Tech
Nvidia’s new $500B plan is risky but brilliant, especially for aging GPUs
Nvidia announced this week that Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR were willing to commit up to $500 billion to build AI data centers. That eye-popping figure got a lot of the attention, but the bigger story is Nvidia’s effort to create a secondary market for aging GPUs.
To convince those big-name financial companies, Nvidia has agreed to guarantee, with its own money, that its chips used as collateral in these deals will retain their value.
Many have now commented on how unusual, smart , and dangerous this plan is. It is all of those things. The bond markets got so spooked that Nvidia CEO Jensen Huang took to X and business TV to better explain how Nvidia’s risk would be limited.
But underneath the financial maneuvering to fund AI data centers (and keep revenue for Nvidia flowing), is something, perhaps, far more interesting for startups and enterprises: Huang wants to ensure an ecosystem of used AI hardware flourishes, helping sustain demand for Nvidia hardware as it ages.
Specifically, Nvidia is promising that if GPUs used as collateral don’t retain their value as expected, the company will cover up to 25% of the difference. So, if a data center owner defaults on a loan and the lender must liquidate, but the chips can’t command the price the books say they should, Nvidia will chip in.
The dangerous part for Nvidia is that this creates something financiers call “wrong way” risk. That is, Nvidia’s obligations will grow as demand weakens. Should that happen, its revenues will likely be squeezed as well.
Still, the scheme is deliberately unlike the comparison to Lucent Technologies that some have been making. Lucent was the telecommunications equipment provider that rose and crashed with the dotcom bubble after lending its customers money to buy its wares.
The Lucent comparison is a shadow over Nvidia, Huang knows. And not an unfair one. Nvidia definitely has committed billions towards those who buy its chips, including frontier AI labs OpenAI and Anthropic, neoclouds like CoreWeave (the originator of using Nvidia chips as collateral) as well as Nebius, Firmus, and Lambda. And it has been working on another $750 billion worth of circular deals this summer, Bloomberg has calculated.
“Is this circular financing?” Huang wrote on X about the new scheme. “This initiative is designed to address that concern. We are bringing independent, long-term institutional capital into the AI infrastructure market.”
That’s true. Unlike Lucent, Nvidia is getting others to shoulder the bulk of the capital and risk, merely by agreeing to protect a portion of its chips’ value in the future.
Should this plan work, Nvidia will have found new sources of money for AI data center builds, after many of the traditional methods have begun to wear thin. For instance, some of the hyperscalers have already taken on a lot of debt (like Oracle), issued new tranches of equity (Google), and burned much cash (Meta).
The situation has become so dicey that Microsoft CEO Satya Nadella recently recommended the book “1873” during his latest earnings call. It’s about the railroad-era financial engineering that crashed the nation’s economy.
The risk is that today’s AI boom, where demand far outstrips capacity, doesn’t continue for much longer. Rather than being in the early innings, what if enterprises and consumers temper AI usage? Or new technologies come along to make existing infrastructure more effective and/or all of today’s AI infrastructure obsolete?
Then, like so many buggy whips in the face of automobiles (to paraphrase Danny Devito’s Lawrence Garfield), demand dries up and everything crashes.
Yet, Huang is arguing that won’t happen by selling a vision of AI as a long-term “investable infrastructure,” as he describes it. That makes his AI servers, which he calls “AI factories” akin to railroads or airlines rather than quickly depreciating assets like PCs.
“When needs change, the factory can be used by another customer, another cloud or another operator. This broad ecosystem gives NVIDIA compute a deep market of potential users and offtakers, helping protect residual value,” he promised.
In that future, Nvidia cares as much about aging architecture as it does the new chips. And perhaps startups, enterprises, and even researchers will tap into a broader variety of hardware, each tuned to different AI needs, just like they are beginning to pick affordable open-weight models alongside the frontier choices.
As the king of AI, Nvidia has the power, and the window of opportunity, to make that happen.
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Tech
OpenAI hires new CRO as executive shake-up continues
OpenAI has replaced chief revenue officer Denise Dresser after just nine months on the job, tapping Wiz president and chief operating officer Dali Rajic to take on frontier lab’s top sales job.
