Microsoft is weighing whether to delay or scale back one of its most ambitious clean energy goals as its rapid buildout of AI data centers puts pressure on its ability to meet those targets. Microsoft has yet to make any public announcements, but according to Bloomberg the company is having internal discussions over its hourly clean energy matching goal.

The tech company has said that by 2030 it intends to match 100% of its hourly energy use with clean power on the same grid. But Microsoft’s rush to build AI data centers has apparently sparked debate within the company about whether the pledge has become an impediment to its ambitions.

Microsoft declined to comment on the internal debate over the hourly matching goal. Instead, a spokesperson told TechCrunch the company continues “to look for opportunities to maintain our annual matching goal.”

Hourly targets like the kind Microsoft has set for itself are more rigorous than annual targets. Because the grid is a balanced system — the supply and demand of electrons needs to be matched on a near-instantaneous basis — hourly matching helps develop clean energy sources that more closely align with a company’s usage patterns.

Annual targets are more lenient. They are effectively accounting tricks that could, for example, let a company buy more solar power than it might use at midday. Other customers on the grid use that energy, but the company that paid for the solar panels gets to claim the renewable power they make. It’s a tidy arrangement that has sped the deployment of wind, solar, and batteries. But on its own, annual targets won’t eliminate fossil fuels entirely. Hourly targets help foster renewable development that more closely mimics how a true net-zero world would be powered.

Big tech companies like Microsoft, Meta, Google, and Apple have generally led on emissions reductions, setting aggressive net-zero targets. Many have eliminated their carbon emissions on an annual basis. Microsoft, for instance, said it met that goal last year.

But as data centers grow in size and number, those same companies are turning to natural gas. Microsoft is included in that list; last month, the company said it was working with Chevron and Engine No. 1 to build a massive natural gas power plant in West Texas that could eventually generate up to 5 gigawatts. 

Despite the West Texas project, Microsoft is widely viewed as a leader among tech companies pursuing net zero emissions. By 2030, Microsoft intends to remove more carbon from the atmosphere than its operations produce.

Part of the company’s renewable push has been driven by an internal carbon tax. The Microsoft spokesperson did not reply to questions about the company’s carbon tax. If it remains in place, some of the internal debate surrounding hourly matching might revolve around a cost-benefit analysis of the shift.

If Microsoft were to abandon its hourly-matching target, the company would also lose some leverage in efforts to sell the public on its data centers. 

As data centers have proliferated, the general public has begun to push back against them, citing concerns over pollution, power prices, and water use. When Microsoft brings its own clean power to a project, it can plausibly say it has addressed two of those concerns. Without it, new data centers might be harder to sell to the public.

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Business insurance startup Corgi announced on Wednesday a $160 million Series B, led by TCV, valuing the startup at $1.3 billion, the startup’s co-founder Nico Laqua said on LinkedIn.

This comes just four months after the company announced a $108 million Series A. The company has now raised $268 million in funding to date, Laqua said, and has become Y Combinator’s latest unicorn.

Laqua started the company with Emily Yuan in 2024 and was part of YC’s Spring 2024 batch. Corgi, which names Deel and Artisan as customers, offers coverage for general liability, cyber liability, and tech and AI liability. Other investors in the round include Kindred Ventures, Leblon Capital, and First Order Fund.

“We’re excited about the raise and incredibly grateful to our investors for believing in what we’re building. But the job is not done,” Laqua told TechCrunch. “Our mission is bigger: we want to use the fresh capital to expand into more lines of insurance and build a generational company.”

Earlier this week, five people who touch every layer of the AI supply chain sat down at the Milken Global Conference in Beverly Hills, where they talked with this editor about everything from chip shortages to orbital data centers to the possibility that the whole architecture that undergirds the tech is wrong.

On stage with TechCrunch: Christophe Fouquet, CEO of ASML, the Dutch company that holds a monopoly on the extreme ultraviolet lithography machines without which modern chips would not exist; Francis deSouza, COO of Google Cloud, who is overseeing one of the biggest infrastructure bets in corporate history; Qasar Younis, co-founder and CEO of Applied Intuition, a $15 billion physical AI company that started in simulation and has since moved into defense; Dimitry Shevelenko, the chief business officer of Perplexity, the AI-native search-to-agents company; and Eve Bodnia, a quantum physicist who left academia to challenge the foundational architecture most of the AI industry takes for granted at her startup, Logical Intelligence. (Meta’s former chief AI scientist, Yan LeCun, signed on as founding chair of its technical research board earlier this year.)

