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AI NewsMicrosoft hits 10 quarters of record-breaking revenue in a row

Microsoft hits 10 quarters of record-breaking revenue in a row

3:58 AM IST · October 29, 2026

Whatever the future of AI might be, the current situation is still very good for Microsoft. The tech giant reported yet another quarterly revenue record on Wednesday:$92.3 billion, a 19% increase over the year-ago quarter. Microsoft has now reported record-breaking revenuefor 10 quarters in a row. This is profitable revenue, too. Microsoft reported that net income reached $36.6 billion, an increase of 32% on a GAAP basis, over the year-ago quarter. The record-breaking income was largely driven on the strength of Microsoft Cloud, which reached $63.9 billion in revenue this quarter, an increase of 30% over the year-ago quarter, Microsoft said. Microsoft has reorganized its business reporting into two basic segments: 1) Agents and Infra and 2) Devices and Consumer. On the flip side, revenue in Devices and Consumer was $15.5 billion, decreased of 3%, with Windows OEM and devices revenue falling 22% from the year-ago quarter. True, not all of Microsoft’s healthy cloud revenue can be attributed to AI. Microsoft 365 accounted for nearly $28 billion. But Azure brought in the most, at $33.2 billion. And again, not all of that is necessarily AI-related. Enterprises run all sorts of the traditional software and SaaS applications on Azure. Still, a good chunk of the revenue was from AI, because Microsoft alsoreported $10 billion in revenue from OpenAI. Despite branching out to AWS, Oracle, and others, OpenAI still runs a lot of its services on Microsoft’s cloud. (Anthropic runs on Azure as well.) Microsoft also said its “commercial remaining performance obligation increased 73% to $677 billion.” That represents future revenue committed under existing contracts of the AI labs. But, like all the big cloud providers, Microsoft is also spending big to prepare to deliver on those commitments. Capital expenditures topped $45.2 billion, another all-time quarterly record.

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The ‘Vulnpocalypse’ is coming for the internet’s aging code, says Wiz’s CTO

The PHP scare may have faded from your feed, but the cleanup is far from over. Last week, two critical bugs in the open source language behind roughly 70% of websitesleft organizations scrambling to fix their systems, with some taking sites offline until the vulnerabilities could be patched. According to researchers who found the bugs, this is only the beginning, as AI changes how quickly novel vulnerabilities can be uncovered and exploited. On this episode of TechCrunch’sEquitypodcast, Rebecca Bellan talks withTomer Schwartz, CTO of Exposure Management & AppSec at Google-owned Wiz, who was part of the scramble — alongside OpenAI, research group Calif, and the PHP team — to fix the bugs. On the show, Schwartz and Bellan get into what the race looked like from the inside, how early-stage startups can build with security in mind, and why patching by hand won’t scale. Listen to the full episode to hear more about: Subscribe to Equity onYouTube,Apple Podcasts,Overcast,Spotify, and all the casts. You can also follow Equity onXandThreads, at @EquityPod.

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Wiz’s CTO on PHP2Shell and the coming ‘vulnpocalypse’

Loading the player… The PHP scare may have faded from your feed, but the cleanup is far from over. Last week, two critical bugs in the open source language behind roughly 70% of websitesleft organizations scrambling to fix their systems, with some taking sites offline until the vulnerabilities could be patched. According to researchers who found the bugs, this is only the beginning, as AI changes how quickly novel vulnerabilities can be uncovered and exploited. On this episode of TechCrunch’sEquitypodcast, Rebecca Bellan talks withTomer Schwartz, CTO of Exposure Management & AppSec at Google-owned Wiz, who was part of the scramble — alongside OpenAI, research group Calif, and the PHP team — to fix the bugs. On the show, Schwartz and Bellan get into what the race looked like from the inside, how early-stage startups can build with security in mind, and why patching by hand won’t scale. Subscribe to Equity onYouTube,Apple Podcasts,Overcast,Spotify, and all the casts. You can also follow Equity onXandThreads, at @EquityPod.

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Anthropic just launched tools to show companies what their AI spending is buying

The financials Anthropic shared with investors this week suggest that its AI coding tool, Claude Code, is every bit the business success the frontier lab wanted. The company recorded an operating profit in the second quarter, and 900% net revenue retention — that is, spending from the same customers in Q3 2025 grew nine times in Q3 2026. Anthropic has focused on gettingenterprise users to stick aroundand Wednesdayannounceda new suite of reporting tools designed to give those customers a more granular understanding of the value they get from their token spend. Using internal analytics and employee surveys, managers can view a team’s work over a given date range, what the team produced, how much it cost, and where its workflows broke down. Anthropic says it won’t use this data for model training by default. The reports will also offer recommendations on how to improve the results. This is a clear attempt to demonstrate the return on investment of Anthropic’s tools and to show customers how to improve it — a major concern among large customers. Anthropic is also creating pooled group budgets to allow teams to better manage token spending. Businesses have been hunting for that kind of information as they figure out how best to deploy AI tools. While usage among venture-backed firms is driving token spend, most of the Fortune 500 are not seeing major gains from deploying AI. Jellyfish, a company that makes tools for software developers, released anew report yesterdaybased on the use of agentic coding tools by 276,000 of its customers. Claude Code and other tools built on Anthropic models remain the most widely used in the survey, responsible for nearly a third of all proposed code changes submitted for review. More importantly, spending on those tools has grown 10x since the beginning of the year, with their cost now equal to about 2% of total engineering cost. “Even if you’re saying 2% is still small, that 10x growth is growth that most CFOs didn’t plan for,” Nicholas Arcolano, the chief scientist at Jellyfish, told TechCrunch. We’re certainly getting to the point where people are realizing, okay, it’s not enough to just spend a bunch. You now have to optimize it, make it work.” Anthropic’s new reports speak to that challenge. Anthropic leads among enterprise customers, but what makes an agentic coding tool truly sticky is difficult to identify. The release of OpenAI’s Astra model this summer seemed like a potential threat to Anthropic, but so far it hasn’t slowed Anthropic’s adoption numbers. “It took a long time for us to see Codex really pop in our data at all, and it has come up with Astra [OpenAI’s latest model],” Nikolas Albarran, the AI product lead at Jellyfish, told TechCrunch. Looking at X, he continued, you’d get the distinct impression that the competition is more heated. “Everyone in the Valley is like, ‘Oh, Codex is better.’” In reality, Albarran continued, “Anthropic was just there with great enterprise motions, and they had an incumbent weight of their own — this stuff just worked and worked well, and a lot of people just put a lot of effort into standardizing workflows around it. And there’s not a big enough reason to switch.” It’s not just a question of models and workflows, either. The realities of business mean that once a frontier lab has gotten its hooks into a customer, switching is harder than it is compared to an individual programmer or small organization. Albarran points to the investment all the major AI labs are making in forward-deployed engineering (FDE) organizations, teams that embed AI consultants inside customer organizations to help them get off the ground. These new reporting tools can be seen as a kind of FDE-lite, a lighter version of that hands-on help, for Anthropic customers. “We’re at a point now where it’s mature enough, where once you’re in, it’s going to be harder and harder to switch because you’ve spent the effort,” he said. “Even if they’re all mostly the same, they’re all going to be slightly different, have their own slight quirks of how they handle agents to ND files, etc., and so that cost to switch is not zero.”

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