Latest AI News
AI4Bharat Builds 4,000-Test Benchmark, Finds AI Judges Fail Half the Time
AI4Bharat has created FOCUS, a benchmark designed to measure how well evaluator VLMs detect mistakes across both image-to-text (I2T) and text-to-image (T2I) tasks.
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GitHub Tightens Bug Bounty Rules as AI Changes Security Research
GitHub is introducing a VIP bug bounty tier and limiting new researchers' submissions as it seeks to curb low-quality, AI-generated vulnerability reports.
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ServiceNow Invests $40 Mn in BUSINESSNEXT to Accelerate Autonomous Banking
The companies said the investment will be used to expand autonomous banking capabilities and private AI deployments for banks and financial institutions.
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India Has ‘No Chance’ of Building a Mythos Model From Scratch
Experts say India should focus on adapting open foundation models first, even as the government explores sovereign AI for cybersecurity.
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After shocking quarter, IBM insists that AI isn’t killing the mainframe
On Wednesday, IBM officially reported earnings and the news was as bad as everyone knew it would be. While the 115-year-old company still generates boatloads of cash — $17.2 billion in revenue, $9.9 billion in gross profit, nearly 58% margins, and $2.2 billion in net earnings for the quarter — its results fell well short of Wall Street’s expectations. It was such a bad miss that IBM CEO Arvind Krishna and the board took an unprecedented step of warning investors ahead of time that the earnings “was worse than our expectations,” offering everyone a sneak peek. He publisheda “letter to investors,”last week sharing preliminary results. It warned of abysmal revenue in the company’s all-important “infrastructure” category and said that profit margins were also going to take a hit. The company’s stock instantly tanked 25%,it’s biggest single-day decline ever. Until then, the stock had performed well under Krishna’s six years of leadership, buoyed by the AI data center boom that had been lifting all boats. On Wednesday, IBM also lowered its full-year growth forecasts, meaning this horrible quarter would impact the rest of the year. The culprit? IBM’s cash-cow mainframe business was down 42%. That’s a cascading problem, because as CFO Jim Kavanaugh explained on the quarterly call with investors, IBM earns $3 in software revenue for every $1 of mainframe hardware it sells. However, the CEO and CFO spent the call insisting that this was a temporary blip and all would be well soon. What happened, they said, was that “tens” of customers that were due to buy a new mainframe during the quarter opted not to do so. That may not sound like a lot of customers, but mainframes are systems that cost hundreds of thousands to millions of dollars, and with maintenance contracts and software, generate many millions more. The same AI boom that lifted IBM’s boat also sank it. Instead of buying a new mainframe, these clients bought other hardware, Krishna explained. They were faced with astronomically high cost increases of 15% to 30% for data center gear and PCs. “When they were faced with that issue, then they decided to move budget to those areas where they were having that extreme price,” Krishna said. Enterprise hardware makers like Dell and HP have warned that rising costs on components like memory, caused by the AI build-out boom,have forced them to raise prices.Apple has said the same. But Krishna promised that those customers will still buy their new mainframes eventually — along with their new software contracts. In fact, he said some of them have already done so this quarter. “We see no evidence of clients moving off the mainframe,” he said. We’ll have to wait and see. But the tech industry has predicted the death of the mainframe for many decades now. Maybe even AI won’t kill it.
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Treasury threatens sanctions after White House claims Moonshot distilled Anthropic’s Fable
U.S. Treasury secretary Scott Bessent doubled down on hiswarnings to Chinese AI companieson Wednesday, saying that sanctions remain on the table after a White House official accused Moonshot of improperly distilling Anthropic’s Fable model. Model distillationis a common AI training technique in which a smaller model learns from the outputs of a larger one. While this process can infringe on intellectual property rights, it’s also widely used as a legitimate optimization method. “Open source is not open season on American IP,”Bessent posted on X. “When [Chinese] firms conduct covert, industrial-scale distillation attacks that cross the line into IP theft, sanctions and Entity List designations will be on the table.” Earlier this week, Bessent stated that the U.S. government would examine open source models from China for signs of intellectual property theft and impose sanctions if found. Bessent’s latest remarks come hours after the White House’s science and technology policy chief Michael Kratsiosaccusedthe China-based Moonshot of conducting large-scale distillation against U.S. models. He alleged that Moonshot had acquired Nvidia’s “GB300-equipped servers and has accessed GB300s in Thailand, likely to train its AI models,” raising questions about whether the firm violated U.S. export-control rules. The GB300 servers are part of Nvidia’s Blackwell generation, which are banned from being sold to Chinese companies. Some experts dispute the idea that Kimi K3 could have been developed primarily through distillation from Fable, which has only been publicly available since July 1. Moonshot released K3 last week as an open-weight model, and its advanced capabilities have called into question the underlying business models of leading U.S. AI labs, casting doubt on whether they can continue to justify the enormous capital requirements underpinning the frontier AI race. The episode has also intensified a broader debate in Washington over the influx of Chinese open models. Some, including former White House AI adviser and current OpenAI Head of Strategic Futures, Dean Ball, have argued that the U.S. should restrict or effectively ban the use of Chinese open-weight models to preserve America’s technological advantage and mitigate potential national security risks. TechCrunch has reached out to Moonshot and the Treasury for comment.
