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DeepSeek 2.0 moment, but it feels very close. The leading Chinese AI labs have been on a roll lately, releasing a series of almost cutting-edge open-source models. Z.ai released GLM 5.2 in June, Moonshot AI released Kimi K3 last week, and Alibaba released Qwen 3.8 this Monday.

Silicon Valley and Washington started talking about the models immediately, especially K3, which is widely seen as the best of the bunch. David Sacks, a venture capitalist and AI adviser to President Donald Trump, called the performance of Moonshot’s model “concerning.” Earlier this week, Commerce Secretary Scott Bessent suggested the US might impose sanctions on Chinese AI companies.

On Wednesday, Michael Kratsios, director of the White House Office of Science and Technology Policy, alleged that the Trump administration has “information that Moonshot AI distilled Anthropic’s Fable for the development of its K3 model,” which he said amounted to “stealing proprietary US technology and undermining American research” and was “unacceptable.” (Moonshot AI did not immediately respond to a request for comment.)

The new Chinese models have a few things in common: Third-party benchmarks show that they perform nearly as well as the best Western models; they are optimized for agentic coding tasks (the hottest thing in AI this year); and they are or will soon be released with open weights, making them accessible and transparent.

But perhaps the biggest parallel between the current moment and January 2025—when the world was shocked by DeepSeek’s R1 model—is that it reaffirms how American and Chinese AI labs are taking diverging paths when it comes to being open or closed.

When it first burst onto the scene, DeepSeek challenged the premise that only closed-source models built with billions of dollars of investment in compute infrastructure and training could achieve frontier performance. But since then, Western AI labs have continued developing AI the same way, and now American frontier models feel more roped-off than they were a year ago.

Anthropic said for months that its latest Mythos model was so dangerously good at hacking that only approved collaborators could use it. When it was finally released more widely, the White House responded by issuing broad export controls, which forced Anthropic to take Mythos and its less capable sister model, Fable 5, offline temporarily. OpenAI similarly delayed the release of GPT 5.6 after it received a request from the White House.

In China, meanwhile, the situation looks very different. Chinese startups and tech giants have doubled down on open source: Anyone with a good enough computer environment can now download an open-weight model, run it locally, add customizations, and overall enjoy a much greater degree of freedom than OpenAI and Anthropic would ever allow. In many ways, the open versus closed debate is more entangled with the US versus China debate than ever before.

There are a lot of reasons why Chinese labs have chosen a business strategy built atop open-source models. Being the newer, smaller fish in the AI field, making their models free and open can help Chinese firms attract more users, collaborators, and media spotlight. It also puts them in a separate lane of competition from the one that OpenAI, Anthropic, Google, SpaceX, and other deep-pocketed giants are in.

Earlier this year, rumors spread that Alibaba might be considering joining the closed-source race after it rearranged its corporate AI model development teams. But the tech giant announced on Monday that it would again release the latest version of Qwen—its line of open-source models beloved by the global tech community—with open weights, signaling to customers and the public it is not pivoting away yet.

