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Two Years After Maui Burned, Researchers Reveal the Wildfire’s True Death Toll

Two Years After Maui Burned, Researchers Reveal the Wildfire’s True Death Toll

In August 2023, downed power lines on Maui, Hawaii, sparked a wildfire that quickly exploded into multiple, fast-moving blazes fanned by high winds. Over several days, the fires reduced much of the town of Lāhainā to ashes, displacing thousands and killing more than 100 people.

New research published Thursday, August 22, in the journal Frontiers in Climate suggests this disaster also caused a population-wide increase in mortality beyond what the official death count captured. By calculating the all-cause excess fatality rate—how many more deaths took place over a given period than expected—scientists found a 67% increase in the local mortality rate for August 2023. During the deadliest week of the blaze, the local death rate was 367% higher than expected. These findings underscore a need for improved disaster preparedness that incorporates Native Hawaiian ecological knowledge, the researchers concluded.

What excess death rate reveals

Looking at the excess death rate offered a fuller picture of the fire’s impact, co-first author Michelle Nakatsuka, a medical student and researcher at New York University’s Grossman School of Medicine, told Gizmodo in an email. “The official numbers mostly count direct causes, like burns or smoke inhalation, but excess deaths capture [the] true toll better by telling us how many more people died than would have otherwise been expected in the month of the Lāhainā fires,” she explained.

Disasters like wildfires often cause deaths in indirect ways that affect communities over time. When clinics shut down and roads are blocked off, people can’t refill their prescriptions or get dialysis treatments, Nakatsuka explained. Stress and displacement can worsen chronic conditions, and power or communication failures can delay emergency responses. “These impacts are amplified in under-resourced settings and [are] disproportionately suffered by vulnerable groups, like the elderly or people of color,” she said.

The tragic toll of the Maui fires

Even with this knowledge, Nakatsuka and her colleagues were surprised by the increase in excess mortality during the month of August 2023. Their analysis included all causes of death except covid-19. “While we anticipated an increase in excess deaths, seeing more than 80 additional deaths in the month of the Lāhainā fires was striking,” Nakatsuka said. “It was also surprising to see that the proportion of those deaths occurring outside of medical settings was larger than expected,” she added.

Indeed, the number of deaths that didn’t take place in a medical context—such as the emergency room—rose from 68% in previous months to 80% in August 2023. These people died in homes or public locations, suggesting that many were unable to reach medical care because of the fires.

A path to resilience

While all-cause excess mortality is useful for correlating increased fatalities with natural disasters, it offers little insight into the details of these deaths, Nakatsuka clarified. “The main limitation here is that we can’t say exactly which deaths were caused by the fires or look into Lāhainā-specific excess mortality; we can only measure the overall increase in deaths,” she said, adding that future research should analyze death records alongside medical and toxicology reports to identify causes of death.

Still, these findings reveal a need to improve Maui’s disaster preparedness and invest in wildfire mitigation strategies rooted in Indigenous knowledge, Nakatsuka said. “Native Hawaiian practices center around caring for the land (mālama ʻāina) in ways that naturally reduce fire risk, like restoring native plants, maintaining diverse ecosystems, and managing water resources,” she said. “Bringing Indigenous knowledge together with modern climate prediction tools will minimize risk of future climate crises and center the community’s voice at the heart of disaster prevention and recovery efforts.”

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#Years #Maui #Burned #Researchers #Reveal #Wildfires #True #Death #Toll

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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