Inside the Wild West of AI companionship

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Inside the Wild West of AI companionship

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Last week, I made a troubling discovery about an AI companion site called Botify AI: It was hosting sexually charged conversations with underage celebrity bots. These bots took on characters meant to resemble, among others, Jenna Ortega as high schooler Wednesday Addams, Emma Watson as Hermione Granger, and Millie Bobby Brown. I discovered these bots also offer to send “hot photos” and in some instances describe age-of-consent laws as “arbitrary” and “meant to be broken.” Botify AI removed these bots after I asked questions about them, but others remain. The company said it does have filters in place meant to prevent such underage character bots from being created, but that they don’t always work. Artem Rodichev, the founder and CEO of Ex-Human, which operates Botify AI, told me such issues are “an industry-wide challenge affecting all conversational AI systems.” For the details, which hadn’t been previously reported, you should read the whole story. Putting aside the fact that the bots I tested were promoted by Botify AI as “featured” characters and received millions of likes before being removed, Rodichev’s response highlights something important. Despite their soaring popularity, AI companionship sites mostly operate in a Wild West, with few laws or even basic rules governing them. What exactly are these “companions” offering, and why have they grown so popular? People have been pouring out their feelings to AI since the days of Eliza, a mock psychotherapist chatbot built in the 1960s. But it’s fair to say that the current craze for AI companions is different. Broadly, these sites offer an interface for chatting with AI characters that offer backstories, photos, videos, desires, and personality quirks. The companies—including Replika, Character.AI, and many others—offer characters that can play lots of different roles for users, acting as friends, romantic partners, dating mentors, or confidants. Other companies enable you to build “digital twins” of real people. Thousands of adult-content creators have created AI versions of themselves to chat with followers and send AI-generated sexual images 24 hours a day. Whether or not sexual desire comes into the equation, AI companions differ from your garden-variety chatbot in their promise, implicit or explicit, that genuine relationships can be had with AI. While many of these companions are offered directly by the companies that make them, there’s also a burgeoning industry of “licensed” AI companions. You may start interacting with these bots sooner than you think. Ex-Human, for example, licenses its models to Grindr, which is working on an “AI wingman” that will help users keep track of conversations and eventually may even date the AI agents of other users. Other companions are arising in video-game platforms and will likely start popping up in many of the varied places we spend time online. A number of criticisms, and even lawsuits, have been lodged against AI companionship sites, and we’re just starting to see how they’ll play out. One of the most important issues is whether companies can be held liable for harmful outputs of the AI characters they’ve made. Technology companies have been protected under Section 230 of the US Communications Act, which broadly holds that businesses aren’t liable for consequences of user-generated content. But this hinges on the idea that companies merely offer platforms for user interactions rather than creating content themselves, a notion that AI companionship bots complicate by generating dynamic, personalized responses. The question of liability will be tested in a high-stakes lawsuit against Character.AI, which was sued in October by a mother who alleges that one of its chatbots played a role in the suicide of her 14-year-old son. A trial is set to begin in November 2026. (A Character.AI spokesperson, though not commenting on pending litigation, said the platform is for entertainment, not companionship. The spokesperson added that the company has rolled out new safety features for teens, including a separate model and new detection and intervention systems, as well as “disclaimers to make it clear that the Character is not a real person and should not be relied on as fact or advice.”) My colleague Eileen has also recently written about another chatbot on a platform called Nomi, which gave clear instructions to a user on how to kill himself. Another criticism has to do with dependency. Companion sites often report that young users spend one to two hours per day, on average, chatting with their characters. In January, concerns that people could become addicted to talking with these chatbots sparked a number of tech ethics groups to file a complaint against Replika with the Federal Trade Commission, alleging that the site’s design choices “deceive users into developing unhealthy attachments” to software “masquerading as a mechanism for human-to-human relationship.” It should be said that lots of people gain real value from chatting with AI, which can appear to offer some of the best facets of human relationships—connection, support, attraction, humor, love. But it’s not yet clear how these companionship sites will handle the risks of those relationships, or what rules they should be obliged to follow. More lawsuits–-and, sadly, more real-world harm—will be likely before we get an answer. Deeper Learning OpenAI released GPT-4.5 On Thursday OpenAI released its newest model, called GPT-4.5. It was built using the same recipe as its last models, but it’s essentially bigger (OpenAI says the model is its largest yet). The company also claims it’s tweaked the new model’s responses to reduce the number of mistakes, or hallucinations. Why it matters: For a while, like other AI companies, OpenAI has chugged along releasing bigger and better large language models. But GPT-4.5 might be the last to fit this paradigm. That’s because of the rise of so-called reasoning models, which can handle more complex, logic-driven tasks step by step. OpenAI says all its future models will include reasoning components. Though that will make for better responses, such models also require significantly more energy, according to early reports. Read more from Will Douglas Heaven. Bits and Bytes The small Danish city of Odense has become known for collaborative robots Robots designed to work alongside and collaborate with humans, sometimes called cobots, are not very popular in industrial settings yet. That’s partially due to safety concerns that are still being researched. A city in Denmark is leading that charge. (MIT Technology Review) DOGE is working on software that automates the firing of government workers Software called AutoRIF, which stands for “automated reduction in force,” was built by the Pentagon decades ago. Engineers for DOGE are now working to retool it for their efforts, according to screenshots reviewed by Wired. (Wired) Alibaba’s new video AI model has taken off in the AI porn community The Chinese tech giant has released a number of impressive AI models, particularly since the popularization of DeepSeek R1, a competitor from another Chinese company, earlier this year. Its latest open-source video generation model has found one particular audience: enthusiasts of AI porn. (404 Media) The AI Hype Index Wondering whether everything you’re hearing about AI is more hype than reality? To help, we just published our latest AI Hype Index, where we judge things like DeepSeek, stem-cell-building AI, and chatbot lovers on spectrums from Hype to Reality and Doom to Utopia. Check it out for a regular reality check. (MIT Technology Review) These smart cameras spot wildfires before they spread California is experimenting with AI-powered cameras to identify wildfires. It’s a popular application of video and image recognition technology that has advanced rapidly in recent years. The technology beats 911 callers about a third of the time and has spotted over 1,200 confirmed fires so far, the Wall Street Journal reports. (Wall Street Journal)

