Latest AI News
Research repository ArXiv will ban authors for a year if they let AI do all the work
ArXiv, a widely used open repository for preprint research, is doing more to crack down on the careless use of large language models in scientific papers. Although papers are posted to the site before they are peer-reviewed, arXiv (pronounced “archive”) has become one of the main ways that research circulates in fields like computer science and math, and the site itself has becomea source of data on trends in scientific research. ArXiv has already taken steps to combat a growing number of low-quality, AI-generated papers, for example by requiring first-time posters toget an endorsement from an established author. And after being hosted by Cornell for more than 20 years, the organization is becoming an independent nonprofit, which should allow it toraise more money to address issues like AI slop. In its latest move, Thomas Dietterich — the chair of arXiv’s computer science section —postedThursdaythat “if a submission contains incontrovertible evidence that the authors did not check the results of LLM generation, this means we can’t trust anything in the paper.” That incontrovertible evidence could include things like “hallucinated references” and comments to or from the LLM, Dietterich said. If such evidence is found, a paper’s authors will face “a 1-year ban from arXiv followed by the requirement that subsequent arXiv submissions must first be accepted by a reputable peer-reviewed venue.” Note that this isn’t an outright prohibition on using LLMs, but rather an insistence that, as Dietterich put it, authors take “full responsibility” for the content, “irrespective of how the contents are generated.” So if researchers copy-paste “inappropriate language, plagiarized content, biased content, errors, mistakes, incorrect references, or misleading content” directly from an LLM, then they’re still responsible for it. Dietterichtold 404 Mediathat this will be a “one-strike” rule, but moderators must flag the issue and section chairs must confirm the evidence before imposing the penalty. Authors will also be able to appeal the decision. Recent peer-reviewed research has found thatfabricated citations are on the risein biomedical research, likely due to LLMs — though to be fair, scientists aren’t the only ones getting caughtusing citations that were made up by AI.
View
The haves and have nots of the AI gold rush
The vibes around the current AI boom aren’t great, even in the tech industry, according toa lengthy social media postfrom Menlo Ventures partner Deedy Das. Das described San Francisco as “pretty frenetic right now,” as “the divide in outcomes is the worst I’ve ever seen.” Using a “back of the envelope AI calculation,” he projected that there are around 10,000 people — founders and employees at companies like OpenAI, Anthropic, and Nvidia — that have “hit retirement wealth of well above $20M,” while everyone else worries “they can work their well-paying (but <$500k) job for their whole life and never get there.” Plus, “layoffs are in full swing,” and “many software engineers feel that their life’s skill is no longer useful,” leading to confusion about the best career paths and “a deep malaise about work (and its future),” Das said. This prompted some eye-rolling on X, with entrepreneur Deva Hazarikaarguingthat “most of the people in this post” are “incredibly fortunate and can simply make a choice to be happy.” Another usersuggestedit’s “pretty damn novel & also kinda nasty” that in the current cycle, “the same technology is both the lottery ticket & the thing eating your fallback.” The vibes in SF feel pretty frenetic right now. The divide in outcomes is the worst I've ever seen.Over the last 5yrs, a group of ~10k people – employees at Anthropic, OpenAI, xAI, Nvidia, Meta TBD, founders – have hit retirement wealth of well above $20M (back of the envelope…
View
OpenAI co-founder Greg Brockman reportedly takes charge of product strategy
OpenAI co-founder and president Greg Brockman is officially taking the reins of the company’s product strategy,according to Wired. This seems to solidify an already-existing change, with Brockman overseeing OpenAI’s products on an interim basis while the company’s CEO of AGI deployment Fidji Simo is out on medical leave. Wired also reports that in a staff memo, Brockman described plans to combine ChatGPT and its programming product Codex into a single unified experience. “We’re consolidating our product efforts to execute with maximum focus toward the agentic future, to win across both consumer and enterprise,” Brockman reportedly said. This is just the latest OpenAI shakeup since CEO Sam Altmandeclared a “code red”at the end of last year and said the company needs to refocus on the core ChatGPT experience. Since then, OpenAI hashalted “side quests”including video generator Sora and OpenAI for Science, and it’s beenhighlighting its ambitions to build an AI “super app.” TechCrunch has reached out to OpenAI for comment. The company told Wired that Simo, who remains on medical leave, worked with Brockman on these changes.
View
From Panchatantra to Prompts: How AI Platforms are Saving Indian Bedtime Stories
Personalised narration, familiar characters, and educational themes are turning bedtime stories into an interactive experience powered by artificial intelligence.
