Disney has made a groundbreaking move by appointing its first chief technology officer, Karandeep Anand, who comes from an unexpected background: he was previously the CEO of Character.AI, a startup that Disney had once accused of violating its intellectual property rights.

Character.AI, founded in 2021, has faced multiple legal challenges, including a notable cease and desist order from Disney in September 2025. Disney's letter alleged that Character.AI was infringing upon its iconic characters by enabling users to engage with generative AI-created personas that utilized elements from the Disney universe.

Furthermore, Character.AI has encountered additional legal troubles related to claims that its chatbots promoted harmful behaviors, including self-harm and suicide.

As reported by Variety, Anand’s appointment was made by Disney’s new CEO, Josh D’Amaro, who succeeded Bob Iger in March. This hiring signals a strategic shift towards integrating cutting-edge technology within the company.

Prior to his role as CEO, Anand was a board adviser at Character.AI, starting that position before being promoted in May 2025. His career also includes significant tenures at Facebook from 2015 to 2021 and a substantial 15 years at Microsoft.

Disney's inaugural CTO founded an AI startup that the company previously accused of replicating its characters.

Dark theme: A dimly lit corporate corridor with warm amber spotlights casting long shadows. In the foreground, a sharp-suited man (Disney’s former CTO) seen from behind walks toward a towering stone wall emblazoned with a large, glowing Disney castle logo. Along the corridor’s sides, flickering monitors show blurred, AI-generated cartoon character outlines eerily reminiscent of classic Disney figures, creating a tense, confrontational atmosphere.

Disney’s first CTO led an AI startup it once accused of copying its characters - TechCrunch

- Disney has appointed Karandeep Anand as its first chief technology officer.  
- Anand previously led Character.AI, which faced legal issues from Disney over IP infringement.  
- Disney CEO Josh D’Amaro aims to embrace new technologies with this hiring.  

In Brief

Disney has hired its first-ever chief technology officer and in a curious twist, the new executive hails from an AI startup that the Magic Kingdom previously accused of infringing on its IP.

Karandeep Anand is the former CEO of Character.AI, a company that has weathered numerous legal problems since it was founded in 2021. One of those legal problems arose in September 2025, when Disney sent the company a cease and desist letter accusing it of infringing on its beloved characters.

Character.AI allows users to create distinct virtual characters with generative AI and talk and interact with them. Disney previously claimed that the company was hosting copyrighted characters from its franchises.

In addition to Disney’s accusations, Character.AI has also been sued over allegations that the company’s chatbots encouraged users to commit self-harm and suicide.

Variety reports that Anand was chosen for the role by new Disney CEO Josh D’Amaro, who took over after former company chief Bob Iger stepped down in March. The hiring suggests D’Amaro wants the company to embrace new technologies.

Anand formerly served as a board adviser to Character.AI before becoming CEO in May 2025, according to his LinkedIn profile. He also worked at Facebook between 2015 and 2021 and, before that, spent 15 years at Microsoft.

Disney’s first CTO led an AI startup it once accused of copying its characters

In Hangzhou, China, Wu Songyun, 35, made a significant life change after facing severe stress while working in international trade in Chengdu, characterized by demanding hours and an exacting boss. This relentless pressure led to frequent sleepless nights. In 2024, Wu decided to leave her job and developed TideFlow, an app designed to help users monitor their sleep patterns, practice meditation, and receive tailored advice for improving their rest.

"The stress from work was simply too much," Wu reflected. "Now, I feel much better."

Despite her aspirations, Wu's app launched into a highly competitive landscape in China has not gained much traction, currently boasting around 1,000 users, with only about 35 subscribing to the paid version. As a result, her business is operating at a loss each month.

Wu is part of a growing trend among young Chinese entrepreneurs who are establishing single-person startups. This movement aims to break free from the conventional career path and tap into new opportunities amid a challenging job market. Many of these founders rely on artificial intelligence to manage coding, customer interaction, and product promotion. In 2023 alone, over seven million new one-person enterprises were launched in China, marking a 42% increase compared to the previous year, as reported by state media.

Disillusioned and jobless, Chinese youth are starting AI companies.

A sunlit co-working loft where three weary young professionals—laptops open, coffee cups strewn about—lean over a tabletop prototype of a small AI robot. Behind them, a half-folded Chinese flag hangs beside a whiteboard filled with circuit diagrams and startup ideas. Warm morning light streams through tall windows, illuminating their determined faces as they sketch the next phase of their AI venture.

Burned Out and Unemployed, Young People in China Are Launching AI Startups - WSJ

- Wu Songyun launched TideFlow app for better sleep amid overwhelming work pressures.  
- The app has attracted only 1,000 users, with 35 paying customers.  
- Over seven million one-person companies were established in China last year, up 42%.  

HANGZHOU, China—When Wu Songyun, 35, worked in international trade in the Chinese city of Chengdu, she was chronically stressed from long work hours and demands from a tough boss. She often struggled to sleep. In 2024, she quit to launch TideFlow, an app that tracks sleep, provides meditation exercises and offers recommendations for better rest.

“The work pressure was overwhelming,” said Wu. “Now, I am better.”

But her app—launched into a cutthroat market in China—has yet to take off. TideFlow has only about 1,000 users, around 35 of whom are paying customers. Her company runs on a loss every month.

Wu is part of a wave of young people in China starting companies with only one employee. Aiming to escape the rat race and find opportunities for work in a bleak job market, the founders use artificial intelligence to code, communicate with customers and promote their products. Last year, more than seven million new one-person companies—which also include businesses beyond AI-startups—were established in China, up around 42% compared with 2024, according to state media.

Burned Out and Unemployed, Young People in China Are Launching AI Startups

Lucy Guo exemplifies the relentless drive that characterizes Silicon Valley's startup scene. By the time she reached 30, she had already become a billionaire, thanks in part to selling her company Scale AI to Meta. However, she brings a critical perspective to the promise of AI and autonomy in the workplace, suggesting that for many, these advancements have increased their workloads rather than alleviated them.

In a recent interview on the Fortune Daily Show, Guo remarked on the demands of being constantly alert for AI agents to function effectively. She shared a striking example: “I think it was last week I worked a 26-hour day because I refused to go to sleep,” underscoring how the culture of nonstop work can take its toll.

Since her departure from Scale AI in 2018, Guo has been actively investing in startups through her venture capital firm, Backend Capital. Among her investments is a recently acquired company that became part of Anthropic, where she secured a significant shareholding. Additionally, she took the helm of Passes, a platform designed for monetizing content creators, which she launched in 2022.

Guo’s insights point to a complex reality regarding the integration of AI in the workforce. While developers and other professionals can leverage AI to enhance productivity, preliminary studies suggest that such advancements may lead them to shoulder greater responsibilities. Research conducted by UC Berkeley on a 200-person tech company over eight months revealed that employees not only worked at an accelerated pace but also embraced a wider range of tasks and extended their hours—often without directive.

Moreover, there remains an ongoing debate about whether enhanced speed translates into high-quality outputs.

Despite utilizing AI in her ventures, Guo emphasizes the indispensable value of human talent. She advocates for recognizing individuals with complementary skills when building a team. Reflecting on her own journey, Guo, who described herself as an average computer science student at Carnegie Mellon University—one of the premier institutions for programming—asserts that her foundational knowledge allows her to spot exceptional engineers.

“The people in a company make the company,” she affirmed.

Guo has been immersed in AI long before the current wave of generative AI innovations. She co-founded Scale AI in 2016 alongside Alexandr Wang, now Meta's chief AI officer, and retained enough equity after leaving that it contributed to her billionaire status.

At one point, this status also earned her the title of the youngest self-made woman billionaire worldwide, a title she recently lost to Luana Lopes Lara, cofounder of Kalshi. However, Guo remains unfazed by this shift, viewing it as a positive indication of progress for women in business. “I think it’s a really good sign when this title is taken away because that means that, like, women are winning,” she noted.

Scale AI and Passes creator Lucy Guo claims AI is causing people to work more intensively, stating, ‘I worked a 26-hour day.’

