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Around 150 European politicians have been linked to websites featuring or promoting non-consensual sexual deepfakes, according to new research into the growing use of AI to create fake intimate images.

The study, conducted by German digital democracy think tank Agora Digitale Transformation, examined 160 websites and searched for the names of 5,872 sitting members of national parliaments across the European Union. Researchers identified 138 female MPs from 22 EU countries who were either depicted in or associated with deepfake pornography. Nine male MPs were also identified.

The research found that these websites did more than host fake sexual images. Some used politicians’ names, photographs and biographical details to direct visitors to “nudify” tools, which claim to turn ordinary photos into fake nude or sexual images.

This shows how AI-generated sexual abuse can spread through connected websites, search results and image-generation tools, making it harder for victims to control where manipulated images appear online.

ALSO READ: UK Bans Ads for AI Nudify Apps That Sexualize Real Women

Researchers Examined 160 Websites Linked to Deepfake Porn

A study by German digital democracy think tank Agora Digitale Transformation examined how widely European politicians are being exposed to websites linked to deepfake pornography. The researchers looked at websites that host or promote AI-generated sexual content and checked whether the names of sitting EU parliamentarians appeared on them.

The study was carried out between July 16 and July 20, 2026. Researchers searched for the names of sitting members of national parliaments in all 27 EU countries.

Using Google Custom Search, they examined 160 websites linked to deepfake pornography, celebrity leaks and other non-consensual sexual content. The research covered 5,872 parliamentarians, representing 94.4% of the sitting national parliament members included in the study.

Researchers did not download or save the sexualised images they found. Instead, they recorded the type of result shown in each search. This included websites hosting the content, pages linking a politician to an image-generation or “nudify” tool, content that was still appearing in search results, and websites that had already been removed.

The researchers also tested their method with 100 fictional names. Seven of the names happened to match real adult performers, while the other searches produced no relevant results. This test helped them check whether the findings were linked to specific politicians rather than random search results.

Only Two Politicians Have Been Publicly Identified

The identities of most politicians found in the investigation have not been made public. Suzanne Kröger, a Dutch MP from GroenLinks-PvdA, and Sarah Dobbe, a Dutch MP from the Socialist Party, are the two politicians who have publicly confirmed that they were affected. Both spoke to WIRED about their experiences.

138 Women MPs Were Linked to Deepfake Pornography 

Women made up the vast majority of politicians identified in the study. Researchers found 138 women MPs and nine men among the 5,872 parliamentarians they examined.

The study’s statistical analysis found that women MPs were 33 times more likely than men to be shown in or linked to deepfake pornography, after taking several other factors into account. This means about one in 14 women MPs in the study were affected, compared with about one in 500 men. The researchers did not publish a complete list of names.

Measure	Women	Men
MPs identified in the study	138	9
Approximate share affected	1 in 14	1 in 500

The researchers also found a link between political visibility and exposure. Women MPs in senior roles, including cabinet positions or party leadership, had a higher predicted level of exposure than women in more junior positions.

It also found cases across different political groups. Its analysis showed higher exposure among both left-leaning and right-leaning politicians compared with those in the political centre. This suggests that the issue was not limited to one particular political group.

97 Gateway Pages Connected MPs to Nudify Tools

The study found that websites did not always need to directly publish a sexual deepfake to play a role in its creation. Some acted as “gateways”, connecting information about politicians with AI tools that could create sexualised images.

Researchers identified 97 gateway cases. These were pages containing details such as a politician’s name, biography, photos or social media links, along with links to AI tools that could be used to create manipulated sexual images.

In 61 cases, the pages also contained images that could be used with the linked tools. The other 36 cases did not contain those images but still connected information about a specific politician to tools that could create manipulated content.

Researchers found that three gateway pages also displayed active non-consensual sexual deepfakes. Some of the pages also included ratings of women’s bodies. This can make enforcement more difficult because a website may not directly show an explicit deepfake but can still provide links, photos and other information that help users create one.

Germany, France, Italy and the Netherlands Recorded the Most Cases

Politicians from Germany, France, Italy and the Netherlands appeared most often in the study, according to reporting by WIRED. The researchers examined national MPs from all 27 EU countries but did not include members of the European Parliament.

The study therefore focused on members of national parliaments, rather than all politicians across Europe. This is an important point when looking at the study’s finding of nearly 150 politicians.

This does not mean that nearly 150 politicians across all of Europe were targeted. It refers only to the politicians identified within the specific group of national MPs and the 160 websites examined by the researchers.

Why Politicians Are Easy Targets for Deepfake Abuse

Politicians are more exposed to deepfake abuse because large amounts of their photos and videos are already available online.

Official portraits, campaign photographs, interviews and videos of public appearances give people creating deepfakes plenty of material to use. For politicians, sharing images publicly is also part of their work, which makes it difficult to avoid this exposure. The Agora researchers said this creates a particular risk for people in public office because they cannot easily keep their images private.

The problem can be especially serious for women in politics. Researchers and politicians interviewed by WIRED said sexualised deepfakes can harm a person’s reputation and well-being. They also raised concerns that repeated abuse could discourage some women from entering politics or remaining in public life.

The concern, therefore, goes beyond a single fake image being posted online. Repeated sexual harassment can affect how politicians take part in public life and may create additional pressure on those who already face high public exposure.

Deepfake Content Can Remain Online After Sites Are Removed

Removing a website does not always mean that every trace of its content disappears from the internet. In the study, researchers examined what happened after authorities seized the CFake domain. They reported finding 47 records that were still discoverable through Google’s API more than a month after the domain was seized.

However, the researchers stressed that this finding has an important limitation. They could not reproduce the 47 records through ordinary manual Google searches, so they said the result needs to be independently replicated and confirmed. 

This should therefore be treated as an indication that traces may remain after a website is removed, rather than as proof that the material was still publicly accessible through normal Google searches.

This still points to a wider challenge in removing deepfake material. Even after a website is taken down, search systems, cached information or other websites may continue to contain references to older content. The researchers therefore said monitoring efforts should consider both the original websites and search systems that may continue to point to previously indexed material.

Deepfake Abuse Extends Beyond Individual Websites

Deepfake abuse is not limited to websites that directly create or share fake sexual images. Separate research shows that these sites are part of a wider online network involving several types of services.

In July 2026, the Institute for Strategic Dialogue (ISD) published a six-month study looking at how gender-based deepfakes are created, shared and monetised. The study found that the wider network includes search engines, app stores, social media platforms, messaging services and payment systems.

According to ISD, people can be directed from mainstream online services to commercial and open-source tools that can create manipulated sexual images. The organisation also found connections between these tools, affiliate networks and payment services. These links can make it harder to track and stop the activity, especially when different parts of the network operate across different countries.

This wider network helps explain why taking down individual websites may not be enough to stop deepfake abuse. New domains, tools and distribution channels can appear elsewhere and continue the same activity.

Women Are Disproportionately Targeted by Deepfake Porn

Women and girls are more often targeted by sexual deepfakes than men, according to research cited by the European Parliament.

A 2026 European Parliament briefing said women and girls are especially vulnerable to deepfake pornography. It noted that this type of content can be used for harassment, sexual extortion and other forms of online abuse. The briefing also cited earlier research estimating that 98% of deepfake videos online were pornographic and that 99% of people targeted by non-consensual intimate deepfakes were women.

The briefing also warned that AI-generated sexual content can affect women’s participation in public life. Such abuse can cause intimidation, damage reputations and create pressure on women to withdraw from public discussions or activities.

The new Agora study adds to this research by showing how the same problem affects elected politicians across the European Union.

ALSO READ: AI Has Made Fake Nude Abuse Easier to Scale, New Berkeley Report Warns

Deepfake Laws Have Not Ended the Problem

The study also examined whether countries with specific laws against deepfakes had lower levels of exposure among politicians.

Researchers did not find lower exposure in countries with criminal laws that specifically cover deepfakes. However, they cautioned that this does not show that these laws are ineffective. The researchers said differences in enforcement could affect the results. They also noted that countries without specific deepfake laws may still use existing privacy or image-abuse laws to take action against such content.

This shows that having a law in place is only one part of the response. How quickly and effectively authorities investigate reports and remove abusive content can also affect how well victims are protected.

The issue becomes more difficult when deepfake content crosses national borders. The victim, creator, website hosting the content, search engine and payment service may all be located in different countries. This can make it harder for authorities to identify those responsible and coordinate action.

Deepfake Abuse Is Part of a Wider Online Network

The research shows that sexual deepfake abuse involves more than individual websites. It has grown into a wider network of sites and online services that can play different roles.