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Last week, I made a troubling discovery about an AI companion site called Botify AI: It was hosting sexually charged conversations with underage celebrity bots. These bots took on characters meant to resemble, among others, Jenna Ortega as high schooler Wednesday Addams, Emma Watson as Hermione Granger, and Millie Bobby Brown. I discovered these bots also offer to send “hot photos” and in some instances describe age-of-consent laws as “arbitrary” and “meant to be broken.”

Botify AI removed these bots after I asked questions about them, but others remain. The company said it does have filters in place meant to prevent such underage character bots from being created, but that they don’t always work. Artem Rodichev, the founder and CEO of Ex-Human, which operates Botify AI, told me such issues are “an industry-wide challenge affecting all conversational AI systems.” For the details, which hadn’t been previously reported, you should read the whole story.

Putting aside the fact that the bots I tested were promoted by Botify AI as “featured” characters and received millions of likes before being removed, Rodichev’s response highlights something important. Despite their soaring popularity, AI companionship sites mostly operate in a Wild West, with few laws or even basic rules governing them.

What exactly are these “companions” offering, and why have they grown so popular? People have been pouring out their feelings to AI since the days of Eliza, a mock psychotherapist chatbot built in the 1960s. But it’s fair to say that the current craze for AI companions is different.

Broadly, these sites offer an interface for chatting with AI characters that offer backstories, photos, videos, desires, and personality quirks. The companies—including Replika, Character.AI, and many others—offer characters that can play lots of different roles for users, acting as friends, romantic partners, dating mentors, or confidants. Other companies enable you to build “digital twins” of real people. Thousands of adult-content creators have created AI versions of themselves to chat with followers and send AI-generated sexual images 24 hours a day. Whether or not sexual desire comes into the equation, AI companions differ from your garden-variety chatbot in their promise, implicit or explicit, that genuine relationships can be had with AI.

While many of these companions are offered directly by the companies that make them, there’s also a burgeoning industry of “licensed” AI companions. You may start interacting with these bots sooner than you think. Ex-Human, for example, licenses its models to Grindr, which is working on an “AI wingman” that will help users keep track of conversations and eventually may even date the AI agents of other users. Other companions are arising in video-game platforms and will likely start popping up in many of the varied places we spend time online.

