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The data analysis company Palantir delivered such a huge third-quarter earnings report on Monday that its press release even bragged that the numberswere “crushing consensus expectations.” Yet retail investors were lukewarm on the company on Tuesday, with the stock trading slightly down. That’s because Palantir’s enterprise sales, its big growth area, showed signs of slowing. First, the good news for the company’s investors. Revenue grew 96% from a year earlier, and 20% from the previous quarter, to $2.31 billion, beating analysts’ expectations of $2.18 billion. It was the first time Palantir has topped $2 billion in a quarter. Palantir also reported adjusted earnings per share (profit per share, excluding certain one-time items) of 44 cents, compared with analysts’ expectations of 41 cents. The company even raised its revenue guidance, or forecast, for the full year. Normally, that alone would send a stock soaring. The clincher was that Palantir’s U.S. commercial revenue surpassed U.S. government revenue for the first time ever. The company reported $984 million in commercial contracts (up 148% from the year-ago quarter), versus $953 million in U.S. government revenue. The reason for all this love from enterprises? They are signing up for the company’s AI software that can run frontier models and custom-train open-weight models securely, without sharing any of their data and business processes with outside AI labs. Plus, Palantir has long offered what is today known as “forward-deployed” software engineering, in which its own engineers work directly with customers to customize the software. However, Palantir also revealed a less-than-rosy signal: Even as enterprises are moving faster than ever, they are often signing short-term trials. “What Ryan and I are seeing on the front line and commercial is this much quicker interaction, much quicker to deployment, shorter duration of the contract but then an ability to expand that contract dramatically,” CEO Alex Karp (pictured above) told Wall Street analysts on the quarterly earnings call. Palantir is experiencing one of theunique phenomena of AI in the enterprise right now.Corporations are willing to quickly try AI software, skipping the usual bureaucracy, but they are also more willing to cancel contracts. Karp used one off-color metaphor after another to describe how unconcerned he was about holding onto his enterprise business. “We’ve gone from being the most attractive person in the physics department to being the captain of the football team, kind of thing,” he said, describing the influx of prospective corporate customers flowing in. “Conversely, you know, once you go Palantir, you don’t go back, and that’s just how it is, and you see it in our numbers.” The numbers don’t yet confirm Palantir’s long-term success. While the company closed a hefty $1.46 billion of total contract value from U.S. enterprises in the quarter, that’s actually a decline from the record $2.13 billion in the previous quarter. That suggests slowing sales, at least in the most recent quarter. The next earnings will show whether Palantir has solidified its role as a rising U.S. enterprise AI provider. Historically, many enterprises sign their biggest tech purchases in the final quarter of the year. Karp and his team certainly sound confident. They increased guidance for U.S. commercial revenue to be “in excess of $3.559 billion, representing a growth rate of at least 143%.”
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Ballistic Ventures, the cybersecurity-focused venture capital firm co-founded by former Kleiner Perkins partner Ted Schlein (pictured above, second from the right), is seeking to raise $490 million across two new funds, TechCrunch has learned through regulatory filings. The San Francisco-based VC firm filed with the U.S. Securities and Exchange Commission this week toraise $360 millionfor its third flagship fund,matching the size of its previous fundraised in 2024, andanother $130 millionfor a separate vehicle called Ballistic Ventures Fund B. Neither fund had recorded any sales at the time of filing. The fundraising plans come as the growing use of artificial intelligence has raised concerns about more sophisticated cyberattacks. The rise of AI agents has also created new opportunities for cybersecurity startups and the venture capital firms backing them. Founded in late 2021, Ballistic Ventures’ founding team includes Schlein, former AT&T Cybersecurity president Barmak Meftah, and Roger Thornton, who co-founded security companies Fortify Software and AlienVault. The firm has backed companies, including Talon Cyber Security,acquired by Palo Alto Networks, and Pangea,bought by CrowdStrike, alongside startups such asArmadin,GetReal, Nudge Security, and Oligo Security. Ballistic’s ties to storied venture capital firm Kleiner Perkins, where Schlein spent more than two decades, help set it apart from rivals, a cybersecurity investor at another VC firm told TechCrunch. Its investment strategy, however, is similar to those of other cybersecurity-focused investors, including Forgepoint Capital and Ten Eleven Ventures, the person said. In 2022, Ballisticannounced its $300 million debut fundand raised another $360 million for its second flagship fund two years later. It also raised about $130 million for a separate vehicle called Ballistic Ventures Fund A, per PitchBook data. The new Fund B is targeting a similar amount. U.S. cybersecurity startups raised $11.5 billion in 2025, the highest since 2022, a recentreportby JPMorgan Chase said, citing PitchBook data. AI-enabled companies accounted for about 72% of U.S. cybersecurity venture deals through May this year, compared with 36% in 2019, the report said. Separately, Microsoft, in its annual Digital Defense Report released last month,warnedthat hackers are increasingly using AI for activities ranging from phishing and social engineering to malware development. The technology is allowing attackers to operate faster and at greater scale, even as many of their underlying techniques remain unchanged. Ballistic Ventures declined to comment on its fundraising plans.
