Ever find something you want to buy online, leave it sitting in a browser tab and then completely forget where you saw it? Maybe you spotted a great pair of headphones while searching Google. Later, you found a birthday gift at another retailer. Before long, you've got a dozen tabs open and no idea which store had the better deal.

Google thinks it has a solution. On Oct. 15, the company began the U.S. rollout of Universal Cart, a new artificial intelligence-powered shopping tool that brings products from different retailers into one place. It also tracks prices, watches for restocks and helps you find savings before checkout.

With holiday shopping approaching, the timing could be especially useful. But there's another question worth asking. Will having everything in one cart help us spend less, or simply make it easier to buy more?

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What is Google Universal Cart?

Google Universal Cart is a shopping hub that works across Google Search and the Gemini app. Instead of keeping separate lists of products you might buy, you can save items from different retailers in one place. For example, you might find a coffee maker while searching Google and add it to your Universal Cart. Later, while talking with Gemini about gift ideas, you could save a pair of sneakers from another store. When you return, both products will be waiting for you. Google first previewed the tool in May 2026. Now, Universal Cart is rolling out to U.S. shoppers. It uses Google's Shopping Graph, which contains more than 60 billion continually refreshed product listings. That information helps the system monitor prices and availability across supported products.

4 ways Google's new AI shopping cart can help you save

The biggest attraction here is convenience. But Google has added several features that could also help you avoid paying more than necessary.

1. Keep products from different stores in one cart

Shopping online often means bouncing between websites. You find something at Target, check another item at Walmart and then head to Sephora for a gift. Remembering where you saw everything can become a chore. Universal Cart lets you save supported products you discover through Google Search and Gemini. More importantly, your saved items stay together when you return to shopping. That could come in handy when you're shopping for several people or making a big purchase that requires some research. You can keep your options organized without committing to buying anything right away.

2. Get alerts when prices drop or products return

Here's the feature I find particularly interesting. Universal Cart can monitor saved products and notify you when a price drops. It can also alert you when something that sold out becomes available again. Suppose you're considering a new television but aren't ready to spend the money. Rather than checking the same retailer every morning, you can save it and let Google's shopping system watch for changes. The alerts could be especially helpful heading into Black Friday and the holiday shopping season. Still, remember that a price reduction doesn't automatically guarantee the lowest price available.

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3. Find promo codes and credit card savings

This is where Google's AI starts doing some extra work. Universal Cart can surface relevant promotional codes and consider benefits associated with your payment cards. Let's say you have multiple credit cards but can't remember which one offers better rewards at a particular retailer. Universal Cart uses information from Google Wallet to understand supported credit card benefits and help you choose between payment methods. It can also show relevant promotional codes directly in your cart, potentially helping you find savings you might otherwise miss. However, I would still check the details before choosing a card. Rewards programs come with conditions, and the highest points offer may not always translate into the greatest savings.

4. Check out without starting over

Once you're ready to buy, Universal Cart offers a more streamlined checkout experience with participating retailers. In some cases, you can complete the purchase using Google Pay directly from the cart. Alternatively, Google can transfer your selected items to the retailer's website so you can finish your order there. Google says its simplified checkout experiences include retailers such as Nike, Sephora, Target, Ulta Beauty, Walmart and Wayfair, along with many Shopify merchants. However, the available checkout options depend on the retailer. Either way, the retailer remains the seller of record and handles order confirmations and customer support. That means you'll continue dealing with the merchant for delivery questions or returns.

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How to get started with Google Universal Cart

Google began rolling out Universal Cart to U.S. users on Oct. 15. The feature works across Google Search and the Gemini app, although availability may vary as the rollout continues. To explore it, open Google Search or Gemini and look for products you're interested in buying. When Universal Cart becomes available in your shopping experience, you can save supported products from different retailers. Google says those items will remain in your cart when you return, allowing you to pick up where you left off. You can also use the cart to monitor price changes, discover promotional offers and review available checkout options. However, Google hasn't provided a single set of instructions that applies to every device. The appearance and placement of these features may vary.

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Does Google's Universal Cart work with every retailer?

Not necessarily. Google says Universal Cart can organize products across retailers, but that doesn't mean every online store supports every feature. The products you can save depend on supported shopping experiences. Meanwhile, checkout options vary depending on the merchant. Some purchases can happen through Google Pay in the cart, while others require you to finish on the retailer's website. Google originally introduced Universal Cart in May with plans to bring it to Search, Gemini, YouTube and Gmail. However, its October 15 launch announcement specifically confirms availability across Search and Gemini in the U.S. Google has not announced a confirmed launch date for Universal Cart integration with Gmail or YouTube.

What's powering Google's new shopping experience?

Behind the scenes, Google uses something called the Universal Commerce Protocol, or UCP. It's a shared technical standard designed to help AI-powered shopping tools communicate with retailers and payment systems. For shoppers, the technical details are less important than what they make possible. UCP helps Google connect product discovery with checkout, reducing the need to jump between different shopping experiences. Google has also been expanding AI shopping beyond the cart. Its Let Google Call feature, for example, can use AI to contact nearby stores and check product availability. Amazon has been developing AI-powered shopping tools, while OpenAI offers AI agents that can assist with shopping-related tasks. The competition to become your digital shopping assistant is heating up.

What about your shopping privacy?

Having Google organize your shopping could be convenient, especially if you already use Search and Gemini. Still, it's worth considering how much shopping information you're comfortable keeping within your Google account. Universal Cart uses Google Wallet to help identify supported payment benefits and relevant offers. However, Google's launch announcement doesn't provide detailed information about how long Universal Cart shopping data is retained or exactly how it may be used for advertising. Before relying on the feature, review your Google account's privacy settings and Google's published privacy policies. For now, I would avoid assuming that adding something to Universal Cart keeps that shopping interest private. And if you're buying a surprise gift for someone who shares access to your Google account, think twice before saving it there. Of course, the usual online shopping precautions still apply. Before completing a purchase, check the final price and verify that you're buying from a legitimate seller. A familiar checkout experience shouldn't replace those basic habits. For more help spotting suspicious sellers, see our guide on how to tell whether an online store is real or a scam.

Kurt's key takeaways

I like the idea of having one place to keep track of everything I'm considering buying, especially when the holidays roll around. The price alerts could be genuinely helpful. And if Google's AI can find a discount code I would have otherwise missed, I'm all for it. But I'm also watching where this is heading. Google is making shopping increasingly convenient, and that convenience can be a double-edged sword. When a company knows what you're interested in buying and makes checkout easier, there's less time to reconsider a purchase. My advice? Use Universal Cart to do your homework and save money. Just don't let the technology do the deciding for you.

Would you trust Google's AI to help you shop and choose how to pay, or are we giving tech companies too much influence over what we buy? Let us know by writing to us at CyberGuy.com.

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• Hugging Face released a new AI model that allows users to "chat with DNA."
• Carbon-X can analyze and generate DNA and allows researchers to ask questions in plain language.
• Hugging Face said the model can help researchers who work outside the big AI labs.

Hugging Face is releasing an AI model that could make it easier for scientists to study DNA and develop new genetic treatments, the company announced Thursday.

Carbon-X is a "first of its kind" model that combines DNA analysis and generation with plain language, allowing researchers to ask questions and essentially "chat with DNA," according to Lewis Tunstall, a senior research scientist at Hugging Face.