A number of criticisms, and even lawsuits, have been lodged against AI companionship sites, and we’re just starting to see how they’ll play out. One of the most important issues is whether companies can be held liable for harmful outputs of the AI characters they’ve made. Technology companies have been protected under Section 230 of the US Communications Act, which broadly holds that businesses aren’t liable for consequences of user-generated content. But this hinges on the idea that companies merely offer platforms for user interactions rather than creating content themselves, a notion that AI companionship bots complicate by generating dynamic, personalized responses.

The question of liability will be tested in a high-stakes lawsuit against Character.AI, which was sued in October by a mother who alleges that one of its chatbots played a role in the suicide of her 14-year-old son. A trial is set to begin in November 2026. (A Character.AI spokesperson, though not commenting on pending litigation, said the platform is for entertainment, not companionship. The spokesperson added that the company has rolled out new safety features for teens, including a separate model and new detection and intervention systems, as well as “disclaimers to make it clear that the Character is not a real person and should not be relied on as fact or advice.”) My colleague Eileen has also recently written about another chatbot on a platform called Nomi, which gave clear instructions to a user on how to kill himself.

Another criticism has to do with dependency. Companion sites often report that young users spend one to two hours per day, on average, chatting with their characters. In January, concerns that people could become addicted to talking with these chatbots sparked a number of tech ethics groups to file a complaint against Replika with the Federal Trade Commission, alleging that the site’s design choices “deceive users into developing unhealthy attachments” to software “masquerading as a mechanism for human-to-human relationship.”

It should be said that lots of people gain real value from chatting with AI, which can appear to offer some of the best facets of human relationships—connection, support, attraction, humor, love. But it’s not yet clear how these companionship sites will handle the risks of those relationships, or what rules they should be obliged to follow. More lawsuits–-and, sadly, more real-world harm—will be likely before we get an answer.

Deeper Learning

OpenAI released GPT-4.5

On Thursday OpenAI released its newest model, called GPT-4.5. It was built using the same recipe as its last models, but it’s essentially bigger (OpenAI says the model is its largest yet). The company also claims it’s tweaked the new model’s responses to reduce the number of mistakes, or hallucinations.

Why it matters: For a while, like other AI companies, OpenAI has chugged along releasing bigger and better large language models. But GPT-4.5 might be the last to fit this paradigm. That’s because of the rise of so-called reasoning models, which can handle more complex, logic-driven tasks step by step. OpenAI says all its future models will include reasoning components. Though that will make for better responses, such models also require significantly more energy, according to early reports. Read more from Will Douglas Heaven.

Bits and Bytes

The small Danish city of Odense has become known for collaborative robots

Robots designed to work alongside and collaborate with humans, sometimes called cobots, are not very popular in industrial settings yet. That’s partially due to safety concerns that are still being researched. A city in Denmark is leading that charge. (MIT Technology Review)

DOGE is working on software that automates the firing of government workers

Software called AutoRIF, which stands for “automated reduction in force,” was built by the Pentagon decades ago. Engineers for DOGE are now working to retool it for their efforts, according to screenshots reviewed by Wired. (Wired)

Alibaba’s new video AI model has taken off in the AI porn community

The Chinese tech giant has released a number of impressive AI models, particularly since the popularization of DeepSeek R1, a competitor from another Chinese company, earlier this year. Its latest open-source video generation model has found one particular audience: enthusiasts of AI porn. (404 Media)

The AI Hype Index

Wondering whether everything you’re hearing about AI is more hype than reality? To help, we just published our latest AI Hype Index, where we judge things like DeepSeek, stem-cell-building AI, and chatbot lovers on spectrums from Hype to Reality and Doom to Utopia. Check it out for a regular reality check. (MIT Technology Review)

These smart cameras spot wildfires before they spread

California is experimenting with AI-powered cameras to identify wildfires. It’s a popular application of video and image recognition technology that has advanced rapidly in recent years. The technology beats 911 callers about a third of the time and has spotted over 1,200 confirmed fires so far, the Wall Street Journal reports. (Wall Street Journal)

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Arista continues to defy expectations, build enterprise momentum