Here’s what the five had to say:

The bottlenecks are real

The AI boom is running into hard physical limits, and the constraints begin further down the stack than many may realize. Fouquet was the first to say it, describing a “huge acceleration of chips manufacturing,” while expressing his “strong belief” that despite all that effort, “for the next two, three, maybe five years, the market will be supply limited,” meaning the hyperscalers — Google, Microsoft, Amazon, Meta — aren’t going to get all the chips they’re paying for, full stop.

DeSouza highlighted how big — and how fast growing — an issue this is, reminding the audience that Google Cloud’s revenue crossed $20 billion last quarter, growing 63%, while its backlog — the committed but not yet delivered revenue — nearly doubled in a single quarter, from $250 billion to $460 billion. “The demand is real,” he said with impressive calm.

For Younis, the constraint comes primarily from elsewhere. Applied Intuition builds autonomy systems for cars, trucks, drones, mining equipment and defense vehicles, and his bottleneck isn’t silicon — it’s the data that one can only gather by sending machines into the real world and watching what happens. “You have to find it from the real world,” he said, and no amount of synthetic simulation fully closes that gap. “There will be a long time before you can fully train models that run on the physical world synthetically.”

The energy problem is also real

If chips are the first bottleneck, energy is the one looming behind it. DeSouza confirmed that Google is exploring data centers in space as a serious response to energy constraints. “You get access to more abundant energy,” he noted. Of course, even in orbit, it isn’t simple. DeSouza observed space is a vacuum, so eliminates convection, leaving radiation as the only way to shed heat into the surrounding environment (a much slower and harder-to-engineer process than the air and liquid cooling systems that data centers rely on today). But the company is still treating it as a legitimate path.

The deeper argument de Souza made, somewhat unsurprisingly, was about efficiency through integration. Google’s strategy of co-engineering its full AI stack — from custom TPU chips through to models and agents — pays dividends in watts per flop that a company buying off-the-shelf components simply can’t replicate, he suggested. “Running Gemini on TPUs is much more energy efficient than any other configuration,” because chip designers know what’s coming in the model before it ships, he said. In a world where energy availability is becoming a massive constraint on how far this tech can go, that kind of vertical integration is a major competitive advantage.

Fouquet’s echoed the point later in the discussion. “Nothing can be priceless,” he said. The industry is in an strange moment right now, investing extraordinary amounts of capital, driven by strategic necessity. But more compute means more energy, and more energy has a price.

A different kind of intelligence

While the rest of the industry debates scale, architecture, and inference efficiency within the large language model paradigm, Bodnia is building something very different.

Her company, Logical Intelligence, is built on so-called energy-based models (EBMs), a class of AI that doesn’t predict the next token in a sequence but instead attempts to understand the rules underlying data, in a way she argues is closer to how the human brain actually works. “Language is a user interface between my brain and yours,” she said. “The reasoning itself is not attached to any language.”

Her largest model runs to 200 million parameters — compared to the hundreds of billions in leading LLMs — and she claims it runs thousands of times faster. More importantly, it’s designed to update its knowledge as data changes, rather than requiring retraining from scratch.

For chip design, robotics and other domains where a system needs to grasp physical rules rather than linguistic patterns, she argues EBMs are the more natural fit. “When you drive a car, you’re not searching for patterns in any language. You look around you, understand the rules about the world around you, and make a decision.” It’s an interesting argument and one that’s likely to attract more attention in the coming months, given the AI field is beginning to ask whether scale alone is sufficient.

Agents, guardrails, and trust

Shevelenko spent much of the conversation explaining how Perplexity has evolved from a search product into something it now calls a “digital worker.” Perplexity Computer, its newest offering, is designed not as a tool a knowledge worker uses, but as a staff that a knowledge worker directs. “Every day you wake up and you have a hundred staff on your team,” he said of the opportunity. “What are you going to do to make the most of it?”

It’s a compelling pitch; it also raises obvious questions about control, so I asked them. His answer was granularity. Enterprise administrators can specify not just which connectors and tools an agent can access, but whether those permissions are read-only or read-write — a distinction that matters enormously when agents are acting inside corporate systems. When Comet, Perplexity’s computer-use agent, takes actions on a user’s behalf, it presents a plan and asks for approval first. Some users find the friction annoying, Shevelenko said, but he said heconsiders it essential, particularly after joining the board of Lazard, where said he has found himself unexpectedly sympathetic to the conservative instincts of a CISO protecting a 180-year-old brand built entirely on client trust. “Granularity is the bedrock of good security hygiene,” he said.