The move comes as part of a broader shake-up in the organization in the last month, which has seen the departures of COO Brad Lightcap, and the company’s number two executive, CEO of AGI deployment Fidji Simo.
OpenAI co-founder and president Greg Brockman has taken a larger role in management following Simo’s departure, and announced Rajic’s arrival today in a blog post. Wiz, Rajic’s previous employer, was acquired by Google for $32 billion this year in the tech giant’s largest-ever acquisition.
“Denise has led our revenue organization through a formative period for the business and has worked tirelessly to get the team to where it is today,” Brockman wrote. “The way we’re deploying this technology is changing rapidly, and Dali will turn what we’ve learned into repeatable execution as we build out the full system to make AI broadly useful for people and businesses.”
OpenAI says its products reach more than one billion weekly active users, and two million businesses. Despite the incredible growth and its powerful models, however, executives have suggested both privately and publicly that the company hasn’t hit all of its revenue goals.
The company says it has filed confidentially with the SEC ahead of a potential IPO, but it’s not clear when that will take place. Private firms often try to round out their executive ranks ahead of a public markets debut. OpenAI purchased $7 billion worth of shares from employees this week in a tender offer that allowed them to cash in on some of their equity compensation, which may suggest a delay in the public offering.
Bloomberg News’ coverage of the change-up referenced an OpenAI blog post that said the company needed to have a “relentless focus” on “measurable business impact,” but those comments appear to have been removed from the published version.
However, this year CEO Sam Altman has spoken about focusing the company on enterprise deployment and cut back on technology projects and experiments seen as distracting from that goal.
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Tech
Ford on track to complete $2B factory overhaul for Fathom EV truck
Ford made a $2 billion bet two years ago when it closed its Louisville Assembly Plant in Kentucky and scrapped the assembly line system it had used for more than a century. Its goal was to transform the factory into one capable of making a new generation of affordable EVs.
The auto giant provided an update on Thursday, stating that its factory overhaul is on track and in 2027 will be ready to start producing the Fathom, an all-electric midsize truck that costs less than $30,000 and the first EV built on its new universal platform.
Ford said it expects to begin prototype builds of the Fathom in the first quarter of 2027, with customer vehicles to follow later in the year. The U.S. automaker is already testing production-level tooling ahead of the prototypes.
The stakes for Ford are high. The company’s previous EV efforts were a drag on its profitability, while Chinese competitors and Tesla leapt ahead with vehicles that sell at volume and with a profit margin.
To catch up, Ford ditched the moving assembly line system that its founder Henry Ford launched and turned to a system developed by its skunkworks team led by former Tesla executive Alan Clarke.
This “universal production system,” as Ford calls it, uses a three-branched assembly tree. Ford will use large single-piece aluminum unicastings that use far fewer parts — a technique that Tesla has popularized — and that will allow the front and rear of the vehicle to be assembled separately on two of the branches. The third branch is where the structural battery will be assembled with seats, consoles, and carpeting. The three components will come together at the end of the line to form the vehicle.
As part of the factory rebuild, Ford has also nearly tripled the Wi-Fi coverage density in the factory to 1,080 access points to the kind of high-bandwidth, low-latency connectivity required for software quality checks.
Ford said it will be able assemble the Fathom a net 15% faster than the former vehicles that were built at the Louisville plant.
The company said some employees have already spent months training on the new system at its New Models Program Development Center in Allen Park, Michigan.
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Tech
Product Engineer Mindset: From Task-Taker to Owner

If you haven’t already seen a job listing for a “product engineer,” you probably will soon. The job everyone’s suddenly hiring for, this role is like a cross between a product manager and an engineer (as the name suggests). And it’s a hiring trend worth paying attention to.
Companies are opening more of these roles every single month, but they’re struggling to fill them. The reason has almost nothing to do with engineers’ coding skills or years of experience.
The best career move you can make to prepare for these types of roles has almost nothing to do with getting more technical. Instead, it comes down to one of the fluffiest, most overused, and potentially cringiest words in all of tech: mindset.
