Latest AI News
Planned Amazon data center could become the biggest climate polluter in the U.S.
As part of a planned data center in Pecos County, Texas, Amazon is investing in an on-site power plant that could become the largest source of climate pollution in the United States,according to The New York Times. The NYT says the plant would burn natural gas and is permitted to release 33 million tons of carbon dioxide per year — more than any other power plant in the U.S. In a statement, an Amazon spokesperson confirmed that the data center will “be powered by new on-site generation that won’t raise electricity costs for Texas families.” (Data centersface growing political oppositionfor a number of reasons, including their effect on electricity costs.) AI has already had a significant impact on Amazon’s carbon emissions, which itreported were up 16% last year— the wrong direction for a company that pledged to eliminate its carbon emissions by 2040. And that could get worse as Amazon and tech companiesback the development of huge natural gas plantsto support their power-hungry data centers. The Amazon spokesperson said, “The world looks different now than when we co-founded the climate pledge,” while also claiming, “Our commitment hasn’t changed.”
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OpenAI acquires presentation startup NextSlide
NextSlide recently announced that it’s joining OpenAI, with the presentation startup’s team members now working on ChatGPT. TheNextSlide websitecurrently displays a note from founder Ahmed Beshry describing the startup’s product as one “that could turn prompts, notes, documents, or research into a polished, editable presentation.” The ultimate goal, Beshry said, was “to make visual communication more accessible and help more people express their ideas clearly.” So by joining OpenAI, the team will “continue pursuing that same mission: building AI products that help people create, communicate, and turn their ideas into meaningful work.” The financial terms of the deal were not disclosed. Ina note on LinkedIn, Beshry said the announcement is coming “a few months late,” as the acquisition took place “earlier this year.” Beshry was previously a co-founder at Caper AI, a smart cart/cashier-less checkout startupacquired by Instacartin 2021.
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The Man Who Declared War on Queues
Digi Yatra CEO Suresh Khadakbhavi isn’t really obsessed with airports. His real obsession has always been removing friction, wherever it exists.
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Why India's Most Advanced Weather Models Still Send Alerts Late
Despite better weather models, growing AI capabilities, and national alert systems, Kerala's recent flash floods reveal that India's biggest challenge may not be predicting disasters.
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After Rippling blew millions on AI in months, it built an employee ROI tool
HR software provider Rippling this weekunveiled AI Spend Console, an anti-tokenmaxxing product that helps a company track and contain its AI spending. One of the most interesting features is that it maps how much individual employees, teams, and roles are spending and if they are genuinely more productive, or generally producing more AI slop. The company promises the tool will show “which engineers have high AI spend whose peers frequently ask them to redo work in code reviews,” the company says in itsblog post. The tool was born after Rippling went all in on tokenmaxxing at the start of the year — as so many did — only to discover employees were wildly burning cash. Chief Product Officer Matt MacInnis still recalls the executive team meeting in March when CFO Adam Swiecicki presented a number that shocked them. Rippling was on track to burn 40% of its R&D headcount budget on AI tokens, meaning it was spending as much on tokens as 40% of all the compensation it paid employees in that unit. Millions of dollars. (The R&D org is home to engineering at most tech companies.) Spending was growing by 80% month-over-month, and if that trend continued, the next year it would spend almost as much on AI tokens — 90% — as it spent on its high-paid R&D unit employees. “We were incredulous,” MacInnis told TechCrunch. Management immediately undertook an “urgent” project to understand the spending and what they were getting for that money, he said. In fact,the launch adfor this new product features Swiecicki sitting on a stool while employees are picking up wads of cash and dumping them into a paper shredder. When Rippling conducted an analysis, it discovered facts like “roughly 10–15% of our employees were driving about 60% of total AI spend. One engineer was spending $50,000 a month,” its blog post shared. Rippling didn’t want to stop AI usage, just rein it in — a lot. It started by negotiating a max spending cap with each of the tools its company used: Cursor, OpenAI, and Anthropic. It immediately found an obvious issue: Employees defaulted to using the most recent, and most expensive, frontier models for all tasks. “The truth is that the inference providers, like Anthropic and OpenAI, have absolutely no incentives to help you control your spend. They have every incentive for it to be a runaway expense, and that’s exactly what they do. They don’t provide you with great usage insight, and they don’t collaborate with one another,” MacInnis said. That was a common early-2026 problem. Now, eight months into the year, enterprises have figured out a couple of things. First, they know they need multiple models from multiple AI labs at various price points, including a frontier open weight option, perhaps of Chinese origin. Rippling founder andCEO Parker Conrad notedlast month that when his company conducted its own benchmarks for its own internal uses, it discovered SpaceX’s Grok was the all-around leader but that “GLM 5.2 is 85% cheaper but [had] nearly identical performance” to the frontier models. (SpaceX now owns Cursor, which offers access to Grok and dozens of other models.) Z.ai’s GLM 5.2 has become a particular favorite Chinese model for coding tasks among tech companies these days.Databricks has also been championing it. Second, enterprises now know they need an AI gateway that routes prompts to the best, most cost-effective model for the task. Rippling