Inc. common stock, in violation of the beneficial ownership reporting requirements under the Securities Exchange Act of 1934 (“Exchange Act”).

The SEC simultaneously moved for entry of a consent final judgment as to the Revocable Trust. Without admitting or denying the allegations of the complaint as to the Revocable Trust, the Revocable Trust consented to entry of a final judgment, subject to court approval, that would permanently enjoin it from violating Section 13(d) of the Exchange Act and Rule 13d-1 thereunder and order it to pay a civil penalty of $1.5 million.

As explained in the consent motion, if the court enters the proposed final judgment as to the Revocable Trust as proposed by the Revocable Trust and the SEC, the SEC will file a stipulated dismissal of Elon Musk in his personal capacity, which will resolve this case in its entirety.

#Elon #Musk #settle #feds #Twitter #lawsuit #pocket #changeElon Musk,Law,News,Policy,Tech,Twitter – X">Elon Musk will settle the feds’ Twitter lawsuit with pocket changeOn May 4, 2026, the U.S. Securities and Exchange Commission filed an amended complaint to add the Elon Musk Revocable Trust dated July 22, 2003 (the “Revocable Trust”) as a defendant to this action. The amended complaint alleges that the defendants failed to timely file a beneficial ownership report with the Commission after the Revocable Trust acquired beneficial ownership of more than five percent of the outstanding shares of Twitter, Inc. common stock, in violation of the beneficial ownership reporting requirements under the Securities Exchange Act of 1934 (“Exchange Act”).The SEC simultaneously moved for entry of a consent final judgment as to the Revocable Trust. Without admitting or denying the allegations of the complaint as to the Revocable Trust, the Revocable Trust consented to entry of a final judgment, subject to court approval, that would permanently enjoin it from violating Section 13(d) of the Exchange Act and Rule 13d-1 thereunder and order it to pay a civil penalty of .5 million.As explained in the consent motion, if the court enters the proposed final judgment as to the Revocable Trust as proposed by the Revocable Trust and the SEC, the SEC will file a stipulated dismissal of Elon Musk in his personal capacity, which will resolve this case in its entirety.#Elon #Musk #settle #feds #Twitter #lawsuit #pocket #changeElon Musk,Law,News,Policy,Tech,Twitter – X


Image model releases are driving growth for AI mobile apps, generating 6.5x more downloads than traditional model updates, according to a new report from app intelligence provider Appfigures.

This marks a shift from earlier days, when the release of new models powering the conversational experiences drove more demand, alongside the new features like a voice chat interface.

For instance, ChatGPT and Gemini each added tens of millions of new downloads after releasing their respective image models, Appfigures found.

For Google’s Gemini, the release of its image model Nano Banana drove an additional 22+ million downloads in the 28 days following the introduction of the Gemini 2.5 Flash image model last August. This launch lifted the app’s downloads by more than 4x over that period, the data showed.

Image AI models now drive app growth, beating chatbot upgrades | TechCrunch
Image model releases are driving growth for AI mobile apps, generating 6.5x more downloads than traditional model updates, according to a new report from app intelligence provider Appfigures.

This marks a shift from earlier days, when the release of new models powering the conversational experiences drove more demand, alongside the new features like a voice chat interface.







For instance, ChatGPT and Gemini each added tens of millions of new downloads after releasing their respective image models, Appfigures found.

For Google’s Gemini, the release of its image model Nano Banana drove an additional 22+ million downloads in the 28 days following the introduction of the Gemini 2.5 Flash image model last August. This launch lifted the app’s downloads by more than 4x over that period, the data showed.

Image Credits:Appfigures

Meanwhile, ChatGPT added more than 12 million incremental installs in the 28 days after the introduction of its GPT-4o image model in March of last year. That’s roughly 4.5x more downloads than it saw for its GPT-4o, GPT-4.5, and GPT-5 model releases, Appfigures pointed out.

Other model releases followed similar trends, though on a smaller scale. Meta AI’s introduction of its AI video feed Vibes added an estimated 2.6 million incremental downloads in the 28 days after its September 2025 release. (Yes, technically, this is a video model, but it’s ultimately about visual content, not just text.)

Image Credits:Appfigures

Still, the report cautioned, additional downloads don’t always translate into increased mobile revenue.