View
Silicon Valley’s vacationland needs a new energy provider just as AI is driving prices up
It’s no secret that AI data centers have been straining the grid. But Silicon Valley has been relatively insulated from it all, thanks to high land and power prices that have pushed hyperscaler projects elsewhere. The tech elite might soon get a taste of the power crunch, though. The Bay Area’s vacationland, Lake Tahoe, has less than a year to find a new energy supplier. By May 2027, Liberty Utilities’ agreement with NV Energy will come to an end. NV Energy’s power will be redirected elsewhere in Nevada, where data centers have been booming. Both Liberty Utilities and NV Energy have said the wind-down has been long planned, and NV Energy said data centers aren’t to blame. But it’s hard to see how they don’t play a role. NV Energy alone has requests for more than 22 gigawatts of load, which as aBloomberg reportpoints out, is more than 40x what Lake Tahoe uses at its peak. If data centers weren’t in play, it’s easy to see a world in which Liberty Utilities and NV Energy renew their contract. But with data center customers willing to pay whatever it takes to get electricity, it was inevitable that traditional customers in Lake Tahoe would be left out in the cold. The timing couldn’t be worse. Energy markets are harsh environments these days, squeezed by surging demand and tightened supplies made worse by the Trump administration’s decision to attack Iran. Lake Tahoe’s circumstances are compounded by the fact that its power lines share more connections with Nevada’s grid than California’s. That means the community must find another power provider from within NV Energy’s territory or elsewhere in the West. Given that NV Energy has already prioritized data centers over the mountain town, it’s likely that Lake Tahoe residents — and second-home owners — will have to find another regional power producer. That won’t be easy, either. One state over, in Utah, a county commissionrecently approveda 40,000-acre data center development that could consume up to 9 gigawatts of electricity when completed. Today, the entire state of Utah usesabout 4 gigawatts. Demand at that scale is almost certain to drive prices up throughout the region. The confluence of those factors means that Lake Tahoe will likely pay more for electricity next year than it does today. Locals will get hit the hardest, but people who own second homes in the area, many of whom are from Silicon Valley, might feel the pinch, too. The injustice of the AI energy crunch is that the people who suffer the most have had very little say in the technology or its rollout. Lake Tahoe’s power predicament shows that’s starting to change, though probably not enough to make a difference.
View
The OpenAI trial wraps up, and the Musk founder machine keeps spinning
The Musk v. Altman trial came to a close this week, and the final arguments kept circling back to one question:can we trust the people in charge of AI?All of this is playing out as SpaceX charges toward what could be one of the largest IPOs in American history, witha whole generationof foundersalready spinning out of the Musk empire. On this episode of TechCrunch’sEquitypodcast, Kirsten Korosec, Anthony Ha, and Sean O’Kane break down the trial’s closing stretch and what the growing Elon Musk founder ecosystem looks like on the ground, and the other deals that caught our eye this week. Listen to the full episode to hear about: Subscribe to Equity onYouTube,Apple Podcasts,Overcast,Spotifyand all the casts. You also can follow Equity onXandThreads, at @EquityPod.
View
Runway started by helping filmmakers — now it wants to beat Google at AI
AI video-generation startupRunwaydoesn’t have the typical Silicon Valley pedigree. No Stanford founders, no ex-Google founders, no nine-figure seed round that bought them time to ignore revenue. Its three founders — two from Chile, one from Greece — met at NYU’s Tisch School of the Arts and built the company in New York. Runway also could be, depending on who you ask, one of the most consequential AI companies today. Not because of what it has built, but because of what it is trying to build next. For the past several years, the AI industry has largely operated on the premise that intelligence lives in language. Large language models like OpenAI’s ChatGPT and Anthropic’s Claude reflect that bet. Runway, alongside other competitors, is making a different one. Its founders believe the next form of AI intelligence won’t be built from text, but from video and world models that learn how the world works, not just how humans describe it. That distinction sounds academic. Its implications are not. Runway co-founder and co-CEO Anastasis Germanidis said training models directly on observational data from the world is the next frontier of AI. The companies that get there first, he argues, won’t be the ones that perfected language. “We’re basically bound by our own understanding of reality,” Germanidis told TechCrunch from Runway’s homey sunlight-filled headquarters near Union Square. “Language models are trained on the entire internet, on message boards and social media, on textbooks — distilling the existing human knowledge,” Germanidis continued. “But to get beyond that, we need to leverage less biased data.” Founded in 2018, Runway built its reputation onvideo-generation models— including its latest Gen-4.5 — and AI tools that let people turn text prompts into editable, cinematic content. Today, Runway’s technology powers production workflows for filmmakers