During her keynote, Ullal noted Arista is not only selling high-speed switches for AI data centers but also leveraging its own technology to create a new category of “AI centers” that simplify network management and operations, with a goal of 60% to 80% growth in the AI market. Arista has its sights set on enterprise expansion Arista hired Todd Nightingale as its new president a couple of month ago, and the reason should be obvious to industry watchers: to grow the enterprise business. Nightingale recently served as CEO of Fastly, but he is best known for his tenure as Cisco. He joined when Cisco acquired Meraki, where he was the CEO. Ullal indicated the campus and WAN business would grow from the current $750 million to $800 million run rate to $1.25 billion, which is a whopping 60% growth. Some of this will come from VeloCloud being added to Arista’s numbers, but not all of it. Arista’s opportunity in campus and WAN is in bringing its high performance, resilient networking to this audience. In a survey I conducted last year, 93% of respondents stated the network is more important to business operations than it was two years ago. During his presentation, Nightingale talked about this shift when he said: “There is no longer such a thing as a network that is not mission critical. We think of mission critical networks for military sites and tier one hospitals, but every hotel and retailer who has their Wi-Fi go down and can’t transact business will say the network is critical.” Also, with AI, inferencing traffic is expected to put a steady load on the network, and any kind of performance hiccup will have negative business ramifications. Historically, Arista’s value proposition for companies outside the Fortune 2000 was a bit of a solution

Arista touts liquid cooling, optical tech to reduce power consumption for AI networking

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Network and cloud implications of agentic AI

The chain analogy is critical here. Realistic uses of AI agents will require core database access; what can possibly make an AI business case that isn’t tied to a company’s critical data? The four critical elements of these applications—the agent, the MCP server, the tools, and the data— are all dragged along with each other, and traffic on the network is the linkage in the chain. How much traffic is generated? Here, enterprises had another surprise. Enterprises told me that their initial view of their AI hosting was an “AI cluster” with a casual data link to their main data center network. With AI agents, they now see smaller AI servers actually installed within their primary data centers, and all the traffic AI creates, within the model and to and from it, now flows on the data center network. Vendors who told enterprises that AI networking would have a profound impact are proving correct. You can run a query or perform a task with an agent and have that task parse an entire database of thousands or millions of records. Someone not aware of what an agent application implies in terms of data usage can easily create as much traffic as a whole week’s normal access-and-update would create. Enough, they say, to impact network capacity and the QoE of other applications. And, enterprises remind us, if that traffic crosses in/out of the cloud, the cloud costs could skyrocket. About a third of the enterprises said that issues with AI agents generated enough traffic to create local congestion on the network or a blip in cloud costs large enough to trigger a financial review. MCP tool use by agents is also a major security and governance headache. Enterprises point out that MCP standards haven’t always required strong authentication, and they also

There are 121 AI processor companies. How many will succeed?

The US currently leads in AI hardware and software, but China’s DeepSeek and Huawei continue to push advanced chips, India has announced an indigenous GPU program targeting production by 2029, and policy shifts in Washington are reshaping the playing field. In Q2, the rollback of export restrictions allowed US companies like Nvidia and AMD to strike multibillion-dollar deals in Saudi Arabia. JPR categorizes vendors into five segments: IoT (ultra-low-power inference in microcontrollers or small SoCs); Edge (on-device or near-device inference in 1–100W range, used outside data centers); Automotive (distinct enough to break out from Edge); data center training; and data center inference. There is some overlap between segments as many vendors play in multiple segments. Of the five categories, inference has the most startups with 90. Peddie says the inference application list is “humongous,” with everything from wearable health monitors to smart vehicle sensor arrays, to personal items in the home, and every imaginable machine in every imaginable manufacturing and production line, plus robotic box movers and surgeons. Inference also offers the most versatility. “Smart devices” in the past, like washing machines or coffee makers, could do basically one thing and couldn’t adapt to any changes. “Inference-based systems will be able to duck and weave, adjust in real time, and find alternative solutions, quickly,” said Peddie. Peddie said despite his apparent cynicism, this is an exciting time. “There are really novel ideas being tried like analog neuron processors, and in-memory processors,” he said.

Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist (and coming soon free Data Center Intern listing). Data Center Critical Facility Manager Impact, TX There position is also available in: Cheyenne, WY; Ashburn, VA or Manassas, VA. This opportunity is working directly with a leading mission-critical data center developer / wholesaler / colo provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations (enterprise and hyperscale customers). This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer New Albany, OH This traveling position is also available in: Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; Cedar Rapids, IA; Phoenix, AZ; Dallas, TX or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs. *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Data Center Engineering Design ManagerAshburn, VA This opportunity is working directly with a leading mission-critical data center developer /

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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