#Chinas #Open #Models #Challenging #Silicon #Valleys #Playbookchina,open source,alibaba,generative ai,national security,artificial intelligence"> China’s Open AI Models Are Challenging Silicon Valley’s PlaybookThe AI industry is not quite experiencing a DeepSeek 2.0 moment, but it feels very close. The leading Chinese AI labs have been on a roll lately, releasing a series of almost cutting-edge open-source models. Z.ai released GLM 5.2 in June, Moonshot AI released Kimi K3 last week, and Alibaba released Qwen 3.8 this Monday.Silicon Valley and Washington started talking about the models immediately, especially K3, which is widely seen as the best of the bunch. David Sacks, a venture capitalist and AI adviser to President Donald Trump, called the performance of Moonshot’s model “concerning.” Earlier this week, Commerce Secretary Scott Bessent suggested the US might impose sanctions on Chinese AI companies.On Wednesday, Michael Kratsios, director of the White House Office of Science and Technology Policy, alleged that the Trump administration has “information that Moonshot AI distilled Anthropic’s Fable for the development of its K3 model,” which he said amounted to “stealing proprietary US technology and undermining American research” and was “unacceptable.” (Moonshot AI did not immediately respond to a request for comment.)The new Chinese models have a few things in common: Third-party benchmarks show that they perform nearly as well as the best Western models; they are optimized for agentic coding tasks (the hottest thing in AI this year); and they are or will soon be released with open weights, making them accessible and transparent.But perhaps the biggest parallel between the current moment and January 2025—when the world was shocked by DeepSeek’s R1 model—is that it reaffirms how American and Chinese AI labs are taking diverging paths when it comes to being open or closed.When it first burst onto the scene, DeepSeek challenged the premise that only closed-source models built with billions of dollars of investment in compute infrastructure and training could achieve frontier performance. But since then, Western AI labs have continued developing AI the same way, and now American frontier models feel more roped-off than they were a year ago.Anthropic said for months that its latest Mythos model was so dangerously good at hacking that only approved collaborators could use it. When it was finally released more widely, the White House responded by issuing broad export controls, which forced Anthropic to take Mythos and its less capable sister model, Fable 5, offline temporarily. OpenAI similarly delayed the release of GPT 5.6 after it received a request from the White House.In China, meanwhile, the situation looks very different. Chinese startups and tech giants have doubled down on open source: Anyone with a good enough computer environment can now download an open-weight model, run it locally, add customizations, and overall enjoy a much greater degree of freedom than OpenAI and Anthropic would ever allow. In many ways, the open versus closed debate is more entangled with the US versus China debate than ever before.There are a lot of reasons why Chinese labs have chosen a business strategy built atop open-source models. Being the newer, smaller fish in the AI field, making their models free and open can help Chinese firms attract more users, collaborators, and media spotlight. It also puts them in a separate lane of competition from the one that OpenAI, Anthropic, Google, SpaceX, and other deep-pocketed giants are in.Earlier this year, rumors spread that Alibaba might be considering joining the closed-source race after it rearranged its corporate AI model development teams. But the tech giant announced on Monday that it would again release the latest version of Qwen—its line of open-source models beloved by the global tech community—with open weights, signaling to customers and the public it is not pivoting away yet.#Chinas #Open #Models #Challenging #Silicon #Valleys #Playbookchina,open source,alibaba,generative ai,national security,artificial intelligence
Tech-news

DeepSeek 2.0 moment, but it feels very close. The leading Chinese AI labs have been on a roll lately, releasing a series of almost cutting-edge open-source models. Z.ai released GLM 5.2 in June, Moonshot AI released Kimi K3 last week, and Alibaba released Qwen 3.8 this Monday.

Silicon Valley and Washington started talking about the models immediately, especially K3, which is widely seen as the best of the bunch. David Sacks, a venture capitalist and AI adviser to President Donald Trump, called the performance of Moonshot’s model “concerning.” Earlier this week, Commerce Secretary Scott Bessent suggested the US might impose sanctions on Chinese AI companies.

On Wednesday, Michael Kratsios, director of the White House Office of Science and Technology Policy, alleged that the Trump administration has “information that Moonshot AI distilled Anthropic’s Fable for the development of its K3 model,” which he said amounted to “stealing proprietary US technology and undermining American research” and was “unacceptable.” (Moonshot AI did not immediately respond to a request for comment.)

The new Chinese models have a few things in common: Third-party benchmarks show that they perform nearly as well as the best Western models; they are optimized for agentic coding tasks (the hottest thing in AI this year); and they are or will soon be released with open weights, making them accessible and transparent.

But perhaps the biggest parallel between the current moment and January 2025—when the world was shocked by DeepSeek’s R1 model—is that it reaffirms how American and Chinese AI labs are taking diverging paths when it comes to being open or closed.