Stick with me, I promise this goes somewhere useful.
The problem: We were trained to be task-takers
When I started out, my job looked like this:
Drive to an office. Sit through meetings that led to other meetings until a project manager handed me a task they’d already chopped into tiny pieces.
My job was to turn that task into code.
It took years for me to get good at a coding language and tech stack, and once I did, I executed that knowledge against specs that somebody else wrote.
You know what’s freakishly good at that exact job? I’ll give you a hint: It starts with A and ends with I.
Boris Cherny, the creator of Claude Code, recently said: “coding is basically solved,” and “the bottleneck is going to be good ideas.”
So if your entire value is “hand me a task and I’ll build it,” you’re in a footrace with the robots. I don’t like that for you.
The bad news… that is also good news
Many companies are flattening. Middle management is getting stripped out, for better or worse (mostly for worse), which means many of us are doing more with less.
This might sound like purely more work, but it’s also an opening for anyone who cares about what they’re building and can put on their manager hat. Companies are no longer just hunting for the strongest engineer in one narrow domain.
What’s rare, and what actually moves revenue, is an engineer who can spot the thing that’s quietly costing money and either flag it to leadership or just go fix it.
What this actually looks like
Being product-minded has NOTHING to do with your tech stack.
Here’s where to start:
Have an opinion and back it up. As a former engineering manager, the worst thing I ever heard was silence. I’d often ask the team what they thought because I doubted myself and wanted a gut check. I was grateful to the ones who said “nope, bad idea, here’s why.” Pushback is a gift.
Learn the domain, casually. Work for a plumbing company? You don’t need to become a plumber, but spend an hour on Reddit threads where plumbers vent. Now your ideas come from your potential customers.
Make experiments cheap and safe. This is where any engineer has massive leverage. Experiments are not free. A bad one loses customers and frustrates users. Tools like LaunchDarkly and Optimizely let you ship a change to 5 percent of users and roll it back the second it tanks. Learn them, or build a scrappy version yourself. A team that can quickly run safe experiments will out-learn everyone else in the building.
Be data-driven. Stop fighting about button colors. Pick a goal: making money, finding product-market fit, or making the product sticky so people come back. Then measure it. If your gorgeous redesign tanks time-on-site, it failed, no matter how good it looked to you. If the ugly version makes more money, ship the ugly version.
You don’t have to be the ideas person. Maybe you’re not a visionary. That’s fine. Organize a hackathon around an actual company goal. Pull up your company’s quarterly targets and build something against one of them. Don’t know what those targets are? That’s your first assignment.
Good ideas are the new bottleneck—and they always have been
When I was a manager, I asked myself one question every week: What’s the single most impactful thing I could do right now? The answer was almost never “write more code.” It was understanding a gnarly problem nobody had defined yet. Building a deck to spread knowledge that was in one person’s head. Getting the right three people in a room to actually make a decision we’d been putting off.
Code is cheap, and it always has been. We just couldn’t see it, because for decades the typing took so long that it felt like the hard part. It never was. The hard part was always knowing what’s worth building.
— Brian
In January 2025, Siobahn Day Grady launched the first AI research institute at a historically Black college or university. The institute aims to help expand AI skills for all students at North Carolina Central University, where Grady is an associate professor, through both AI research opportunities and skills training. Though the institute is the first of its kind, Grady hopes it could serve as a model for other HBCUs.
Read more here.
AI is increasingly used in the scientific research process. So does publishing need to change to keep up? Jiachen Liu recently co-authored a paper published on ArXiv arguing that the PDF should be replaced with an “Agent-Native Research Artifact” designed with AI in mind. In this interview with IEEE Spectrum, Liu lays out a provocative vision of AI-driven research and an infrastructure that captures—and learns from—details that often get left out of today’s papers.
Read more here.
Astronomers still don’t know exactly what dark matter is, but they can detect it—and so can you. With a small radio telescope and a few other pieces, you can create a DIY setup to gauge how fast hydrogen clouds are moving across the Milky Way. Feed those measurements into a spreadsheet, and you can see the same signals that have baffled the astronomical community for decades.
Read more here.
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