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Google justifies its massive AI spending with a booming cloud business
Alphabet investors havevery publicly worriedthat the company’smassive AI spendingisn’t worth the money. With the company’s latest earnings report, those investors should be able to relax a little. The takeaway: Google’s cloud business — driven largely by enterprise AI adoption — is booming. The search giant saw Google Cloud revenue spike 82% from where it was this time last year, climbing to $24.8 billion. That’s well above last quarter’s generous year-over-year growth, which showed a revenue jump of 63% to $20 billion — and it handily beats what Wall Street analysts expected for this quarter’s growth (theexpectation was $22.46 billion). Those cloud gains were driven largely by enterprise AI solutions and enterprise AI infrastructure adoption, the company said, while also noting that its backlog of cloud contracting work — that is, work that it hasn’t yet converted into revenue — had climbed to $514 billion. The company’s profit hit $112.1 billion, which is a massive jump from this time last year, when the company reported $28.1 billion in profit, the company’s earnings report shows. Meanwhile, Alphabet’s overall revenue grew 24% year-over-year during the past quarter to $119.8 billion. The company also saw Google Services revenue jump 15% to $94.5 billion. “Our AI investments are redefining what’s possible across every part of our business,” said Google CEO Sundar Pichai during Wednesday’s earnings call. “We have exciting momentum across the board.” More people are also adopting Gemini, Google’s AI chatbot, as the app currently enjoys 950 million monthly active users, the company said. In Q4 of 2025, Googlereported thatthe app had 750 million users. It’s worth noting that spiking revenue isn’t unusual for Google. This marks the company’s 12th consecutive quarter of double-digit revenue growth. But even by that standard, this quarter represents a particularly bountiful period for the tech giant. Alphabet’s spending is still hefty, with its capital expenditures — the money it spends building data centers, buying chips, and expanding infrastructure — estimated to be between $180 billion and $190 billion for the year — a fact not lost on analysts during Wednesday’s earnings call. Several pressed Pichai on when, and how much, those investments will pay off. “I think our compute capacity investments in ’27,” he said. “We are seeing strong demand indicators, including long-term deals,” he continued. “I think, if anything, the dynamics look healthier than where we were about a year ago, so that’s what gives us the confidence to undertake those investments,” he said.
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AMD and Kimi Deliver 3.2× Lower Latency for Coding
AMD and Moonshot AI rebuilt the inference software behind Kimi's coding model for AMD GPUs, reporting up to 3.2× lower tail latency and 7.7% higher token throughput on agentic workloads.
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Uber Co-Founder’s Startup Raises $1.7 Bn Led by a16z
The funding consolidates Travis Kalanick’s businesses under a single equity structure as Atoms expands its industrial AI strategy across manufacturing, mining, transport and food.