When it first burst onto the scene, DeepSeek challenged the premise that only closed-source models built with billions of dollars of investment in compute infrastructure and training could achieve frontier performance. But since then, Western AI labs have continued developing AI the same way, and now American frontier models feel more roped-off than they were a year ago.

Anthropic said for months that its latest Mythos model was so dangerously good at hacking that only approved collaborators could use it. When it was finally released more widely, the White House responded by issuing broad export controls, which forced Anthropic to take Mythos and its less capable sister model, Fable 5, offline temporarily. OpenAI similarly delayed the release of GPT 5.6 after it received a request from the White House.

In China, meanwhile, the situation looks very different. Chinese startups and tech giants have doubled down on open source: Anyone with a good enough computer environment can now download an open-weight model, run it locally, add customizations, and overall enjoy a much greater degree of freedom than OpenAI and Anthropic would ever allow. In many ways, the open versus closed debate is more entangled with the US versus China debate than ever before.

There are a lot of reasons why Chinese labs have chosen a business strategy built atop open-source models. Being the newer, smaller fish in the AI field, making their models free and open can help Chinese firms attract more users, collaborators, and media spotlight. It also puts them in a separate lane of competition from the one that OpenAI, Anthropic, Google, SpaceX, and other deep-pocketed giants are in.

Earlier this year, rumors spread that Alibaba might be considering joining the closed-source race after it rearranged its corporate AI model development teams. But the tech giant announced on Monday that it would again release the latest version of Qwen—its line of open-source models beloved by the global tech community—with open weights, signaling to customers and the public it is not pivoting away yet.

#Chinas #Open #Models #Challenging #Silicon #Valleys #Playbookchina,open source,alibaba,generative ai,national security,artificial intelligence">China’s Open AI Models Are Challenging Silicon Valley’s Playbook

The AI industry is not quite experiencing a DeepSeek 2.0 moment, but it feels very close. The leading Chinese AI labs have been on a roll lately, releasing a series of almost cutting-edge open-source models. Z.ai released GLM 5.2 in June, Moonshot AI released Kimi K3 last week, and Alibaba released Qwen 3.8 this Monday.

Silicon Valley and Washington started talking about the models immediately, especially K3, which is widely seen as the best of the bunch. David Sacks, a venture capitalist and AI adviser to President Donald Trump, called the performance of Moonshot’s model “concerning.” Earlier this week, Commerce Secretary Scott Bessent suggested the US might impose sanctions on Chinese AI companies.

On Wednesday, Michael Kratsios, director of the White House Office of Science and Technology Policy, alleged that the Trump administration has “information that Moonshot AI distilled Anthropic’s Fable for the development of its K3 model,” which he said amounted to “stealing proprietary US technology and undermining American research” and was “unacceptable.” (Moonshot AI did not immediately respond to a request for comment.)

The new Chinese models have a few things in common: Third-party benchmarks show that they perform nearly as well as the best Western models; they are optimized for agentic coding tasks (the hottest thing in AI this year); and they are or will soon be released with open weights, making them accessible and transparent.

But perhaps the biggest parallel between the current moment and January 2025—when the world was shocked by DeepSeek’s R1 model—is that it reaffirms how American and Chinese AI labs are taking diverging paths when it comes to being open or closed.

When it first burst onto the scene, DeepSeek challenged the premise that only closed-source models built with billions of dollars of investment in compute infrastructure and training could achieve frontier performance. But since then, Western AI labs have continued developing AI the same way, and now American frontier models feel more roped-off than they were a year ago.

Anthropic said for months that its latest Mythos model was so dangerously good at hacking that only approved collaborators could use it. When it was finally released more widely, the White House responded by issuing broad export controls, which forced Anthropic to take Mythos and its less capable sister model, Fable 5, offline temporarily. OpenAI similarly delayed the release of GPT 5.6 after it received a request from the White House.