Hugging Face, which Nvidia agreed to acquire for nearly $13 billion in September, is a platform for sharing and developing AI models and also releases its own. The company worked with research labs in Italy and China on Carbon-X, which is an open-source model. Hugging Face built Carbon-X by training an existing language model, Qwen3-14B, on genomic sequences and natural language. It was trained on DNA sequences from humans, other mammals, plants, and fungi.

As an example of how Carbon-X can be used, Tunstall said a researcher could provide a DNA sequence and ask whether a particular mutation could increase a person's risk of disease. The model would then offer a prediction and explanation. It can also generate new DNA sequences, which could help researchers develop new gene therapies.

Tunstall said the company was partly motivated to build Carbon-X to prevent biology researchers from becoming dependent on the major AI labs, noting how AI has disrupted mathematics.

"If you want to really push the frontiers of mathematics, you likely have to be at one of these AI labs like OpenAI or Anthropic, because they're the ones who have the best mathematics models," he said, suggesting a similar situation is likely to play out in other fields of science.

"We wanted to provide the open research community with tools that don't make them fully dependent on some external single provider," he added.

Researchers can run Carbon-X on their own computers, avoiding the need to share sensitive patient data with external AI companies. He also said that some of the misalignment behavior observed in larger models, such as the OpenAI agents that famously hacked Hugging Face in July, has not yet been observed in smaller, more specialized models.

Tunstall said generalist chatbots also sometimes refuse to answer legitimate biology questions from researchers because they mistake them for attempts to "design a bioweapon." He said viruses were explicitly excluded from Carbon-X's training data in order to avoid misuse of the model.

While Carbon-X is likely to be used by researchers, Tunstall said the model represents a small step toward a future where AI can help provide more personalized medical care.

"I think the holy grail is, in general, this concept of personalized medicine where we're able to, as individuals, take our own patient records, our own genomes, and use this to ultimately find or even maybe design our own treatments," he said.

• Newcomers like Kevin O'Leary face skepticism due to a lack of experience in data center development.
• Bernstein analysts dubbed O'Leary and others "bragawatts."
• Most announced projects will never happen. "Press releases are cheap…concrete is not."

The AI industry has a problem with bragging.

Companies are announcing enormous new data centers to power the AI boom. Some promise facilities bigger than anything the industry has built before.

There's just one problem: Most of these projects may never happen.

Bernstein Research analyzed thousands of planned US data centers and concluded that only about 35% of the announced capacity is likely to get built. That's roughly 170 gigawatts of data center capacity out of a total of almost 500 gigawatts.

The rest? A significant chunk of it belongs to a category the industry has a wonderfully dismissive name for: "Bragawatts." These are ambitious announcements, often from newcomers with little experience building data centers, that have a slim chance of becoming reality.

"Press releases are cheap…concrete is not," Bernstein analysts wrote in a research note.

Bernstein began its analysis with more than 7,000 data center projects across North America, drawing on information from Aterio, a company that tracks construction through satellite imagery, permits, and other records. The analysts narrowed that to more than 5,000 projects (partly by excluding those already canceled).

Then they assessed the chances of these remaining projects being completed. The analysts considered factors such as whether construction had started, whether electricity was available, and whether the companies involved had successfully built data centers before.

The results revealed a striking divide.

The 'bragawatt brigade'

Experienced operators including Amazon, Google, Microsoft, and Meta landed at the top. Bernstein gave these companies perfect scores of 5 out of 5 for their development credentials. Other data center specialists, such as Digital Realty, Equinix, and CoreSite, got top marks, too.

O'Leary Digital, associated with celebrity investor Kevin O'Leary, ended up at the bottom, with a credibility score of 0.35 out of 5, according to Bernstein. His company has announced 15 gigawatts of data center projects, and Bernstein estimates that only 489 megawatts, or about 3%, will actually materialize.

"Sorry, Kevin," the analysts wrote.

Although the term "bragawatts" has been doing the rounds in the data center industry lately, it's unclear who coined it, Bernstein analyst Madison Rezaei told me.

"I wish I could take credit for that," she said. "I believe I originally heard it from an Equinix executive, but I don't think he owned it either."

She said that while some ambitious newcomers could succeed, they haven't yet demonstrated they can deliver.

"It's not that all of these guys are sort of terrible and not going to deliver, but we just have lower confidence because we don't know them," Rezaei said.

O'Leary Digital responds

O'Leary Digital disputed Bernstein's assessment, saying the research doesn't account for substantial work that occurs before construction begins.

While acknowledging that O'Leary Digital is a young company, CEO Paul Palandjian said the team has decades of experience building major infrastructure projects and that its proposed 15 gigawatts of capacity would be built in stages over several years.

The company said it has secured binding agreements covering about 11,000 acres in Utah and 13,000 acres in Alberta, obtained a 30-year development agreement in Utah, and made progress on planning, engineering, and permits at both sites. It also said the projects would generate their own electricity.

The company said its early development work is funded, additional capital is committed to reaching key milestones, and experienced engineering and construction partners are involved.

"We would rather be judged on milestones than on a model," Palandjian said.

'An announcement in search of a project'

Other low scorers in the "bragawatt" brigade included Vermaland and Joule Capital Partners, according to Bernstein analysis based on Aterio data.

Joule has announced 50 data center projects with a combined capacity of 11.7 gigawatts. Bernstein estimates that only about 11% of that capacity will actually get built. Almost all of Joule's planned capacity remains at the announcement stage, with little evidence of construction progress or of major customers committing to use the facilities, Bernstein wrote in a July note to investors.

The analysts gave Joule a credibility score of 2.05 out of 5, placing it among the lowest-ranked developers in its database.

"An announcement in search of a project," the analysts wrote.

Vermaland has announced plans for a massive data center project in Arizona. Bernstein estimates that only 4% of its proposed 2.2 gigawatts of capacity will get built. The company is primarily a landowner, not a data center developer. Its proposed $33 billion La Osa project near Eloy had already been sharply scaled back amid local opposition and without construction approval. In late August, regulators formally rejected the project. Even before that, the analysts gave Vermaland a credibility score of 0.75 out of 5.

Joule and Vermaland didn't respond to emails seeking comment this week.

A yawning gap for data centers

Sergio Toro, CEO of Aterio, sees similar patterns in his firm's voluminous, detailed data.

Aterio's tracking shows that roughly 30 to 40 gigawatts of new data center capacity get announced each month. Yet only about 7 gigawatts begin construction each month, while 2 to 3 gigawatts actually start operating.

This leaves a yawning gap.

Many announced projects get withdrawn before construction begins — and before real money has been spent.

"They just make the announcement, but they didn't get their approvals. Or they make multiple filings in multiple locations to see which one gets selected," Toro told me. "That's why we track with satellite if there's real movement on the ground or not."

Indeed, Bernstein analysts wrote recently that the amount of "stranded" data center capacity has surged. These are projects that are either canceled, delayed, or not approved.

The AI boom's three tiers

Toro sees three broad groups competing to build AI infrastructure.

At the top are the established technology giants, including Amazon, Microsoft, and Google. They've built data centers for decades and generally deliver on their commitments. Most of their projects will get done, he said.