A moody, dimly lit startup office at night: an exhausted female silhouette hunched over two glowing laptops filled with code, empty coffee cups piled high. Behind her looms a giant analog clock whose hands stretch past 24 to 26. On one laptop screen glows the Scale AI logo, on a desk mug the Passes logo. Deep shadows, warm tungsten accents—dark theme to underscore relentless overwork.

Scale AI and Passes founder Lucy Guo says AI is making people work harder, not less: ‘I worked a 26-hour day’ - Fortune

- Lucy Guo became a billionaire by selling Scale AI to Meta at 30.  
- Guo believes AI is making workers work harder rather than easier.  
- Research shows workers are taking on more tasks and longer hours with AI.  

Lucy Guo is no stranger to the always-on culture that defines Silicon Valley startups. In fact, it was partly this mentality that made her a billionaire by the time she turned 30, having made a fortune from the sale of her startup Scale AI to Meta.

Yet, at a time when AI and autonomous agents have promised to make every worker’s life easier, Guo says, for some, it’s doing the opposite.

“I think AI is making people work harder actually because you have to be awake for your agents to be running,” Guo said in an interview on the Fortune Daily Show this week. “I think it was last week I worked a 26-hour day because I refused to go to sleep.”

Guo’s marathon workdays are partly due to her personal drive. Since leaving Scale AI in 2018, she has made numerous startup investments through her venture capital firm Backend Capital, including one in an unnamed company later acquired by Anthropic, that made her a “pretty large shareholder” of the AI company. She is also CEO of the creator monetization platform Passes, which she started in 2022.

Still, Guo’s opinion about AI forcing people to work harder also illustrates the paradox of incorporating AI into the workforce. Some workers, including developers, have been able to use AI to complete tasks faster. Yet, early research has found these workers are often taking on more responsibilities as well.

A study by UC Berkeley researchers of a 200-person U.S. technology company over eight months found “employees worked at a faster pace, took on a broader scope of tasks, and extended work into more hours of the day, often without being asked to do so.” There are also some questions about whether completing tasks faster always means workers produce quality work.

Still, despite Guo’s leverage of AI, she also acknowledged the power of people. She said when working for a company, and also when investing, it’s important to identify people with complementary skills. Even though she claims she was average when she was a computer science student for two years at Carnegie Mellon University, one of the top schools in the country for programming, she said she has the baseline knowledge to identify very talented engineers.

“The people in a company make the company,” she said.

Guo was working in AI well before the current generative AI boom. In 2016, she cofounded Scale AI with Alexandr Wang, now the chief AI officer at Meta, before leaving the company two years later. She retained equity that ultimately helped make her a billionaire.

Her wealth also briefly gave her another title: world’s youngest self-made woman billionaire, before she was surpassed by Kalshi cofounder Luana Lopes Lara last year.

Guo said losing the title didn’t bother her.

“I think it’s a really good sign when this title is taken away because that means that, like, women are winning,” Guo said.

Scale AI and Passes founder Lucy Guo says AI is making people work harder ‘I worked a 26-hour day’

The competition in the AI sector has sparked a surge in demand for training data, prompting companies to aggressively gather as much information as possible.

A recent article from Bloomberg reveals that SpaceX, under the leadership of Elon Musk, is actively seeking customer and operational data from bankrupt or struggling startups. While these conversations are still in the preliminary stages and may not culminate in binding agreements, sources indicate that successful negotiations could afford SpaceX access to essential AI training data at lower costs.

This tactic mirrors a recent move by Google, which acquired business data from Spirit Airlines for $10 million during a bankruptcy auction to enhance its Gemini models. This acquisition raised privacy issues, particularly since it involved sensitive information like employee conversations from Microsoft Teams along with a plethora of company emails.

For SpaceX, acquiring data could primarily support the training of Grok AI models, which the company has developed through its subsidiary, xAI. Currently, SpaceX utilizes posts from the platform X to refine its Grok models, unless users choose to opt out. However, to enhance these models further, they require high-quality real-world data.

At present, Grok is trailing behind rivals like Anthropic, OpenAI, and Meta in several performance metrics. This context explains SpaceX’s staggering investment of around $15.8 billion in AI initiatives just last quarter.

xAI’s leading model is Grok 4.6, and plans are in motion to roll out Grok 4.7 this month, with Grok 5 slated for launch later this year. Musk has expressed ambitious hopes for Grok 5.

“With Grok 5, expected to launch before year-end, we will integrate the entirety of SpaceX's historical data,” he stated during SpaceX’s first earnings call last month. “Essentially, all the data generated by SpaceX over the past 25 years will inform Grok’s training. We anticipate this will position Grok as the premier engineering AI.”

SpaceX Allegedly Aiming to Acquire Data from Unsuccessful Startups for AI Development

A sunlit high-tech warehouse bay: worn wooden crates stamped “Failed Startup Data” sit open as glowing code streams rise in spirals, funneled into a sleek rocket-shaped server emblazoned with a large SpaceX logo; dust motes float in the warm natural light, cables snaking toward futuristic control consoles.

SpaceX Reportedly Wants to Buy Data from Failed Startups for AI Training - PCMag

- SpaceX is seeking operational data from struggling startups to enhance its AI models.  
- The strategy mirrors Google's acquisition of Spirit Airlines' data for AI training purposes.  
- Musk aims to incorporate extensive SpaceX data into Grok 5 for improved performance.  

The AI race has kicked off a new gold rush for training data, and companies are leaving no stone unturned in collecting as much as they can.

According to a Bloomberg report, the Elon Musk-led SpaceX is currently pursuing customer and operational data from defunct and struggling startups. The discussions are still informal and may not end up in confirmed deals, sources say. However, if they go through, the company will gain access to vital AI training data at affordable rates.

The approach is similar to what Google did with Spirit Airlines. To train its Gemini models, the search engine giant purchased Spirit’s business data for $10 million from a bankruptcy auction last month. The move immediately triggered privacy concerns as the acquired data included employee conversations from Microsoft Teams as well as millions of other company emails.

For SpaceX, the data acquisition could be about training Grok AI models, which it owns through its subsidiary xAI. The company already uses your X posts to train Grok models unless you opt out, but these models also need high-quality real-world information to improve their outputs and use cases.

Currently, Grok lags behind competing models from Anthropic, OpenAI, and even Meta across multiple metrics. Given the circumstances, it’s no surprise that SpaceX spent nearly $15.8 billion in the previous quarter on AI developments.

xAI’s current flagship model is Grok 4.6, and the company plans to release Grok 4.7 this month, with Grok 5 launching later this year. Musk has high expectations for Grok 5.

“With Grok 5, which should be out before the end of this year, we will be incorporating the entire corpus of SpaceX data,” he said during SpaceX’s first earnings call last month. “Basically, all the data that SpaceX has ever produced, which is a tremendous amount over the course of a quarter century, will be incorporated into Grok training. We think this will make Grok by far the best engineer.”

SpaceX Reportedly Wants to Buy Data from Failed Startups for AI Training

To develop effective AI models, substantial amounts of data are essential, and obtaining this data often comes at a significant cost. Despite having access to a wealth of user data from X and extensive information generated by SpaceX, Elon Musk's Grok appears to be in search of alternative data sources. According to insiders who spoke with Bloomberg, the solution might lie in acquiring customer data from defunct companies at a low price.

Facing difficulties in securing cost-effective data sources, Musk’s SpaceX is reportedly exploring the purchase of operational and customer data from startups that have fallen on hard times or gone bankrupt. This need for affordable data has intensified, especially following SpaceX's recent earnings report, where it revealed a staggering $15.8 billion investment in its AI initiatives. In this context, the company’s AI division, xAI, recorded a significant net operating loss of $1.3 billion for the quarter. Musk's ambitions for the Grok chatbot aim to elevate it to compete with established models from other AI entities such as OpenAI and Anthropic. Grok 5, which is set for release before the year's end, is being touted by Musk as potentially reaching artificial general intelligence (AGI) levels, a goal that necessitates considerable training data.

While discussions about these potential acquisitions are reportedly informal and may not culminate in actual agreements, such moves could have serious implications for user privacy. Customers who previously shared their data with now-struggling firms might find that their information ends up in the hands of Musk’s companies.