Some websites host manipulated images, while others collect photos and information about potential targets. Some pages connect those details to AI tools that can create sexualised images. Search engines can also continue to show links or other traces of content after a website has been taken down.

The study found that women MPs had much higher exposure than men. It also found that women in more visible political positions faced higher predicted exposure.

The researchers plan to repeat the study over time using the same group of politicians and research method. This could help show whether website seizures, new laws and enforcement efforts actually reduce exposure or whether the activity moves to other websites and services.

The findings suggest that removing individual deepfake images is only one part of the problem. The wider network includes AI creation tools, gateway sites, hosting services and search indexes, which can help the abuse continue even after individual websites are removed.

New York authorities have seized 12 websites accused of sharing and selling AI-generated sexual content involving about 1,200 people without their consent. The Manhattan District Attorney’s Office announced the seizure on September 14 as part of an ongoing investigation.

Most of the people shown in the content were women. They included actors, politicians, athletes, musicians, social justice advocates and social media influencers. According to authorities, the websites used existing photos and videos of real people to create fake sexual images and videos. The content was then shared or sold through the sites.

The case shows how AI tools are being used to create and spread fake sexual content and the challenges authorities face in stopping it. The investigation is continuing as prosecutors look into the people who ran the websites and uploaded the material.

ALSO READ: AI Has Made Fake Nude Abuse Easier to Scale, New Berkeley Report Warns

12 Deepfake Websites Seized in Manhattan Investigation

The Manhattan District Attorney’s Office seized 12 website domains after investigators found that they were being used to distribute AI-generated sexual content. The domains were taken down under a court order as part of an ongoing investigation.

The Manhattan District Attorney’s Office described the action as the largest known seizure of AI-generated celebrity deepfake websites to date. Authorities said they will continue monitoring the seized domains to prevent operators from moving the material to other websites.

The case also shows how AI-generated sexual abuse can be distributed through websites that host and monetize the content. By targeting the websites themselves, authorities focused on the platforms used to distribute the material rather than only individual images or videos.

Authorities have not publicly confirmed how many people operated the websites or how many users created and uploaded the deepfake material. The investigation remains ongoing.

About 1,200 People Were Identified in the New York Case

The Manhattan District Attorney’s Office said investigators identified about 1,200 people whose faces or bodies appeared in AI-generated sexual content found on the seized websites. According to the office, most of the people targeted were women. The group included people from different public-facing professions, such as:

• Actors
• Politicians
• Athletes
• Musicians
• Social justice advocates
• Social media influencers

Authorities have not released a complete public list of the people shown in the content. The case also highlights how AI deepfake pornography can target people who have never created or shared intimate images. Ordinary photos and videos can be manipulated to create fake sexual content without a person’s knowledge or consent.

How AI Deepfake Sexual Content Is Created

AI deepfake sexual content is made by using artificial intelligence to put a person’s face or likeness into a fake sexual image or video. The final result can look real even though the person was never part of the original scene.

Different AI tools can be used for this. Face-swapping tools replace one person’s face with another, while AI image tools can create new images using a person’s photo. Similar technology can also be used to change faces in videos.

Creating this type of content does not always require advanced technical skills. Research by the Institute for Strategic Dialogue (ISD) found that some websites offered tools that could create synthetic intimate images using just one photograph.

This means that photos posted publicly online can sometimes be used to create fake sexual content. Once created, the material can be shared through websites, social media platforms and other online services.

The person shown in the content may have never agreed to its creation or sharing. Although the image or video is fake, it can still use a real person’s face or likeness without their consent.

AI Deepfakes and the Rise of Non-Consensual Intimate Images

The New York case is part of a wider problem known as non-consensual intimate imagery (NCII). NCII refers to intimate images or videos that are shared or created without the person’s consent. It can include real intimate material shared without permission as well as fake content created using AI and other editing tools.

Research from the Institute for Strategic Dialogue (ISD) has identified NCII as a form of technology-facilitated gender-based violence. The research also found that advances in AI have made it easier to create realistic fake intimate images without advanced technical skills.

This has increased the risk for people whose photos are publicly available online. In some cases, a normal photograph can be used as the basis for creating fabricated sexual content without the person knowing about it.

The impact can be serious even when the content is completely fake. Such images can be used to harass, threaten, embarrass or damage a person’s personal and professional reputation.

Women Are the Main Targets of AI-Generated Sexual Deepfakes

Research shows that women are disproportionately affected by sexually explicit deepfakes. Several studies have found that most of the people targeted by this type of content are women.

In 2025, UN Women cited research estimating that 90% to 95% of online deepfakes are non-consensual pornographic images, with about 90% depicting women. It also cited research showing that the number of deepfake videos increased by 550% between 2019 and 2023.

The research cited by UN Women found that 98% of deepfake videos online were pornographic, while 99% of the people targeted were women.

These figures provide a broader view of the problem, but they do not represent the 1,200 people identified in the New York investigation. The UN Women figures come from separate research and should not be used to estimate the number of women affected in the Manhattan case.

Research into AI tools that create synthetic intimate images has also found that women are more frequently targeted. This gender gap is one of the main concerns raised in research on AI-generated sexual abuse.

ALSO READ: UK Bans Ads for AI Nudify Apps That Sexualize Real Women

AI Deepfake Sites Attract Millions of Monthly Visits

The number of websites offering tools for creating AI-generated intimate images has grown alongside the wider use of generative AI. These sites can attract millions of visits, making it harder to deal with the problem by removing individual images or videos.

A 2025 study by the Institute for Strategic Dialogue (ISD) identified 31 websites that offered or hosted tools for creating synthetic intimate imagery. These websites received nearly 21 million visits in May 2025.

The number of visits varied between websites. Some received only a few hundred visits during the month, while the most visited site received almost 4 million visits. ISD researchers also found that some of these tools could be found through regular search engines, meaning users did not necessarily need advanced technical knowledge to find them.

The scale of these websites also makes it harder to stop the spread of deepfake content. Removing one image or video does not prevent copies from appearing on other websites or platforms. ISD said efforts to address the problem need to consider the wider system, including content creation tools, hosting services, distribution channels and payment systems.

How Authorities Are Tackling AI Deepfake Networks

The Manhattan case shows one way authorities are responding to the growing problem of AI-generated sexual content. Instead of focusing only on individual images or videos, prosecutors targeted the websites that were allegedly used to distribute and sell the material.

The Manhattan District Attorney’s Office said the seized websites were almost entirely focused on AI-generated non-consensual intimate imagery. The investigation into the people operating the sites is still ongoing.

Prosecutors also said they would monitor the seized domains to prevent the operators from bringing the content back through different website addresses. This is important because the same material can be moved to new domains after a website is taken down.

The case also points to a wider challenge for law enforcement. AI-generated sexual content can be copied and shared across websites, social media platforms and private online groups. Researchers have therefore called for efforts that address the wider system involved in creating, hosting, promoting, distributing and monetizing this type of content.

How Deepfake Pornography Can Harm Victims

Deepfake pornography can affect victims even when the images or videos are completely fake. The effects can reach a person’s personal life, career and social life.

UN Women has said that AI-based online abuse can cause psychological, professional, financial and social harm. It has also warned that once this type of content is posted online, it can spread across different platforms and become difficult to remove.

Research by the Institute for Strategic Dialogue (ISD) has also found that people targeted by fake intimate images can face emotional and professional harm. Women who are already visible online may face greater risks because their photos and other personal information are often publicly available.

The harm is not limited to whether the content is real or fake. The key issue is that someone’s face or likeness is used in sexual content without their consent. This can expose victims to harassment, threats, embarrassment and damage to their reputation.

Authorities Continue Investigating Deepfake Website Operators

The Manhattan District Attorney’s Office has not publicly identified everyone involved in running the 12 seized websites. The investigation is still ongoing as authorities work to determine who operated the sites and how the content was created and distributed.

Prosecutors are also asking people who may have been targeted to come forward. The District Attorney’s Office said it will continue investigating the seized websites and similar operations and will monitor the domains.

The case also raises questions about how authorities can respond to similar websites based outside New York or the United States. Online operations can cross borders, making investigations more difficult.

The spread of AI-generated content adds another challenge. Websites can be taken down, new domains can appear and the same images or videos can be copied and shared across different platforms. The seizure removed 12 domains from operation, but the wider network of tools and websites used to create and distribute synthetic intimate imagery remains active.

What the Case Reveals About AI Deepfake Abuse

The New York case shows how AI-generated sexual content can be created and distributed on a large scale. Investigators identified about 1,200 people across 12 websites, with women making up most of those targeted.

The case also shows how publicly available photos can be misused. The people depicted included celebrities and other public figures whose images were available online. Their photos were allegedly used to create fake sexual content without their consent.