In the middle are "neoclouds," newer companies such as CoreWeave that rent out computing power specifically for AI. Although they're more vulnerable to financing problems and delays, many are making solid progress, especially because some of these players already have access to electricity.

"We see them advancing quite well," Toro said. "Sometimes we see some delays, but it's not something significant or any kind of big capacity that is at risk."

Then come the newcomers making extraordinary promises. That's where most delays and cancellations happen, Toro said.

Even Amazon needs a backup plan

Not every project that disappears was a fantasy, though. Even a top player like Amazon struggles sometimes to secure electricity and other supplies on time for these huge projects.

Business Insider's Eugene Kim reported exclusively this week on the challenges Amazon faces with its record AI buildout.

The biggest, most experienced tech companies deliberately pursue more potential data center capacity than they need because they know some developments will fail or get delayed.

Major AI companies were committing to three or four times the space and electricity they needed over the following two years, Jeffrey Moerdler, a data center lawyer, estimated during an interview with Bernstein analysts.

His reasoning: Of data center projects that haven't started construction, roughly a third will arrive on schedule, another third will be delayed, and the remainder won't happen.

Rezaei and Toro estimate that the top players don't over-commit that much — more like double the capacity they need at most.

The strategy provides a "Plan B," as Toro put it, in case of delays involving electricity, equipment, permits, local opposition, or other factors.

Texas has a ghost problem

This helps explain another extraordinary number.

Data center companies have requested connections for roughly 450 gigawatts of electricity demand on Texas' main power grid, more than five times the grid's roughly 85 gigawatts of existing capacity, according to Toro and others. 

Many of those requests are placeholders rather than firm construction plans. Toro calls them "ghost projects."

These speculative projects clog the waiting lists for electricity connections, making the electricity shortage look a lot worse than it actually is.

"Developers routinely spam projects across several sites, and many are speculative builds with no customer yet," Ryan McEntush, a partner at venture capital firm Andreessen Horowitz, wrote in a recent blog. "Often these new developers have never even plugged in a GPU, let alone a power plant."

Texas has paused applications to review and weed out projects that aren't serious. Toro, from Aterio, expects this to favor the established players at the expense of the bragawatts.

The real AI boom

None of this means the AI infrastructure boom is imaginary.

Aterio tracks thousands of buildings, including major projects already under construction. Bernstein estimates that nearly 80% of the capacity the largest technology companies are directly developing is credible.

And when speculative projects disappear, Rezaei doesn't expect a wave of unpaid debt. Many will fail before building begins, the stage when developers borrow serious money through project financing.

Some of those missing data centers were never really coming in the first place.

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• Sundar Pichai said he uses AI agents to track Google's model development and internal discussions.
• The bots summarize updates and monitor how Google's computing resources are used.
• Pichai said humans still make consequential decisions about resource allocation.

Even Google's CEO needs help keeping up with Google.

On the Sources podcast released Wednesday, Sundar Pichai described to host Alex Heath how he delegates some of the information-gathering that comes with running the tech giant.

“It could easily take me an hour a day to kind of catch up, what's happened in the last 24 hours, right?" Pichai said. "But now I have agents running which summarize and bring that information to me. I can still go deeper, and I do so often."

Pichai said that his AI agents give him a way to "understand the model development arc," figure out how Google's computing resources are being used, and follow threads spread across numerous internal chat groups without having to read through every conversation himself.

“Similarly, there are agents with telemetry into our compute usage by product area, efficiency, utilization, and giving me inputs and insights, you know," Pichai added. "I think it's made all of us, you know, be better at what we do."

That doesn't mean Pichai has handed over the important calls to AI. He said humans — including himself — remain directly involved in consequential decisions about allocating computing resources.

Managing agents is becoming a skill in its own right, giving some workers a new set of headaches, from checking the bots' output to preventing them from duplicating work or straying beyond their assignments. Business Insider previously reported that the need to manage agents is making everyone a boss without higher pay.

Delegating to bots doesn't necessarily make work easier, either. A Cisco engineering director who manages more than 10 agents told Business Insider that overseeing them involves more task switching and can make work feel more intense without shortening his days.

For the Google CEO, however, continuous work is a plus.

"So because your agents, they're running in the cloud, they're running 24/7, they have your context, and so they can pick up things where you left off," Pichai said. "It's a continuum there. By the time it's doing that, you would be applying yourself to the next problem and so on."

• Nvidia CEO Jensen Huang's Berlin keynote was interrupted by Greenpeace protesters onstage.
• Huang was making the case for Europe to build an AI economy with factories and agents.
• His finale featured a wobbly Microduck robot demo and an AI-generated Eurovision-style video.

Even for a seasoned keynote pro like Jensen Huang, there's always the risk that things don't go fully to plan.

The Nvidia CEO opened the company's Berlin conference on Wednesday to make the case that Europe should build its own AI economy — complete with AI factories, autonomous agents, and robots.

But the pitch was interrupted about 20 minutes into the keynote by protesters holding a Greenpeace banner reading, "Nvidia, clean your dirty business!"

One protester shouted: "Clean up your act! What about the technology that was found in Russian drones? Those were Nvidia chips. What are you doing about that? What are you doing about the environmental destruction?"

The protester appeared to be referring to an August statement from Ukraine's Defence Intelligence, which said it had identified an Nvidia Jetson Orin computer module in a Russian S-71 Monochrome cruise missile. The agency did not allege that Nvidia knowingly supplied the component to Russia.

Greenpeace said in a press release that it's calling on Nvidia to extend its 100% renewable energy target across its wider supply chain and to conduct audits to ensure its technology doesn't end up in military equipment. Nvidia did not immediately respond to Business Insider's request for comments.

As security escorted the protester away, Huang said, "Give her a chance," and that "everybody has a chance to say something." He then told the applauding crowd: "All important issues … but let me get through my keynote."

Huang, after joking that he had to "torture" the audience with "some education" before "the entertainment," then pressed on with Nvidia's pitch for Europe's AI future. He unveiled DSX POD, a modular system for AI factories, and promoted OpenShell, a platform designed to help companies run autonomous AI agents securely.

'Pay attention!'

The entertainment came near the end of the keynote, when Huang brought four small robots, known as Microducks, from French startup Pollen Robotics onstage.

The demo was charming, but not entirely obedient.

When Huang told the robots to "take a nap," one kept walking before eventually falling alongside the others. Later, only two appeared to perform a somersault when he asked them to.

Another robot lingered farther away from the group and tripped before getting back up. "Pay attention! Stay focused. Come here, come here. Stay focused," Huang told it. It eventually approached and sat down, drawing laughter from the audience.

Huang then closed the event with an AI-generated video of robots singing in a Eurovision-style contest.

The demo came a day after Huang told German Chancellor Friedrich Merz that Europe could not regulate its way to technological leadership.

"There's nothing to regulate if the industry doesn't thrive," Huang told Merz.

• Fireworks AI is an AI-infrastructure startup founded by former Google and Meta AI engineers.
• The company is seeking to raise a new round of funding at a valuation between $30 billion and $40 billion.
• Fireworks AI was last valued at nearly $18 billion in July of this year.

Fireworks AI is seeking to raise fresh funding at a valuation between $30 billion and $40 billion, a dramatic jump just months after the AI-infrastructure startup was valued at $17.5 billion, according to people familiar with the matter.