	
		
		Techcrunch event
		
			
			
									San Francisco, CA
													|
													October 13-15, 2026
							
			
		
	


Instead, new image model releases give people a reason to install the app and try out its improved image-generation capabilities. That doesn’t mean they’ll necessarily convert to paying subscribers. For example, Appfigures noted that Nano Banana drove only 1,000 in estimated gross consumer spending during the 28-day window following its release, even though it produced a larger spike in downloads than ChatGPT’s 4o image model release.

Meta AI’s launch of Vibes also led to additional downloads, but no meaningful revenue.

Among the three, only ChatGPT turned the increased attention into actual dollars. 







OpenAI’s 4o image-generation model led to an estimated  million in gross consumer spending over the 28 days after its launch, compared with its prior baseline, Appfigures said.

Image Credits:Appfigures

The company also looked at DeepSeek in its analysis, but it didn’t fit the pattern. 

While DeepSeek R1 drove 28 million downloads after its January 2025 release, it wasn’t a typical model comparison event. This was DeepSeek’s breakout moment, when it went from being relatively unknown to an overnight sensation as the tech industry learned about the techniques it used to train its AI models at a fraction of the cost of its competitors. This case highlights how curiosity can drive downloads — though in this instance, the interest wasn’t tied to an image model.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Image #models #drive #app #growth #beating #chatbot #upgrades #TechCrunchai apps,ChatGPT,gemini,image models,meta ai
Image Credits:Appfigures

Meanwhile, ChatGPT added more than 12 million incremental installs in the 28 days after the introduction of its GPT-4o image model in March of last year. That’s roughly 4.5x more downloads than it saw for its GPT-4o, GPT-4.5, and GPT-5 model releases, Appfigures pointed out.

Other model releases followed similar trends, though on a smaller scale. Meta AI’s introduction of its AI video feed Vibes added an estimated 2.6 million incremental downloads in the 28 days after its September 2025 release. (Yes, technically, this is a video model, but it’s ultimately about visual content, not just text.)

Image Credits:Appfigures

Still, the report cautioned, additional downloads don’t always translate into increased mobile revenue.

Techcrunch event

San Francisco, CA | October 13-15, 2026

Instead, new image model releases give people a reason to install the app and try out its improved image-generation capabilities. That doesn’t mean they’ll necessarily convert to paying subscribers. For example, Appfigures noted that Nano Banana drove only $181,000 in estimated gross consumer spending during the 28-day window following its release, even though it produced a larger spike in downloads than ChatGPT’s 4o image model release.

Meta AI’s launch of Vibes also led to additional downloads, but no meaningful revenue.

Among the three, only ChatGPT turned the increased attention into actual dollars.

OpenAI’s 4o image-generation model led to an estimated $70 million in gross consumer spending over the 28 days after its launch, compared with its prior baseline, Appfigures said.

Image Credits:Appfigures

The company also looked at DeepSeek in its analysis, but it didn’t fit the pattern.

While DeepSeek R1 drove 28 million downloads after its January 2025 release, it wasn’t a typical model comparison event. This was DeepSeek’s breakout moment, when it went from being relatively unknown to an overnight sensation as the tech industry learned about the techniques it used to train its AI models at a fraction of the cost of its competitors. This case highlights how curiosity can drive downloads — though in this instance, the interest wasn’t tied to an image model.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Image #models #drive #app #growth #beating #chatbot #upgrades #TechCrunchai apps,ChatGPT,gemini,image models,meta ai">Image AI models now drive app growth, beating chatbot upgrades | TechCrunch
Image model releases are driving growth for AI mobile apps, generating 6.5x more downloads than traditional model updates, according to a new report from app intelligence provider Appfigures.

This marks a shift from earlier days, when the release of new models powering the conversational experiences drove more demand, alongside the new features like a voice chat interface.







For instance, ChatGPT and Gemini each added tens of millions of new downloads after releasing their respective image models, Appfigures found.

For Google’s Gemini, the release of its image model Nano Banana drove an additional 22+ million downloads in the 28 days following the introduction of the Gemini 2.5 Flash image model last August. This launch lifted the app’s downloads by more than 4x over that period, the data showed.