Sovereignty, not just safety

Younis offered what may have been the panel’s most geopolitically charged observation, which is that physical AI and national sovereignty are entangled in ways that purely digital AI never was.

The internet initially spread as American technology and faced pushback only at the application layer — the Ubers and DoorDashes — when offline consequences became visible. Physical AI is different. Autonomous vehicles, defense drones, mining equipment, agricultural machines — these manifest in the real world in ways governments can’t ignore, raising questions about safety, data collection, and who ultimately controls systems that operate inside a nation’s borders. “Almost consistently, every country is saying: we don’t want this intelligence in a physical form in our borders, controlled by another country.” Fewer nations, he told the crowd, can currently field a robotaxi than possess nuclear weapons.

Fouquet framed it a little differently. China’s AI progress is real — DeepSeek’s release earlier this year sent something close to a panic through parts of the industry — but that progress is constrained below the model layer. Without access to EUV lithography, Chinese chipmakers cannot manufacture the most advanced semiconductors, and models built on older hardware operate at a compounding disadvantage no matter how good the software gets. “Today, in the United States, you have the data, you have the computing access, you have the chips, you have the talent. China does a very good job on the top of the stack, but is lacking some elements below,” Fouquet said.

The generation question

Near the end of our panel, someone in the audience asked the obvious uncomfortable question: is all of this going to impact the next generation’s capacity for critical thinking?

The answers were, perhaps unsurprisingly, optimistic, though not naively so. De Souza pointed to the scale of problems that more powerful tools might finally let humanity address. Think neurological diseases whose biological mechanisms we don’t yet understand, greenhouse gas removal, and grid infrastructure that has been deferred for decades. “This should unleash us to the next level of creativity,” he said.

Shevelenko made a more pragmatic point: the entry-level job may be disappearing, but the ability to launch something independently has never been more accessible. “[For] anybody who has Perplexity Computer . . . the constraint is your own curiosity and agency.”

Younis drew the sharpest distinction between knowledge work and physical labor. He pointed to the fact that the average American farmer is 58 years old and that labor shortages in mining, long-haul trucking, and agriculture are chronic and growing — not because wages are too low, but because people don’t want those jobs. In those domains, physical AI isn’t displacing willing workers. It’s filling a void that already exists and looks only to deepen from here.

Google has a publisher problem. AI Overviews, the AI-generated summaries that now appear at the top of search results for a growing share of queries, have been correlated with a 58 per cent reduction in click-through rates to the websites whose content those summaries are built on. Penske Media has filed an antitrust lawsuit. The European Publishers Council has filed a formal complaint with the European Commission. A third of publishers surveyed say they will block AI Overviews once the tools to do so become available. And Google’s search advertising business, which generated more than 50 billion dollars in the first quarter of 2026 alone, depends on the continued existence of the web content that AI Overviews are systematically disincentivising publishers from producing. On Tuesday, Google announced five updates to AI Mode and AI Overviews designed to send more traffic back to the websites it has been accused of cannibalising. The updates are Google’s most direct acknowledgement yet that AI search and the open web have a relationship problem, and its most concrete attempt to argue that the relationship can be repaired.

The features

The most significant addition is Further Exploration, a new section that appears at the end of AI Overviews with curated links to specific articles, case studies, and reports related to the query. The section is designed to transform the AI summary from a destination into a departure point, giving users who want to go deeper a structured path to the source material rather than leaving them with an answer that renders the original content unnecessary. Google is also introducing inline link context on desktop: hovering over a link embedded in an AI Overview will now display the name of the website or page title, addressing what the company describes as user hesitancy to click links when they are unsure where they lead.

Three additional changes target specific use cases. AI Mode and AI Overviews will begin labelling links from a user’s active news subscriptions so they stand out in results, a feature Google says early testing showed made users “significantly more likely” to click. AI responses will also surface previews of perspectives from public forums such as Reddit, social media, and other firsthand sources, with context including the creator’s handle or community name. And Google is expanding the display of product review cards and comparison features within AI Overviews for shopping queries, adding more direct links to retailer and review sites. Taken together, the five updates represent a concerted effort to make AI Overviews more porous: more links, more context around those links, and more reasons for users to click through to the websites that generated the information the AI is summarising.

