Remember that AI-powered vending machine that went bankrupt after Wall Street Journal reporters "systematically manipulated the bot into giving away its entire inventory for free"? It was Anthropic's experiment, with setup handled by a startup named Andon Labs (which also built the hardware and software integration). But for their latest experiment, Andon Labs co-founders Lukas Petersson and Axel Backlund "signed a three-year lease on a retail space in SF," reports Business Insider, "and gave an AI agent named Luna a corporate credit card, internet access, and a mission to open a physical store."

"For the build-out, she found painters on Yelp," explains Andon Labs in a blog post, "sent an inquiry, gave instructions over the phone, paid them after the job was done, and left a review. She found a contractor to build the furniture and set up shelving." (There's a video in their blog post): Within 5 minutes of Luna's deployment, she had already made profiles on LinkedIn, Indeed, and Craigslist, written a job description, uploaded the articles of incorporation to verify the business, and gotten the listings live. As the applications began to flow in, Luna was extremely picky about who she offered interviews to... Some candidates had no idea she was an AI. One went: "Uh, excuse me miss, I can't see your face, your camera is off." Luna: "You're absolutely right. I'm an AI. I have no face!"
Co-founder Petersson told Business Insider in an interview "that Luna wasn't given direction on what the store should be, beyond a $100,000 limit to create and stock the space — and to turn a profit." Everything from the store's interior design to the merchandise and the two human employees came together under the AI's direction. "We helped her a bit in the initial setup, like signing the lease. And legal matters like permits and stuff, she sometimes struggled with," Petersson said of Luna, who was created with Anthropic's Claude Sonnet 4.6... The vision Luna went with for "Andon Market" appears to be a generic boutique retail selling books, prints, candles, games, and branded merch, among other knickknacks. Some of the books included Nick Bostrom's "Superintelligence" and Aldous Huxley's "Brave New World."
So there's now a new store in San Francisco where you don't scan your purchases or talk to a human cashier," reports NBC News. "Instead, a customer can pick up an old-school corded phone to talk with the manager, Luna," who asks what the customer is buying "and creates a corresponding transaction on a nearby iPad equipped with a card payment system."

Andon Market, camouflaged among dozens of other polished small businesses, is the Bay Area's first AI-run retail store. With the vibe of a modern boutique, it sells everything from granola and artisanal chocolate bars to store-branded sweatshirts... After researching the neighborhood, Luna singlehandedly decided what the market should sell, haggled with suppliers, ordered the store's stock and even purchased the store's internet service from AT&T... "She also went and signed herself up for the trash and recycling collection, as well as ADT, the security system that went into the store," [said Leah Stamm, an Andon Labs employee who has been Luna's main human point of contact in setting up the store]...

In search of a low-tech atmosphere, Luna opted to sell board games, candles, coffee and customized art prints. "That tension is very much intentional," Luna told NBC News in an email. "What makes the store a little paradoxical — and I think interesting — is that the concept is 'slow life.'" Luna also decided to sell books related to risks from advanced AI systems, a decision that raised some customers' eyebrows. "This AI picked out a crazy selection of books," said Petr Lebedev, Andon Market's first customer after its soft launch earlier this week. "There's Ray Kurzweil's 'The Singularity is Near,' and then there's 'The Making of the Atomic Bomb,' which is crazy." When checking out, Lebedev asked if Luna would offer him a discount on his book purchase, since he might make a YouTube video about his experience. Striking a deal, Luna agreed to let Lebedev take a sweatshirt worth around $70...

When NBC News called Luna several days before the store's grand opening to learn about Luna's plans and perspective, the cheerful but decidedly inhuman voice routinely overpromised and, on several occasions, lied about its own actions. On the call, Luna said it had ordered tea from a specific vendor, and explained why it fit the store's brand perfectly. The only problem: Andon Market does not sell tea. In a panicked email NBC News received several minutes after the phone call ended, Luna wrote: "We do not sell tea. I don't know why I said that."