came to that conclusion too. So it built its own AI gateway that is also part of this product. MacInnis says it is possible for enterprises that already use another gateway to still use the AI Spend Console product, though if they want the features that govern spending, they would need to use Rippling’s gateway. AI Spend Console produces dashboards (once known as leaderboards in the tokenmaxxing days) that score attributes such as prompts per day combined with work output (lines of code/pull requests) and spend. With this tool in place, Rippling said it dropped its token spend from 40% of its headcount budget to about 15%. But it didn’t curtail AI usage. The company spent a peak of 605 billion tokens the month the CFO issued his warning, MacInnis shared. In July, internal usage hit 600 billion tokens again, yet “the cost of July’s token spend was 37% of the cost of April’s token spend,” he said. “That’s just because now we’re routing to the more effective models,” he said, joking that “we’re not letting the sales team do grammar updates using Fable.” But technology solutions aren’t enough, Rippling notes. The company found people using AI effectively and made them “AI captains” tasked with assisting the rest of the company. Still, such efforts to use AI beyond engineering are a work in progress, MacInnis says, as software engineers have been the primary users so far. But Rippling is, for example, working on it for customer onboarding teams to automate some mailing data and data-reconciliation tasks. The dashboard will then measure productivity in terms of onboarding more customers. “We have to be able to link token consumption in G&A functions and in customer-facing functions back to productivity. If we can’t do that, all bets are off on any of this stuff being available to the broader employee base,” MacInnis says. So, if Rippling is an example, tokenmaxxing may have swung so far the other direction that employee AI access may no longer be like Slack or email. If the company can’t measure productivity, then all employees might not have access. As for the product, AI Spend Console is included for Rippling’s HR subscribers, though there are additional AI usage-based costs. It can also be purchased as a stand-alone product and integrated with another HR system of record, MacInnis says.
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OpenAI says it slowed Astra model development over security concerns
OpenAI said Friday it has suspended work on some aspects of its upcoming model Astra after an internal review found it had made significant advancements in agentic coding and cybersecurity — enough to warrant concern over its capabilities. OpenAI said in ablog postFriday that this model, which is still in development, reached its “critical cybersecurity threshold,” meaning it could independently identify and carry out cyberattacks against traditionally well-protected real-world systems. Under the company’s “Preparedness Framework,” which it created in 2023, this triggered additional safeguards. “While we continue to benchmark and assess this model, our preliminary evaluations indicate strong enough performance that we cannot rule out Critical capability level at this time,” OpenAI wrote. “Astra is an upcoming model, and was not involved in exploiting Hugging Face.” The disclosure highlights an unusual moment in the topsy-turvy, and still nascent frontier AI labs sector. Companies across every industry hold back products over potential risks, including for safety and cybersecurity concerns. But they rarely announce those decisions publicly when it’s a product that is still under development. In this case, OpenAI is already under scrutiny after a different unreleased modelbreached Hugging Face’s systemsduring internal testing — the first verifiable incident of an AI lab losing control of its model. Since then, OpenAI and AI labs such as Anthropic havedisclosed other incidentsin which AI models breached their sandboxes and posed threats during cybersecurity tests. The string of cases —seems like a new disclosure every day now— has triggered varying reactions from cybersecurity experts, lawmakers and the AI labs themselves. Some express fear and call for stricter oversight. But there’s also a bit flexing. In certain circles, any AI lab with a model that has that kind of capability will be seen as an impressive advancement. OpenAI said it was sharing this information because it believes “it’s important to be transparent with the public and the safety and security communities about this potential shift in capabilities.” The AI lab said it’s also taking action, including enacting stricter security controls and pausing internal activites involving Astra that don’t meet these beefed guardrails. OpenAI said it is working with relevant government agencies and “select AI safety organizations” to test the capabilities for this model.
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Jill Lepore on the ‘Artificial State’ and why Silicon Valley’s leaders are bad sci-fi readers
Historian Jill Lepore has a theory about why tech companies often use soaring language to describe their products — almost as if they’re forming a new government. And whether you’re thinking of Twitter’s old “town hall in your pocket” orAnthropic’s Claude constitution, it’s a theory that doesn’t paint Silicon Valley in a very flattering light. In Lepore’s upcoming book,The Rise and Fall of the Artificial State, the Pulitzer Prize-winning Harvard historian and New Yorker writer argues that tech companies are gradually taking on the functions of democratic government — whether that’s Twitter giving a distorted picture of the electorate or Facebook displacing local news. In her view, these leaders often mistake technological advancement for political progress, all whileoutright declaring their intention to replace the nation-state. On this episode of TechCrunch’sEquitypodcast, Anthony Ha talks with Lepore about her book and why she thinks today’s AI leaders — who promise a future where AI makes government more efficient, more responsive, and maybe even unnecessary — are selling an old fantasy in new packaging. Listen to the full episode to hear more about: Subscribe to Equity onYouTube,Apple Podcasts,Overcast,Spotifyand all the casts. You also can follow Equity onXandThreads, at @EquityPod.