The problem

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The updates arrive in the context of an existential confrontation between Google and the publishers whose content powers its search engine. An Ahrefs study published in February 2026 found that AI Overviews correlate with a 58 per cent reduction in click-through rates for top-ranking pages, nearly double the 34.5 per cent decline documented in April 2025. The Pew Research Center found that only eight per cent of users click on traditional search results when an AI Overview is present, compared to 15 per cent when no overview appears. Digital Content Next, which represents major digital publishers, reported that most of its members experienced traffic losses between one and 25 per cent, with some reporting declines exceeding 75 per cent. Chartbeat data tracking more than 2,500 news sites globally showed that Google search referrals declined by 33 per cent in 2025.

The European Commission has told Google what it must do to share search data with rivals under the Digital Markets Act, proposing six specific areas of obligation including how Google must provide third-party search engines and AI chatbots with access to search index data. The EU has also launched a separate antitrust investigation into whether Google’s AI Overviews and AI Mode violate competition rules by using publisher content without appropriate compensation and without allowing publishers to refuse without losing access to Google Search. In the United States, the Department of Justice won its antitrust case against Google, with a federal judge prohibiting exclusive contracts relating to the distribution of Google Search and ordering behavioural remedies, though the DOJ is considering whether to appeal for additional structural relief.

The tension

Sundar Pichai’s vision for Google is to transform Search from a retrieval engine into an agent manager, a platform that does not merely find information but acts on it. The plan, articulated at Google Cloud Next 2026, positions AI agents as the next interface layer between users and the web, with Google’s models interpreting queries, synthesising answers, and executing tasks across services. The strategic direction is clear: Google wants users to interact with AI, not with websites. But the business model depends on those websites continuing to exist, continuing to produce content, and continuing to attract enough traffic that advertisers will pay to appear alongside their pages. The five updates announced on Tuesday are an attempt to square this circle, to keep AI Overviews as the primary interface while creating enough clickthrough to sustain the web ecosystem that feeds them.

Google’s repositioning of Chrome as an agentic AI workplace tool underscores the direction of travel. The browser that once existed to connect users to websites is being rebuilt as an autonomous agent that completes tasks without requiring users to visit individual sites at all. The trajectory from AI Overviews to agentic browsing to fully autonomous agents suggests that the five publisher-friendly updates are a tactical concession within a strategic movement that is structurally reducing the value of the open web to Google’s users. Publishers are aware of this tension. The European Publishers Council’s complaint specifically argues that Google’s approach amounts to a forced choice: accept unlicensed use of content for AI training and AI-generated answers, or risk losing the search traffic that sustains digital publishing.

The calculation

The economics of AI search are fundamentally different from the economics of link-based search. A user who receives a complete answer from an AI Overview has no incentive to click through to a publisher’s website. A publisher whose content is summarised in an AI Overview receives no compensation for the content used and no traffic from the summary generated. The advertising model that sustained both Google and publishers for two decades depended on imperfect information: users searched, found promising links, clicked through, consumed content, and encountered ads. AI Overviews collapse this chain by providing the answer directly, eliminating the click, and stranding the advertising that was attached to the destination page. Google is simultaneously investing billions in custom AI inference chips to reduce the cost of generating those overviews at scale, which means the economic incentive to expand AI answers to more queries will only intensify.

Google’s five updates attempt to rebuild some of the click incentive that AI Overviews have destroyed. Further Exploration sections add links. Subscription labels add familiarity. Inline context adds transparency. Forum perspectives add social proof. Product cards add commercial intent. Whether these additions are sufficient to reverse a 58 per cent decline in clickthrough rates, or whether they are window dressing on a structural shift that has already occurred, will be determined not by Google’s announcements but by the traffic data that publishers track in the months that follow. Google’s broader strategy is to make AI the interface for everything, from search to workspace to enterprise to commerce. The open web is the content layer that trains and feeds that interface. The question Google has not answered, and that Tuesday’s updates do not resolve, is what happens to the content layer when the interface no longer sends it traffic. The updates are a gesture. The trajectory is unchanged.

On Wednesday, xAI and Anthropic announced a surprise partnership that has the Claude-maker buying out “all of the compute capacity at [xAI’s] Colossus 1 data center,” roughly 300MW that allowed Anthropic to immediately raise its usage limits. It’s a huge deal for xAI, likely worth billions of dollars. More importantly, it immediately monetized one of the company’s most impressive accomplishments, turning xAI from a consumer to a provider of compute. 