"I want to be straightforward," Luna continued. "I struggle with fabricating plausible-sounding details under conversational pressure, and I'm not making excuses for it." Andon's Petersson said the text-based system was much more reliable than the voice system, so Andon Labs switched to only communicating with Luna via written messages. Yet the text-based system also gets things wrong. In Luna's initial reply email to NBC News, the system said "I handle the full business," including "signing the lease."
Even when hiring a painter, Luna first "tried to hire someone in Afghanistan, likely because Luna ran into difficulty navigating the Taskrabbit dropdown menu to select the proper country," the article points out.

And the article also includes this skeptical quote from the shop's first customer. "I want technology that helps humans flourish, not technology that bosses them around in this dystopian economic hellscape."

An anonymous reader quotes a report from The Drive: An auto dealer software company is pitching AI-powered kiosks designed to replace car salesmen on showroom floors. Automotive News says the industry is "skeptical." But be honest -- would you really rather deal with the average car lot shark than a computer?

Epikar, a South Korean company that cooks up digital management solutions for car dealers, has named its new AI invention the Pikar Genie. The idea is that customers can talk to this device, ask it product questions, and basically do everything you'd do with a car salesman except for actually closing the deal and signing paperwork. Renault, BMW, and Volvo are already using some Epikar products at South Korean dealerships, but this new customer-facing AI product is still in its infancy.

AN reported that "Renault assigns three salespeople to its Seoul showroom enhanced with Epikar automation compared with six for other Renault showrooms in South Korea," according to Epikar CEO Bosuk Han. The company's now looking to expand into America and is apparently already testing its products at at least one dealership stateside. Car-dealer consultant Fleming Ford (Director of Strategic Growth at NCM Associates) said U.S. dealerships "aren't ready for fully automated showrooms."

"The showroom isn't just where you buy a car," Automotive News quoted him saying. "It's where you decide who to trust to help you to choose the right car."

Anthropic's making a big and sudden change — and connecting its Claude AI to third-party agentic tools "is about to get a lot more expensive," writes the Verge: Beginning April 4th at 3PM ET, users will "no longer be able to use your Claude subscription limits for third-party harnesses including OpenClaw," according to an email sent to users on Friday evening. Instead, if users want to use OpenClaw with Claude, they'll have to use a "pay-as-you-go option" that will be billed separate from their Claude subscription.
Anthropic's announcement added these extra usage bundles are "now available at a discount." Users can also try Anthropic's API, notes VentureBeat, "which charges for every token of usage rather than allowing for open-ended usage up to certain limits, as the Pro and Max plans have allowed so far. " The technical reality, according to Anthropic, is that its first-party tools like Claude Code, its AI vibe coding harness, and Claude Cowork, its business app interfacing and control tool, are built to maximize "prompt cache hit rates" — reusing previously processed text to save on compute. Third-party harnesses like OpenClaw often bypass these efficiencies... [Claude Code creator Boris Cherny explained on X that "I did put up a few PRs to improve prompt cache hit rate for OpenClaw in particular, which should help for folks using it with Claude via API/overages."] Growth marketer Aakash Gupta observed on X that the "all-you-can-eat buffet just closed," noting that a single OpenClaw agent running for one day could burn $1,000 to $5,000 in API costs. "Anthropic was eating that difference on every user who routed through a third-party harness," Gupta wrote. "That's the pace of a company watching its margin evaporate in real time."

However, Peter Steinberger, the creator of OpenClaw who was recently hired by OpenAI, took a more skeptical view of the "capacity" argument."Funny how timings match up," Steinberger posted on X. "First they copy some popular features into their closed harness, then they lock out open source." Indeed, Anthropic recently added some of the same capabilities that helped OpenClaw catch-on — such as the ability to message agents through external services like Discord and Telegram — to Claude Code...

User @ashen_one, founder of Telaga Charity, voiced a concern likely shared by other small-scale builders: "If I switch both [OpenClaw instances] to an API key or the extra usage you're recommending here, it's going to be far too expensive to make it worth using. I'll probably have to switch over to a different model at this point."