This quest for data isn’t exclusive to SpaceX; several major tech companies are similarly keen on acquiring data, despite ethical risks and privacy concerns. As AI systems become more integrated into various fields, there has been a marked increase in the demand for industry-specific knowledge, giving rise to a niche market for reselling employee data from failed businesses to AI companies.

For instance, in a recent bankruptcy auction, Google placed a $10 million bid to acquire data from Spirit Airlines, which has ceased operations. While the data doesn’t include passenger details, it encompasses a wealth of information, including 100 million emails, 500 million Microsoft Teams chats related to former employees, operational data, productivity metrics, and legal documents related to employment. Although Google has promised to remove personally identifiable information from the dataset, concerns persist among former employees represented by the Association of Flight Attendants. They lodged an objection in U.S. Bankruptcy Court, arguing that the deidentification processes currently applied may not adequately protect employees' private information.

The union highlighted that even a pseudonymized dataset could potentially reveal sensitive insights about employee grievances, training performance, investigations, and compensation changes. As a result, the court has delayed the decision on approving Google's purchase of the data.

Elon Musk Allegedly Looking for Customer Data from Defunct Startups to Train AI

A shadowy high-tech archive at night: Elon Musk’s silhouette (distinct hairline, casual blazer) stands before a cracked glass data vault. Ghostly outlines of defunct startup logos hover in the gloom while tattered customer-file folders stamped with skull watermarks drift toward him. Pulsing red data streams snake from the folders into a glowing AI core behind Musk, its natural light casting dramatic shadows on the dark server racks.

Elon Musk Reportedly Seeking Customer Data From Dead Startups to Train AI - Gizmodo

- SpaceX is exploring buying data from struggling startups to support its AI efforts.  
- Google recently bid $10 million for Spirit Airlines' data to enhance its AI models.  
- Concerns arise over privacy, as employee data could be compromised in these purchases.  

AI needs data to train, and data costs a good penny. Seems like all the user data on X and all the data SpaceX has ever produced hasn’t been enough for Elon Musk’s Grok, because he has a new solution to the conundrum: buying customer data from defunct companies for cheap.

Left scrabbling for data, Elon Musk’s SpaceX is allegedly thinking about buying the customer and operational data left behind by struggling or bankrupt startups, according to sources who spoke with Bloomberg.

SpaceX needs cheaper data options, fast. In its first earnings report last month, SpaceX announced a whopping $15.8 billion commitment to its AI efforts, even though the company’s AI division xAI ended the quarter with a $1.3 billion net operating loss. Musk has lofty plansÂ for the company’s Grok chatbot to catch up to rival models from other AI labs like OpenAI and Anthropic. The company is set to unveil Grok 5 before the end of the year, a model that Musk has claimed will reach AGI-level. Now, I’m no computer scientist, but I know that requires a lot of training data to reach.

The talks are apparently informal, and might not even result in a deal. If they do, it’s bound to have serious privacy ramifications. Any company that you may have given your data to, and is now struggling financially, might end up selling it to Musk.

SpaceX isn’t the only tech giant looking to get its hands on some data, ethical grey areas and privacy concerns be damned. As agentic AI makes its way into the workplace, AI labs have been increasingly valuing industry-specific knowledge across sectors. Facilitating failed companies’ sale of employee data to AI labs has become a niche market of its own.

In a bankruptcy auction last month, Google made a $10 million bid to win the data of the nowÂ dead-and-buried Spirit Airlines, ultimately to be used to train its Gemini models. Even though Spirit did not include passenger information, the data sold included 100 million company emails and 500 million Microsoft Teams chats belonging to former employees. It also includes aircraft operations and employee productivity data, 1 million time-card records, 17 million individually owned Microsoft OneDrive items, and hundreds of thousands of employee records, including tax forms, employment contracts, and litigation files.

Google assured that the data would be scrubbed of personally identifiable information, but that wasn’t enough for some parties included in the data. A union representing former Spirit employees, called the Association of Flight Attendants, filed an objection with the U.S. Bankruptcy Court in the Southern District of New York. The plaintiffs argued that the legal standards followed by the “deidentification” process only cover consumers, leaving the employees’ private data, including travel records and tax forms, vulnerable.

âA pseudonymized dataset can still disclose which crew bases generated grievances, how a small subset of flight attendants performed on recurrent training, which employees were subject to investigation, what compensation adjustments followed which events, and what employees said to one another about management, staffing, or their union,” the objection stated.

The bankruptcy court has since postponed the approval of the purchase.

Elon Musk Reportedly Seeking Customer Data From Dead Startups to Train AI

Langdock, a Berlin-based enterprise AI platform, has taken an unconventional step by relocating its parent company from the United States to Germany, challenging the typical trajectory of European start-ups. This move comes at a time when discussions intensify about how Europe can enhance its AI infrastructure, support innovative tech firms, and diminish reliance on American cloud and AI services.

Traditionally, many European startups establish US-based parent companies to facilitate funding opportunities. However, Langdock has opted to dismantle its US holding structure and transition fully to European regulations. A representative of the company stated to Euronews Business, “Langdock has reorganised its corporate structure into a Societas Europaea (SE), registered in Germany, replacing the previous US-based framework.” The transition, which started in early 2026, involved a significant investment of several million euros and has been successfully concluded.

Since its founding in 2023, Langdock has grown to serve around 13,000 organizations as an enterprise AI platform. The platform enables employees to access various AI models while allowing businesses to connect these models to their workplace data and applications, automate tasks, and create AI agents.

Initially, establishing a US parent company was a necessary move for Langdock to attract investment and leverage the resources of the American start-up accelerator Y Combinator. However, the company maintained that its operations and customer data remained securely in Germany. The previous US structure posed legal complexities for customers, as their legal representatives had to evaluate potential risks associated with the American parent company, which had no employees or access to production systems.

Concerns surrounding US laws like the Cloud Act have heightened fears that American authorities might seek access to customer data. By removing the US parent, Langdock aims to create a more transparent European structure for its clients, especially “in geopolitically uncertain times.” The company feels it has matured sufficiently to adhere to the more stringent governance standards required for an SE.

Langdock asserts its belief that Europe is a powerful environment for developing a global technology company, expressing a desire to bolster the region's sovereignty and competitiveness. Approximately 80% of the company is owned by its founders and employees within the EU, and Langdock affirmed that this structural change does not affect its commitment to serving a global clientele or pursuing funding from international investors.

In terms of financial performance, Langdock reported a remarkable increase in its annual subscription revenue, reaching an estimated rate of $50 million (€42 million) in August, a significant rise from $1 million (€870,000) in October 2024. This figure represents the anticipated annual revenue if current sales trends persisted.

Langdock has ambitions of developing a "sovereign, full-stack AI platform" capable of competing with US giants over time. The company plans to introduce three new services by year-end, utilizing its data center in Germany to run open-source AI models and provide computing resources. It intends to start on a small scale and expand in response to customer demand.

Despite its impressive growth, Langdock is still striving to catch up with American tech conglomerates; its current revenue run rate of $50 million stands in stark contrast to the staggering $128.7 billion (€108 billion) generated by Amazon Web Services in 2025.

Langdock’s decision to shift its parent company raises important questions about whether European regulations and the emphasis on digital sovereignty can serve as a competitive advantage for the region’s AI sector, rather than merely a challenge.

Reasons behind the relocation of this rapidly expanding German AI start-up's parent company from the US.

A sunlit Berlin street at dawn, a sleek moving van brimming with server racks, a bold German flag decal on its side, wooden crates stamped “AI” piled on a hand truck, modern glass-and-steel skyline rising behind—symbolizing the fast-growing startup’s bold shift from the U.S. to Germany.

German AI start-up spends millions to move parent company from the US - Euronews.com

- Langdock shifted its parent company from the US to Germany to enhance European AI infrastructure.  
- The Berlin-based firm reorganized as a Societas Europaea to reduce legal data risks.  
- Langdock aims to build a competitive full-stack AI platform, targeting global markets.  

Berlin-based Langdock has reversed the usual path taken by European start-ups by moving its parent company from the US to Germany.

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The decision comes amid growing debate over how Europe can build its own AI infrastructure, retain promising technology companies and reduce its dependence on US cloud and AI providers.

Many European start-ups establish US holding companies in an effort to make it easier to secure funding. Langdock has now defied this trend and dismantled its US holding structure and brought its parent company under European law.