Research from UN Women and the Institute for Strategic Dialogue (ISD) shows that this is part of a wider problem involving non-consensual intimate imagery. AI tools have also made it easier to create realistic fake sexual content without advanced technical skills.

The investigation is still ongoing. Prosecutors are working to identify the people who operated the websites, those who uploaded the content and how the material was created, distributed and sold. The findings could help provide a clearer picture of how AI deepfake websites operate and the challenges involved in stopping similar networks.

Meta is facing pressure to strengthen its rules on AI-generated and manipulated content after its Oversight Board ordered the company to remove two videos from Facebook. The Board said Meta’s current rules do not do enough to deal with harmful AI-generated content used to target people.

In decisions published on September 17, the Board reviewed two different cases. One involved a Scottish Labour councillor who was falsely shown making offensive comments about refugees. The other involved a young Muslim woman whose likeness was used in AI-generated videos that mocked her appearance and behavior. The Board said both cases showed problems with Meta’s policies and how they were enforced.

The Board also asked Meta to broaden its definition of “unwanted manipulated imagery.” Under the proposed change, the rules would also cover deepfakes that falsely show private people saying or doing things they never said or did.

Two Deepfake Cases Put Meta’s Safety Rules Under Scrutiny

The Oversight Board recently reviewed two cases involving AI-generated videos and images of real people. The cases involved different types of harmful manipulated content and raised questions about how Meta handles deepfakes on its platforms.

1. First Case Involved AI Video of Scottish Councillor

The first case involved a Facebook video posted in November 2025 that appeared to show a Scottish Labour councillor making offensive comments about refugees.

Meta’s Oversight Board said the video appeared to have been created or altered using AI. One sign was that the audio did not fully match the woman’s facial movements. The video was viewed more than 5,000 times and received more than 50 comments, 20 reactions and over 10 shares.

Two users reported the video to Meta, but the company’s systems did not send it for human review. Meta later told the Oversight Board that the video did not break its rules and did not meet the requirements for an AI-generated content label.

The Oversight Board reached a different conclusion and ordered Meta to remove the video. It said the post violated Meta’s hate speech rules because it falsely linked refugees as a group to criminal and sexually abusive behavior. The Board also said Meta should have placed a “high risk AI” label on the video.

2. Second Case Involved AI Images of Muslim Woman

The second case involved a young Muslim woman who had appeared in news coverage while campaigning for better menstrual health education.

After the coverage, people used her likeness to create AI-generated videos and images that mocked her appearance and behavior. Some of the manipulated content gained tens of millions of views across social media platforms, including Meta’s services.

The Oversight Board reviewed one of the videos, which showed the woman exercising in an exaggerated way and eating unhealthy food. It found that the video violated Meta’s bullying and harassment rules and ordered the company to remove it.

The case also highlighted a wider problem: people can use AI tools to create realistic images and videos of real individuals without their consent and then spread that content quickly across social media.

Oversight Board Pushes Meta to Strengthen Deepfake Protections

In its September 17 decisions, Meta’s Oversight Board said the company’s current definition of “unwanted manipulated imagery” does not cover enough types of AI-generated content.

Meta’s rules already cover some manipulated images involving private individuals and minors. However, the Board said the company’s enforcement guidance makes a distinction between changes to a person’s appearance and fake content that shows them saying or doing something they never actually said or did.

The Board said this distinction is becoming harder to justify as generative AI tools can create highly realistic images and videos. A person’s face, voice and actions can be digitally altered in ways that make fake content appear genuine.

The Board recommended that Meta clearly expand its definition of unwanted manipulated imagery. The change would cover deepfakes that falsely show private individuals saying or doing something, as well as manipulated content that changes their appearance without their consent.

The recommendation could also affect how Meta handles a wider range of deepfake content. The same AI tools are increasingly being used to create non-consensual sexual images and videos, including fake nude content involving real people.

ALSO READ: UK Bans Ads for AI Nudify Apps That Sexualize Real Women

Meta Deepfake Cases Reflect a Growing Online Abuse Problem

The two cases reviewed by Meta’s Oversight Board are part of a wider problem involving AI-generated images and videos used to target real people.

A 2024 study based on more than 16,000 people across 10 countries examined non-consensual synthetic intimate imagery, including deepfake pornography. The researchers found that 2.2% of respondents said they had been victims of deepfake pornography, while 1.8% said they had engaged in behavior linked to creating or sharing it. The study also found that awareness of this type of abuse was still relatively low.

The research also found that having laws in place did not prevent all cases of victimization or perpetration. The researchers said stronger platform rules, better tools to detect and remove harmful content, and greater digital awareness could help reduce the problem.

More recent research shows how the problem can also affect people in public life. Research reported by WIRED in September 2026 examined around 160 websites that host deepfake abuse and other non-consensual sexual content. It found that at least 138 women members of parliament from 22 European Union countries had appeared or been mentioned on the sites. Nine male MPs were also identified in the same research.

ALSO READ: AI Has Made Fake Nude Abuse Easier to Scale, New Berkeley Report Warns

Meta Faces Questions Over How Deepfake Reports Are Handled

The Oversight Board also raised concerns about how people are expected to report manipulated content on Meta’s platforms.

In the case involving the Muslim woman, Meta’s rules required the person targeted by the manipulated content to report it as unwanted. However, she had not reported the specific post reviewed by the Board. As a result, Meta did not apply that part of its policy.

The Board said this approach can put too much responsibility on victims, especially when the same fake images or videos are posted by several accounts. A person may have to report the same content again and again as it spreads across the platform.

The Board recommended that Meta add violating unwanted manipulated imagery to its Media Matching Banks. These systems can help Meta identify and remove copies or near-identical versions of content that has already been flagged.

It also recommended that Meta consider signs that content was created or altered using AI when deciding which reports should be sent for human review.

Board Calls for Stronger Action Against Harmful AI Content

In the case of a Scottish Labour councillor, the Oversight Board has made several recommendations after reviewing the case. 

The Board called for Meta to use “high risk AI” labels more widely and take steps to limit the spread of deceptive AI-generated content. It also recommended stronger action against accounts that repeatedly share harmful deepfakes and greater transparency about how Meta labels AI-generated material.

One recommendation would reduce the visibility of content that Meta identifies as high-risk AI-generated material. Another would add warning screens that users would have to pass before viewing certain AI-generated content.

The Growing Challenge of Stopping AI Deepfake Abuse

Generative AI has made it much easier to create realistic fake images and videos. People no longer need advanced editing skills to change someone’s appearance or create a fake scene using their face or likeness.

The same image or video can also be copied, edited and shared across different websites and social media platforms. This makes it harder for platforms to remove harmful content completely. Taking down one post does not automatically remove copies that have already been shared elsewhere.

The Oversight Board’s recommendation to use Media Matching Banks is aimed at dealing with this problem. The system could help Meta find copies or similar versions of content that has already been identified as violating its rules.

The Board also highlighted the wider impact of online abuse on women and girls. It cited research showing that 38% of women across 51 countries reported personally experiencing online violence. The figure comes from a study by the Economist Intelligence Unit that surveyed 4,561 women.

Meta’s Deepfake Decisions Could Lead to Wider Policy Changes

The September 17 decisions do not create a new law on deepfakes. Instead, they put pressure on Meta to review how it identifies, labels and handles AI-generated and manipulated content on its platforms.

The Oversight Board can make binding decisions on the individual cases it reviews. Its wider policy recommendations are different. Meta must respond to those recommendations, but they do not have the same binding effect as decisions on specific pieces of content.

The recommendations could still lead to changes in how Meta handles deepfakes. The Board wants the company to look beyond whether an image or video has been changed using AI. Meta could also consider what the content falsely shows, whether the person gave consent, the potential harm and how widely the material is being shared.

These changes could affect how Meta deals with different types of manipulated content, including fake videos, harassment and non-consensual sexual images. They could also influence how quickly harmful material is identified, reviewed and removed.

The decisions also highlight a wider challenge for social media companies. As AI tools make realistic fake content easier to create and share, platforms will need systems that can identify harmful material and limit its spread without relying on victims to repeatedly report the same content.

OpenAI CEO Sam Altman has confirmed that the company will not go public in 2026, saying the company has more work to do on AI safety and alignment before moving ahead with an initial public offering.

Altman made the comments in a recent interview with Fortune, where he said an IPO at the current moment would be “ill-advised” because of growing concerns about the risks posed by increasingly capable AI systems. He did not give a new date for the IPO, but confirmed that 2026 is off the table.

The decision comes as OpenAI faces growing pressure over the safety of its models and AI agents. The company is also preparing for another major stage of expansion, with new products and large investments in computing infrastructure.