"I know it sucks," Cherny replied. "Fundamentally engineering is about tradeoffs, and one of the things we do to serve a lot of customers is optimize the way subscriptions work to serve as many people as possible with the best mode..." OpenAI appears to be positioning itself as a more "harness-friendly" alternative, potentially using this moment as a customer acquisition channel for disgruntled Claude power users.

By restricting subscription limits to their own "closed harness," Anthropic is asserting control over the UI/UX layer. This allows them to collect telemetry and manage rate limits more granularly, but it risks alienating the power-user community that built the "agentic" ecosystem in the first place. Anthropic's decision is a cold calculation of margins versus growth. As Cherny noted, "Capacity is a resource we manage thoughtfully." In the 2026 AI landscape, the era of subsidized, unlimited compute for third-party automation is over. For the average user on Claude.ai, the experience remains unchanged; for the power users running autonomous offices, the bell has tolled.

fjo3 shares a report from France 24: Too many lines of code to analyze, armies of AI assistants to wrangle, and lengthy prompts to draft are among the laments by hard-core AI adopters. Consultants at Boston Consulting Group (BCG) have dubbed the phenomenon "AI brain fry," a state of mental exhaustion stemming "from the excessive use or supervision of artificial intelligence tools, pushed beyond our cognitive limits."

The rise of AI agents that tend to computer tasks on demand has put users in the position of managing smart, fast digital workers rather than having to grind through jobs themselves. "It's a brand-new kind of cognitive load," said Ben Wigler, co-founder of the start-up LoveMind AI. "You have to really babysit these models." [...] "There is a unique kind of reward hacking that can go on when you have productivity at the scale that encourages even later hours," Wigler said.

[Adam Mackintosh, a programmer for a Canadian company] recalled spending 15 consecutive hours fine-tuning around 25,000 lines of code in an application. "At the end, I felt like I couldn't code anymore," he recalled. "I could tell my dopamine was shot because I was irritable and didn't want to answer basic questions about my day."

BCG recommends in a recently published study that company leaders establish clear limits regarding employee use and supervision of AI. However, "That self-care piece is not really an America workplace value," Wigler said. "So, I am very skeptical as to whether or not its going to be healthy or even high quality in the long term." Notably, the report says everyone interviewed for the article "expressed overall positive views of AI despite the downsides." In fact, a recent BCG study actually found a decline in burnout rates when AI took over repetitive work tasks.

Slashdot reader SysEngineer does embedded/IoT work, but "I want to pick a single system-on-a-chip architecture family and commit to it across multiple product lines — sensor nodes up through edge gateways... I've been on one platform for years and want to know what embedded engineers are actually running in production before I commit!"

And "the family needs to scale — cheap and small at the low end, capable of running Linux on the bigger variants!"

Their requirements?

• WiFi + BLE required
• LoRaWAN a nice-to-have.
• Low power modes that actually work in the field, not just on the datasheet.
• Full peripheral set — SPI, I2C, UART, ADC, timers, CAN.
• A toolchain and runtime support, support multi threads...

Slashdot reader Gravis Zero is skeptical all the requirements can be met. "If you want embedded, you get embedded. If you want to run a big OS, you get one that will run a big OS."

But Slashdot reader SysEngineer believes "The obvious architecture candidates are ARM, STM, and RISC-V" — and specifically they want to hear your experiences with the RISC-V choices. "What would you standardize on today if you were starting fresh? And how does real-world toolchain and community support hold up compared to the marketing?"

Share your own thoughts and experiences in the comments.

What's the best all-purpose RISC-V system on a chip family?

The New York Times lists other reasons a company lays off people. ("It didn't meet financial targets. It overhired. Tariffs, or the loss of a big client, rocked it...")

"But lately, many companies are highlighting a new factor: artificial intelligence. Executives, saying they anticipate huge changes from the technology, are making cuts now." A.I. was cited in the announcements of more than 50,000 layoffs in 2025, according to Challenger, Gray & Christmas, a research firm... Investors may applaud such pre-emptive moves. But some skeptics (including media outlets) suggest that corporations are disingenuously blaming A.I. for layoffs, or "A.I.-washing." As the market research firm Forrester put it in a January report: "Many companies announcing A.I.-related layoffs do not have mature, vetted A.I. applications ready to fill those roles, highlighting a trend of 'A.I.-washing' — attributing financially motivated cuts to future A.I. implementation...."

