Greg Jensen, co–chief investment officer at Bridgewater Associates, thinks the top holders of AI compute warrant extra regulation, similar to what governs the world’s biggest financial institutions.

If a company controls more than, say, 5% of U.S. or global compute, it should be subject to heightened oversight, “like we do with systemically important banks,” Jensen said. In a wide-ranging interview, he also discussed Bridgewater’s growing use of AI in investing and where it still remains cautious, including leaving risk controls and trade execution to what he called “human-driven algorithms.”

CoreWeave said on Thursday that it could sell up to 35 million shares, worth roughly $3 billion based on the prior day’s closing price, through a new at-the-market offering program. At the same time, the company announced plans to raise $3 billion in a convertible note issue, with an option for ...

Remember when OpenAI landed in hot water earlier this month after mathematicians accused the company of trying to steal their thunder for solving an ultra-difficult math problem? (I know it feels like a million years ago given all the news cycles since then.)

Well, it turns out OpenAI is close to solving another math problem from the Millennium Prize Problems, a set of seven famous math challenges including that earlier “Navier-Stokes existence and smoothness problem,” according to a person with knowledge of the solution.

Employees expect the next problem, known as the Hodge Conjecture, to be solved relatively soon, the person said. The Hodge Conjecture asks whether certain geometric features of shapes defined by polynomial equations can always be described using simpler algebraic pieces. It could take the company longer to announce the solution, though, because it’s trying to figure out how to collaborate with the math community to make the announcement without triggering another PR nightmare, the person said. 

Money, technical savvy and disruptive ideas all matter within the technology industry. But the most seismic force is influence. Those who have it build, fund and steer the companies that alter the world—again and again. Those without it can only rise so far.

So who’s got that clout? You’ll find the answer by poring over our new list: the 50 Most Influential People in Tech. It isn’t just a snapshot of influence today. Coming up with such a list would be a relatively simple exercise. Rather, we’re providing a roster of the people we believe will have the most influence over the next decade, a more complex task that The Information is uniquely well positioned to pull off.

Huawei Technologies plans to launch its new AI chip in the first quarter of 2027, nine months earlier than planned, as it steps up its efforts to challenge Nvidia.

Tao Wang, Huawei’s deputy chairman and rotating chairman, said at the Huawei Connect conference in Shanghai on Thursday that the new ...

Put aside, for just a moment, all the arguments about AI safety. Today’s decision by the Federal Reserve to raise interest rates may be a bigger issue for the AI sector in the short term. Long-term bond yields had already risen, of course. But the Fed’s action—pushing up short- and medium-term borrowing costs—seems sure to crimp the debt-fueled AI boom, particularly given the prospects that another rate hike is possible later in the year. 

The most vulnerable to higher rates are surely small companies that have weak or no credit ratings but need to raise money, such as would-be neoclouds hoping to finance data center projects. Think Rum Group, which plans a big data center in Georgia for a deal with Anthropic but doesn’t have financing for the project yet. (For more on startups’ financing challenges, see here). But the ripple effects of higher interest rates will eventually affect everyone, including big tech firms spending a fortune to expand their data centers for AI.

OpenAI released a new framework on Wednesday for how it aims to report unsafe or concerning behavior in its models, and disclosed six incidents of such behavior it had observed in the past six months.

The framework comes after multiple employee warnings and hacking incidents increased public ...

Google must improve the ability of its advertising technology to integrate with the products of competitors, according to the decision of a federal judge made public Wednesday. The opinion follows a ruling last year that Google operated an illegal monopoly in parts of the market for advertising ...

Greetings!

If it suddenly feels like every startup is trying to raise money to throw at some kind of AI infrastructure shortage, you’re probably onto something. 

We reported last night that the startup behind AI assistant Instinct is in talks to raise $1 billion, as it faces compute capacity constraints after growing to more than 100,000 users on an invite-only basis. Meanwhile Coatue Management has been discussing a multibillion-dollar joint venture involving chip startup MatX, to finance purchases of memory and logic dies and the reservation of manufacturing capacity. 

There’s more going on than simply scrounging up a few spare servers or chip components. Getting access to AI infrastructure increasingly means making big, long-term commitments—it’s cheaper for providers to finance new cloud capacity that way, while manufacturers are more willing to allocate scarce capacity to large, established customers. That’s making it tougher for young but fast-growing companies to get a place in line. 

Smaller cloud companies often borrow heavily to finance data center capacity and purchases of graphics processing units, while even the biggest are increasingly using debt to supplement balance sheet cash. On top of that, borrowing costs have risen sharply across the economy, with benchmark yields on both long- and short-term debt climbing.

It’s generally cheaper to borrow money with established, long-term customers lined up, especially ones with good credit ratings, than to finance compute capacity to serve less-proven customers. Iren, for instance, said in August that roughly $3.6 billion in financing for GPUs for Microsoft carried an interest rate of around 6%. But a separate $2.4 billion in financing for GPUs for non–investment-grade customers had a rate around 3 percentage points higher. 