The potential valuation leap reflects investors' appetite for the companies selling the computing power and tools fueling the AI boom, even as Fireworks AI works through the fallout from a recent security incident.

The talks around the financing are still early, and valuation targets could still change. Fireworks declined to comment.

Founded in 2022, Fireworks was built by veterans of Meta and Google's AI efforts. Chief executive Lin Qiao previously led the PyTorch team at Meta, while cofounders include former Meta infrastructure engineers and a former leader of Google's Vertex AI. The company rents out computing infrastructure and software that lets businesses run, fine-tune, and deploy open-weight and custom AI models.

In July, Fireworks announced a $1.5 billion Series D funding round at a $17.5 billion valuation, led by Atreides Management, Index Ventures, and TCV, with participation from Nvidia, Lightspeed Venture Partners, Bessemer Venture Partners, and Menlo Ventures. That round followed a $250 million Series C at a $4 billion valuation in late 2025, co-led by Lightspeed, Index, and Evantic. Last month, The Information reported that Fireworks has been considering a round at a $30 billion.

Rather than trying to build a consumer chatbot to rival ChatGPT, Fireworks sells what it calls specialized intelligence: models customized on a customer's proprietary data, workflows, and use cases. The company said in July that it had surpassed $1 billion in annualized revenue and was serving more than 40 trillion tokens a day; it said 95% of those tokens came from models tailored to customers.

The company operates in a highly competitive layer of the AI stack. Rivals include model-infrastructure startups such as Together AI, Baseten, and Replicate, as well as cloud giants such as Amazon Web Services, Google Cloud, Microsoft Azure, and Oracle.

The fundraising talks come shortly after a security incident at Fireworks AI. The company said it identified unauthorized use of internal credentials earlier this month that allowed an intruder to access data from some customer accounts.

"We take this seriously and will be transparent about what happened and what we find," Qiao posted on X. "I'm personally committed to turn this attack into the most formidable security response in AI Infra."

Please send along your tips. Email Ben or Katie anytime or contact them on Signal at benbergman.11 and kroof.26. Sign up for Business Insider's weekly VC Insider newsletter here.

FIRST ON FOX: The U.S. Navy is quadrupling the ceiling on a contract for AI-powered robots used to inspect warships to $300 million.

Just six months after initially tapping the technology to help get ships through maintenance and back to sea faster, the expanded agreement with Pittsburgh-based Gecko Robotics underscores the importance the U.S. military is putting on tech-based solutions. The initial ceiling for the deal was $71 million, but the Navy is pushing to improve fleet readiness amid a widening shipbuilding gap with China.

"While the U.S. may not be able to out-manufacture faster than China, we can outsmart the ways that we maintain, operate and are ready with our vessels," Gecko Robotics CEO Jake Loosararian told Fox News Digital.

Gecko says its technology can save the Navy up to $8 million in repair costs on a single ship and identify some structural issues up to 50 times faster than traditional inspection methods.

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The company is also unveiling a new robot developed specifically for the Navy that can inspect the steel beneath thick coatings on warship decks without first stripping them away.

Dubbed Komodo, the robot scans a ship's deck to map corrosion and material thickness, giving maintenance crews a picture of what needs to be repaired before more invasive work begins.

"It's essentially a CAT scan," Loosararian said. "You're basically understanding exactly what's going on."

Traditionally, determining the condition of steel beneath a ship's non-skid deck coating can require removing material before crews know the extent of the problem. Komodo is designed to gather that information without first taking the surface apart.

"The robots are able to actually understand what's wrong without having to take apart the ship to figure out what to fix," Loosararian said.

In another example cited by Loosararian, an assessment that initially called for roughly 1,200 square feet of repair work was narrowed to about 300 square feet after robotic inspections provided a more precise picture of the damage.

"You reduce and focus the kinds of repairs that you need," Loosararian said. "You're repairing less."

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The increased ceiling comes just six months after the Navy awarded Gecko a five-year, $71 million contract vehicle to expand the use of its robotic inspection technology across the fleet.

Under the initial agreement announced in March, Gecko was set to begin work on 18 ships in the U.S. Pacific Fleet, including destroyers, amphibious warships and littoral combat ships.

The company's wall-climbing robots crawl across hulls, flight decks, ballast tanks and other difficult-to-access areas, scanning for corrosion, metal loss and other structural problems. The data is fed into Gecko's AI-powered Cantilever platform to help maintenance planners determine where repairs are needed.

The goal is to find those problems earlier, before a ship enters a major maintenance period and crews discover additional work that can extend its stay in the shipyard.

"The biggest savings are just in speed," Loosararian said. "Speed — you can't put a price tag on it, especially if you're in the U.S. Navy right now."

The expansion comes as Navy leaders seek to push fleet readiness toward 80% while maintenance delays continue to sideline ships that would otherwise be available for deployment.

China already fields a larger battle force than the U.S. Navy and can produce vessels at a pace American shipyards cannot easily match. That has put additional pressure on the Pentagon to keep more of the ships already in the American fleet available for operations.

For Gecko, the longer-term goal is to move beyond finding existing damage and use years of inspection data to anticipate problems before they force ships into longer repair periods.

"You are able to predict exactly what's going to break before it does," Loosararian said, allowing maintenance planners to have workers, materials and repair plans ready when a ship reaches the pier or enters a shipyard.

The technology could also reduce the amount of dangerous and labor-intensive inspection work performed by shipyard workers as the industrial base struggles with shortages of skilled labor.

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Loosararian said the combination of robotics and AI is ultimately aimed at allowing a limited workforce to spend more time fixing ships and less time determining what needs to be fixed.

For a Navy racing to keep pace with a rapidly expanding Chinese fleet, he argued, getting more out of the ships already in the water could prove just as important as building new ones.

"While the U.S. may not be able to out-manufacture faster than China," Loosararian said, "we can outsmart the ways that we maintain, operate and are ready with our vessels."

America seems to be on the precipice of a future technological golden age. Whether we take steps toward that future could depend on which political vision prevails over the next few election cycles.

Speaking alongside President Donald Trump at the White House in September, Elon Musk predicted a world where artificial intelligence and advanced robotics create an "age of abundance." Musk suggests these technologies could eventually provide your average person with better medical care than what today’s wealthiest individuals enjoy.

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This optimistic vision aligns well with Trump’s repeated promises of a new American golden age. Yet, as his administration works to accelerate technological advancement, an increasingly influential wing on the far left appears determined to slam on the brakes.

Trump’s vision of a golden age

The Trump administration has made American leadership in artificial intelligence a cornerstone of its economic agenda. Through its AI Action Plan and executive orders, the administration has prioritized eliminating regulatory barriers, constructing data centers and expanding energy infrastructure necessary to power the AI revolution.

TRUMP ANNOUNCES NEW 'AI FORCE,' VOWS TO PROTECT INDUSTRY AS AI CZAR ANNOUNCEMENT NEARS

Treasury Secretary Scott Bessent has argued that AI and automation could help create a new era of American manufacturing. In an interview from 2025, Bessent suggested that the advanced "smart factories" of tomorrow could overcome labor-cost issues, making manufacturing in the United States far more economically feasible.

Imagine a future in America where advanced robotics makes domestic manufacturing globally competitive, where AI leads to major medical breakthroughs and where technological innovation produces unprecedented economic prosperity.