In China, meanwhile, the situation looks very different. Chinese startups and tech giants have doubled down on open source: Anyone with a good enough computer environment can now download an open-weight model, run it locally, add customizations, and overall enjoy a much greater degree of freedom than OpenAI and Anthropic would ever allow. In many ways, the open versus closed debate is more entangled with the US versus China debate than ever before.

There are a lot of reasons why Chinese labs have chosen a business strategy built atop open-source models. Being the newer, smaller fish in the AI field, making their models free and open can help Chinese firms attract more users, collaborators, and media spotlight. It also puts them in a separate lane of competition from the one that OpenAI, Anthropic, Google, SpaceX, and other deep-pocketed giants are in.

Earlier this year, rumors spread that Alibaba might be considering joining the closed-source race after it rearranged its corporate AI model development teams. But the tech giant announced on Monday that it would again release the latest version of Qwen—its line of open-source models beloved by the global tech community—with open weights, signaling to customers and the public it is not pivoting away yet.

#Chinas #Open #Models #Challenging #Silicon #Valleys #Playbookchina,open source,alibaba,generative ai,national security,artificial intelligence

The AI industry is not quite experiencing a DeepSeek 2.0 moment, but it feels very…

flooding online feeds, echoing the White House’s own turn toward cryptic teaser clips and meme-native visuals. This is not just content drift. It is a new front in the information war, one where speed, ambiguity, and algorithmic reach matter as much as accuracy.

One Iran-linked outlet, Explosive News, can reportedly turn around a two-minute synthetic Lego segment in about 24 hours. The speed is the point. Synthetic media does not need to hold up forever; it only needs to travel before verification catches up.

Last month, the White House added to that confusion when it posted two vague “launching soon” videos, then removed them after online investigators and open source researchers began dissecting them.

The reveal turned out to be anticlimactic: a promotional push for the official White House app. But the episode demonstrated how thoroughly official communication has absorbed the aesthetics of leaks, virality, and platform-native intrigue. Even when official accounts adopt the aesthetics of a leak, questioning whether a record is real or synthetic is the only defensive move left.

Real vs. Synthetic: The New Friction

A zero digital footprint used to signal authenticity. Now, it can signal the opposite. The absence of a trail no longer means something is original—it may mean it was never captured by a lens at all. The signal has inverted. Truth lags; engagement leads.

Automated traffic now commands an estimated 51 percent of internet activity, scaling eight times faster than human traffic according to the 2026 State of AI Traffic & Cyberthreat Benchmark Report. These systems don’t just distribute content, they prioritize low-quality virality, ensuring the synthetic record travels while verification is still catching up.

Open source investigators are still holding the line, but they are fighting a volume war. The rise of hyperactive “super sharers,” often backed by paid verification, adds a layer of false authority that traditional open source intelligence (OSINT) now has to navigate.

“We’re perpetually catching up to someone pressing repost without a second thought,” says Maryam Ishani, an OSINT journalist covering the conflict. “The algorithm prioritizes that reflex, and our information is always going to be one step behind.”

At the same time, the surge of war-monitoring accounts is beginning to interfere with reporting itself. Manisha Ganguly, visual forensics lead at The Guardian and an OSINT specialist investigating war crimes, points to the false certainty created by the flood of aggregated content on Telegram and X.

“Open source verification starts to create false certainty when it stops being a method of inquiry—through confirmation bias, or when OSINT is used to cosmetically validate official accounts or knowingly misapplied to align with ideological narratives rather than interrogate them,” Ganguly says.

While this plays out, the verification toolkit itself is becoming harder to access. On April 4, Planet Labs—one of the most relied-upon commercial satellite providers for conflict journalism—announced it would indefinitely withhold imagery of Iran and the broader Middle East conflict zone, retroactive to March 9, following a request from the US government.