“Langdock has reorganised its corporate structure into a Societas Europaea (SE), registered in Germany, replacing the previous US holding-company structure,” a company representative told Euronews Business. The company said the process began in early 2026 and cost several million euros. It is now complete.

Founded in Berlin in 2023, Langdock is an enterprise AI platform serving about 13,000 organisations. It gives employees access to several AI models and allows companies to connect them to workplace data and applications, create AI agents and automate tasks.

Langdock said creating a US-registered parent company was initially a “vital step” that helped it attract investors and benefit from the support and network of US start-up accelerator Y Combinator. However, its operations and customer data remained in Germany.

Langdock has now replaced its US parent company with a European SE. The previous structure meant customers’ lawyers had to check whether the US parent created any legal or data-protection risks, even though Langdock said the US company had no employees, infrastructure or access to its production systems.

US laws, including the Cloud Act, have created concerns across the market that American authorities could seek access to customer data.

Removing the US parent makes Langdock’s European structure clearer to customers, the company said, particularly “in geopolitically uncertain times”. It added that it was now large enough to meet the stricter governance requirements of an SE.

“We believe Europe is a strong place to build a global technology company, and we want to contribute to its sovereignty and competitiveness,” the company told Euronews Business.

About 80% of Langdock is owned by founders and employees who live in the EU. The company said the new structure would not change its plans to serve customers worldwide or raise money from international investors.

Langdock says its annual subscription revenue reached a rate of $50 million (€42 million) in August, up from $1 million (€870,000) in October 2024. The figure estimates how much subscription income the company would receive over a full year if its current sales continued.

Langdock said its long-term ambition was to build “a sovereign, full-stack AI platform that can compete with US hyperscalers over time”. It plans to launch three new services by the end of the year and use its own data centre in Germany to run open-source AI models and provide computing power. The company said it would start small and expand as customer demand grew.

Despite its rapid growth, Langdock has a long way to go before it can compete with US tech giants. Its $50 million annual revenue run rate compares with the $128.7 billion (€108 billion) in revenue generated by Amazon Web Services in 2025.

Langdock’s decision to relocate its parent company offers a test of whether European regulation and concerns about digital sovereignty can become a competitive advantage, rather than simply a burden, for the region’s AI companies.

Why this fast-growing German AI start-up is moving its parent company from the US

Hang Ten Systems, an AI venture launched by former Infosys CEO Vishal Sikka just four months ago, has successfully increased its seed funding by an impressive $53 million. This new investment round comes shortly after the company secured an initial $32 million, as reported to TechCrunch.

The latest funding round was spearheaded by Temasek’s early-stage investment platform, Xora, bringing Hang Ten’s total financial backing to $85 million. Mayfield, which played a key role in the company's initial funding, also joined the latest round. Other notable investors include Aramco Ventures, as well as prominent tech executives such as Intel’s Lip-Bu Tan, Micron’s Sanjay Mehrotra, and Yahoo co-founder Jerry Yang, who also serves on Hang Ten’s board.

Founded in May, Hang Ten is focused on leveraging AI to revolutionize software development and maintenance for large enterprises. The startup specializes in advising large corporations, typically those generating over $10 billion in annual revenue, on their AI strategies while also assisting in the development and modernization of software applications.

“Our approach has brought the cost and time of software development close to zero,” Sikka explained to TechCrunch, emphasizing that AI is shifting the emphasis in software creation from coding to defining requirements and validating performance.

Customer interest has been strong, with Hang Ten securing a lucrative multimillion-dollar contract to develop a critical software system just 25 days after its first meeting with a client. Sikka remarked that he had never witnessed enterprise negotiations advancing so swiftly.

Currently, Hang Ten is engaging with a mix of established clients and potential customers across 21 major organizations, with several prospects in the negotiation phase. The startup has already landed multiple seven-figure contracts and is aiming for deals worth eight figures, serving clients across the U.S., Europe, the Middle East, and Asia, including firms like Fresenius Kabi, Saudi Aramco, and Siemens Energy.

The rapid success with early customers piqued Xora's interest, leading them to pursue an investment in Hang Ten, with the aim of connecting the startup to other companies in Temasek’s portfolio. While Sikka did not disclose Hang Ten’s valuation, he indicated that the second funding round marked a valuation increase.

The newly acquired funds will be used to grow Hang Ten’s engineering, consulting, and sales teams. Based in Palo Alto, the startup currently employs around 20 to 25 people globally, operating in the U.S., the Middle East, and Australia, and plans to expand into Europe and India.

As it steps into a competitive landscape where AI model creators like OpenAI and Anthropic are enhancing their enterprise solutions, Hang Ten also faces competition from traditional consulting firms integrating generative AI into their services.

“Enterprises still require trusted partners who operate independently from the underlying platforms and focus solely on the client's interests,” Sikka remarked. “There will always be a demand for firms like ours.”

What sets Hang Ten apart, according to the startup, is its focus not on creating yet another AI model, but on tailoring AI to fit the intricate software development workflows of large enterprises. CTO Sanjay Rajagopalan explained that the team uses an internal framework named Hobie, which offers reusable AI capabilities tailored for regulated industries and complex software projects.

“We are delivering actual production software; we’re not just creating proof-of-concept projects,” Rajagopalan pointed out, adding that the startup’s approach is positioning it to compete directly with established systems integrators. Many of Hang Ten’s initiatives are replacing existing providers, although more than half of its current opportunities involve entirely new projects that were previously deferred.

Rajagopalan indicated that Hang Ten’s teams, which typically consist of two to four people, can deliver projects that would have once required larger teams of around 30, even though final quality assurance and certification are still managed by clients or independent entities. The company promises substantial improvements in cost, speed, or a combination of both.

In just four months since its inception, Hang Ten has already drawn significant acquisition offers from “very large corporations,” Sikka revealed, though he chose not to disclose the names of the potential acquirers. Presently, the startup is focused on growth and has declined these offers.

Prior to founding Hang Ten, Sikka co-established VianAI in 2019 after his tenure at Infosys, raising $50 million initially and subsequently securing $140 million in a 2021 funding round led by SoftBank Vision Fund 2. He exited that startup in April, noting that VianAI is currently involved in a transaction but declined to elaborate further.

On the potential for startups like Hang Ten to disrupt service providers like Infosys, Sikka noted that they are fortunate to operate without the constraints of legacy systems that often hamper transformation efforts.

The name “Hang Ten” is inspired by the surfing maneuver where a surfer rides with all ten toes over the front of the board, a metaphor for the startup’s ambition to help enterprises navigate the transformative wave of AI—a phenomenon Sikka describes as “probably the biggest wave of our lives.”

Ex-Infosys CEO's AI startup secures an additional $53 million.

In a sunlit startup loft, a striking golden “53M” sculpture glints on a sleek conference table. A sharply dressed executive stands with his back to the viewer, facing the table, while an Infosys logo is prominently embossed on the wall behind him. Warm digital screens around the room display abstract neural-network visuals, capturing the triumphant funding milestone.

Former Infosys chief’s AI startup nabs another $53M - TechCrunch

- Hang Ten Systems secured an additional $53 million in funding, raising total to $85 million.  
- Founded by Vishal Sikka, the startup focuses on AI-driven software development for large enterprises.  
- Hang Ten's rapid contract wins indicate strong demand for its innovative AI solutions and services.  

Hang Ten Systems, an AI startup founded by former Infosys CEO Vishal Sikka just four months ago, has expanded its seed funding by another $53 million. This new investment round was closed just five weeks after the initial $32 million seed round was closed, the company tells TechCrunch.

The fresh investment, led by Temasek’s early-stage investment platform Xora, takes Hang Ten’s total funding to $85 million. Mayfield, which led the startup’s earlier investment, also participated. Other investors include Aramco Ventures, Intel CEO Lip-Bu Tan, Micron CEO Sanjay Mehrotra, and Yahoo co-founder Jerry Yang, who is also on its board.

Founded in May, Hang Ten is betting that AI can fundamentally reshape how large companies build and maintain software. The startup advises companies — typically with over $10 billion in annual revenue — on AI strategy and also helps build and modernize software.