and ad agencies, and the company has signed deals with major media players likeLionsgateand AMC Networks. Its tools have even been used in films such as “Everything Everywhere All At Once.” Runway is now valued at$5.3 billionand, according to one of its founders,added $40 million in annual recurring revenuein the second quarter of 2026. If Runway’s bet that video generation is the path to world models pays off, the result will be felt from Hollywood to drug discovery. If it doesn’t, Runway risks being outpaced by competitors with far deeper pockets — Google chief among them. Within the last six months, the startup has put its plan into action and expanded beyond video generation, launching itsfirst world model in December, with plans to launch another this year. (World models are AI systems that simulate environments well enough to predict how they’ll behave.) Runway isn’t alone in its pursuit of turning physics-aware video models to world models, with near-term use cases in interactive entertainment, gaming, and robotics training. StartupsLumaandWorld Labsare on asimilar trajectory, and Google has pointed itsGenie world modelin the same direction. Everyone is after some version of the same thing: AI that solves humanity’s hardest problems. That’s far from Runway’s original product, but it’s the result of both emergent capabilities in the technology and founders who were predisposed to follow where it led. For his part, Germanidis sees world models as scientific infrastructure. The more sensory data and observations you train a single model on, the closer you get to a working digital twin of the universe — one you can run experiments on faster than any lab could. Much of the scientific process is just waiting on results, he points out. If you could compress that waiting, you could compress progress itself. “If we can build a better scientist than human scientists, we can accelerate progress in how we understand the universe and how we solve problems,” Germanidis said. Germanidis fell in love with programming as an 11-year-old in Athens and came to the U.S. at 18 to study neuroscience and film. He turned back to computer science, working at several Silicon Valley tech firms before deciding he’d had enough of the culture.Co-CEO Cristóbal Valenzuela, born and raised in Santiago, studied economics as an undergraduate before working in film and then software. Another Santiago native, Chief Innovation Officer Alejandro Matamala-Ortiz studied advertising and ran a design firm. The three met in 2016 while attending NYU’s ITP (Interactive Communications Program), a graduate program that Valenzuela described as an “art school for engineers.” The co-founders had all aspired to befilmmakersat certain points in their lives, according to Matamala-Ortiz. So Runway started with a simple mission: Can we use AI to makeeveryone a filmmaker? After releasing their first video generation model in February 2023 — which isstaggeringly unimpressivecompared to what Runway is putting out today — that mission evolved into: Could we make everyone agreatfilmmaker, according to Matamala-Ortiz. It required growing the team to what it is today. The company has 155 workers spread across offices in New York, London, San Francisco, Seattle, Tel Aviv, and most recently, Tokyo. "But throughout this process, we learned that these models can understand how the world works, and if you scale them, they can be useful for many other different things,” he added. Things like robotics, drug discovery, and climate modeling — the kinds of problems that have stumped researchers for decades. Last year,Runway launched a robotics unitwhich Germanidis says has already resulted in real-world testing and deployments. Germanidis,like others,sees the field heading towardtraining a single model on many different modalities— text, video, voice, and other sensors — and thinks the compounding effect is the point. His own moonshot goal for Runway’s technology, given enough time and resources, is biological world models and anti-aging research. Whether Runway can carry its video dominance into world models is far from settled, and the competition isn’t waiting around. Runway was among the first to AI video generation, but world models are a different race with deep-pocketed and well-respected competitors. Google, formerMeta chief scientist Yann LeCun, AI’s ‘godmother’Fei-Fei Li, and a growing field of startups are all chasing the same goal. Kian Katanforoosh, CEO of AI skills benchmarking companyWorkeraand a lecturer at Stanford, pointed out that no one has yet proven the jump between video intelligence and generalized reasoning via world models, but that doesn’t mean it’s impossible. He said that if Runway wants to turn its world model bet into reality, it will need to continue gathering resources — compute chief among them. Runway has deals withCoreWeaveandNvidia, but wouldn’t confirm whether it has dedicated cluster access — the kind of guaranteed, large-scale compute that training frontier models requires. “How are you going to build a foundational model without a cluster?” Katanforoosh asked. “I don’t think anybody can do that.” Runway has raised $860 million to date, including a$315 million roundin February from strategic partners like AMD Ventures and Nvidia. That’s roughly in line with its most immediate competitors, Luma AI and World Labs, which have raised $900 million and $1.29 billion, respectively, according to PitchBook. But Runway is also going up against incumbents like OpenAI, which has raised around $175 