The response from US defense secretary Pete Hegseth to concerns about the delay was unambiguous: “Open source is not the place to determine what did or did not happen.”

That shift matters. When access to primary visual evidence is restricted, the ability to independently verify events narrows. And in that narrowing gap, something else expands: Generative AI doesn’t just fill the silence—it competes to define what’s seen in the first place.

Generative AI Is Getting Harder to Spot

Generative AI platforms have been learning from their mistakes. Henk van Ess, an investigative trainer and verification specialist, says many of the classic tells—incorrect finger counts, garbled protest signs, distorted text—have largely been fixed in the latest generation of models. Tools like Imagen 3, Midjourney, and Dall·E have improved in prompt understanding, photorealism, and text-in-image rendering.

But the harder problem is what van Ess calls the hybrid.

#Internet #Broke #Everyones #Bullshit #Detectorspropaganda,artificial intelligence,open source,satellite images,iran,war,politics"> How the Internet Broke Everyone’s Bullshit DetectorsLego-style propaganda videos alleging war crimes are flooding online feeds, echoing the White House’s own turn toward cryptic teaser clips and meme-native visuals. This is not just content drift. It is a new front in the information war, one where speed, ambiguity, and algorithmic reach matter as much as accuracy.One Iran-linked outlet, Explosive News, can reportedly turn around a two-minute synthetic Lego segment in about 24 hours. The speed is the point. Synthetic media does not need to hold up forever; it only needs to travel before verification catches up.Last month, the White House added to that confusion when it posted two vague “launching soon” videos, then removed them after online investigators and open source researchers began dissecting them.The reveal turned out to be anticlimactic: a promotional push for the official White House app. But the episode demonstrated how thoroughly official communication has absorbed the aesthetics of leaks, virality, and platform-native intrigue. Even when official accounts adopt the aesthetics of a leak, questioning whether a record is real or synthetic is the only defensive move left.Real vs. Synthetic: The New FrictionA zero digital footprint used to signal authenticity. Now, it can signal the opposite. The absence of a trail no longer means something is original—it may mean it was never captured by a lens at all. The signal has inverted. Truth lags; engagement leads.Automated traffic now commands an estimated 51 percent of internet activity, scaling eight times faster than human traffic according to the 2026 State of AI Traffic & Cyberthreat Benchmark Report. These systems don’t just distribute content, they prioritize low-quality virality, ensuring the synthetic record travels while verification is still catching up.Open source investigators are still holding the line, but they are fighting a volume war. The rise of hyperactive “super sharers,” often backed by paid verification, adds a layer of false authority that traditional open source intelligence (OSINT) now has to navigate.“We’re perpetually catching up to someone pressing repost without a second thought,” says Maryam Ishani, an OSINT journalist covering the conflict. “The algorithm prioritizes that reflex, and our information is always going to be one step behind.”At the same time, the surge of war-monitoring accounts is beginning to interfere with reporting itself. Manisha Ganguly, visual forensics lead at The Guardian and an OSINT specialist investigating war crimes, points to the false certainty created by the flood of aggregated content on Telegram and X.“Open source verification starts to create false certainty when it stops being a method of inquiry—through confirmation bias, or when OSINT is used to cosmetically validate official accounts or knowingly misapplied to align with ideological narratives rather than interrogate them,” Ganguly says.While this plays out, the verification toolkit itself is becoming harder to access. On April 4, Planet Labs—one of the most relied-upon commercial satellite providers for conflict journalism—announced it would indefinitely withhold imagery of Iran and the broader Middle East conflict zone, retroactive to March 9, following a request from the US government.The response from US defense secretary Pete Hegseth to concerns about the delay was unambiguous: “Open source is not the place to determine what did or did not happen.”That shift matters. When access to primary visual evidence is restricted, the ability to independently verify events narrows. And in that narrowing gap, something else expands: Generative AI doesn’t just fill the silence—it competes to define what’s seen in the first place.Generative AI Is Getting Harder to SpotGenerative AI platforms have been learning from their mistakes. Henk van Ess, an investigative trainer and verification specialist, says many of the classic tells—incorrect finger counts, garbled protest signs, distorted text—have largely been fixed in the latest generation of models. Tools like Imagen 3, Midjourney, and Dall·E have improved in prompt understanding, photorealism, and text-in-image rendering.But the harder problem is what van Ess calls the hybrid.#Internet #Broke #Everyones #Bullshit #Detectorspropaganda,artificial intelligence,open source,satellite images,iran,war,politics
Tech-news