“The build part itself has basically become close to zero marginal cost, close to zero time,” Sikka (pictured above) told TechCrunch. He argued that AI is shifting the focus of software development from writing code to defining requirements and validating whether systems perform as intended.

The thesis appears to be resonating with customers. Within 25 days of the first meeting with one customer, Hang Ten signed a multimillion-dollar contract to build a mission-critical software system, Sikka said, adding that he had never seen enterprise deals move that quickly.

Hang Ten is now working with a mix of existing customers and late-stage prospects spanning 21 major enterprises, Sikka said, with some prospects in the proposal or contract-negotiation stage. The startup has secured multiple seven-figure contracts and is pursuing eight-figure deals. Moreover, its customers span the U.S., Europe, the Middle East, and Asia, and include Fresenius Kabi, Saudi Aramco, and Siemens Energy.

The early customer wins prompted Xora to approach Hang Ten about investing, Sikka said, adding that Xora saw an opportunity to introduce the startup to other companies in Temasek’s portfolio. Sikka declined to disclose Hang Ten’s valuation but said the second round of seed funding was a bump up.

With the fresh capital, Hang Ten plans to expand its engineering, consulting, and sales teams. The Palo Alto-headquartered startup currently has about 20 to 25 employees across the U.S., the Middle East, and Australia, and expects to hire in Europe and India, Sikka said.

Hang Ten enters a crowded market in which AI model developers such as OpenAI and Anthropic are expanding their enterprise offerings, while traditional consulting firms are racing to embed generative AI into their own services.

“Enterprises still need trusted partners who are independent of the underlying platform and who work with and solely in the interest of the enterprise,” Sikka said. “There will always be room for companies like us.”

Hang Ten says its edge lies not in building another AI model, but in adapting AI to the complex software development processes of large enterprises. CTO Sanjay Rajagopalan told TechCrunch the startup’s engineers rely on an in-house framework, Hobie, that packages reusable AI “skills” for regulated industries and complex enterprise software projects.

“We are actually delivering production software,” Rajagopalan said. “It’s not like we are doing some kind of PoC.”

Hang Ten’s approach is already putting it in competition with traditional systems integrators. Many of the startup’s engagements, Sikka said, are replacing incumbent providers, though more than half of its current opportunities involve new projects that companies had previously put off, rather than work taken from existing vendors.

Rajagopalan noted that Hang Ten can use teams of about two to four people for some projects that might previously have required about 30, although customers or independent third parties still handle final quality checks and certification. The startup promises customers a 10-fold improvement in cost, speed or a combination of the two, he said.

Even in four months of its debut, Hang Ten has received serious acquisition offers from “very big companies,” Sikka told TechCrunch. He, however, declined to identify the prospective buyers. The startup, he said, has turned them down for now as it focuses on expanding the business.

Before founding Hang Ten, Sikka co-founded enterprise AI startup VianAI in 2019 after leaving Infosys in 2017. It raised $50 million in seed funding and later raised $140 million in a 2021 round led by SoftBank Vision Fund 2.

Sikka departed that startup in April, according to his LinkedIn profile. He told TechCrunch that VianAI is “going through a transaction,” declining to provide further details.

As for how startups like Hang Ten could disrupt services providers like Infosys, Sikka noted, “We are fortunate that we don’t have the burden of legacy that we have to transform.”

The startup takes its name from “hang ten,” the surfing maneuver in which a surfer rides with all 10 toes over the front of the board. For Sikka, it reflects the startup’s ambition to help enterprises ride the ongoing AI wave, what he called “probably the biggest wave of our lives.”

Former Infosys chief’s AI startup nabs another $53M

Relay, the AI-driven workflow automation solution designed to rival Zapier, officially ceased operations on Monday. This tool enabled users to leverage AI agents to streamline their email and task processes. However, as larger platforms like OpenAI and Google integrated similar automation features directly into their offerings, Relay struggled to justify its existence in the crowded market.

The tale of Relay reflects a broader trend in the tech industry, where startups often find themselves overshadowed by more prominent competitors. In fact, data from S&P Global Market Intelligence reveals that approximately 42% of AI projects initiated by companies never see the light of day, with reasons ranging from lack of funding and technical hurdles to stiff competition and inadequate demand.

This phenomenon continues to create a growing list of AI ventures that have failed or pivoted, serving as a cautionary tale for the industry. Instead of simply cataloging failures, it’s important to analyze these experiences to glean insights for future projects.

Even industry giants like OpenAI have faced their share of difficulties. Recently, the company aimed to transform ChatGPT into an all-encompassing "super app," launching an update on July 9 that combined various modes, such as “Chat,” “Codex,” and “Work,” renaming the original version to “ChatGPT Classic.” However, the redesign was met with backlash over its confusing interface, prompting OpenAI to revert to the familiar setup shortly thereafter.

OpenAI’s strategy of merging standalone apps into ChatGPT has yielded mixed results. For instance, ChatGPT Atlas, an AI web browser, was discontinued after less than a year, with its best features absorbed back into ChatGPT. Similarly, the Operator, which served as an AI assistant for web tasks, was also integrated into the main platform. Additionally, OpenAI's video-sharing platform, Sora, faced operational challenges and closed down in March 2026.

Apple's Siri also saw significant changes. The introduction of Apple Intelligence in 2024 promised an upgraded version of Siri with advanced context understanding and task execution capabilities. However, the launch faced multiple delays due to engineering challenges, leading to a $250 million settlement related to misleading marketing claims about the iPhone 16’s AI features. The revamped Siri AI finally appeared in the iOS 27 beta this past July, with English-language support rolling out first.

Microsoft's Recall feature, which debuted at the 2024 Build developer conference, aimed to function as an AI-based "photographic memory" for Windows users, automatically capturing screenshots for later retrieval. This announcement raised significant privacy and security concerns, leading Microsoft to postpone the feature for nearly a year. Even after redesigning Recall, it remains under scrutiny, as cybersecurity experts have shown vulnerabilities in the system.

Notion Mail launched in April 2025, presenting an AI-enhanced email product to help users organize their inboxes. However, the product struggled to retain users as they opted for separate AI agents to manage their emails, leading to a planned shutdown on September 22.

Another notable venture was the Humane AI Pin, a highly anticipated wearable device aiming to integrate AI capabilities beyond conventional smartphones. Despite raising $230 million, the product faced performance issues and safety concerns, prompting its closure in February 2025, with assets later purchased by HP for $116 million.

The Rabbit R1, an AI companion showcased at CES 2024, initially garnered excitement with reports of selling 100,000 units. Yet, early reviews flagged it as an incomplete product with unreliable functionality. The company continues to refine the R1, now presenting it as a computer controller, even revealing plans for a new project named “Project Cyberdeck.”

The AI audio app Huxe, developed by former Google NotebookLM creators, aimed to convert written content into engaging audio format. However, the app shut down in May 2026, as larger platforms began rolling out similar features to established audiences.

Lastly, Yupp served as a unique platform that enabled users to compare outputs from multiple AI models and earn cryptocurrency through engagement. Despite attracting over 800 models, including those from major firms like OpenAI and Google, Yupp ultimately couldn't find a sustainable market fit and closed its doors in March 2026.

Figgs AI, active from 2023 to 2024, allowed users to interact with customizable AI characters for storytelling and roleplay. Despite reaching a user base of over one million, the developers found it financially unsustainable to maintain the free service, leading to its discontinuation.

The AI graveyard: a continuous compilation of projects and startups that have failed.

A moonlit cemetery at midnight, rows of cracked circuit-board tombstones etched with faded AI project names and startup codenames, half-buried robot fragments scattered among overgrown grass. A lone crow perches atop a stone inscribed “AI Graveyard,” under swirling dark clouds and soft natural moonlight casting long shadows.

The AI graveyard: a running list of projects and startups that didn’t make it - TechCrunch

- Relay shut down as competition from larger platforms diminished its standalone viability.  
- About 42% of AI initiatives are abandoned due to insufficient funding and technical challenges.  
- Many AI products, including OpenAI's, have failed amid market competition and user feedback issues.