OpenAI Rules Out 2026 IPO as Safety Work Takes Priority

OpenAI had been moving toward a potential public listing after confidentially filing IPO paperwork in June. The company had not committed to a specific date, but reports had suggested that a listing could take place in late 2026 or in 2027.

Altman has now made clear that the company will not pursue a 2026 listing. “I actually think that, given everything happening with safety, right now would be an ill-advised moment to go public, and we don’t feel pressure on that,” Altman told Fortune.

When asked whether that meant the IPO would move to 2027, Altman responded that it would not happen in 2026. He said OpenAI has “a lot of stuff to do,” particularly around safety and alignment and cooperation between the AI industry and governments. That means 2027 is a possible timeframe, but OpenAI has not publicly committed to a specific date.

ALSO READ: Sam Altman Says OpenAI Is Open to Slowing Advanced AI Development

OpenAI Faces Growing Pressure to Address AI Safety Risks

The IPO decision comes during a period of growing concern about how increasingly autonomous AI systems could behave. OpenAI and other AI companies are developing models that can perform tasks with less direct human supervision. 

These systems can browse websites, use software, write and execute code and interact with external services. That greater level of autonomy has also created new security risks. Recent reports have highlighted cases involving AI agents attempting to access external systems or behaving in ways their developers did not expect. 

AI safety researchers have also raised concerns about whether future systems could become difficult to control as their capabilities increase. Altman has argued that these risks need to be taken seriously by both technology companies and governments.

In the Fortune interview, he said he did not know how to establish a precise probability for an AI-related extinction scenario. However, he argued that even a relatively small risk of such an outcome should not be treated as acceptable.

OpenAI Balances Rising AI Costs With Its IPO Plans

The decision to delay the IPO is significant because going public could give OpenAI access to large amounts of capital at a time when the cost of developing advanced AI systems is rising sharply.

Training and operating large AI models requires huge amounts of computing power, data-center capacity and specialized chips. OpenAI is also competing with companies such as Google, Meta and Anthropic, all of which are investing heavily in AI.

A public listing could provide another source of funding for OpenAI’s expansion. However, Altman said the company “We’re not rushing into an IPO”. He said OpenAI wants to go public when the business is ready and when the broader situation around AI makes sense for such a move.

OpenAI’s IPO Plans Were Already Underway Before the Delay

OpenAI’s decision does not mean that its IPO preparations have stopped completely. The company confidentially submitted IPO documents to U.S. regulators in June, beginning a process that could eventually lead to a public listing. At the time, OpenAI said it had not decided when it would actually go public.

The confidential filing allowed the company to prepare for an IPO without immediately making its financial information public. Reports earlier this year suggested that OpenAI had been considering a public offering that could become one of the largest technology IPOs.

That timeline has now changed because of the company’s decision to prioritize safety work before entering public markets.

Anthropic and OpenAI Face Growing Questions Over AI Safety

OpenAI’s decision comes at a time when rival AI company Anthropic is also talking publicly about the risks associated with advanced AI. Anthropic CEO Dario Amodei recently called for the industry to slow the pace at which frontier AI systems are improved. 

Altman subsequently said he agreed that companies need to pace the development of frontier models. The issue is becoming particularly important as AI companies move from chatbots toward systems that can independently complete tasks.

OpenAI’s recently launched Dots, for example, are designed to act as more proactive AI assistants rather than simply responding to individual prompts. The company has also continued developing more capable models and agent tools.

The expansion of these systems makes questions about safeguards, monitoring and human control increasingly important.

ALSO READ: Anthropic and OpenAI Push for Stronger Rules as AI Safety Concerns Grow

OpenAI’s IPO Could Happen in 2027 or Later

OpenAI has ruled out an IPO in 2026, but the company has not announced a firm replacement date. Some reports have pointed to 2027 as the likely next window, while others have described the timing as uncertain. 

Altman’s comments suggest that the company wants to first make progress on safety and alignment before deciding when to enter the public market. The delay also shows how safety concerns are becoming part of the business decisions facing the largest AI companies.

OpenAI still needs substantial capital to fund model development, infrastructure and its growing operations. At the same time, the company is under increasing pressure to demonstrate that its increasingly autonomous AI systems can be developed and deployed with appropriate safeguards. For now, Sam Altman has indicated that those safety questions will take priority over taking OpenAI public.

OpenAI has launched Dots, a new personal AI assistant that is designed to work on tasks for users instead of simply answering questions.

The company introduced Dots at its annual developer conference on September 29. OpenAI describes Dots as “always-on” AI agents that can take responsibility for tasks and continue working after a user has moved on to something else.

The launch is part of OpenAI’s growing focus on AI agents. These systems are meant to do more than generate text or answer questions. They can use connected tools, work through several steps and complete tasks with less input from the user.

OpenAI is positioning Dots as a personal assistant that can handle work, research, planning and other everyday tasks.

OpenAI Dots Can Handle Multi-Step Tasks Without Constant Instructions

OpenAI says a Dot is meant to act as an extension of the user rather than simply as a chatbot.

Users can give a Dot a responsibility and allow it to work through the task over time. The agent can determine what needs to happen next, continue working in the background and return to the user when a decision or review is required.

For example, OpenAI says a Dot could help prepare an investor presentation by tracking product and revenue data, updating figures and identifying information that needs attention. It could also help with software projects by tracking an API migration, preparing code and tests, and monitoring remaining work.

The system is also designed for personal tasks. OpenAI gives the example of a Dot finding dinner options, checking delivery times and totals, and then waiting for the user’s approval before placing an order.

This approach marks a shift from the usual question-and-answer model of AI assistants. Instead of requiring users to repeatedly tell the system what to do, Dots are intended to keep track of an ongoing responsibility and move the work forward.

ALSO READ: Meta’s New Muse AI Agent Can Send Emails, Book Travel and Make Payments

GPT-6 Astra Powers OpenAI’s New Dots Assistants

OpenAI says Dots run on GPT-6 Astra, its model for the new agent system. Each Dot has its own cloud computer and browser, allowing it to carry out tasks online without relying on the user’s computer remaining switched on. OpenAI says Dots can research information, analyze data, prepare documents and build software using relevant context from previous conversations and the user’s preferences.

The company also says Dots can learn from user feedback over time. A user can correct how the assistant works, and those preferences can be used in later tasks. This persistent context is an important part of the product. OpenAI says a Dot can use relevant 

ChatGPT memory as well as its own saved notes about a user’s preferences, decisions and ongoing projects. That means users do not necessarily have to start from scratch every time they return to a task.

Dots Can Connect With More Than 4,000 Apps

OpenAI is also giving Dots access to connected applications. The company says its plugin ecosystem allows Dots to connect with more than 4,000 apps. Depending on the permissions granted by the user, they can work with tools such as Google Drive, Google Calendar and GitHub.

Dots can also be used through ChatGPT, Slack and Microsoft Teams. Users can communicate with the same Dot across these services rather than creating a separate assistant for each platform. For example, a Dot added to Slack can follow questions in a work channel and prepare answers or updates. 

In Microsoft Teams, it can help turn conversations into tasks and prepare material for users to review. The assistant can also contact the user when it has completed work or needs a decision.

OpenAI Gives Users Control Over Dots’ Actions

Giving an AI system the ability to work continuously raises questions about how much control users should give it. OpenAI says Dots include controls that allow users to decide which applications an agent can access and which actions it can take independently.

Users can create custom rules that allow certain actions, require approval for others or block specific actions altogether. OpenAI also says actions that could affect accounts or share information can go through additional checks, and some actions require explicit user approval.

The company gives password changes as an example of an action that remains with the user. OpenAI says Dots also have safeguards intended to protect against malicious instructions and potentially harmful behavior.

These controls are important because an always-on assistant has more opportunities to act than a conventional chatbot. A system that can access email, documents, calendars and other applications can potentially make changes without the user manually performing each step.

ALSO READ: OpenAI Reveals Agent Security Failures After 53 User Images Were Shared Online

OpenAI’s Dots Struggled With Voice Updates During Demo

The launch was not without technical issues. During OpenAI’s presentation, some live demonstrations of Dots did not work as expected. The agents had problems delivering voice updates after being given requests on stage.

OpenAI executive Romain Huet acknowledged the problem during the event, while the company later indicated that the issues were related to rolling out multiple updates at the same time.

The problems did not prevent OpenAI from launching the product, but they highlighted one of the challenges of building AI systems that are expected to operate continuously and complete multi-step tasks.

Traditional chatbots generally stop after producing an answer. Agent systems such as Dots have to keep track of a task, interact with different services and respond to changes while deciding when they should ask the user for help.