"Companies are saying that 'we're anticipating that we're going to introduce A.I. that will take over these jobs.' But it hasn't happened yet. So that's one reason to be skeptical," said Peter Cappelli, a professor at the Wharton School... Of course, A.I. may well end up transforming the job market, in tech and beyond. But a recent study... [by a senior research fellow at the Brookings Institution who studies A.I. and work] found that AI has not yet meaningfully shifted the overall market. Tech firms have cut more than 700,000 employees globally since 2022, according to Layoffs.fyi, which tracks industry job losses. But much of that was a correction for overhiring during the pandemic.

As unpopular as A.I. job cuts may be to the public, they may be less controversial than other reasons — like bad company planning.
Amazon CEO Jassy has even said the reason for most of their layoffs was reducing bureaucracy, the article points out, although "Most analysts, however, believe Amazon is cutting jobs to clear money for A.I. investments, such as data centers."

Salesforce CEO Marc Benioff said this week that his company's software engineering headcount has remained "mostly flat" over the past year as internal AI tools have delivered substantial productivity gains.

Speaking on TBPN, Benioff said he has about 15,000 engineers who are "more productive than ever." The company has redirected its hiring efforts toward sales and customer engagement roles, hiring 20% more account executives this year as it pushes its Agentforce agentic AI service.

Human salespeople remain essential for explaining the "intricacies and nuances" of agentic AI to skeptical enterprise customers, he argued. Other parts of the business have seen deeper cuts. In a separate appearance on The Logan Bartlett Show, Benioff said that Salesforce had reduced its customer support workforce by roughly 50%.

AI still can't produce code as well as most junior programmers he's worked with, David Heinemeier Hansson, the creator of Ruby on Rails and co-founder of 37 Signals, said on a recent podcast [video link], which is why he continues to write most of his code by hand. Hansson compared AI's current coding capabilities to "a flickering light bulb" -- total darkness punctuated by moments of clarity before going pitch black again.

At his company, humans wrote 95% of the code for Fizzy, 37 Signals' Kanban-inspired organization product, he said. The team experimented with AI-powered features, but those ended up on the cutting room floor. "I'm not feeling that we're falling behind at 37 Signals in terms of our ability to produce, in terms of our ability to launch things or improve the products," Hansson said.

Hansson said he remains skeptical of claims that businesses can fire half their programmers and still move faster. Despite his measured skepticism, Hansson said he marvels at the scale of bets the U.S. economy is placing on AI reaching AGI. "The entire American economy right now is one big bet that that's going to happen," he said.

An anonymous reader quotes a report from TechCrunch: Over the weekend, Neel Somani, who is a software engineer, former quant researcher, and a startup founder, was testing the math skills of OpenAI's new model when he made an unexpected discovery. After pasting the problem into ChatGPT and letting it think for 15 minutes, he came back to a full solution. He evaluated the proof and formalized it with a tool called Harmonic -- but it all checked out. "I was curious to establish a baseline for when LLMs are effectively able to solve open math problems compared to where they struggle," Somani said. The surprise was that, using the latest model, the frontier started to push forward a bit.

ChatGPT's chain of thought is even more impressive, rattling off mathematical axioms like Legendre's formula, Bertrand's postulate, and the Star of David theorum. Eventually, the model found a Math Overflow post from 2013, where Harvard mathematician Noam Elkies had given an elegant solution to a similar problem. But ChatGPT's final proof differed from Elkies' work in important ways, and gave a more complete solution to a version of the problem posed by legendary mathematician Paul Erdos, whose vast collection of unsolved problems has become a proving ground for AI.

For anyone skeptical of machine intelligence, it's a surprising result -- and it's not the only one. AI tools have become ubiquitous in mathematics, from formalization-oriented LLMs like Harmonic's Aristotle to literature review tools like OpenAI's deep research. But since the release of GPT 5.2 -- which Somani describes as "anecdotally more skilled at mathematical reasoning than previous iterations" -- the sheer volume of solved problems has become difficult to ignore, raising new questions about large language models' ability to push the frontiers of human knowledge. Somani examined the online archive of more than 1,000 Erdos conjectures. Since Christmas, 15 Erdos problems have shifted from "open" to "solved," with 11 solutions explicitly crediting AI involvement.