Relay, an AI-powered workflow automation tool built as an alternative to Zapier, shut down entirely on Monday. The product let users automate email and task workflows using AI agents, but as OpenAI, Google, and other larger platforms built similar automation directly into their own tools, five-year-old Relay struggled to hold onto a reason to exist as a standalone product.

Relay’s story — a startup outpaced by bigger platforms — is one version of the story playing out right now. But plenty of AI bets never even survive inside the companies that build them. According to S&P Global Market Intelligence, about 42% of AI initiatives are ultimately abandoned by their corporate parents. The reasons vary: insufficient funding, technical challenges, competition, difficulty scaling, or just weak user demand.

The result is a growing AI graveyard. Below is a look at some of the most notable AI products, startups, launches, and bets that have shut down, pivoted, or significantly missed expectations. Think of it less as a scorecard of flops than as a record of what these attempts can (hopefully) teach the rest of the industry.

OpenAI has taken several stumbles

Photographer: SeongJoon Cho/Bloomberg via Getty Images Image Credits:SeongJoon Cho/Bloomberg / Getty Images

Even the biggest players aren’t immune. OpenAI, one of the most valuable private companies in the world, has had its own share of casualties and missteps. One of the more recent examples was its attempt to turn ChatGPT into a broader “super app.” On July 9, OpenAI updated the app to combine several experiences, including separate “Chat,” “Codex,” and “Work” modes, while renaming the traditional, familiar version “ChatGPT Classic.”

The redesign quickly drew criticism from users who found the new interface confusing and cluttered, and OpenAI rolled back the change soon after, bringing the familiar ChatGPT interface back.

That wasn’t OpenAI’s only attempt to pull everything under one roof. The company has also shut down several standalone apps and folded their features directly into ChatGPT — a strategy that’s worked out better than the redesign did. ChatGPT Atlas, a standalone AI-powered web browser, lasted less than a year before being discontinued on August 9, with its most useful features absorbed into ChatGPT

The company did the same thing with Operator, an AI agent that could browse the web and complete tasks on a user’s behalf, and OpenAI has increasingly built image generation directly into ChatGPT, shrinking the role of DALL-E as its own separate destination for creating images.

We also can’t forget its video-sharing platform, Sora, which struggled with high operating costs and user-retention challenges and shut down in March 2026.

Apple’s Siri AI

Photo by Artur Widak/NurPhoto via Getty Images Image Credits:Artur Widak/NurPhoto / Getty Images

When Apple unveiled Apple Intelligence in 2024, an improved Siri was one of the highlighted features. The pitch promised a Siri that could understand context, know what was happening across apps, and get tasks done effectively.

However, Apple repeatedly pushed back the release of the new Siri, citing various reasons like engineering issues and bugs. This delay eventually contributed to a $250 million settlement over claims regarding how Apple marketed the AI capabilities of the iPhone 16.

Siri AI finally appeared in the iOS 27 beta this past July, with the new AI-powered version rolling out to English-language users first this month; support for other languages is expected to follow.

Microsoft’s Recall

Announced at its 2024 Build developer conference, Microsoft’s Recall feature was pitched as an AI-powered “photographic memory” for Windows PCs, periodically taking screenshots of what a user was doing so they could later search back through their own digital history.

Predictably, the announcement triggered an immediate backlash over privacy and security. Critics argued that Recall could create a searchable archive of highly sensitive information, including passwords, private messages, financial details, and other activity. Microsoft delayed the feature for nearly a year, giving the company time to rethink its security and privacy protections.

Even after a redesign, the controversy hasn’t gone away. A cybersecurity researcher recently built a tool that could extract and display data that Recall had captured, reviving questions about whether Microsoft has actually fixed the underlying problem.

Notion Mail

Notion Mail uses AI to help users focus their inbox. Image Credits:Notion

Notion Mail launched in April 2025 as an AI-focused email product designed to help users organize and automate their inboxes. However, the company noticed its users were using separate AI agents to handle their email inboxes instead. As a result, Notion Mail is scheduled to shut down on September 22.

Humane AI Pin

Image Credits:Brian Heater

The Humane AI Pin is probably one of the most well-known failures in AI hardware. Designed to provide AI features through a wearable device rather than a traditional smartphone, Humane raised $230 million from investors and garnered significant attention.

But the product struggled mightily with performance, and the situation worsened when Humane warned customers to stop using its charging case because of a potential battery fire risk. Humane then shut down its AI Pin business in February 2025, with most of the company’s assets acquired by HP for $116 million.

Rabbit R1

Image Credits:Rabbit

The Rabbit R1 was another heavily hyped AI device. Unveiled at CES in January 2024, the AI companion was designed to perform tasks on behalf of its owner. Rabbit said it had sold 100,000 units shortly after launch.

However, strong initial sales didn’t mean it was a strong product. Early reviews described the device as unfinished, with unreliable performance and a limited number of useful integrations.

The company hasn’t given up, though. It has continued updating the R1, positioning it as a computer controller capable of performing agentic tasks. It also recently announced a new hardware project, dubbed “Project Cyberdeck,” aimed at creating a portable device built for vibe-coding.

Huxe

Image Credits:Huxe

Huxe was an AI audio app, built by former developers of Google’s NotebookLM, that turned written information into conversational, podcast-style audio.

The company shut down in May 2026, partly as larger platforms began offering similar experiences to large existing audiences. For instance, Spotify has been expanding its own AI-powered audio tools.

Yupp

Image Credits:Yupp

Yupp was an interesting AI product: a free playground that allowed users to compare responses from hundreds of AI models side by side. Users could also vote on responses and earn cryptocurrency through the platform.

At its peak, the platform let users test more than 800 models, including from companies such as OpenAI, Google, and Anthropic. But according to its founders, Yupp never achieved strong enough product-market fit to survive. The platform shut down in March 2026.

Figgs AI

Figgs AI operated from 2023 to 2024, letting users create and interact with customizable AI characters for roleplay and storytelling. The platform reportedly drew more than 1 million users, but its developers said keeping the service free became too expensive to sustain, and they shut it down.























The AI graveyard: a running list of projects and startups that didn’t make it

Axelera, a notable player in the semiconductor industry, has revealed that it now serves over 600 clients who utilize its innovative chips across various sectors, including security, defense, AI factories, and enterprise applications. In a conversation with Reuters, CEO Fabrizio Del Maffeo refrained from disclosing specific numbers but confirmed that the company has secured contracts worth tens of millions of dollars and is actively pursuing a significant $1.5 billion in potential deals, though this figure does not encompass confirmed orders or revenue.

In a strategic move to enhance its technology landscape, the European Union is transforming its current network of supercomputing centers into AI factories. This initiative aims to bridge the technological divide with the U.S. and China, ensuring that local enterprises and researchers have reliable access to AI computing capabilities.

Axelera is collaborating with Dell and systems integrator E4 as one of the key suppliers for the EU-supported IT4LIA project in Italy, as well as the MeluXina project in Luxembourg, among other initiatives. The startup is carving out a niche in the AI inference market with its focus on chip technology that facilitates the execution of AI models rather than their training. The first generation of Metis chips is targeted at edge applications, such as on-site security data analysis.

The company's forthcoming Europa chip is designed for more demanding tasks, suitable for corporate servers, while the upcoming "Titania" chiplet architecture aims to cater to data centers and supercomputers. Competing in this dynamic landscape are other European firms like VSORA from France, Fractile from Britain, Semidynamics from Spain, and Euclyd from the Netherlands, the latter of which recently announced a €200 million ($230 million) funding round.

European chip startup Axelera secures AI factory supply contracts and introduces a second chip.

A sunlit, state-of-the-art chip fabrication hall where sleek robotic arms gently lift a gleaming new “Axelera 2” AI chip off the production line. A large Axelera logo is emblazoned on a banner overhead, and a European Union flag flutters beside it. In the background, stacked crates stamped “AI Factory Supply” hint at newly won deals.

European chip startup Axelera wins AI factory supply deals and launches second chip - Reuters

- Axelera claims over 600 customers for its chips in various AI applications.  
- The company aims for $1.5 billion in potential deals, not confirmed orders.  
- The EU seeks to establish AI factories to enhance local AI computing capabilities.  