billionper CEO Sam Altman, and tech behemoth Google, whose parent company Alphabet is worth $4.86 trillion.Google is Runway’s biggest threat.The company’s Veo model competes directly with Runway’s video generation business, while its Genie world model targets the same longer-term territory Runway is racing towards. Katanforoosh nodded at OpenAI, whichshuttered its video platform Sorain March after burning roughly$1 million per dayin compute costs with barely $2.1 million in revenue according to some estimates. His point: resources alone don’t guarantee survival. Theydon’t guarantee itfor Runway either. Katanforoosh isn’t writing Runway off. He pointed to AI audio startupElevenLabs, which hasoutperformedOpenAI and Google on their own benchmarks, despite lacking the resources and pedigree of either. Runway, he argues, could follow a similar playbook. The comparison isn’t lost on Runway’s founders. Valenzuela says the startup’s lack of Bay Area “standardization” gives them an edge. Not only do they have diversity of thought, he contends, but without Silicon Valley ties, they had to be scrappier, lacking the war chest many of their peers have access to that would have insulated them from the need to generate revenue early. And according to Michelle Kwon, Runway’s chief operating officer, the company isn’t in a rush to raise more funds, even as compute demands increase with scale. “Their background has led them to be early, to be right more often than not, and to build a culture that moves incredibly quickly,” early investor Michael Dempsey, managing partner at Compound, told TechCrunch. For Valenzuela, that culture starts with how he sees the world in the first place.He spends whatever free time he has — not much, as a co-CEO and new father — reading books, including the Chilean poet Nicanor Parra, whom he describes as the antithesis of Pablo Neruda: less formal, less academic, holding a view that poetry belongs to the people rather than to rules. “Rules are just rules they invented,” Valenzuela said. “That’s a driving force of how we do things at Runway. They say Silicon Valley is here and that’s where the startups are. Why? Those are just made up rules. Scrub them all and start again.”
View
OpenAI launches ChatGPT for personal finance, will let you connect bank accounts
On Friday, OpenAI launched a new set of personal finance tools in preview for ChatGPT Pro subscribers in the U.S., letting them connect their accounts and ask questions ranging from spending analysis to future financial planning. OpenAI has partnered with the financial connection service Plaid to manage the account connections. Users can connect to over 12,000 financial institutions, including Schwab, Fidelity, Chase, Robinhood, American Express, and Capital One. Once users connect these accounts, they will see a dashboard of their portfolio performance, spending, subscriptions, and upcoming payments. The new product comes just one month after OpenAIacquired the team behind personal finance startup Hiro, which was backed by firms like Ribbit, General Catalyst, and Restive, in April. OpenAI said that the Hiro team’s expertise in finance was useful in launching this product but didn’t specify if the entire feature was built by them. OpenAI users can access the tool by selecting “Get started” in the “Finances” option in the sidebar, or typing “@Finances, connect my accounts” in a ChatGPT conversation. Once users do that, the chatbot will guide them about linking accounts through Plaid. The company said it plans to support Intuit soon, which would enable analysis such as the impact of a stock sale on taxes or the odds of a credit card approval. According to OpenAI, more than 200 million users already ask financial questions to ChatGPT every month. The company also noted that the new GPT-5.5 model is stronger at reasoning with context, which is crucial for answering finance-related questions. The company said it worked with finance experts to create a benchmark for the model to improve on personal finance questions. With the new financial tool integration, users can get detailed answers to questions such as “I feel like I’ve been spending more recently. Has anything changed?” or “Help me build a plan to be ready to buy a house in my area in the next 5 years.” Users can go to Settings > Apps > Finances to remove connections to certain accounts if they want. Once they disconnect a service, the synced data will be removed from ChatGPT in 30 days. What’s more, users can also view and delete financial memories from the Finances page. Generalized chatbots are designed to answer anything, leading people to ask questions about data-sensitive topics such as health, finance, and personal life. AI companies are realizing this and making specialized products for these sectors. BothOpenAI and Anthropichave launched health-related tools. Earlier this month, Perplexity launched its ownfinancial research product based on its Computer agent. OpenAI said its personal finance tools will be available on ChatGPT on the web and on iOS for Pro users. It noted that, based on the feedback from these users, it wants to improve the product before making it available to Plus users.
View
Rajasthan Enters Semiconductor Race With First Chip Packaging Plant in Bhiwadi
the facility currently has an annual packaging capacity of 60 million semiconductor units
View
Telangana's Life Sciences Hub Hits $145 Bn, Draws ₹84,000 Cr in 2 Years
Hyderabad now hosts technology and innovation centres of nine of the world’s 10 ten life sciences companies.