Image Credits:Appfigures

Meanwhile, ChatGPT added more than 12 million incremental installs in the 28 days after the introduction of its GPT-4o image model in March of last year. That’s roughly 4.5x more downloads than it saw for its GPT-4o, GPT-4.5, and GPT-5 model releases, Appfigures pointed out.

Other model releases followed similar trends, though on a smaller scale. Meta AI’s introduction of its AI video feed Vibes added an estimated 2.6 million incremental downloads in the 28 days after its September 2025 release. (Yes, technically, this is a video model, but it’s ultimately about visual content, not just text.)

Image Credits:Appfigures

Still, the report cautioned, additional downloads don’t always translate into increased mobile revenue.

	
		
		Techcrunch event
		
			
			
									San Francisco, CA
													|
													October 13-15, 2026
							
			
		
	


Instead, new image model releases give people a reason to install the app and try out its improved image-generation capabilities. That doesn’t mean they’ll necessarily convert to paying subscribers. For example, Appfigures noted that Nano Banana drove only 1,000 in estimated gross consumer spending during the 28-day window following its release, even though it produced a larger spike in downloads than ChatGPT’s 4o image model release.

Meta AI’s launch of Vibes also led to additional downloads, but no meaningful revenue.

Among the three, only ChatGPT turned the increased attention into actual dollars. 







OpenAI’s 4o image-generation model led to an estimated  million in gross consumer spending over the 28 days after its launch, compared with its prior baseline, Appfigures said.

Image Credits:Appfigures

The company also looked at DeepSeek in its analysis, but it didn’t fit the pattern. 

While DeepSeek R1 drove 28 million downloads after its January 2025 release, it wasn’t a typical model comparison event. This was DeepSeek’s breakout moment, when it went from being relatively unknown to an overnight sensation as the tech industry learned about the techniques it used to train its AI models at a fraction of the cost of its competitors. This case highlights how curiosity can drive downloads — though in this instance, the interest wasn’t tied to an image model.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Image #models #drive #app #growth #beating #chatbot #upgrades #TechCrunchai apps,ChatGPT,gemini,image models,meta ai

new models powering the conversational experiences drove more demand, alongside the new features like a voice chat interface.

For instance, ChatGPT and Gemini each added tens of millions of new downloads after releasing their respective image models, Appfigures found.

For Google’s Gemini, the release of its image model Nano Banana drove an additional 22+ million downloads in the 28 days following the introduction of the Gemini 2.5 Flash image model last August. This launch lifted the app’s downloads by more than 4x over that period, the data showed.

Image AI models now drive app growth, beating chatbot upgrades | TechCrunch
Image model releases are driving growth for AI mobile apps, generating 6.5x more downloads than traditional model updates, according to a new report from app intelligence provider Appfigures.

This marks a shift from earlier days, when the release of new models powering the conversational experiences drove more demand, alongside the new features like a voice chat interface.







For instance, ChatGPT and Gemini each added tens of millions of new downloads after releasing their respective image models, Appfigures found.

For Google’s Gemini, the release of its image model Nano Banana drove an additional 22+ million downloads in the 28 days following the introduction of the Gemini 2.5 Flash image model last August. This launch lifted the app’s downloads by more than 4x over that period, the data showed.

Image Credits:Appfigures

Meanwhile, ChatGPT added more than 12 million incremental installs in the 28 days after the introduction of its GPT-4o image model in March of last year. That’s roughly 4.5x more downloads than it saw for its GPT-4o, GPT-4.5, and GPT-5 model releases, Appfigures pointed out.

Other model releases followed similar trends, though on a smaller scale. Meta AI’s introduction of its AI video feed Vibes added an estimated 2.6 million incremental downloads in the 28 days after its September 2025 release. (Yes, technically, this is a video model, but it’s ultimately about visual content, not just text.)

Image Credits:Appfigures

Still, the report cautioned, additional downloads don’t always translate into increased mobile revenue.

	
		
		Techcrunch event
		
			
			
									San Francisco, CA
													|
													October 13-15, 2026
							
			
		
	


Instead, new image model releases give people a reason to install the app and try out its improved image-generation capabilities. That doesn’t mean they’ll necessarily convert to paying subscribers. For example, Appfigures noted that Nano Banana drove only 1,000 in estimated gross consumer spending during the 28-day window following its release, even though it produced a larger spike in downloads than ChatGPT’s 4o image model release.