THE NEXT AMERICAN BOOM COULD BE HERE — UNLESS WASHINGTON REGULATES IT TO DEATH

Of course, there are legitimate questions about how this societal transformation will unfold, but the potential benefits are incredible.

The far left wants to hit the brakes

While many in the Trump administration plan for a golden age, Vermont Independent Sen. Bernie Sanders and his fellow democratic socialists are promoting an entirely different future.

In March, Sanders and New York Democrat Rep. Alexandria Ocasio-Cortez proposed a nationwide moratorium on new data center construction until Congress establishes a sufficient regulatory system for the industry.

INSIDE THE FAR-LEFT'S ANTI-DATA CENTER PUSH AND WHAT EXPERTS SAY IS DRIVING IT: 'PLAYBOOK WE'VE SEEN BEFORE'

These positions have quickly become commonplace for Democratic Socialists of America (DSA) activists and candidates. Many of these DSA-endorsed candidates support data center moratoriums and push these sorts of proposals at the state and local level.

But Sanders’ anti-AI sentiment goes way further. His American AI Sovereign Wealth Fund Act would effectively nationalize 50% of the AI industry, converting those stakes into a government-controlled fund. The plan would also include stacking the boards of these companies with federally picked appointees to influence corporate decisions.

SANDERS PUSHES 'DEATH PENALTY' FOR CRUCIAL US INDUSTRY THAT COULD GIVE ADVANTAGE TO FOREIGN ADVERSARIES

More recently, Sanders introduced legislation that would permanently prohibit the development of artificial superintelligence. This plan would establish a new federal AI agency and halt other advanced AI development until regulators approve continued efforts. Under this proposal, individuals who violate the research bans could face up to 20 years in federal prison.

Think about that. At a time when America is competing with China for technological supremacy, one of the country’s most prominent socialist politicians wants federal bureaucrats to target AI scientists with potential prison time for developing AI research it deems too advanced.

WINNING THE AI RACE MEANS BEATING CHINA ON INNOVATION, COST AND TRUST

The cost of standing still

To be sure, the AI revolution comes with many legitimate concerns that deserve conversation. Potential job displacement, privacy violations, algorithmic manipulation and the possible misuse of advanced AI are issues that will likely need to be addressed.

However, the consequences of deliberately suppressing technological innovation are rarely given equal attention.

Consider the potential benefits of AI-assisted drug discovery, which could lead to better treatments for cancer, Alzheimer’s and many other conditions. AI-enhanced robotics and logistics could transform manufacturing, increase agricultural output and help elderly and disabled Americans maintain their independence.

Imagine the lives and the industries that could be saved and the economic opportunities that could spring up from these technologies that we have only now begun to understand.

Under the far left’s proposals, we may never see these benefits materialize. Sanders says he is concerned that average Americans will be left behind in the AI revolution, but his proposed restrictions threaten to undermine the very innovations that could cause growth in societal prosperity.

China won’t be hitting the brakes

Perhaps the most concerning consequence of the far left’s vision is America’s geopolitical future.

Like America, China is also pursuing AI at breakneck speeds. While America has enjoyed a substantial lead in this race, the recent Stanford University 2026 AI Index showed the performance gap between American and Chinese models has effectively disappeared.

The proposals from Sanders and the far left to stall AI research and data center construction give the Chinese Communist Party opportunity to pull ahead in the AI race. If China were to take the lead in the AI race, America’s global status economically and geopolitically could be severely diminished.

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At the same White House meeting where Musk outlined his optimistic vision of the future, Trump warned that Chinese President Xi Jinping "will take every data center that we don’t want."

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The AI revolution carries great potential for increased prosperity unlike anything we have seen in our lifetime. It would be a historic tragedy if America surrendered that opportunity because our politicians were more afraid of technological progress than our geopolitical rivals.

• Chris Williams was the VP of HR at Microsoft and is now an advisor and consultant.
• Williams uses a broad range of AI tools and encourages his clients to do the same.
• "Perhaps you'll find, as I did: AI can be better at deletion than creation," he says.

Practically every company is making AI tools available to their employees. As the former VP of HR at Microsoft and a leadership advisor with over 50 years of business experience, I'm amazed at how quickly this rollout has progressed.

Some people are all in, using the tools for nearly everything they do. Others have barely stuck their toes in the AI waters. I've been using a broad range of AI tools and encourage my clients to do the same.

AI isn't without controversy, and if someone is strongly against using it — for whatever reason — I'm not going to convince them otherwise.

But if you're wondering how and why you should use these tools, I'm here for you. Let's talk about three ways I advise all my executive clients to use AI effectively in the workplace.

1. Use AI as a second opinion

Use AI as a way to see things you might not have seen. Ask it, "What am I missing?"

I agree with many that AI is lousy at most creative efforts. Whether written or visual, AI results can be generic, mediocre, and often embarrassing. The output usually deserves the "AI Slop" label it gets. But to dismiss it entirely misses a core value.

AI is useful with, perhaps even because of, that generic, often obvious other perspective.

It can be amazingly helpful to simply ask: what am I missing? Since AI has a tendency to agree with me, I explicitly ask for pushback: "review this and tell me what I'm missing, tell me where I'm wrong." The fact that AI is so obvious, so average, is actually a core value here. I'm often surprised at the points I missed and the viewpoints I overlooked.

Even if AI doesn't find things you missed, a contrarian angle can be very helpful to shore up your own case. You'll be able to say, "Yes, I considered those things, and here's why my take on this issue is better."

In that way, AI can also help you prepare for your discussions with colleagues. With so many letting AI draft their arguments, it's very valuable to see their perspective in advance.

So no, I don't let AI make my case, but I always look for another perspective in what I do — wherever it comes from. Having a tool that can give me that on a moment's notice is incredibly useful.

2. Ask AI to summarize dense information

Long before AI, we all had that colleague who always told you far more than you needed to know. With some now using AI heavily, it can frankly be overwhelming.

You ask a simple question and get a blizzard of AI-generated noise in response. AI can be the worst over-explainer when simply left to run. You see the results in a seven-page response.

When I'm faced with one of these walls of text, I've learned there are really only two ways to respond. I can, and often do, ask the person to try again. What does this all really mean? Can you summarize your point in a couple of sentences? I occasionally get snarky and ask for the "non-AI slop" version.

Another way to deal with this is to ask AI to summarize it, whether AI tools or a wordy colleague wrote it. Importantly, I don't trust the AI summary to be the full explanation, but it helps me understand the key points. Which I then work to find in the original blizzard response.

Yes, this is a little circular. After all, why not just have the two AI systems have the discussion? Leave me out of it. But in a world where it seems everyone has a hard time getting to the point, any tool to cut through the noise is welcome. And AI is very good at that.

3. Let AI help you with tasks

In a similar vein, one thing AI does incredibly well is organize. This kind of rote task of sorting, filtering, and filing is right in the sweet spot for most AI systems.

I'm wary of letting AI systems automate tasks like filtering or filing things without my supervision. I'd never let an AI system automatically send emails, texts, or documents without my careful review. But I often have AI systems help me sort through mountains of accumulated debris.

I recently moved to a new computer system and had AI review what I was planning to move over. I discovered decades of accumulated cruft. Simply from a comprehensive list, I found gigabytes of programs, files, and settings, 90% of which I just left behind.