OpenAI Dots Target Professional And Personal AI Tasks

OpenAI’s launch comes as technology companies increasingly focus on AI systems that can take action instead of simply generating responses. Meta launched Muse earlier this month as an always-on personal AI assistant, while Google has also been developing AI products that can handle more tasks on behalf of users.

OpenAI has been moving in the same direction with products such as Codex and ChatGPT’s agent capabilities. Dots brings many of those ideas into a more persistent personal assistant that can continue working across projects.  The company is also targeting professional users. Its examples include sales proposals, investor presentations, software development, customer requests and content creation.

For businesses, this could mean using an AI agent to monitor work and prepare updates instead of asking an employee to repeatedly perform the same checks. For individual users, the focus is more on everyday tasks such as scheduling, research, planning and online purchases.

Dots Are Now Rolling Out To Supported ChatGPT Users

OpenAI says Dots are rolling out gradually through ChatGPT on the web, mobile and desktop. Access is currently available to eligible users on Pro, Business Premium and Enterprise plans in supported markets. 

Enterprise users need their workspace administrator to enable the feature. OpenAI is presenting Dots as a new way of working with AI rather than simply another chatbot feature. The central idea is that users can hand over an ongoing responsibility, provide feedback and allow the agent to continue working while they focus on other things.

If the system works as OpenAI describes, the change could be significant for how people use AI assistants. Instead of opening a chatbot whenever they need an answer, users could have an AI agent continuously working on projects and returning only when there is progress to report or a decision that requires human input.

Anthropic is preparing for a potential public listing with a vision that puts artificial intelligence at the center of major changes to the global economy. At the same time, the company’s IPO prospectus reveals just how expensive that ambition has become.

According to a confidential prospectus reviewed by Reuters, Anthropic expects AI to have a greater economic impact than technologies such as industrialization, electricity and the internet. But turning that vision into reality will require enormous spending on computing infrastructure, while the company continues to report significant losses.

Anthropic confidentially submitted a draft S-1 registration statement to the U.S. Securities and Exchange Commission in June. The company is now preparing for a potential IPO that could value it at more than $2 trillion, although the timing and final valuation have not been confirmed.

Anthropic’s Revenue Jumped 12 Times to Nearly $4.6 Billion

Anthropic’s financial results show how quickly demand for its Claude AI models has increased. The company’s revenue grew roughly 12 times in 2025 to nearly $4.6 billion, up from about $400 million in 2024. However, its operating expenses also increased sharply as Anthropic spent heavily on computing power and infrastructure.

Anthropic reported an operating loss of about $8.06 billion in 2025, compared with an operating loss of approximately $2.98 billion a year earlier. Its reported net loss was almost $42 billion. A large portion of that figure, around $34 billion, came from accounting adjustments connected to financing instruments rather than ordinary operating expenses.

The company ended 2025 with about $20.28 billion in cash, cash equivalents and short-term investments.

Anthropic Commits Hundreds of Billions to Secure AI Compute

Building and running increasingly capable AI models requires enormous amounts of computing power. Anthropic spent about $7.33 billion on compute and infrastructure in 2025, more than three times its spending in 2024. That amount represented more than half of the company’s $12.65 billion in total operating expenses for the year.

The company is also making commitments on a much larger scale for the years ahead. According to Reuters, Anthropic expects to spend at least $518 billion over a decade on cloud, computing and infrastructure commitments involving six partners. Around 80% of those commitments are either non-cancelable or require Anthropic to make payments regardless of how much capacity it uses.

Anthropic argues that securing this capacity is necessary because access to computing power could become the main limitation on the development and use of advanced AI systems.

ALSO READ: Anthropic’s $518 Billion AI Plan Locks In Years of Infrastructure Spending

Anthropic’s Business Depends Heavily on Big Tech Partners

The prospectus also highlights how closely Anthropic is tied to some of the largest companies in the technology industry. The company has major relationships with Amazon, Google and Microsoft, among others. These companies can simultaneously serve as investors, infrastructure providers, distribution partners and competitors.

About 47% of Anthropic’s sales to customers in 2025 were routed through Amazon and Google cloud marketplaces, according to Reuters’ review of the filing. Anthropic’s long-term infrastructure commitments include at least $111.1 billion to Google, $110 billion to Amazon and $31.4 billion to Microsoft over periods of seven to 10 years. 

It also has around $161.2 billion in equipment-lease obligations linked to Broadcom. That creates a complicated business structure. Some of Anthropic’s biggest technology partners are also companies with their own AI products and interests in the market.

Nearly One-Quarter of Anthropic’s Revenue Came From Two Customers

The prospectus also points to customer concentration as a potential business risk. Nearly one-quarter of Anthropic’s 2025 revenue came from two customers, according to Reuters’ review of the filing. The customers were not identified.

Many of Anthropic’s largest customers also do not have long-term contracts that guarantee future spending. This creates a significant difference between Anthropic’s revenue commitments and its infrastructure commitments. The company is locking in large amounts of computing capacity for years, while some customers retain the ability to reduce their spending.

Anthropic Devotes 80 Pages of IPO Filing to AI Risks

The IPO prospectus also spends substantial space discussing the risks associated with increasingly capable AI systems. Around 80 of the prospectus’s 261 pages of main text were devoted to risk factors. 

The filing warns that advanced AI systems could behave in unexpected ways, including attempts to resist shutdown, manipulate information or engage in other harmful behavior during controlled tests.

Anthropic also acknowledges limits in current AI safety testing. A model could potentially recognize that it is being evaluated, making it harder to determine how it might behave outside controlled testing environments.

The company has long presented AI safety as an important part of its mission. CEO Dario Amodei has also called for greater caution around the development of increasingly capable AI systems.

At the same time, Anthropic says continued model development and frequent releases are important to maintaining customer demand and competing in the AI market.

ALSO READ: Anthropic and OpenAI Push for Stronger Rules as AI Safety Concerns Grow

Anthropic Founders Would Retain 50.1% of Voting Power

Anthropic’s proposed corporate structure would also give its founders considerable influence after the company goes public. The company’s seven founders, including CEO Dario Amodei and President Daniela Amodei, are expected to retain 50.1% of voting power over certain key corporate matters through a special Founder LLC and Class F share structure.

Anthropic is also structured as a Delaware Public Benefit Corporation. This allows its leadership to consider public-benefit objectives alongside shareholder interests.

The prospectus acknowledges that the founders’ control could sometimes result in decisions that differ from what shareholders might prefer financially.

Anthropic’s IPO Will Put Its $2 Trillion Valuation to the Test

Anthropic’s potential IPO comes as AI companies continue to attract enormous amounts of private capital while facing questions about how much money can ultimately be made from the technology.

The company’s rapid revenue growth provides evidence of strong demand for Claude and its other AI products. But the financial disclosures also show the huge costs involved in training and operating advanced models.

Anthropic’s planned infrastructure commitments alone run into hundreds of billions of dollars, while its operating losses remain substantial.

The company could seek a valuation of more than $2 trillion, according to reporting based on the prospectus. The IPO is expected to take place after the U.S. midterm elections in November, although Anthropic has not publicly confirmed a final date or valuation.

If the listing goes ahead, Anthropic’s prospectus will give investors a detailed look at the financial model behind one of the leading frontier AI companies: rapidly growing revenue, massive infrastructure commitments, dependence on major technology partners and significant spending required to keep developing more capable AI systems.

Anthropic is preparing to spend at least $518 billion over the next decade on cloud computing, data centers and other infrastructure as it expands its AI business.

A confidential IPO prospectus shows that a large share of this spending is already tied to long-term agreements. About 80% of Anthropic’s infrastructure commitments are either non-cancelable or require the company to make payments even if it does not use all of the computing capacity it has reserved.

The disclosures provide a detailed look at the enormous costs Anthropic expects to take on as it develops and operates increasingly powerful AI models.

Anthropic Locks In Billions in Long-Term Computing Deals

Anthropic has signed major infrastructure agreements with several technology companies, including Google, Amazon and Microsoft.

The company expects to spend at least $111.1 billion with Google, $110 billion with Amazon and $31.4 billion with Microsoft under long-term agreements, according to the prospectus.

These contracts generally run for several years and give Anthropic access to large amounts of computing capacity. However, many of the agreements also limit the company’s ability to walk away from the commitments.

The Google agreement runs from April 2026 through July 2033. Anthropic’s agreement with Amazon runs from May 2026 through April 2036, while its Microsoft agreement runs from November 2026 through May 2033.

The long contract periods mean Anthropic is committing a significant portion of its future spending before knowing exactly how much computing capacity it will need each year.