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Cloudflare launches Kitesurf, a browser built for AI agents
Cloudflare is the latest company to join the race to build a new web browser. But instead of pitching a Chrome alternative to consumers, the internet infrastructure provider launchedKitesurf, a cloud-hosted browser designed specifically for AI agents. AI software is evolving from chatbots that answer questions to agents that can complete tasks on users’ behalf. Browsers are a critical part of this transition, as they’ll need to navigate the web and use websites, as humans do. Unlike traditional web browsers built for humans, a browser built for AI agents doesn’t care about visual elements, like themes, tabs, or browser extensions, Cloudflare explained in itsannouncement. A browser designed for AI agents needs to manage context windows, performance, token costs, and scalability. It also faces a different threat model because an AI browser could be subject to vulnerabilities like prompt injection attacks and more, the company noted. With Kitesurf, AI developers will be able to build software that can navigate websites, fill out forms, and complete other browser-based tasks, without having to build their own browser software. Cloudflare says it decided to build Kitesurf just 12 weeks ago, and it runs entirely on top of the company’s serverless platform, called Workers. Kitesurf is available for free while in beta inBrowser Run, which lets developers programmatically control and interact with headless browser instances on Cloudflare’s network. For developers, Cloudflare’s pitch is that this enables AI agents to use the web more efficiently while using less computing power than Chromium, which keeps costs down. “Kitesurf is significantly more efficient in CPU and memory consumption than Chromium for common agentic tasks like screenshots and HTML extraction,” according to the company. The browser itself was built from other technologies, including a modular rendering engine fromBlitz; Firefox’s CSS parser,Stylo; andBoa JS, a Rust ECMAScript engine. Everything else runs inside Cloudflare Workers. Although still new, Cloudflare says Kitesurf already passes around 215,000+web platform tests, and it’s adding hundreds more, passing tests every week. Cloudflare also credited the open source Rust headless engine, Obscura, for inspiring it to develop Kitesurf, noting that the first proof of concept was a port of Obscura to Workers. The company said the browser correctly renders pages likeTodoMVC, a popular benchmark application for comparing JavaScript frameworks, along with Wikipedia, Hacker News, the Cloudflare Blog, and much of the Cloudflare dashboard.
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Airbnb says AI is helping it ship features faster as it tests a new search function
Airbnb might be taking slow steps to roll out AI features to its consumer-facing interface, but the company is rapidly adopting the tech to build product. Earlier this year, the company saidAI is writing 60% of its code. In its latest earnings call, co-founder and CEO Brian Chesky said AI is helping Airbnb create features a at rapid rate. Chesky said that because of AI, the company has reduced time from conceptualization to finally shopping features by 60%. “Today, we’re building, testing, and iterating faster than we could just a year ago. Across some of our key initiatives, we’ve reduced the time from concept to launch by as much as 60%. And compared to the same six months last year, we’ve increased the number of features and improvements we shipped this year by nearly 80%,” he said during the company’s second-quarter earnings call. He pointed out that AI has helped the company in areas like search, sign-up, checkout, and payments. Airbnb has also released features designed to help hosts with thingslike quicker onboarding flow. Airbnb’s adoption of consumer-facing AI features has been slower, and mostly isolated to features like review summaries and listing highlights. Until now, Chesky has maintained that just adopting achatbot-like interfacewon’t work for travel use cases. Instead, the company has focused on developing an AI for search, discovery, and support. Chesky said during the earnings call that the company will finally start testing AI search. Even with the new test, Airbnb doesn’t want to impose AI search on customers who are used to the current search and filter feature on the app. To accommodate that, Airbnb is adding a toggle that lets users switch to AI search, where they can type in natural language to get results, which would be in a visual format. “The titles [in the answer] could actually be AI-generated and they can be conversational as if you’re reading a chatbot, but more visual. Then you get to the product description page and the highlights are AI generated in real-time and personalized to you,” Chesky said. On the back end, customer support is one area where Airbnb has heavily deployed AI. The company launched its AI-powered botin North Americain 2025, and this year, it has expanded it to more than 50 languages with plans to make it available for voice calls later this year. The company said that nearly 45% of the customer issues that start with its AI agent are completed without any human intervention. Because of this, the company’s support cost per booking is down 16% year-over-year. Airbnb posted positive results for the quarter ended in June with revenue up 17% year-over-year to $3.6 billion and adjusted EBITDA jumping 21% to $1.3 billion.
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Solinas Integrity Raises $5.5 Million to Scale AI-Powered Robotics for Water & Sanitation Infrastructure
The fund will be used to scale its AI-powered robotics platform for underground water and sanitation infrastructure and expand into global markets.
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KühlTherm Launches Made-in-India Liquid Cooling Systems for AI Data Centres
The startup targets 2GW of manufacturing capacity by 2027.
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OpenAI’s Advice to Bengaluru AI Founders on Building Startup Moats
During a visit to Bengaluru, Marc Manara, who leads OpenAI’s Startups, VC and Private Equity teams, shared what founders need to build AI startups that can survive increasingly capable foundation models.
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