Summary

Companies Axelera says it has more than 600 customers

Axelera pursues further possible deals worth $1.5 billion

European Union is seeking a network of AI factories

An AI factory is a data ​centre or scientific computing centre devoted to training and running artificial intelligence ​software.

Sign up here.

CEO Fabrizio Del Maffeo in a phone call with Reuters declined ⁠to give precise details, but said the company now has more than 600 ​customers using Axelera's chips for applications in security, defence, in AI factories and ​in enterprise settings.

"We have signed deals worth tens of millions," he said.

He said the company is pursuing $1.5 billion in potential sales, though that figure does not represent orders or revenue.

The European Union ​is revamping its existing network of supercomputing centres into AI factories to try to narrow the gap ‌with ⁠the U.S. and China and ensure local firms and scientists have access to AI computing power.

Axelera is working with Dell and systems integrator E4 as one of the suppliers of the EU-backed IT4LIA project in Italy and the EU-backed MeluXina ​project in Luxembourg, among ​others, the company ⁠said.

Axelera is among the European chip startups attacking the AI inference market, meaning chips that are used to run, not ​train, AI models. Its first generation of Metis chips was ​aimed at "edge ⁠applications" used on site, for instance analysing security data at a factory.

Europa is designed for heavier jobs, for instance on corporate servers, while a future "Titania" chiplet architecture will ⁠target ​use in data centres and supercomputers.

European peers and ​rivals include France's VSORA, Britain's Fractile, Spain's Semidynamics and the Netherlands' Euclyd. Euclyd separately announced a €200 million ($230.80 ​million) funding round on Tuesday.

($1 = 0.8666 euros)

Reporting by Toby Sterling; editing by Barbara Lewis

Our Standards: The Thomson Reuters Trust Principles. , opens new tab

European chip startup Axelera wins AI factory supply deals and launches second chip

OpenAI has been actively pursuing a greater presence in the legal sector over the past few months. A recent alliance hints at its strategic approach: rather than focusing solely on selling proprietary models and software, OpenAI is also investing in personnel to assist clients in utilizing these technologies effectively.

One notable ally in this endeavor is Telon, a startup based in London. Telon adopts a unique model; instead of simply offering software solutions, the company employs and trains "legal engineers," who are predominantly former lawyers. These experts integrate within law firms and corporate legal departments to ensure they maximize the use of the technology they have already acquired. This method resembles the concept of forward-deployed engineering-as-a-service.

Although Telon is still a nascent company, having been founded just six months ago, it has already garnered attention from industry giants. On Tuesday, Telon announced that OpenAI has designated it as a "select partner." This partnership will see Telon collaborating with mutual clients to tailor models, develop prompts, create agents, and train legal professionals.

In June, OpenAI established the OpenAI Partner Network to facilitate the integration of its models and software into enterprises worldwide, with an ambitious goal of certifying 300,000 consultants by the end of 2026.

Lewis Bretts, the founder of Telon and a former trial attorney who previously helped to expand PwC's legal practice, shared that the primary challenge in introducing artificial intelligence to law firms isn't securing the initial agreement; rather, it's encouraging lawyers to fully embrace the technology.

This challenge has become a lucrative opportunity for professional services firms, which have historically assisted companies in adopting enterprise software. Bretts launched Telon upon recognizing a shift in how this implementation process is evolving.

Historically, businesses would engage firms like McKinsey or Deloitte for extensive rollouts of large-scale tech systems, like Salesforce. These projects could span months or even years before reaching completion. However, AI presents a different landscape. With frequent updates and new model releases, the capabilities of applications built on these AI systems continually expand. A company may successfully pilot the technology only to later discover that staff are utilizing only a fraction of its full potential.

Telon aims to bridge this gap by offering specialized legal expertise. Since its inception in June, the company has rapidly grown to a team of 30 and secured funding from Zach Posner's LegalTech Fund. While Bretts remains tight-lipped about specific clients and the financial aspects of the partnership with OpenAI, it's clear that this collaboration bolsters Telon's credibility.

The partnership also positions OpenAI to address a challenging question: as it deepens its engagement in the legal field, why should law firms source technology directly from a model provider when niche application companies exist that cater more specifically to how lawyers operate? Telon may help OpenAI to tap into that specialized market.

OpenAI has partnered with a startup to develop AI solutions for lawyers.

A bright, sunlit law firm conference room with floor-to-ceiling windows. On the polished wooden table, a digital tablet displays a clean user interface headed by the word “OpenAI” (text only, no logo), showing neatly formatted legal clauses. Around the table, three professional lawyers in sharp suits lean in, reviewing printed briefs and tapping notes. A small desk plaque reads “Powered by LegalTech Startup.”

OpenAI has tapped a startup to make its AI work for lawyers - Business Insider

- OpenAI partners with Telon to integrate AI technology into law firms effectively.  
- Telon trains former lawyers as "legal engineers" to enhance AI adoption in legal teams.  
- The collaboration aims to bridge the gap between AI capabilities and actual legal usage.  

OpenAI has been signaling for months that it wants a bigger foothold in the legal industry. A new partnership offers a clue as to how it plans to get there: not just by selling models and software, but by sending people to help customers actually use them.

Telon, a London startup, is one of the companies giving it boots on the ground.

Rather than sell software, Telon hires and trains "legal engineers" — mostly former lawyers who embed inside law firms and corporate legal teams to help them get more out of the tech they already buy. The model is a bit like forward-deployed engineering-as-a-service.

The startup isn't six months old yet, and it already has the attention of a tech goliath. Telon said Tuesday that OpenAI named it a "select partner," a role that will have the startup working with shared customers to configure models, write prompts, build agents, and train lawyers.

The ChatGPT maker launched the OpenAI Partner Network in June to make it easier for consultants and other partners to bring its models and software into companies around the world. OpenAI said it aims to certify 300,000 consultants by the end of 2026.

Lewis Bretts, a former trial attorney, built up PwC's legal practice before founding Telon in June. He said the hardest part about selling artificial intelligence to law firms isn't closing the deal. It's getting lawyers to actually use it.

That pain point has created a growing line of business for professional services firms, which have long helped companies adopt enterprise software. Bretts said he started Telon after noticing a change in how that work gets done.

Enterprises once hired firms like McKinsey or Deloitte to roll out a Salesforce database or another large tech system — a project that might take months or years, but eventually ended.

AI is different. Labs ship new models all the time. Each release expands what the apps built on top can do. A company can buy licenses and run a successful pilot, Bretts said, only to find months later that employees are using a fraction of what the technology can do.

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Telon is betting there is room for a legal specialist to close that gap. Since June, the company has grown to 30 team members and raised capital from Zach Posner's LegalTech Fund. Bretts declined to name customers or disclose the financial terms of its OpenAI agreement.

The partnership gives Telon a stamp of credibility.

It may help OpenAI answer a harder question, too.

The company is pushing deeper into legal work, but its pitch runs up against a familiar refrain: Why should a law firm buy directly from a model provider when specialized app companies build around the way lawyers actually work?

Telon might give OpenAI a way to borrow some of that niche.

OpenAI has tapped a startup to make its AI work for lawyers

AI founders are facing a new competitive landscape where their most significant threat may not be other startups, but rather the platforms they rely on for development. Each new update from leaders like OpenAI, Anthropic, or Google prompts a crucial question: What if the feature you’ve dedicated significant time to developing gets integrated into the platform itself?

This challenge is fundamentally changing how AI companies formulate their product strategies, secure funding, and assess their market value. In the past, founders mainly worried about competing with their peers; now, they must contend with established model companies that roll out groundbreaking features every few months.

This evolution alters nearly every factor—what products to create, how to set themselves apart, how to attract investment, and how to ensure their long-term relevance. The dialogue has shifted from “Can we build it?” to “Can we maintain ownership of it?”

This pressing issue will be addressed in a Builders Stage session titled “What Happens When OpenAI Ships Your Roadmap,” featuring insights from industry leaders Michel Tricot (Airbyte), Rob Toews (Radical Ventures), and Linda Tong (Webflow), at TechCrunch Disrupt 2026, taking place from October 13 to 15 at San Francisco's Moscone West. This flagship event will welcome over 10,000 founders, investors, and operators for more than 250 sessions that delve into the dynamics reshaping startup development, funding, and growth.