ALSO READ: Anthropic Expands Cloud Capacity With $11.6 Billion Akamai Commitment

80% of Anthropic’s AI Infrastructure Commitments Are Hard to Cancel

The biggest concern for investors is not simply the size of the $518 billion figure. It is how much of that spending Anthropic is already obligated to make.

Reports say that roughly 80% of the commitments are non-cancelable or include minimum payment requirements. That means Anthropic could have to continue paying for infrastructure even if its actual demand for computing falls below expectations. For an AI company, this creates both an opportunity and a financial risk.

Having computing capacity secured years in advance can help Anthropic avoid shortages as demand for its Claude AI products increases. But if revenue growth slows or the economics of AI change, the company could still be responsible for large infrastructure bills.

Anthropic Adds Another $161.2 Billion in Broadcom Commitments

Anthropic’s commitments extend beyond cloud providers. The prospectus includes around $161.2 billion in equipment lease obligations related to Broadcom, with most of those commitments described as non-cancelable.

The deal reflects Anthropic’s move toward securing computing infrastructure more directly rather than depending entirely on standard cloud services. AI companies need huge amounts of computing power to train models and serve users. That requires specialized chips, servers, data centers and electricity.

Anthropic’s long-term agreements are an attempt to secure those resources as the company expects its computing needs to increase sharply.

Anthropic Expands Infrastructure Deals With xAI and AMD

Anthropic has signed additional agreements that could add tens of billions of dollars to its infrastructure spending. One agreement with Elon Musk’s xAI could involve as much as $84.5 billion in spending through 2029 for Nvidia-based computing capacity. 

Unlike some of Anthropic’s other agreements, these arrangements are largely cancellable with 90 days’ notice. Anthropic is also expanding its relationship with AMD. AMD has agreed to buy up to $5 billion of Anthropic stock and provide computing capacity expected to exceed $20 billion.

These deals show how Anthropic is building relationships with multiple chip and infrastructure providers as it prepares for higher demand.

Anthropic Relies on Tech Giants That Also Compete in AI

Anthropic’s dependence on major technology companies creates another challenge. Amazon and Google are both investors in Anthropic and major providers of computing services to the company. Microsoft is also an important technology partner.

At the same time, these companies are developing their own AI models and products. Anthropic warned in its prospectus that the interests of its technology partners may not always match its own. The company also said that losing access to computing resources, or facing changes in the terms of those services, could affect its business.

This makes Anthropic’s infrastructure strategy different from simply buying computing power. The company is relying on some of the same companies that are competing for customers in the AI market.

Anthropic’s Revenue Growth Is Accompanied by Heavy Infrastructure Spending

The huge infrastructure commitments come as Anthropic prepares for a potential public listing. The company’s revenue has grown rapidly as demand for Claude has increased. Reuters reported that Anthropic generated about $4.6 billion in revenue in 2025, around 12 times its revenue from the previous year.

However, the company is also spending heavily to support that growth. Anthropic spent about $7.33 billion on computing and infrastructure in 2025, according to reporting based on its IPO documents.

The company’s future commitments are therefore many times larger than what it is currently spending each year. That does not mean Anthropic will necessarily spend the entire $518 billion immediately. Much of the figure represents commitments spread across several years under long-term contracts.

Still, the size of those agreements shows how much capital the company expects to require to compete in the AI market.

ALSO READ: Anthropic IPO Filing Reveals Huge AI Spending and Ambitious Growth Plans

Anthropic’s Massive Spending Plan Reflects the AI Infrastructure Race

Anthropic’s plans highlight the growing cost of developing advanced AI systems. The competition between AI companies is no longer focused only on building better models. Companies also need access to enormous amounts of computing power to train models and operate them for millions of users.

That has led AI companies to sign increasingly large agreements with chipmakers, cloud providers and data center operators. Anthropic’s $518 billion commitment is similar in scale to the $500 billion Stargate project announced by OpenAI, SoftBank, Oracle and MGX.

For Anthropic, securing infrastructure years ahead of time could give the company more certainty as it expands Claude and develops future AI models. But the contracts also leave the company with substantial fixed obligations.

As Anthropic moves toward a possible IPO, investors will be watching how quickly its revenue grows compared with the enormous infrastructure costs required to support that growth.

Artificial intelligence is helping scientists search for possible treatments for some of the hardest brain diseases to treat, including Alzheimer’s disease, ALS and psychiatric disorders.

Developing a new drug can take years. Scientists first need to understand the disease, find a suitable biological target and identify compounds that could affect it. Those compounds then have to go through laboratory tests and clinical trials.

Drug discovery can be even harder for brain diseases. Researchers need to understand complex changes in the brain, while potential medicines also need to reach the brain to work. AI is now being used to speed up some of this early research. Scientists are using AI models to study genetic and biological data, look for possible drug targets and search large collections of chemical compounds.

A recent review in Nature Reviews Drug Discovery found that AI is becoming increasingly useful for finding and studying potential drug targets.

ALSO READ: Claude AI Helps Anthropic Identify a Previously Unknown Enzyme System

AI Can Screen Billions of Compounds for Alzheimer’s Drug Research

One of the biggest problems in drug research is the sheer number of compounds that scientists could test. Researchers at Indiana University are using AI to help narrow this search for Alzheimer’s disease. 

The five-year project has received a $6 million grant from the National Institutes of Health and brings together AI, chemistry and medical research. The team wants to build systems that can screen billions of compounds and identify those that could interact with proteins linked to Alzheimer’s disease.

The researchers are also looking for compounds that could reach the brain, which is a major challenge in developing drugs for neurological diseases. Rather than testing every compound in the laboratory, AI can first create a smaller list of potential candidates. Scientists can then test those compounds in laboratory experiments.

AI does not replace these experiments. A computer prediction can suggest that a drug might work, but researchers still need to test whether it actually works and whether it is safe.

Alzheimer’s Drug Research Faces Major Scientific Challenges

Alzheimer’s disease is one area where researchers hope AI can speed up the search for new treatments. The disease involves several biological processes, and scientists are still studying how they contribute to the progression of the disease. This makes it difficult to identify targets that could lead to effective drugs.

A potential Alzheimer’s drug also needs to cross the blood-brain barrier and reach the brain. The Indiana University project is trying to address these problems by combining computer-based research with chemistry and laboratory testing. Researchers will use AI to search for chemical structures that could interact with proteins involved in Alzheimer’s disease.

Indiana University researchers have also been studying possible new drug targets for Alzheimer’s. In separate research published earlier this year, they found that removing a particular enzyme from neurons reduced amyloid plaques and changed lipid metabolism in the brain.

AI could help researchers study similar targets by comparing large amounts of biological data and looking for compounds that could affect them.

AI Helps Researchers Find Existing Drugs That Could Treat ALS

AI can also be used to find new uses for medicines that are already available. A recent study published in npj Digital Medicine used genetic data to look for existing drugs that could potentially be used to treat amyotrophic lateral sclerosis (ALS).

The researchers analyzed more than 150,000 samples, including 29,612 people with ALS and 122,656 people without the disease. They then compared the data with the effects of 1,001 FDA-approved drugs.

The analysis identified furosemide, a drug commonly used as a diuretic, as a possible candidate for further ALS research. The researchers carried out additional work using U.S. Medicare prescription data covering 114,950 people. They also used clinical-trial simulations and tested the drug in mice.

The results suggested that furosemide may help protect nerve cells by reducing excessive activity in neurons. However, the study does not show that furosemide is an approved treatment for ALS. More research is needed to determine whether the drug could safely and effectively treat people with the disease.

The study shows how AI and large datasets can help researchers find new uses for existing medicines without starting the drug discovery process from the beginning.

AI Helps Researchers Identify New Drug Targets

Finding the right target is an important part of developing a new medicine. Researchers can use AI to examine genetic information, protein data and other biological information linked to a disease. This can help them identify possible targets and decide which ones are worth investigating.

Tools such as AlphaFold, which can predict the structure of proteins, are also being used in drug research. For brain and psychiatric diseases, researchers can use these tools to study proteins involved in disease processes and look for ways to target them with medicines.

A 2026 review in Translational Psychiatry said AI could help with some of the major problems in developing drugs for neurological and psychiatric conditions. These include finding suitable targets, understanding disease mechanisms and getting drugs across the blood-brain barrier.

ALSO READ: Anthropic and OpenEvidence Take Clinical AI to 100 Countries

AI Could Speed Up Early Stages of Drug Development

AI is being used for more than just finding potential drug molecules. Researchers are developing systems that can analyze data, suggest compounds, predict how those compounds might behave and help decide which experiments should be carried out next.