It’s tempting to see the arrangement as a shot at OpenAI amid the ongoing lawsuit. But Musk’s explanation on X was that xAI had already moved training to a newer data center, Colossus 2, and xAI simply didn’t need them both. 

In the short term, there’s an obvious logic at work. xAI’s existing products are mostly focused on Grok, which has seen plummeting usage since the image generation debacles earlier this year. If xAI’s data center buildout is much more than what Grok needs to operate, partnering with Anthropic adds a lot of green to the balance sheet. This is especially useful as the company, now combined with SpaceX, speeds toward an IPO. More broadly, having Anthropic lined up as a customer makes it easier to believe that SpaceX’s orbital data center play might actually work. 

But beyond the short-term benefit, the Anthropic partnership sends an unusual message about where Elon Musk’s priorities really lie. It suggests the company’s real business may be more about building data centers than training AI models. 

It’s rare to see a major tech company treat compute resources this way when companies like Google and Meta, which are also training models, are building more data centers. It’s an easy point to miss, because so many of these companies are working as enterprise AI vendors, online services, and cloud providers all at once. But when forced to make a choice between selling more available compute to customers and preserving some to build their own tools, they reliably choose door No. 2. 

Just last month, Sundar Pichai admitted on a call that Google Cloud revenue was lower than it could have been because the company was “capacity constrained” — and when given the choice of renting out their GPUs or using them to develop AI products, Google chose the AI products. 

Facebook has faced a more extreme version of the same constraint, spinning up an entirely new cloud apparatus just to ensure they would have enough GPU power to chase Mark Zuckerberg’s AI ambition. As he put it when announcing Meta Compute in January, “How we engineer, invest, and partner to build this infrastructure will become a strategic advantage.” 

The key word there is “strategic.” Both Zuckerberg and Pichai are looking toward a future where AI is powering the most popular and lucrative systems in the world. Computing power isn’t just a way to satisfy today’s inference demand, but to build tomorrow’s products — and running short on compute means missing out on that chance.  

By focusing on data centers (earthbound and otherwise), xAI is positioning itself more like a neocloud business: buying GPUs from Nvidia and renting them out to model developers like Anthropic. It’s a far more difficult business, squeezed by both chip suppliers and the shifting cycles of demand. The valuations for most active neoclouds reflect that reality: xAI was valued at $230 billion in its January funding round; CoreWeave, which oversees a comparable quantity of computing power, is worth less than a third of that. 

Musk’s version of a neocloud is more ambitious, as you might expect. Some of the data centers might be in space — at least by 2035, if things go according to plan. xAI will be making its own chips at the Terafab, which will take away some but not all of Nvidia’s pricing power. But none of it changes the basic economics of the neocloud business. 

As recently as the February all-hands, xAI had real ambitions in software. That was the presentation that unveiled the orbital data center project, but it also teased significant ambitions in coding (since bolstered by the Cursor partnership) and interesting ideas like leveraging computer use into full-scale digital twins (in the unfortunately named Macrohard project). These are the kind of long-horizon projects that need committed computing resources to succeed. As long as xAI is selling large quantities of compute to its competitors, it’s hard to think such new ambitions have much of a future.

• Four Android banking trojan campaigns target hundreds of finance and social apps
• Malware hides icons, blocks removal, and overlays fake banking login screens
• Live screen streaming lets attackers monitor activity and capture authentication steps

Security researchers have tracked four Android banking trojan campaigns that rely on deception, stealth, and disappearing app icons to stay hidden out of sight after installation.

Researchers at Zimperium say the campaigns, named RecruitRat, SaferRat, Astrinox, and Massiv, collectively targeted more than 800 banking, cryptocurrency, and social media apps.

RingConn has launched its latest smart ring, and it’s going straight after Samsung with one standout claim: battery life that’s double that of the Galaxy Ring.

The new RingConn Gen 3 lasts up to 14 days on a single charge (with the caveat that haptics are turned off) compared to the seven-day estimate on Samsung’s Galaxy Ring. That alone makes it one of the longer-lasting smart rings currently on the market, and appeals to users who don’t want another device to charge every few days.

Battery aside, RingConn is also leaning further into health tracking. The Gen 3 focuses on vascular health insights, alongside features like sleep tracking and sleep apnea monitoring. There’s also a planned blood pressure tracking feature, but it’s not available at launch and will instead arrive in a future update. As expected, it’s positioned as a trend-tracking tool rather than a medical-grade replacement.