Some cloud providers are also demanding more up-front payments, which helps lower the amount they have to raise. Iren said recent customers were paying as much as 55% of the cost of GPUs and related equipment up front. Nebius, similarly, said that the share of deals with prepayments reached an all-time high in the second quarter, with roughly 70% of newly inked deals requiring money up front. 

Nebius expects to receive more than $9 billion in prepayments this year, compared with projected 2026 revenue of up to $3.4 billion, and called them the “market standard for securing capacity in a supply-constrained environment.” 

Renting GPU capacity as needed from startups like Runpod is another option, while larger providers like CoreWeave are introducing flexible ways to buy inference capacity. But that approach can be expensive, and needed capacity might not always be available. Either way, scaling up can get pricey fast for AI-heavy startups, and they might need to reserve capacity before knowing exactly how usage growth will pan out.

In the pre–AI craze days, a consumer app might chase user growth without worrying about revenue, while computing needs were generally less intensive and cheaper, so didn’t require the same kind of hefty upfront payments. Photo sharing app BeReal, for instance, grew to nearly 8 million daily users within months of taking off in 2022, having raised roughly $90 million, though growth later fizzled. 

On the chipmaking side, startups are also facing challenges in securing resources ahead of growth, in that manufacturers are more likely to allocate capacity to customers that can make large commitments and have the financial resources to back them up. The joint-venture financing for MatX, which doesn’t expect to have its chip design ready for trial production until next year, would be aimed partly at helping it reserve capacity like customers such as Broadcom or Marvell Technology do.  

It’s not clear how much money Coatue would put into a joint venture, which remains in the early stages of discussion, and whether the venture could also pull in outside financing. But Coatue already has some experience helping finance capital-heavy infrastructure deals on the data center side, sometimes in creative setups. 

Coatue’s Next Frontier venture participated in a joint venture with AI infrastructure startup Fluidstack to finance a planned Indiana data center campus. A subsidiary of that JV raised $5.7 billion of debt to help buy land and build the facilities and related power infrastructure. (The financing doesn’t cover chips.) Fluidstack will lease those planned facilities to provide AI cloud services, but there’s yet another company involved—Google, which is guaranteeing Fluidstack’s lease obligations.

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Apollo Global Management, known for leveraged buyouts and hefty financing deals, has started investing in AI and related hardware startups with the ambition to lend money to them in the future.

In one previously unreported deal, Apollo has already invested in the low tens of millions of dollars in Mercor’s latest funding round, which is set to value the data-labeling startup at $20 billion, according to people familiar with the matter. It has also invested in chip startup SiFive and in Hadrian, which says it’s building AI-powered factories, in transactions the companies disclosed earlier this year. It expects to make more investments in these categories, the people said.

Zipline, a startup making drones that can deliver small packages like meals and medical supplies, is in talks to raise $1 billion at a valuation of roughly $20 billion, according to three people with knowledge of the fundraise.

The funding round, if it closes, would more than double its valuation from its last funding round, which valued the company at $7.6 billion after the investment. It follows a partnership announced last month with Uber to expand delivery by drones across the U.S. and expectations by investors that the costs of drone delivery are falling, expanding what was once a niche market.

OpenAI said Wednesday it is testing allowing some U.S. advertisers to sponsor AI agents working in ChatGPT, a move that would bring ChatGPT ads closer to what Google and Meta Platforms already offer.

ChatGPT users who click an ad can now start a conversation with an AI agent to ask follow-up ...

Stablecoin issuer Circle officially launched Arc blockchain, which will support the trading of stablecoins, tokenized equities, and other assets.

The blockchain will also support perpetuals, lending protocols, and social trading apps, Jeremy Allaire, CEO of Circle, said in a press briefing. ...

Even for technology leaders, it can feel impossible to keep up with constant advances in the field.

This is especially true in the age of AI, with vendors racing to roll out model updates with new capabilities. Even if technology leaders had time to attend every conference and read every tech publication, it would be a full-time job to simply figure out which trends represent real transformation opportunities, and which are merely hype.

With Google Gemini, you can generate a tailored digest that summarizes the developments most likely to have an impact on your specific industry and organization. Armed with this up-to-the-minute information, you can keep your leadership and workforce apprised of market shifts and technology breakthroughs, protect your organization from expensive distractions, and make high-stakes procurement decisions with confidence.

Step 1: Generate Your Tech Forecast

Open up a new chat, and prompt Google Gemini to provide you with a summary of current technology trends. For a more tailored summary, provide as much information as possible about your company and industry. You might, for example, tell Gemini that you’re the CTO at a mid-sized regional commercial and retail bank in the Pacific Northwest, with a mix of legacy on-premises and modernized cloud infrastructure.