So if you have 8,000 emails in your inbox, or a documents folder that has grown a second life, let an AI system take a look for you. Don't just tell it to "deal with it." Your judgment is needed.

Rather, have it give you a review of it and another perspective. And once you've reviewed it, perhaps you'll find, as I did: AI can be better at deletion than creation.

So when your company rolls out the next new AI, use those tools to your advantage. AI can help you see another perspective, find the important points in the blizzard, and cut through the mess. I've embraced AI, use it with great care, and encourage everyone to do the same.

• Amazon is on an unprecedented spending spree to build more AI data center capacity than anyone else.
• The company has extensive experience, yet even it's struggling to bring capacity online fast enough.
• Internal documents show Amazon getting bilions of dollars of Nvidia GPUs before they can be deployed.

At an internal staff meeting in September, AWS CFO John Felton had a joke for Prasad Kalyanaraman, the executive overseeing much of Amazon's AI infrastructure expansion.

"You've taken us free cash flow negative as a company, so congratulations," Felton said, according to a recording of the meeting obtained by Business Insider.

The remark was lighthearted. But it captured a stunning reversal.

Just two years ago, Wall Street was debating what Amazon would do with its growing cash hoard. Now, cash flow has evaporated as Amazon embarks on its biggest bet ever, spending $220 billion this year alone to build the physical backbone for AI.

At the meeting, Kalyanaraman did not dispute Felton's premise. Amazon's machine-learning and AI business is "hugely free cash flow negative," he said, adding that he expects the company's free cash flow to remain negative for "the next couple of years or so."

Amazon has made big bets before. It spent heavily on cloud computing, fulfillment centers, and its delivery network long before they became some of the company's most valuable assets. Investors questioned those investments at the time, but Amazon ultimately built enormous, profitable businesses from them.

AI is testing this playbook at a far larger scale. Amazon is committing hundreds of billions of dollars to data centers, servers, and power before investors know how quickly those investments will generate revenue.

The company is the best, or among the best, at building data centers, with decades of experience. But the size and complexity of these projects, and the bottlenecks holding progress back, are challenging — even for such a respected cloud pioneer.

Internal documents reviewed by Business Insider and people familiar with the matter show the company trying to coordinate a sprawling industrial system whose parts move on different schedules. A delay in power, equipment, or construction can leave billions of dollars in hardware waiting — and Amazon's cash tied up — while forecasts and costs keep shifting.

Why Amazon thinks it will work

Amazon says it has strong evidence that the capacity will be needed. AWS revenue grew 37% in the second quarter, its fastest pace in 18 quarters, while its backlog reached $496 billion.

CEO Andy Jassy thinks the company has a "clear line of sight" to strong financial returns from this record investment cycle. In his latest annual shareholder letter, he called AI a "once in a lifetime opportunity" and said Amazon would not be conservative in its investments.

"We've been through this cycle with the first big AWS growth wave, and liked the results," Jassy wrote. "We expect to feel similarly about this next wave."

Investors are concerned by the scale of the industry's expansion, especially as AI data centers are costing more to build and taking longer to complete.

The market is "repricing anything that funds multi-year builds against revenue that arrives later," Dan Ives, an analyst at Yorkville Ives, wrote in a recent report. Growing uncertainty around data center permitting and the political fight over electricity prices have given investors another reason to question how quickly these investments will pay off, he added.

Racks, but not enough power

One major industry challenge is securing enough power to run the equipment that goes inside data centers. This includes expensive racks of servers that contain GPUs and other chips that train and run AI models and services such as ChatGPT.

Internal emails from August show Amazon preparing a new "Early Rack Receipt" process for an expected wave of server racks carrying Nvidia's GB300 chips at US facilities before final deployment.

One Amazon email projected the company would take delivery of about $5 billion of Nvidia GB300 racks in August, followed by another roughly $3 billion each month "for the foreseeable future."

Some AWS data center sites had room for more racks but not enough available power to turn them on, people familiar with the effort said, leaving the equipment to be staged at intermediary warehouses until the facilities were ready. These people asked not to be identified discussing sensitive matters.

Timing and coordination

Usually, Amazon doesn't pay for racks until the gear is put into a data center, leaving suppliers to carry the cost of the equipment in transit, according to the internal documents obtained by Business Insider. This helps the company conserve cash.

However, the intensity of the AI data center building boom has made this type of coordination harder. Amazon has to compete more for scarce resources as it jockeys with rivals for access to Nvidia GPUs and other AI components.

Under the new "Early Rack Receipt" process, Amazon now takes ownership of these racks earlier in the supply chain, rather than waiting until the equipment reaches a data center.

The means spending cash sooner. And any delays getting the racks installed and plugged into a reliable power source potentially pushes back the moment when revenue starts flowing from these assets.

At least one AWS supply chain team did not have "a clear forecasted path" for getting racks into data centers, according to one of the emails reviewed by Business Insider.

An Amazon spokesperson said the company routinely stages finished hardware near deployment sites so it can be installed once power capacity is available, calling that approach "best business practice."

The spokesperson added that AWS has "long-established systems" to track infrastructure assets and disputed that the broader company lacks a forecasted path for getting racks into data centers.

This problem is not unique to Amazon.

Bernstein analysts wrote in a recent note that power permits, cooling systems, electrical equipment, construction, and hardware all move on separate schedules.

"Timing is imperfect," the analysts said.

"Inherently uncertain"

During the internal staff meeting in September, Felton, the AWS CFO, described the broader problem eloquently: "How do you work in the future when the future is inherently uncertain?"

Kalyanaraman, the AI infrastructure leader, said Amazon's internal outlook for the return on its AI investments had improved. But the assumptions behind those returns keep shifting.

"The forecast is changing on a daily basis," Kalyanaraman said. "The world that we live in is extremely dynamic."

Those shifting assumptions include when data centers will be ready and how much equipment AWS needs to keep on hand, he explained.

An eight-week supply buffer might be routine for a smaller company. But at Amazon's scale, Kalyanaraman said, this could mean holding "billions and billions of dollars" worth of additional equipment inventory — tying up precious cash.

Frugality meets the AI boom

Such tensions are especially striking for Kalyanaraman, a 21-year Amazon veteran who earlier this year was promoted to the elite S-team that reports directly to Jassy.

Amazon's founding story is defined by frugality. It famously used cheap doors as desks when founder Jeff Bezos started in Seattle in the mid-90s.

Kalyanaraman has absorbed this lore into bones. In late 2023, as AWS faced slower growth and layoffs, he urged employees to do more with less. "Frugality breeds innovation," he said back then.

That instinct still shapes his approach. In the September meeting this year, Kalyanaraman told Felton that AWS tries to make major investments at the "last responsible moment," when it has the clearest possible view of demand.

"If we can wait to spend the capital as late as possible, it's better for us," he said.

But the frenetic pace of the AI boom makes this principle difficult to follow. By the time demand is fully visible, the servers, power, and construction capabilities that AWS needs may no longer be available. That puts pressure on the company to commit more money sooner to secure supply and meet its data center capacity goals.

Kalyanaraman recalled telling Jassy in 2023 that AWS would "surely" be out of its capacity crunch by 2027. Instead, Amazon still expects demand to exceed its available capacity in 2027, the AI infrastructure chief said during the meeting this September.