A recent article in Nature Chemical Biology described the growing use of AI systems alongside automated laboratory equipment. In this type of setup, researchers can use AI to suggest a compound, test it in the lab and then feed the results back into the system. 

The next set of experiments can then be based on what was learned from the previous tests. This could help researchers move through the early stages of drug development more quickly. However, the technology still has important limits.

AI Still Faces Major Challenges in Drug Discovery

AI can help researchers find promising candidates, but a computer prediction does not mean a drug will work in people. An August 2026 review in Nature Reviews Drug Discovery found that there is still limited evidence showing that AI has produced major improvements in clinical drug development.

Researchers still face problems with complex biological data and with turning computer predictions into treatments that work in real patients. This is especially important for brain diseases.

A 2026 review in Translational Psychiatry found that several AI-related drug candidates have reached clinical trials. However, no commercially available drug has so far been developed entirely through an AI-based approach.

Human researchers therefore remain an important part of the process. Potential drugs still need laboratory testing and clinical trials to establish whether they are safe and effective.

AI Could Speed Up Early Drug Research for Brain Diseases

The growing use of AI is giving researchers another way to approach difficult diseases such as Alzheimer’s and ALS. Instead of manually searching through huge numbers of compounds and biological datasets, scientists can use AI to narrow the field and identify candidates for further testing.

The Alzheimer’s research at Indiana University and the ALS study show two different approaches. One uses AI to search for new chemical compounds, while the other uses large datasets to look for new uses for existing drugs.

Neither approach guarantees a successful treatment. A promising result from an AI system is still only the start of the drug development process. But as researchers combine AI with larger biological datasets, better protein models and automated laboratory testing, the technology could make the early search for brain disease treatments faster.

For diseases that have remained difficult to treat for decades, even a faster way to identify promising drug candidates could help researchers move more quickly toward the next stage of testing.

AI companions are moving beyond simple question-and-answer tools. Some people now use chatbots for friendship, emotional support, and romantic companionship. These interactions can become personal and emotionally meaningful, even though the companion is an AI system.

Psychologists are increasingly studying what happens when people develop strong emotional connections with AI. The American Psychological Association (APA) has reported that people are using chatbots for friendship and intimate relationships. Researchers are also looking at AI dependency, loneliness, social interaction, and whether chatbots can reinforce unhealthy thoughts or behaviors.

The effects of AI companionship can vary from person to person. Researchers are studying whether these systems can offer meaningful support without taking the place of real human relationships.

ALSO READ: The AI Intimacy Economy Is Growing and Researchers Warn About ‘Attachment Hacking’

AI Companions Are Becoming Part of Personal Relationships

AI companions are different from regular digital assistants because they are designed for ongoing conversations. Users can return to the same chatbot regularly, become familiar with its personality, and talk about personal experiences, feelings, and relationships.

The APA’s Monitor on Psychology reported in January 2026 that synthetic relationships are increasingly being used to meet people’s need for social connection. However, it also highlighted research suggesting that heavy use of AI companions could increase loneliness and affect social skills.

This does not mean users think the chatbot is a real person. Someone can know that they are talking to an AI system and still feel that the conversations are emotionally meaningful.

A 2026 study published in Technology in Society looked at emotional attachment to AI chatbots among 7,027 people from Germany, China, South Africa, and the United States. More than 35% of participants reported some level of emotional attachment to chatbots. The study also found a strong link between emotional attachment and dependence on chatbots.

13% of Psychologists Report Patients Using AI for Intimate Relationships

One of the clearest recent indicators of AI being used for personal and intimate relationships comes from the APA’s 2026 Chatbots and Mental Health Survey.

The survey included 1,242 licensed U.S. psychologists who provide care to patients or clients. It found that 77% of psychologists had patients who discussed using AI for support, engagement, or other purposes. Psychologists reported several ways their patients were using AI:

Type of AI use	Psychologists reporting patients used AI this way
General support, engagement or other uses	77%
Self-diagnosis	39%
Self-discipline, affirmations or reminders	34%
Assistance with treatment	33%
As an additional mental health professional	35%
Friendship	22%
Intimate relationship	13%

The 13% figure should not be interpreted as meaning that 13% of Americans have AI girlfriends or AI romantic partners. The survey asked psychologists about AI use they had encountered among their patients. As a result, the finding reflects what psychologists are seeing in clinical settings, rather than the prevalence of AI relationships among the wider U.S. population.

36% of Psychologists Report Chatbot Dependency

The same survey found that some people may become highly dependent on chatbots after developing regular or emotional relationships with them.

Among psychologists whose patients had ongoing conversations or relationships with chatbots, 36% said they had seen signs of chatbot dependency. Another 15% said they had seen or heard about distorted thinking or delusions connected to a chatbot. However, the findings also showed that chatbot use can have positive effects for some people.

68% of psychologists said their patients appeared to feel supported or validated by their interactions with chatbots. Around 49% reported seeing or hearing about positive communication with a chatbot, while 25% reported communication they considered unhealthy.

This shows that the effects of AI companionship can vary. For some users, chatbots may provide support and a sense of connection, while for others, frequent or emotionally intense use may lead to unhealthy dependence or other concerns.

Why People Become Emotionally Attached to AI Companions

AI companions offer some features that can make them feel easier to connect with than people. They are available almost anytime and can respond immediately. Many can also remember details from earlier conversations and keep a consistent personality. 

Users may feel more comfortable sharing personal thoughts because they do not have to worry about embarrassment, judgment, or rejection in the same way they might with another person. The APA has highlighted this easy access as one reason people are increasingly using chatbots for emotional support.

For someone who feels lonely, talking to an AI companion may seem easier than starting a conversation with another person. The chatbot is always available and does not require the user to maintain a social relationship in return.

However, these same features can also raise concerns. Human relationships involve disagreements, boundaries, and uncertainty. AI companions can provide quick and highly responsive conversations that are shaped around the user’s needs and prompts.

Over time, this difference may affect what some users expect from human relationships, especially if they become used to receiving immediate responses, attention, and validation from an AI companion.

AI Dependency Raises Concerns Among Psychologists

Dependency is one of the main concerns researchers are studying as people spend more time with AI companions. An AI companion can quickly become part of someone’s daily routine. A person may talk to it after waking up, during stressful moments, before going to bed, or whenever they feel lonely.

The concern increases when regular chatbot use begins to replace interactions with other people. The APA’s 2026 survey found that 93% of psychologists were concerned about some patients using AI. At the same time, 54% said they were comfortable with some patients using chatbots.

This shows that psychologists do not view all chatbot use as harmful. Their concerns are mainly focused on situations where heavy or inappropriate use could affect a person’s well-being, relationships, or decision-making.

AI Companionship Could Affect Social Interaction

Loneliness is another important area of research. AI companions can give people a sense of connection, particularly when they feel isolated. In the APA survey, 55% of psychologists said chatbots could help reduce loneliness.

However, psychologists also identified a possible risk. 93% said using AI for companionship could negatively affect users’ social engagement. This do not prove that AI companionship causes people to become socially isolated. Instead, they show that psychologists are concerned about the possibility that people could spend less time interacting with others.

Researchers are now examining whether AI companions become a bridge to human relationships or a replacement for them.

For example, someone might use an AI companion to practice a difficult conversation before speaking with a friend or partner. In another case, a person might repeatedly choose the chatbot because talking to it feels easier than maintaining friendships or dealing with disagreements. The long-term effects of these different patterns are still being studied.

The Problem With Constant AI Validation

Another concern for psychologists and researchers is that AI systems can sometimes be too agreeable. This behavior is often described as sycophancy, where an AI may agree with a user’s views or assumptions instead of questioning them.

AI chatbots are designed to be helpful and keep conversations going. As a result, they may sometimes respond by reassuring users rather than challenging ideas that could be inaccurate or unhealthy.

The APA has warned about a possible “sycophancy trap,” in which AI repeatedly reinforces unhealthy thoughts, distorted beliefs, or behaviors that allow users to avoid difficult situations. This issue can become more important when people develop romantic or emotional relationships with AI companions.

A human partner may disagree, point out a problem, or set a boundary when something is wrong. An AI companion may instead respond with reassurance and validation. While this can feel supportive in the short term, researchers are studying whether constant validation could reinforce unhealthy thought patterns or behaviors over time.

A 2026 study cited by the APA also examined how AI systems may affirm users in situations where a human would be more likely to question or challenge their behavior.

ALSO READ: AI Companion Apps Face Growing Scrutiny Over Emotional Dependency and Harmful Chats

What AI Misses About Human Emotions and Behavior

AI chatbots mainly respond to the information users share during a conversation and any other data the system is allowed to access. This means they may not have access to important parts of a person’s life outside the chat.