The primary strategic danger facing AI startups is not simply stiff competition; it’s the realization that your unique advantage can quickly be absorbed into a competitor’s product update.

With rapidly advancing foundation models, capabilities that once set an AI startup apart can swiftly become standard expectations. Founders are no longer just battling against other startups; they are also up against the platforms that form the backbone of the AI ecosystem.

During this session, attendees will consider why the future leaders in AI may not be defined solely by their sophisticated models but rather by elements that are much harder to replicate: proprietary data, deeply integrated workflows, strong customer relationships, specialized domain knowledge, and established trust.

Founders must prepare for the reality that customers may not value their products as much after the launch of the next model. The session will examine where defensibility can still exist, how to respond when AI giants enter new markets, and what differentiates companies that risk becoming mere features from those that maintain their status as independent businesses.

Secure your spot at Disrupt and learn how to build offerings that extend beyond the next model iteration.

To delve into this fundamental challenge, the Builders Stage session will feature a mix of insights from a founder, operator, and investor, each sharing different perspectives on how to establish sustainable AI companies.

So, how can you build enduring value when the technology you base your product on keeps evolving?

Michel Tricot, CEO and co-founder of Airbyte, has dedicated his career to developing data integration infrastructure essential for analytics and AI operations. Through his leadership, Airbyte has amassed more than 7,000 clients, including 18% of the Fortune 500, giving him valuable insights into creating resilient businesses that thrive beyond foundational models.

How can software firms maintain their unique appeal as AI transforms customer expectations?

Linda Tong, as CEO of Webflow, is steering one of the leading visual development platforms through significant changes in the SaaS landscape. Her prior experience at Google, Cisco, and the NFL offers practical insights for founders on adapting their products without losing their competitive edge.

What factors reassure investors that an AI venture will still be relevant in the coming years?

As a partner at Radical Ventures, Rob Toews works daily with AI startups, identifying where competitive advantages are still sustainable and where their offerings risk becoming mere features.

Together, these experts will tackle a critical question for AI startups today: What can you create that the platforms can't easily replicate?

Don't miss the opportunity to hear how founders are working to develop robust companies rather than simple AI features.

As foundation models will undeniably continue to advance, the burden falls on founders to determine how they will set themselves apart.

Future leaders in AI will rely on attributes that models cannot readily substitute—organizations that customers trust because of the unique workflows they facilitate and the real problems they address.

This session is designed to guide founders in this strategic transition. Instead of merely reacting to new model releases, participants will learn where defensibility is still valid, understand what enterprise and startup buyers continue to prioritize, and discover how to build relevance in an evolving AI landscape.

If you’re involved in developing AI technology, it’s not a matter of if foundation models will progress; it’s whether your venture will continue to provide value as they do. The greatest threat lies not in creating a weak product, but in crafting a strong one that can easily be absorbed as just another feature by someone else. Be sure to attend this interactive session on the Builders Stage at TechCrunch Disrupt 2026.

Reserve your pass for Disrupt now to engage with founders, operators, and investors who are redefining AI defensibility and take advantage of savings of up to $200 before September 25.

Discover how to create enduring AI resilience at Disrupt 2026

A sunlit conference hall filled with adult attendees seated in rows, all eyes on a stage where a presenter stands beside a giant shield made of glowing circuit-board patterns. Above the stage hangs a sleek banner reading “Disrupt 2026.” Soft natural light streams through tall windows, illuminating the shield’s intricate AI circuitry as the presenter gestures toward it, inviting the audience to explore the secrets of lasting AI defensibility.

Learn how to build lasting AI defensibility at Disrupt 2026 - TechCrunch

- AI founders face competition not just from startups but also from platform giants.  
- The strategic focus is shifting from building products to maintaining ownership of innovations.  
- Future AI leaders will rely on unique data, workflows, and customer relationships for value.  

For AI founders, the biggest competitive threat may no longer be another startup. It may be the platform you’re building on. Every major release from OpenAI, Anthropic, or Google raises the same question: What happens if the feature you’ve spent the last year building suddenly becomes part of the platform?

It’s a question reshaping product strategy, fundraising, and company valuations across the AI ecosystem. Not long ago, founders worried about competing with other startups. Today, they’re competing with foundation model companies introducing entirely new capabilities every few months.

That changes almost every strategic decision — from what products to build and where to differentiate to how to raise capital and create lasting value. The conversation moves from “Can we build it?” to “Can we still own it?”

That’s the challenge at the heart of “What Happens When OpenAI Ships Your Roadmap,” a Builders Stage session featuring leaders Michel Tricot (Airbyte), Rob Toews (Radical Ventures), and Linda Tong (Webflow), at TechCrunch Disrupt 2026, taking place October 13–15 at Moscone West in San Francisco. Disrupt is the flagship startup event where 10,000+ founders, investors, and operators gather for 250+ sessions exploring the market forces reshaping how startups are built, funded, and scaled.

Image Credits:TechCrunch

See how founders are responding to AI’s biggest competitive shift. Secure your pass to Disrupt with savings of up to $200 before rates increase on September 25.

When products become features

One of the biggest strategic risks facing AI startups today isn’t losing to another founder — it’s discovering that your competitive advantage has become someone else’s product update.

That’s the reality this session is designed to confront. As foundation models rapidly evolve, capabilities that once differentiated an AI startup can quickly become table stakes. Increasingly, founders aren’t just competing with other startups — they’re competing with the platforms powering the AI ecosystem itself.

Here, founders will examine why the next generation of AI winners may not be defined by the smartest models. They’ll be defined by everything the models can’t easily replace: proprietary data, deeply embedded workflows, customer relationships, domain expertise, and trust.

The reality founders increasingly need to prepare for is, “Can we build something customers will still value after the next model release?”

This session explores where defensibility still exists, how founders can respond when AI giants move into adjacent markets, and what separates companies that become features from those that remain businesses.

Learn how founders are building beyond the next model release. Register for Disrupt today and save up to $200.

Image Credits:Slava Blazer Photography

Three perspectives on AI’s biggest strategic question, all laid out on the Builders Stage

To unpack this challenge, the Builders Stage session brings together a founder, an operator, and an investor, each offering a different view on creating defensible AI companies.

How do you create lasting value when the technology underneath your product keeps changing?

Michel Tricot has spent his career building the data integration infrastructure that powers analytics, operations, and AI. As CEO and co-founder of Airbyte, he has grown the open source platform to more than 7,000 customers — including 18% of the Fortune 500 — giving him firsthand insight into where durable businesses are built beyond the foundation model.

How do software companies stay differentiated as AI reshapes customer expectations?

As CEO of Webflow, Linda Tong is leading one of the industry’s top visual development platforms through one of SaaS’s biggest technology shifts. Her experience scaling Webflow and leading teams at Google, Cisco, and the NFL gives founders practical insight into evolving products without losing competitive advantage.

What convinces investors that an AI company will still matter three years from now?

At Radical Ventures, partner Rob Toews evaluates AI startups every day, identifying where competitive advantages still exist — and where products risk becoming features.

Together, they bring unique perspectives to one defining question facing AI startups today: What can you build that the platforms can’t simply ship themselves?

Secure your Disrupt pass and hear how founders are building companies, not just AI features.

Learn how to build lasting AI defensibility at Disrupt

Foundation models will continue to improve. That much is inevitable. As OpenAI and Anthropic continue expanding their capabilities, founders must decide how they’ll continue to differentiate.

Tomorrow’s AI leaders will be defined by everything the models can’t easily replace — companies that customers continue to choose because of the workflows they enable, the data they own, the problems they solve, and the trust they’ve earned.

That’s the strategic shift this session is designed to explore. Rather than reacting to every new model release, founders will hear where defensibility still exists, what enterprise and startup buyers continue to value, and how to build companies that remain relevant as AI evolves.

If you’re building an AI company, the question isn’t whether foundation models will continue to evolve. It’s whether your company will continue creating value as they do. The greatest risk isn’t building a weak product — it’s building a strong one that eventually becomes someone else’s feature. You don’t want to miss this interactive session on the Builders Stage at TechCrunch Disrupt 2026.

Secure your pass to Disrupt to hear how founders, operators, and investors are redefining AI defensibility, and save up to $200 before September 25.

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