Here’s a sample prompt:

I am the CTO of [company] in [industry]. I want to understand emerging technology trends. Summarize the top five emerging technologies with the most significant potential impact on [industry]. For each technology, list its potential benefits and challenges, and suggest how it could impact [company] in the next two to three years.

Gemini will quickly generate a list of technologies, broken down by their potential benefits, challenges, and overall impact. For the regional bank, Gemini might suggest using agentic AI for continuous risk assessment, pointing out the opportunity to proactively surface lending opportunities and flag potential risks.

Step 2: Refine the Output

Gemini’s summary will be based on the information you provided. To generate a more custom tech forecast, add more information about your existing environment and current challenges.

For example: Gemini might suggest deploying API-led middleware to bridge the gap between your legacy core and your modern cloud app; but maybe you already have middleware running, and the problem is that its fragility is leading to downtime. By adding that context, you can help Gemini focus its output on your specific needs.

Go back and forth with Gemini, providing additional context and feedback until the tech trends summary is exactly what you’re looking for.

Step 3: Automate Delivery

If you have to manually enter a new prompt every time you want an update on tech trends, you might not be as consistent as you’d like. Instead, prompt Gemini once to automatically provide you with an update each day, week, or month.

These sorts of prompts work best when they are completely self-contained, rather than the result of a long, messy chat history. So, ask Gemini to look back at your conversation and synthesize all of your real-world feedback into a single master prompt. Then, paste that master prompt into a new chat and explicitly tell Gemini your desired delivery schedule.

Gemini’s Scheduled Actions feature will take over, allowing you to manage or pause the recurring prompt in your account settings.

Step 4: Move from Knowledge to Action

Staying on top of tech trends is only really useful insofar as it spurs action. As you peruse Gemini’s scheduled summaries, consider where digging deeper into specific topics may help you optimize your operations, mitigate emerging risks, or prepare your IT environment for the future. Try this prompt:

Recommend three areas where [my company] can take proactive steps to stay ahead of the curve on [specific areas].

The CTO of the regional bank might, for example, ask Gemini for specific tips on network-based fraud intelligence. In this step, the role of Gemini is shifting slightly, from industry analyst to strategic sounding board. Because Gemini already has detailed information about your organization’s challenges, goals, and existing environment, its insights will be grounded in your specific reality.

By moving from high-level summaries to tactical next steps, you can use your understanding of tech trends to guide your organization’s IT investments in a marketplace that never stops moving.

Anthropic’s call to slow down AI development to keep humans safe has dominated the news cycle and drew similar calls from the leaders of OpenAI, Microsoft and others (and subtle disagreement from Mark Zuckerberg). 

But Mustafa Suleyman, who runs Microsoft’s AI model development, has a warning about Anthropic itself. 

Suleyman, who co-founded DeepMind (now part of Google), says Anthropic’s approach of treating its Claude models as if they were sentient, similar to humans, could produce a dangerous outcome. He called out Anthropic’s Claude Constitution, a document that treats the model as an intelligent entity and offers guidelines for how it should handle ethical dilemmas. Suleyman said that by incorporating the document into its training, Anthropic could make Claude harder to control. 

Suleyman’s concerns emerged when Anthropic updated the constitution at the start of this year to leave room for the possibility that Claude has some sort of “consciousness” or “moral status,” a change from the original document.

Apple has been working on plans for an enterprise server using its own chips that could incorporate networking equipment from Nvidia, people familiar with the project said, as the Mac maker looks to capitalize on surging interest in its computers for AI.

Apple has been developing the server with plans to sell it to AI developers, other businesses and governments, the people said. The product under consideration would come in two versions—a smaller one with two of Apple’s planned M8 Ultra chips clustered together and a bigger one with four M8 Ultra chips. The M-series Ultra chips are Apple’s highest-performance chips and power products including the Mac Studio.

Nvidia CEO Jensen Huang plans to attend President Donald Trump’s state dinner for Chinese leader Xi Jinping in Washington next week, according to a person familiar with the matter. His attendance comes as AI chips and the pace of AI development become growing sources of tension between the U.S. ...

South Korean memory chipmaker SK Hynix is in talks with Intel about a deal to manufacture memory chips on U.S. soil for the first time, Reuters reported, citing three people familiar with the discussions.

SK Hynix would either lease part of Intel’s long-planned Ohio fab, or form a joint venture ...

Meta Platforms chief Mark Zuckerberg said ensuring the safety of AI is a commercial necessity for the companies that make models, and took an implicit swipe at calls for a collective slowdown in the technology’s development.

“Every lab has the responsibility and incentive to move at the pace ...

Two researchers who worked on AI safety left their roles at Google DeepMind, stating that they departed over concerns that powerful systems could endanger humans.

The two, Bilal Chughtai and Josh Engels, both of whom are joining organizations dedicated to AI safety, add their voices to those of ...