flooding online feeds, echoing the White House’s own turn toward cryptic teaser clips and meme-native visuals. This is not just content drift. It is a new front in the information war, one where speed, ambiguity, and algorithmic reach matter as much as accuracy.

One Iran-linked outlet, Explosive News, can reportedly turn around a two-minute synthetic Lego segment in about 24 hours. The speed is the point. Synthetic media does not need to hold up forever; it only needs to travel before verification catches up.

Last month, the White House added to that confusion when it posted two vague “launching soon” videos, then removed them after online investigators and open source researchers began dissecting them.

The reveal turned out to be anticlimactic: a promotional push for the official White House app. But the episode demonstrated how thoroughly official communication has absorbed the aesthetics of leaks, virality, and platform-native intrigue. Even when official accounts adopt the aesthetics of a leak, questioning whether a record is real or synthetic is the only defensive move left.

Real vs. Synthetic: The New Friction

A zero digital footprint used to signal authenticity. Now, it can signal the opposite. The absence of a trail no longer means something is original—it may mean it was never captured by a lens at all. The signal has inverted. Truth lags; engagement leads.

Automated traffic now commands an estimated 51 percent of internet activity, scaling eight times faster than human traffic according to the 2026 State of AI Traffic & Cyberthreat Benchmark Report. These systems don’t just distribute content, they prioritize low-quality virality, ensuring the synthetic record travels while verification is still catching up.

Open source investigators are still holding the line, but they are fighting a volume war. The rise of hyperactive “super sharers,” often backed by paid verification, adds a layer of false authority that traditional open source intelligence (OSINT) now has to navigate.

“We’re perpetually catching up to someone pressing repost without a second thought,” says Maryam Ishani, an OSINT journalist covering the conflict. “The algorithm prioritizes that reflex, and our information is always going to be one step behind.”

At the same time, the surge of war-monitoring accounts is beginning to interfere with reporting itself. Manisha Ganguly, visual forensics lead at The Guardian and an OSINT specialist investigating war crimes, points to the false certainty created by the flood of aggregated content on Telegram and X.

“Open source verification starts to create false certainty when it stops being a method of inquiry—through confirmation bias, or when OSINT is used to cosmetically validate official accounts or knowingly misapplied to align with ideological narratives rather than interrogate them,” Ganguly says.

While this plays out, the verification toolkit itself is becoming harder to access. On April 4, Planet Labs—one of the most relied-upon commercial satellite providers for conflict journalism—announced it would indefinitely withhold imagery of Iran and the broader Middle East conflict zone, retroactive to March 9, following a request from the US government.

The response from US defense secretary Pete Hegseth to concerns about the delay was unambiguous: “Open source is not the place to determine what did or did not happen.”

That shift matters. When access to primary visual evidence is restricted, the ability to independently verify events narrows. And in that narrowing gap, something else expands: Generative AI doesn’t just fill the silence—it competes to define what’s seen in the first place.

Generative AI Is Getting Harder to Spot

Generative AI platforms have been learning from their mistakes. Henk van Ess, an investigative trainer and verification specialist, says many of the classic tells—incorrect finger counts, garbled protest signs, distorted text—have largely been fixed in the latest generation of models. Tools like Imagen 3, Midjourney, and Dall·E have improved in prompt understanding, photorealism, and text-in-image rendering.