Meta AI’s launch of Vibes also led to additional downloads, but no meaningful revenue.

Among the three, only ChatGPT turned the increased attention into actual dollars. 







OpenAI’s 4o image-generation model led to an estimated  million in gross consumer spending over the 28 days after its launch, compared with its prior baseline, Appfigures said.

Image Credits:Appfigures

The company also looked at DeepSeek in its analysis, but it didn’t fit the pattern. 

While DeepSeek R1 drove 28 million downloads after its January 2025 release, it wasn’t a typical model comparison event. This was DeepSeek’s breakout moment, when it went from being relatively unknown to an overnight sensation as the tech industry learned about the techniques it used to train its AI models at a fraction of the cost of its competitors. This case highlights how curiosity can drive downloads — though in this instance, the interest wasn’t tied to an image model.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Image #models #drive #app #growth #beating #chatbot #upgrades #TechCrunchai apps,ChatGPT,gemini,image models,meta ai
Image Credits:Appfigures

Meanwhile, ChatGPT added more than 12 million incremental installs in the 28 days after the introduction of its GPT-4o image model in March of last year. That’s roughly 4.5x more downloads than it saw for its GPT-4o, GPT-4.5, and GPT-5 model releases, Appfigures pointed out.

Other model releases followed similar trends, though on a smaller scale. Meta AI’s introduction of its AI video feed Vibes added an estimated 2.6 million incremental downloads in the 28 days after its September 2025 release. (Yes, technically, this is a video model, but it’s ultimately about visual content, not just text.)

Image Credits:Appfigures

Still, the report cautioned, additional downloads don’t always translate into increased mobile revenue.

Techcrunch event

San Francisco, CA | October 13-15, 2026

Instead, new image model releases give people a reason to install the app and try out its improved image-generation capabilities. That doesn’t mean they’ll necessarily convert to paying subscribers. For example, Appfigures noted that Nano Banana drove only $181,000 in estimated gross consumer spending during the 28-day window following its release, even though it produced a larger spike in downloads than ChatGPT’s 4o image model release.

Meta AI’s launch of Vibes also led to additional downloads, but no meaningful revenue.

Among the three, only ChatGPT turned the increased attention into actual dollars.

OpenAI’s 4o image-generation model led to an estimated $70 million in gross consumer spending over the 28 days after its launch, compared with its prior baseline, Appfigures said.

Image Credits:Appfigures

The company also looked at DeepSeek in its analysis, but it didn’t fit the pattern.

While DeepSeek R1 drove 28 million downloads after its January 2025 release, it wasn’t a typical model comparison event. This was DeepSeek’s breakout moment, when it went from being relatively unknown to an overnight sensation as the tech industry learned about the techniques it used to train its AI models at a fraction of the cost of its competitors. This case highlights how curiosity can drive downloads — though in this instance, the interest wasn’t tied to an image model.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Image #models #drive #app #growth #beating #chatbot #upgrades #TechCrunchai apps,ChatGPT,gemini,image models,meta ai">Image AI models now drive app growth, beating chatbot upgrades | TechCrunch


Image model releases are driving growth for AI mobile apps, generating 6.5x more downloads than traditional model updates, according to a new report from app intelligence provider Appfigures.

This marks a shift from earlier days, when the release of new models powering the conversational experiences drove more demand, alongside the new features like a voice chat interface.

For instance, ChatGPT and Gemini each added tens of millions of new downloads after releasing their respective image models, Appfigures found.

For Google’s Gemini, the release of its image model Nano Banana drove an additional 22+ million downloads in the 28 days following the introduction of the Gemini 2.5 Flash image model last August. This launch lifted the app’s downloads by more than 4x over that period, the data showed.

Image AI models now drive app growth, beating chatbot upgrades | TechCrunch
Image model releases are driving growth for AI mobile apps, generating 6.5x more downloads than traditional model updates, according to a new report from app intelligence provider Appfigures.

This marks a shift from earlier days, when the release of new models powering the conversational experiences drove more demand, alongside the new features like a voice chat interface.







For instance, ChatGPT and Gemini each added tens of millions of new downloads after releasing their respective image models, Appfigures found.

For Google’s Gemini, the release of its image model Nano Banana drove an additional 22+ million downloads in the 28 days following the introduction of the Gemini 2.5 Flash image model last August. This launch lifted the app’s downloads by more than 4x over that period, the data showed.