View
Anthropic Reverses Decision on Third-Party Agentic Tools, Allows OpenClaw Usage in Claude Plans
The artificial intelligence (AI) space has started moving away from an “unlimited free buffet” pricing model for programmatic and agentic automation. After Microsoft announced that GitHub Copilot will move to a metered credit allotment calculated on token consumption instead of premium requests, Anthropic is moving towards a similar model. The company said that a monthly fixed credit will now be provided for programmatic use. However, the upside to the change is that it now reverses the AI startup's older decision not to allow third-party agentic tool usage via Claude plans.
View
Osaurus brings both local and cloud AI models to your Mac
As AI models increasingly become commoditized, startups are racing to build the software layer that sits on top of them. One interesting entrant into this space isOsaurus, an open source, Apple-only LLM server that lets users move between different local AI models, either locally or in the cloud, while keeping their files and tools all on their own hardware. Osaurus evolved out of the idea for adesktop AI companion, Dinoki, which Osaurus co-founderTerence Paedescribed as a sort of “AI-powered Clippy.” Dinoki’s customers had asked him why they should buy the app if they still had to pay for tokens — the usage units AI companies charge for processing prompts and generating responses. That got Pae thinking more deeply about running AI locally. “That’s how Osaurus started,” Pae, previously a software engineer at Tesla and Netflix, told TechCrunch over a call. The idea, he explained, was to try to run an AI assistant locally. “You can do pretty much everything on your Mac locally, like browsing your files, accessing your browser, accessing your system configurations. I figured this would be a great way to position Osaurus as a personal AI for individuals.” Pae began building the tool in public asan open-source project, adding features and fixing bugs along the way. Today,Osauruscan flexibly connect with locally hosted AI models or cloud providers like OpenAI and Anthropic. Users can freely choose which AI models they’re using, and keep other aspects of the AI experience on their own hardware, like the models’ own memory, or their files and tools. Given that different AI models have different strengths, the advantage of this system is that users can switch to the AI model that best fits their needs. Such a structure makes Osaurus what’s called a “harness” — a control layer that connects different AI models, tools, and workflows through a single interface, similar to tools likeOpenClaworHermes. However, the difference is that such tools are often aimed at developers who know their way around a terminal. And sometimes, like in the case of OpenClaw, they may pose security issues and holes to worry about. Osaurus, meanwhile, presents an easy-to-use interface that consumers can use, and addresses security concerns by running things in a hardware-isolated, virtual sandbox. This limits the AI to a certain scope, keeping your computer and data safe. Of course, the practice of running AI models on your machine is still in its early days, given that it’s heavily resource-intensive and hardware-dependent. To run local models, your system will need at least 64 GB of RAM. For running larger models, like DeepSeek v4, Pae recommends systems with about 128 GB of RAM. But Pae believes local AI’s needs will come down in time. “I can see the potential of it, because the intelligence per wattage — which is like the metric for local AI — has been going up significantly. It’s on its own curve of innovation. Last year, local AI could barely finish sentences, but today it can actually run tools, write code, access your browser, and order stuff from Amazon […] it’s just getting better and better,” he said. Osaurus today can run MiniMax M2.5, Gemma 4, Qwen3.6, GPT-OSS, Llama, DeepSeek V4, and other models. It also supports Apple’s on-device foundation models, Liquid AI’s LFM family of on-device models, and in the cloud, it can connect to OpenAI, Anthropic, Gemini, xAI/Grok, Venice AI, OpenRouter, Ollama, and LM Studio. As a full MCP (Model Context Protocol) server, you can give any MCP-compatible client access to your tools as well. Plus, it ships with over 20 native plugins for Mail, Calendar, Vision, macOS Use, XLSX, PPTX, Browser, Music, Git, Filesystem, Search, Fetch, and more. More recently, Osaurus was updated to include voice capabilities as well. Since the project went live nearly a year ago, it has been downloaded north of 112,000 times, according to itswebsite. Currently, Osaurus’ founders (who include co-founder Sam Yoo) are participating in the New York-based startup accelerator Alliance. They’re also thinking about next steps, which could see Osaurus being offered to businesses, like those in the legal space or in healthcare, where running local LLMs could address privacy concerns. As the power of local AI models grows, the team believes it could lower the demand for AI data centers. “We’re seeing this explosive growth in the AI space where [cloud AI providers] have to scale up using data centers and infrastructure, but we feel like people haven’t really seen the value of the local AI yet,” Pae said. “Instead of relying on the cloud, they can actually deploy a Mac Studio on-prem, and it should use substantially less power. You still have the capabilities of the cloud, but you will not be dependent on a data center to be able to run that AI,” he added.
View