But the harder problem is what van Ess calls the hybrid.

#Internet #Broke #Everyones #Bullshit #Detectorspropaganda,artificial intelligence,open source,satellite images,iran,war,politics">How the Internet Broke Everyone’s Bullshit Detectors

Lego-style propaganda videos alleging war crimes are flooding online feeds, echoing the White House’s own turn toward cryptic teaser clips and meme-native visuals. This is not just content drift. It is a new front in the information war, one where speed, ambiguity, and algorithmic reach matter as much as accuracy.

One Iran-linked outlet, Explosive News, can reportedly turn around a two-minute synthetic Lego segment in about 24 hours. The speed is the point. Synthetic media does not need to hold up forever; it only needs to travel before verification catches up.

Last month, the White House added to that confusion when it posted two vague “launching soon” videos, then removed them after online investigators and open source researchers began dissecting them.

The reveal turned out to be anticlimactic: a promotional push for the official White House app. But the episode demonstrated how thoroughly official communication has absorbed the aesthetics of leaks, virality, and platform-native intrigue. Even when official accounts adopt the aesthetics of a leak, questioning whether a record is real or synthetic is the only defensive move left.

Real vs. Synthetic: The New Friction

A zero digital footprint used to signal authenticity. Now, it can signal the opposite. The absence of a trail no longer means something is original—it may mean it was never captured by a lens at all. The signal has inverted. Truth lags; engagement leads.

Automated traffic now commands an estimated 51 percent of internet activity, scaling eight times faster than human traffic according to the 2026 State of AI Traffic & Cyberthreat Benchmark Report. These systems don’t just distribute content, they prioritize low-quality virality, ensuring the synthetic record travels while verification is still catching up.

Open source investigators are still holding the line, but they are fighting a volume war. The rise of hyperactive “super sharers,” often backed by paid verification, adds a layer of false authority that traditional open source intelligence (OSINT) now has to navigate.

“We’re perpetually catching up to someone pressing repost without a second thought,” says Maryam Ishani, an OSINT journalist covering the conflict. “The algorithm prioritizes that reflex, and our information is always going to be one step behind.”

At the same time, the surge of war-monitoring accounts is beginning to interfere with reporting itself. Manisha Ganguly, visual forensics lead at The Guardian and an OSINT specialist investigating war crimes, points to the false certainty created by the flood of aggregated content on Telegram and X.

“Open source verification starts to create false certainty when it stops being a method of inquiry—through confirmation bias, or when OSINT is used to cosmetically validate official accounts or knowingly misapplied to align with ideological narratives rather than interrogate them,” Ganguly says.

While this plays out, the verification toolkit itself is becoming harder to access. On April 4, Planet Labs—one of the most relied-upon commercial satellite providers for conflict journalism—announced it would indefinitely withhold imagery of Iran and the broader Middle East conflict zone, retroactive to March 9, following a request from the US government.

The response from US defense secretary Pete Hegseth to concerns about the delay was unambiguous: “Open source is not the place to determine what did or did not happen.”

That shift matters. When access to primary visual evidence is restricted, the ability to independently verify events narrows. And in that narrowing gap, something else expands: Generative AI doesn’t just fill the silence—it competes to define what’s seen in the first place.

Generative AI Is Getting Harder to Spot

Generative AI platforms have been learning from their mistakes. Henk van Ess, an investigative trainer and verification specialist, says many of the classic tells—incorrect finger counts, garbled protest signs, distorted text—have largely been fixed in the latest generation of models. Tools like Imagen 3, Midjourney, and Dall·E have improved in prompt understanding, photorealism, and text-in-image rendering.

But the harder problem is what van Ess calls the hybrid.

#Internet #Broke #Everyones #Bullshit #Detectorspropaganda,artificial intelligence,open source,satellite images,iran,war,politics

Lego-style propaganda videos alleging war crimes are flooding online feeds, echoing the White House’s own…