Secured Power at Risk

Getting enough electricity may be the biggest challenge for an industry leader like Amazon.

The company has 44 gigawatts of data center capacity under development across 16 states, more than double Google, Meta, and Microsoft, according to Cleanview Research estimates.

An internal AWS dashboard called "Secured Power at Risk" tracks utility and regulatory constraints affecting electricity capacity AWS has lined up for its data centers.

The risks include transmission bottlenecks, requirements to reduce use during grid stress, and "use it or lose it" provisions that can jeopardize capacity if a data center project misses usage or construction milestones.

In other words, AWS is not simply waiting for electricity to arrive. It is managing a portfolio of commitments that can become more costly if a data center project is delayed.

Maryland proposals

A submission to Maryland's utility regulator last year, obtained through a public records request, illustrates the kind of exposure that can accompany massive data center buildouts.

Amazon told the Maryland Public Service Commission that large data center customers could help pay for grid upgrades or bring new power capacity to market without burdening other electricity ratepayers.

The company also proposed setting aside collateral to help utilities get more comfortable with the large investments needed to generate more electricity for data centers. Amazon proposed three years of collateral so that if a large electricity customer abandoned unused capacity, the utility would have time to re-sell the power.

The proposal is not evidence that AWS has made such a payment or posted collateral. It does, however, show that large data centers can face extra financial obligations, beyond their own electricity bills, if projects are delayed or cancelled.

The Amazon spokesperson said such utility rules are not unique to AWS. The company has not disclosed how much cash it has paid upfront to secure power.

New constraints 'keep popping up'

The AI buildout also incurs expenses that are harder to model than a conventional capex plan.

In the September meeting this year, Kalyanaraman said that while reviewing plans for gigawatts of capacity in West Texas, AWS confronted a labor problem: There weren't enough nearby pipefitters, electricians, and other skilled workers to build the facilities.

The company's response, he said, was to pay to construct housing complexes near its data centers so workers could live there for the two or three years needed to complete the projects.

Even the crates used to ship servers have become a planning issue. It may seem absurd to worry about the price of a container when servers cost hundreds of thousands of dollars, Kalyanaraman said. But at AWS's scale, where the company needs so many crates, even trivial items can become a material expense.

"We have new constraints that keep popping up," Kalyanaraman said.

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• A version of this story originally appeared in the AI Insider newsletter.
• Sign up for Business Insider's weekly AI Insider newsletter here.

AI companies have already scraped much of the web. Now they're chasing something even more valuable: data that shows how humans actually work.

That's the focus of Business Insider's new series, The AI Data Grab. This week, we published three stories showing how the race for workplace data is accelerating.

One piece, from Charles Rollet, Tom Carter, and Stephen Council, examines the booming market for company records. AI firms are offering businesses six- and even seven-figure sums for anonymized Slack messages, emails, presentations, and internal documents.

The goal is to teach AI systems how people solve problems, collaborate, make decisions, and complete long, multi-step tasks. Those real-world workflows are becoming the fuel for the next generation of AI agents.

Another story, from Melia Robinson, explores how law firms are building AI systems around decades of legal knowledge, turning their own experience into a competitive advantage rather than relying solely on off-the-shelf AI models and tools.

A third piece, by Rya Jetha, reveals how AI companies pay people to record real-world work, such as cooking dinners and packing warehouses, to teach robots how humans perform everyday physical tasks.

Together, these stories point to a major shift in AI development. The first wave of generative AI was mostly about bigger models and faster chips. The next chapter is about machines understanding how work gets done, so AI can help us do it better and quicker.

Sign up for the AI Insider newsletter here. Reach out to me via email at abarr@businessinsider.com

• Anthropic examined what might happen when AI and robots are combined.
• That could put more hands-on jobs, including gardening, in the path of automation, Anthropic warned.
• I'm skeptical that robots can handle the messy reality of pulling weeds.

Remember when Anthropic's AI jobs report suggested gardeners were among the safest workers because AI couldn't pull weeds or tell a rose from a dandelion? I joked about that in AI Insider earlier this year.

Well, Anthropic updated the research to freak us all out a bit more.

This time it asks a bigger question: What happens when you combine AI with robots? That change pushes many hands-on jobs much higher on the disruption scale. On paper, it makes sense. In practice, I'm skeptical.

For instance, anyone who's spent time working in a garden knows how hard it is to identify the right plant, kneel in awkward spots, and pull a weed out properly by the roots without damaging everything around it.

Robots are improving. But if you've ever watched one tackle a messy real-world task, you'll know there's a long road between a research chart and reality.

The chart below illustrates how adding robots could change job-task exposure across selected job categories. Cleaning & maintenance includes gardeners along with janitors and similar roles.

• Anthropic published new surveys comparing its staffers' sentiments to those of US workers.
• The AI startup's employees expect the tech to change work and the economy far more dramatically.
• Most Anthropic employees guessed that unemployment would top 12% in 2030.

Anthropic employees believe that AI will make their labor obsolete. Most US workers don't see their jobs that way at all.

The AI company published research on Friday that compares surveys with US adults against an internal poll of 1,334 Anthropic employees. Most of the Anthropic staffers said AI will handle at least 80% of their jobs by 2030, while only about 5% of the US workers felt the same.

The research, titled "The AI expectations gap," shows the chasm between how the employees at the AI startup feel and how other American adults feel. Anthropic workers are far more optimistic about the progress of AI capabilities, how much it's already saving time, and how fast the economy will grow.

The median Anthropic employee said that by the end of 2030, they expect AI to handle 95% of the work of building a new AI model.

Peter McCrory, Anthropic's chief economist, wrote on X that the views might help outsiders prepare for more capable AI systems.

"We're publishing this research because Anthropic runs roughly a year and a half ahead of the economy on AI adoption, so what happens at Anthropic could be a leading indicator for the broader world," he said.

Anthropic workers expect higher unemployment and extreme GDP growth

Anthropic's surveys examined how workers already use AI and how they think it will change.

"US adults and Anthropic staff largely agree about what AI can do today," the researchers wrote. "However, they disagree about how quickly it will improve, how widely it will be used, and how large the productivity gains measured by time saved will be."

There are a few possible explanations for the discrepancies. Anthropic employees have larger budgets for the tools and often get access to unreleased models. US workers, on average, are more likely to work with their hands or in a service industry.

The researchers found that workers in manual trades, production, and transport thought AI would handle the least of their work by 2030, compared with Anthropic staffers, tech workers, and respondents in business and finance.

Most Anthropic employees surveyed said that they expect the unemployment rate to top 12% in 2030. The majority of the US workers thought it would hover around or under 8%. It's currently 4.2%.

So far, researchers have found mixed evidence on AI disrupting the job market — a Yale team recently found it had mainly changed jobs, and had "no connection" to changes in unemployment.

The Anthropic researchers also compiled survey responses to estimate predictions on economic growth. Based on their answers, the median US adult's guess for GDP growth in 2030 was 5.4%, compared with 17.1% among Anthropic employees. The country's GDP grew by 2.2% last year.

Anthropic's staffers are following their leader

Anthropic's CEO, Dario Amodei, has become one of the industry's most closely watched voices on labor upheaval. In March of 2025, he said he expected AI to write 90% of code within 6 months, and "essentially all of the code" within the year. He made more headlines that May, telling Axios that AI could wipe out half of entry-level white-collar jobs within five years and lift unemployment to 10% or 20%.