Image Credits:Appfigures

Meanwhile, ChatGPT added more than 12 million incremental installs in the 28 days after the introduction of its GPT-4o image model in March of last year. That’s roughly 4.5x more downloads than it saw for its GPT-4o, GPT-4.5, and GPT-5 model releases, Appfigures pointed out.

Other model releases followed similar trends, though on a smaller scale. Meta AI’s introduction of its AI video feed Vibes added an estimated 2.6 million incremental downloads in the 28 days after its September 2025 release. (Yes, technically, this is a video model, but it’s ultimately about visual content, not just text.)

Image Credits:Appfigures

Still, the report cautioned, additional downloads don’t always translate into increased mobile revenue.

	
		
		Techcrunch event
		
			
			
									San Francisco, CA
													|
													October 13-15, 2026
							
			
		
	


Instead, new image model releases give people a reason to install the app and try out its improved image-generation capabilities. That doesn’t mean they’ll necessarily convert to paying subscribers. For example, Appfigures noted that Nano Banana drove only 1,000 in estimated gross consumer spending during the 28-day window following its release, even though it produced a larger spike in downloads than ChatGPT’s 4o image model release.

Meta AI’s launch of Vibes also led to additional downloads, but no meaningful revenue.

Among the three, only ChatGPT turned the increased attention into actual dollars. 







OpenAI’s 4o image-generation model led to an estimated  million in gross consumer spending over the 28 days after its launch, compared with its prior baseline, Appfigures said.

Image Credits:Appfigures

The company also looked at DeepSeek in its analysis, but it didn’t fit the pattern. 

While DeepSeek R1 drove 28 million downloads after its January 2025 release, it wasn’t a typical model comparison event. This was DeepSeek’s breakout moment, when it went from being relatively unknown to an overnight sensation as the tech industry learned about the techniques it used to train its AI models at a fraction of the cost of its competitors. This case highlights how curiosity can drive downloads — though in this instance, the interest wasn’t tied to an image model.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Image #models #drive #app #growth #beating #chatbot #upgrades #TechCrunchai apps,ChatGPT,gemini,image models,meta ai
Image Credits:Appfigures

Meanwhile, ChatGPT added more than 12 million incremental installs in the 28 days after the introduction of its GPT-4o image model in March of last year. That’s roughly 4.5x more downloads than it saw for its GPT-4o, GPT-4.5, and GPT-5 model releases, Appfigures pointed out.

Other model releases followed similar trends, though on a smaller scale. Meta AI’s introduction of its AI video feed Vibes added an estimated 2.6 million incremental downloads in the 28 days after its September 2025 release. (Yes, technically, this is a video model, but it’s ultimately about visual content, not just text.)

Image Credits:Appfigures

Still, the report cautioned, additional downloads don’t always translate into increased mobile revenue.

Techcrunch event

San Francisco, CA | October 13-15, 2026

Instead, new image model releases give people a reason to install the app and try out its improved image-generation capabilities. That doesn’t mean they’ll necessarily convert to paying subscribers. For example, Appfigures noted that Nano Banana drove only $181,000 in estimated gross consumer spending during the 28-day window following its release, even though it produced a larger spike in downloads than ChatGPT’s 4o image model release.

Meta AI’s launch of Vibes also led to additional downloads, but no meaningful revenue.

Among the three, only ChatGPT turned the increased attention into actual dollars.

OpenAI’s 4o image-generation model led to an estimated $70 million in gross consumer spending over the 28 days after its launch, compared with its prior baseline, Appfigures said.

Image Credits:Appfigures

The company also looked at DeepSeek in its analysis, but it didn’t fit the pattern.

While DeepSeek R1 drove 28 million downloads after its January 2025 release, it wasn’t a typical model comparison event. This was DeepSeek’s breakout moment, when it went from being relatively unknown to an overnight sensation as the tech industry learned about the techniques it used to train its AI models at a fraction of the cost of its competitors. This case highlights how curiosity can drive downloads — though in this instance, the interest wasn’t tied to an image model.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Image #models #drive #app #growth #beating #chatbot #upgrades #TechCrunchai apps,ChatGPT,gemini,image models,meta ai

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