Stuck in years-long lines for grid connections and new turbines, some AI data centers are making their own electricity with converted jet engines,the New York Times reportedTuesday. The engines, pulled from retired aircraft and rebuilt to burn natural gas, aren’t anyone’s idea of a permanent power plant. But they’re another sign of how far power supply has fallen behind the AI buildout. For companies stuck in those lines, the main draw of converted jet engines is how quickly the engines can be up and running. “It’s all about getting power as fast as possible,” Rystad Energy analyst Audun Martinsen told the Times. With jet-powered units, “they can have a data center running in 24 months,” he said. Hooking up 100 megawatts to the grid in Northern Virginia, by comparison, takes aboutseven years, according to the International Energy Agency. Most power capacity is expected to come on by 2031, an executive at Mitsubishi Power, one of the world’s top makers of power generation equipment,told Bloomberg last week. In some cases, these delays are already costing some data center operators business. Last month, Microsoftpulled outas the anchor customer for Nscale’s data center in Loughton, England, after the local utility said it couldn’t deliver enough power until the early to mid-2030s. Elon Musk’s xAI is among the companies already using the engines, having installed eight converted jet-engine turbines in Mississippi to help power its Colossus 2 data center in Memphis, the Times reported, citing state permits and satellite imagery. The units come from ProEnergy, which rebuilds a GE engine commonly found on Boeing jets. In 2025, OpenAIfollowed Musk’s leadwith gas turbines at its first Stargate site in Abilene, Texas. Crusoe, a Denver-based startup that builds AI data centers, is constructing that Abilene campus andordered 13of ProEnergy’s converted units in April. In September, Crusoewalked awayfrom a $1.25 billion deal to buy 29 natural gas turbines from Boom Supersonic, a startup developing a supersonic passenger jet that has also begun selling a version of that jet’s engine as a power plant for data centers. A Crusoe spokesperson told TechCrunch at the time that the company still plans to use turbines. Currently the Trump administrationis adding to the pressureby encouraging data centers to generate their own electricity. Converted jet engines come with trade-offs, though: They’re less efficient and pollute more than turbines designed for conventional gas plants, and it takes more of them to make the same amount of power, according to the Times. That may ultimately make converted jet engines a short-term play with some experts saying that efficiency and environmental concerns may grow in importance in a year or two.
Anthropic is working on an open source system to verify which AI model produced a given output, according to a blog post published Tuesday. The system,called FlashProve, is built to provide what researchers call “provable inference” — a cryptographic proof, essentially a mathematical guarantee, that a given output came from a specific model. It’s meant to let users confirm that the answer they received really came from the model they think they’re using. Similar tocryptographic email signaturesthat let recipients confirm a message really came from its claimed sender, FlashProve would be robust against impostors attempting to disguise one model as another, even when the impostor is the lab that built and runs the model. In particular, the researchers note that the system could be used to prevent an AI company from secretly swapping in a cheaper model to cut costs, or a more expensive model to inflate its performance results. FlashProve could also be used to verify claims by third parties that a specific task was done by an advanced model. “A developer might claim that their software library was scanned for vulnerabilities by a frontier cybersecurity model, or a company might claim that its business plan was given the go-ahead by an appropriate model,” the researchers write. “How can a user verify these claims?” To work, the system requires AI labs to publish a “fingerprint” of each model they release that can later be matched to specific outputs and verified cryptographically. Crucially, the fingerprints don’t reveal a model’s weights, the internal numbers that define how it behaves and which closed-model makers like Anthropic and OpenAI keep private. That means labs could publish the fingerprints without giving up their most valuable asset, enabling the system to work with commercial models, too. The system is also designed to be lightweight, so verification could be performed on a local device. As part of the announcement, researchers releaseda detailed technical specificationand working code for various elements of the system, which is publicly availableunder an open source license on GitHub. Research in the system is ongoing, and there are no plans to deploy it at Anthropic or any other lab. “This research post is meant to share intermediate progress, and not a completed research agenda,” researchers explain in the announcement post. “We are sharing this explicitly with the goal that others look at what we have done, take it, and improve it.”
The platform has not only added 4 million CPU cores and 160 PB of internal storage, but it is also rebuilding core Git primitives by decoupling compute from storage.
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The platform provides a single API to access open AI models, including NVIDIA Nemotron, Sarvam, DeepSeek and Qwen, through NVIDIA GPU infrastructure hosted in India.
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Enmovil's expansion addresses the need for AI across complex supply chains, where solutions must fit unique customer workflows and operational limits.
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"Self-hosting GLM brings our blended cost of tokens to 30 cents per million. Our invoice is now one-tenth of what we were spending on Anthropic and OpenAI,” said David Abitbol, who leads AI operation
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Coforge’s 53.6% Q2 revenue jump includes Encora. The company is moving from selling engineering hours to selling outcomes.
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The models support multiple Indian languages and target applications ranging from agentic AI and banking to secure, offline deployment across critical sectors.
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The company will offer affected employees at least six months’ salary and consolidate more product teams in Sydney as part of an organisational restructuring.
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The model scored 71.8 across three benchmarks, recording a 44 ms response time on an NVIDIA B200 GPU.
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