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Menlo Ventures’ Matt Murphy explains what AI startups founders must do differently
Anthropic leaped toa $47 billion revenue run rate by May,compared to $9 billion in 2025. It’s the kind of growth that Menlo Ventures’Matt Murphysays he’s never seen in 25 years of investing, not in the internet wave, not in mobile, not in the first cloud boom. Menlo led Anthropic’s $500M Series D, and Murphy has had a front-row seat as the company went from a pre-revenue, pre-launch bet to one of the most valuable startups out there. On this episode of TechCrunch’sEquitypodcast, Julie Bort talks with Murphy about backing Anthropic before anyone else would, why a great model was never the point, and what’s driving the fastest-growing startups he’s ever seen. 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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OpenAI’s AI spending spree has ballooned to $750B
OpenAI announced Wednesday that it will spend $750 billion on infrastructure through 2030, some 25% more than it estimated earlier this year, The Wall Street Journalreported. The renewed blitz comes as its Stargate data center projectappears to have stalled. The first salvo in OpenAI’s spending spree will be a $20 billion data center campus in Georgia known as Project Camellia. The development will span 1,400 acres northwest of Savannah and will draw at least 3.2 gigawatts of power from Georgia Power, the region’s utility. The generating capacity is expected to become available between 2028 and 2032. The AI company said it would “pay the full cost of the infrastructure and electric-service costs” for the new data center. The Georgia Public Service Commission (PSC)adopted a rulelast year to prevent utilities from passing on costs associated with new users drawing more than 100 megawatts. Georgia Power also said OpenAI will reduce its power draw by up to 1 gigawatt during periods of high demand on the grid. OpenAI is receiving a 50% property tax abatement for 15 years from Effingham County,accordingto the Effingham Herald. Neither OpenAI nor Georgia Power has said how Project Camellia will be powered. TechCrunch asked both companies for specifics but did not immediately receive a reply. Regulatory filings might provide some clues. In December, Georgia Power receivedapprovalfrom the PSC to produce an additional 9,885 megawatts. The utilitytold the PSCit expects to have all the capacity contracted by the end of 2026. The OpenAI deal accounts for about a third of that. Based ondocumentsGeorgia Power filed with the PSC, most of the new capacity will come from natural gas. The utility said it will build or buy from third parties about 5.8 gigawatts of natural gas generating capacity, about a quarter of which will be from more polluting simple-cycle turbines. Altogether, the new fossil fuel capacity will more than doubleGeorgia Power’s natural gas fleet. The remainder will be supplied by grid-scale batteries and solar. While electricity from Georgia Power is expected to start flowing in 2028, OpenAI did not give a timeline for when the first GPU will be turned on. That could happen sooner than 2028 given that OpenAI recently hired Brett Mayo to lead data center construction. Mayo previously worked at xAI, where he oversaw the Colossus data center in Memphis. Colossus was built in record time, but it has allegedly taken its toll on local air quality, according to alawsuitfiled by the NAACP and the Southern Environmental Law Center. The xAI data center has been runningdozens of unpermitted natural gas turbines, claiming exemption from federal clean air regulations.
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Substack’s new tool tells you who’s been writing their newsletters with AI
Substack has launched a new feature that can show you which of your favorite newsletters are being written using AI. This week, the newsletter and writing platformannouncedan integration with the AI writing detection softwarePangramthat will allow users to scan posts, comments, and replies on Substack’s app to see an estimate of how much of the content was written by a human and how much was AI. In the short term, the move might be bad for Substack’s business, as it could expose many of the newsletters on its platform that aren’t entirely written by people. That could potentially erode trust in the platform’s ecosystem of independent news and blogs, or even damage its reputation as a host of high-quality content. But in the long term, AI-detection features could help keep Substack free of “AI slop” and encourage more users to trust what they’re reading was written by a person, or at least better understand when it’s not. Substack joins several platforms that are leaning towardlabeling AI contentas such, especially now that AI is playing a greater role in the creation process. Photos and videos generated with AI arelabeledon social media sites, whilemusic streaming serviceshave more recently begun labeling and, in some cases,penalizingAI-generated music. “This is good use of AI,” Substack CEO Chris Best said. “When I used to pitch Substack to writers, one way I would do it is … we’ll do everything for you except the hard part,” he explained in anonline chatwith Pangram’s founder, Max Spero. “You have to have something — an idea that’s worth reading, that’s worth caring about, that’s worth sharing. That one thing is very hard and very valuable … [S]oftware should do everything else, but I think you do want the person to do the hard part.” The feature will be available in Substack’s app for any post, note, reply, or comment above 100 characters. Substack will also allow its writers to include an optional AI author’s note, using which creators can properly disclose their use of AI, the company told TechCrunch. The company clarified that the tool is not meant to prohibit or penalize AI-assisted writing, but rather to encourage writers to add a “how I make this” statement, where they explain their process. Publishers can also run Pangram on their own drafts before publication, and report and remove scans on their own work they believe are mistakes.
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