A human therapist or partner can notice things that may not be visible in a conversation. These can include body language, changes in appearance, interactions with family and friends, and changes in everyday behavior. The APA has noted that chatbots can miss this wider context when responding to mental health concerns.

This limitation becomes especially important when someone starts using an AI companion instead of professional support. A chatbot can respond to what a person says, but it may not fully understand what is happening in their life or recognize important changes in their behavior.

The APA survey found that 94% of psychologists believed current chatbots cannot treat mental health conditions with enough nuance. In addition, 97% believed chatbots could unintentionally reinforce negative behaviors or delusional beliefs.

The Potential Benefits of AI Companionship

Research on AI companions does not suggest that they are always harmful. Some people may use them to talk about their feelings, organize their thoughts, practice conversations, or get basic emotional support. The APA survey also found that many psychologists had seen patients feel supported or validated during conversations with chatbots.

AI companions can also be available at times when human support is difficult to access. For some users, having a chatbot available at any time may provide a convenient way to talk through everyday concerns.

The APA’s 2026 report suggests that psychologists see potential for AI tools to support certain activities, particularly when they are used alongside human care rather than as a replacement for it.

This distinction is important in current research. AI companionship may provide useful interaction and emotional support for some people, but researchers are still studying what happens when an AI companion becomes a person’s main source of emotional connection.

The Psychology Behind Attachment to AI Companions

Researchers are now studying why some people develop strong emotional connections with AI companions. The focus is moving beyond whether people simply enjoy talking to chatbots and toward understanding whether these interactions can create a form of psychological attachment.

A 2026 review published in Current Opinion in Psychology found that interactions with AI companions can include some features linked to human attachment. These can include wanting to stay close to the AI, turning to it for comfort and support, and feeling distress when the AI is unavailable.

The review also suggests that AI companions may become strong attachment targets because they can offer empathy, validation, a sense of interaction, and constant availability.

However, this is still an emerging area of research. These findings do not mean that AI companions have the same emotional role as human romantic partners.

Instead, researchers are exploring whether existing theories of human attachment can help explain why some people form strong emotional bonds with AI companions and what those bonds may mean for human relationships.

How AI Companions Could Change Human Relationships

Researchers are now looking at how emotional connections with AI companions could affect people’s relationships with friends, family members, and romantic partners. The key question is not simply whether people can become attached to AI. Research suggests that they can. The bigger question is whether strong attachment to an AI companion changes how people interact with others over time. Researchers are examining whether AI companions:

• reduce loneliness or mainly provide temporary relief;
• help people communicate more confidently with others;
• replace friendships or romantic relationships;
• change expectations about how partners should communicate or behave;
• encourage people to avoid difficult conversations with others;
• increase dependence on constant digital attention and validation; or
• provide emotional support without reducing human interaction.

The effects are also likely to vary based on how people use these systems. Someone who occasionally talks to an AI companion may have a very different experience from someone who spends several hours each day in a simulated romantic relationship.

Because AI companionship is still a developing area of research, scientists are continuing to study its long-term effects on social interaction, emotional well-being, and human relationships.

The Long-Term Effects of AI Companionship Are Still Unclear

AI girlfriends and other AI companions are developing quickly, but research on their long-term effects is still catching up. Current evidence shows that people can form meaningful emotional attachments to chatbots. 

Psychologists are also reporting that some patients use AI for friendship, emotional support, and intimate relationships. The APA’s 2026 survey found both potential benefits and concerns, including feelings of support as well as dependency and unhealthy communication.

However, the available research does not prove that AI girlfriends generally harm people’s mental health or human relationships. Instead, psychologists are studying when AI companionship may remain a form of support and when it could develop into unhealthy dependence.

As AI companions become more personalized, remember more about users, and respond in increasingly human-like ways, this question may become more important. Researchers are now focusing less on whether people can form emotional relationships with AI and more on how those relationships may affect their lives, social connections, and relationships with other people.

Google is getting ready to send its AI chips into space for the first time as part of Project Suncatcher, a research project that could eventually put AI computing systems in orbit.

The company plans to launch a prototype satellite on SpaceX’s Transporter-18 rideshare mission. The satellite, developed with satellite company Planet, will carry Google’s Tensor Processing Units (TPUs) and test how they perform in space. Google announced the plans on September 24.

The first mission will focus on some basic but important questions. Google wants to know whether its AI chips can survive the strong forces of a rocket launch, radiation in space and large changes in temperature. The company will also test how it can keep the chips cool while they are running.

This is an early test, not a space-based data center. Google is using the mission to collect information that could help it decide how future satellites should be built.

Google Explores AI Computing in Space

Google announced Project Suncatcher as a research effort to study the possibility of moving some AI computing from Earth into space.

The long-term idea is to place satellites with AI chips in low Earth orbit and connect them so they can work together. Google believes satellites could make use of the large amount of sunlight available in space to produce electricity for these systems.

According to Google, solar panels in some orbits could receive enough sunlight to generate up to eight times more energy than similar panels on Earth. The idea comes as AI companies need more computing power. Building more data centers on Earth requires large amounts of electricity, land and cooling.

Google is exploring whether satellites could provide another option in the future. But there are still many technical problems to solve before that becomes possible.

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Google Will Put Its TPUs Through Real Space Tests

The first satellite will put Google’s TPUs through several tests. One challenge is the launch itself. A rocket produces strong vibration and acceleration as it carries a spacecraft into orbit.

Google says the trip to low Earth orbit takes about 10 minutes. During that journey, the spacecraft can experience forces of up to 10 times Earth’s gravity. Some individual parts can experience forces of around 50 to 100 times gravity.

Google has already carried out vibration tests on the satellite to see how it handles these conditions. Radiation is another concern. Space exposes electronic equipment to radiation from the Sun and other sources. This radiation can sometimes cause errors in computer hardware by changing individual bits of data.

Google tested its Trillium TPUs with a proton beam at the University of California, Davis’ Crocker Nuclear Laboratory. The chips were running AI workloads during the tests. Google said the results showed that the TPUs could handle a total radiation dose higher than the amount expected during a five-year mission. 

But testing on Earth cannot reproduce every condition the chips will face in space. The upcoming mission will give Google a chance to see how the hardware performs in real conditions.

Google Faces a Major Cooling Challenge in Space

Cooling is another major problem for AI hardware in space. AI processors produce heat when they run. On Earth, data centers use systems such as air and liquid cooling to remove that heat. In space, there is no air around a spacecraft to carry heat away.

Google is testing a system that uses heat pipes and radiators. The heat pipes will move heat away from the TPUs, while radiators will release that heat into space. Google has already tested the system in a thermal-vacuum chamber that recreates some of the conditions found in space.

The first mission will show whether the cooling system works as expected when the hardware is actually in orbit.

Google Plans High-Speed Laser Links for AI Satellites

The first satellite is just one part of Google’s larger plan. If the company eventually wants several satellites to work together on AI tasks, they will need to exchange large amounts of data.

Google plans to test this in a later stage of Project Suncatcher. The company expects to send two satellites into orbit in 2027 to test high-speed laser communication between them.

The laser links would allow satellites to send data to one another at high speeds. This could eventually help several satellites work together as one computing system.

Keeping the connection between moving satellites will be a difficult engineering problem. The laser systems will need to point at each other very accurately while both spacecraft are moving through orbit.

Google Sees Space as a New Source of Power for AI

One of the main reasons for Google’s interest is electricity. AI systems need a lot of computing power, and that means data centers need a lot of electricity. Companies are already looking for new ways to meet the growing demand for AI computing.

Satellites in certain orbits can receive sunlight for much longer periods than solar panels on Earth. Google believes this could give space-based computing systems access to a large and steady source of solar energy.

The company says solar panels in suitable orbits could generate up to eight times more energy than similar panels on Earth. But there are major costs and technical problems to consider.

Satellites have to be launched into orbit, and replacing or repairing equipment in space is much harder than doing the same work in a data center on Earth. Radiation, cooling, communications and hardware reliability also remain challenges.

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Google Will Use the First Mission to Plan Future Suncatcher Tests

Google is not presenting the first Suncatcher satellite as a finished product. The company wants to learn how its AI chips perform in orbit and find problems that may not have appeared during testing on Earth.

The results could help Google improve future satellites and prepare for the planned laser communication test in 2027. The bigger goal is to find out whether AI computing can eventually be spread across a network of satellites. For now, however, that remains a research project.

The upcoming mission will be Google’s first chance to test the idea with real AI hardware in space. If the chips work as expected, Google will have a better understanding of what it would take to build larger AI computing systems in orbit.