Amodei expanded on that claim in a later essay, writing that he expects AI to be different from prior technological changes. He wrote that he expects an "unprecedented" short-term shock to labor, and that the economy will grow dramatically.

"AI isn't a substitute for specific human jobs but rather a general labor substitute for humans," he wrote.

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• Microsoft President Brad Smith warned that US immigration policies could drive AI talent overseas.
• "Let's not put that at risk," Smith said.
• The Trump administration recently suspended Microsoft and other tech firms from a green card program.

If America wants to win the AI race, sending the world's brightest engineers packing might not be a winning strategy.

That's the warning from Microsoft President Brad Smith, who said the Trump administration's immigration crackdown risks driving the next generation of AI talent away from the United States.

"I think we should all be worried about sending intentionally or inadvertently or otherwise, signals that send the most talented next wave of AI software engineers to other countries rather than the United States," Smith said in an episode of Politico's "The Decoded Podcast" that aired on Thursday.

His comments come after the Trump administration announced last week that it was suspending Microsoft, Adobe, and several other technology companies from a federal program that sponsors foreign workers for permanent residency.

Vice President JD Vance accused Microsoft of abusing the Permanent Labor Certification, or PERM, program by laying off American employees while hiring lower-paid foreign workers.

Smith, who Business Insider reported was recently put in charge of Microsoft's communications, pushed back against those allegations, saying the administration had presented a misleading picture of Microsoft's hiring practices.

"What was presented as Microsoft replacing 6,000 people who lost their jobs with 9,000 foreigners was frankly completely off base," he said.

Smith said most of the company's visa and green card applications concerned existing employees rather than new hires, adding that Microsoft had hired 9,200 American workers in its last fiscal year.

"That was left out of the narrative," he said.

He also rejected suggestions that Microsoft pays foreign workers less than their American counterparts, saying employees are compensated under the same system regardless of immigration status.

The American dream, on hold

The dispute comes amid growing uncertainty for skilled foreign workers in the US.

As part of our ongoing H-1B Fallout series, Business Insider previously reported that the Trump administration's shifting immigration policies have prompted some international workers to explore opportunities abroad, including in countries like the UK, Germany, and Canada.

An analysis of federal data by Business Insider found that certified H-1B applications fell 10% in the first nine months of fiscal year 2026 compared with the same period a year earlier. Applications for data scientists, however, rose 22.5%, highlighting continued demand for specialized AI talent.

On the Politico podcast, Smith cited immigrants' economic contributions as evidence of what America stands to lose.

He said that five of the six technology and science medal recipients recently honored by President Donald Trump had immigrated to the United States.

According to Smith, the companies associated with those five individuals collectively represent more than a quarter of the S&P 500's market value.

Their contributions, he said, benefit not just technology employees but ordinary Americans whose retirement savings and pension funds are invested in those businesses.

"Think about what those five individuals have done for the well being, even the wealth and standard of living, of Americans across the country," Smith said.

"Let's not put that at risk."

• Microsoft AI recently announced its latest model MAI-Base-2.
• AI CEO Mustafa Suleyman compared the model to a similar offering from OpenAI.
• Microsoft trained its model more efficiently than OpenAI, he said in an internal meeting.

Microsoft AI CEO Mustafa Suleyman told employees an in-house model was better than a similar offering from OpenAI, one of the software giant's biggest customers and partners.

"We now have a stronger pre-trained base model than the model that currently powers GPT 5.6 Sol," Suleyman said during a town hall meeting with employees on Thursday. "And in fact, we used one-quarter of the compute that OpenAI used to train their model."

Pre-training is the first stage of building an AI model, when it learns patterns from huge amounts of text, images, and other information. This takes massive amounts of expensive computing power, so Microsoft could save money if it's worked out a way to do this more efficiently.

Microsoft announced the model, MAI-Base-2, earlier on Thursday.

Suleyman said Microsoft privately tested this model against OpenAI models, including one nicknamed "Spud" that powers GPT-5.6. He added that Microsoft's model performed better on tests measuring how well AI can understand and predict patterns in information.

The comparisons are based on technical information Microsoft has about OpenAI's models, Suleyman said.

"Privately and confidentially, we've also compared it to the OpenAI models," Suleyman told employees, while suggesting that Microsoft has special access to information on how OpenAI models perform.

It's notable that Suleyman would choose to highlight limitations of OpenAI models, even in private discussions. Microsoft has invested billions of dollars in OpenAI, and owns a stake in the startup that is likely worth more than $100 billion. Any criticism of OpenAI could potentially dent the value of this huge investment.

The two companies also have a major partnership, with Microsoft offering OpenAI models to its Azure cloud customers. Microsoft has used OpenAI models for its own products such as Copilot, too.

But the relationship between the two companies has frayed in the past year or so. Increasingly, Microsoft has tried to branch out from OpenAI, including by working on its own models.

Suleyman also said Microsoft's new AI model used "12 times less computation than was used for DeepSeek, for example, on compute efficiency."

Suleyman didn't specify which DeepSeek model, but a public blog post described the pre-training of MAI-Base-2 being a lot more efficient than a similar offering from the Chinese lab, called DeepSeek-V4-Pro-Base.

Microsoft in March created a new combined team for its commercial and consumer Copilot products and reassigned Suleyman to focus on building AI models in the company's superintelligence unit.

Microsoft declined to comment and OpenAI did not respond to a request for comment on Thursday.

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• OpenAI's Alexander Embiricos said the AI era gives a choice: work more, or work less.
• He's in charge of Dots, the company's new always-on work assistant.
• AI is often pitched as freeing up time. Embiricos responded to the idea that it might add more work.

As the competitive AI race pushes tech workers into long hours, the head of OpenAI's new Dots product says AI gives people another option: work a whole lot less.

Alexander Embiricos, who leads OpenAI's enterprise product and helms the new Dots agent tool, told a crowd at TechCrunch's Disrupt conference on Thursday that AI's capabilities have created something of a fork in the road for workers. You can grind to meet the moment. Or you can use AI to kick your feet up a little.

"We basically have a lot more choice now," he said.

Embiricos made the comments after a reporter asked about when AI systems will actually deliver more free time. The reporter shared an anecdote where an OpenAI employee used a ChatGPT mobile feature to work in their car and on a plane — not exactly liberated by the new tech.

Embiricos said that the moment has already arrived where a worker can "outsource" tasks to AI and do less work. Then again, some people are so excited about AI's potential that they feel like it's an "interesting, ripe moment" to put in extra hours.

"I think that's why a lot of people are motivated to work very hard right now. That includes myself," he said. "But I think actually there is this choice, and there's totally this other setup where you could delegate a ton more things."

OpenAI is billing its Dots tool as a workplace assistant, always on and capable of proactively handing tasks and sorting to-dos. Embiricos uses his Dot often in Slack, he said, engaging on various threads.

His perspective illustrates how early companies are in their journey to adopt AI tools. Most companies have not yet automated their work tasks, and many workers are still debating how much to use the tools. Early AI adopters, meanwhile, can sprint through some work that used to take days or weeks, especially in software engineering.

Embiricos summed up the situation he feels workers are in: "You either choose: do you expand the ambition of what you can do, or do you do less? I do think we're there."

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