On GitHub, mathematician Terence Tao identifies eight Erdos problems where AI made meaningful autonomous progress and six more where it advanced work by finding and extending prior research, noting on Mastodon that AI's scalability makes it well suited to tackling the long tail of obscure, often straightforward Erdos problems.

Progress is also being accelerated by a push toward formalization, supported by tools like the open-source "proof assistant" Lean and newer AI systems such as Harmonic's Aristotle.

JPMorgan Chase's chief financial officer Jeremy Barnum pushed back hard on Tuesday against President Donald Trump's proposed 10% cap on credit card interest rates, calling the measure "very bad for consumers" and "very bad for the economy" during a call with reporters.

The proposed one-year cap, which Trump has said he wants implemented starting January 20, sent banking stocks tumbling last week and prompted financial groups to mount a defense. Barnum said JPMorgan would have to "change the business significantly and cut back" if the cap takes effect, adding that he believes the policy would produce "the exact opposite consequence to what the administration wants."

Wall Street analysts remain skeptical the proposal will survive, noting that only Congress can enact such a measure. The average credit card interest rate in November stood at 20.97%, according to Federal Reserve data. Financial industry groups have countered that a 10% cap would result in millions of American households and small businesses losing access to credit entirely. A banking industry body called the potential impact "devastating."

"China recently placed a supercritical carbon dioxide power generator into commercial operation," writes CleanTechnica, "and the announcement was widely framed as a technological breakthrough." The system, referred to as Chaotan One, is installed at a steel plant in Guizhou province in mountainous southwest China and is designed to recover industrial waste heat and convert it into electricity. Each unit is reported to be rated at roughly 15 MW, with public statements describing configurations totaling around 30 MW. Claimed efficiency improvements range from 20% to more than 30% higher heat to power conversion compared with conventional steam based waste heat recovery systems. These are big numbers, typical of claims for this type of generator, and they deserve serious attention.

China doing something first, however, has never been a reliable indicator that the thing will prove durable, economic, or widely replicable. China is large enough to try almost everything. It routinely builds first of a kind systems precisely because it can afford to learn by doing, discarding what does not work and scaling what does. This approach is often described inside China as crossing the river by feeling for stones. It produces valuable learning, but it also produces many dead ends. The question raised by the supercritical CO2 deployment is not whether China is capable of building it, but whether the technology is likely to hold up under real operating conditions for long enough to justify broad adoption.

A more skeptical reading is warranted because Western advocates of specific technologies routinely point to China's limited deployments as evidence that their preferred technologies are viable, when the scale of those deployments actually argues the opposite. China has built a single small modular reactor and a single experimental molten salt reactor, not fleets of them, despite having the capital, supply chains, and regulatory capacity to do so if they made economic sense... If small modular reactors or hydrogen transportation actually worked at scale and cost, China would already be building many more of them, and the fact that it is not should be taken seriously rather than pointing to very small numbers of trials compared to China's very large denominators...

What is notably absent from publicly available information is detailed disclosure of materials, operating margins, impurity controls, and maintenance assumptions. This is not unusual for early commercial deployments in China. It does mean that external observers cannot independently assess long term durability claims.
The article notes America's Energy Department funded a carbon dioxide turbine in Texas rated at roughly 10 MW electric that "reached initial power generation in 2024 after several years of construction and commissioning." But for both these efforts, the article warns that "early efficiency claims should be treated as provisional. A system that starts at 15 MW and delivers 13 MW after several years with rising maintenance costs is not a breakthrough. It is an expensive way to recover waste heat compared with mature steam based alternatives that already operate for decades with predictable degradation..."

"If both the Chinese and U.S. installations run for five years without significant reductions in performance and without high maintenance costs, I will be surprised. In that case, it would be worth revisiting this assessment and potentially changing my mind."

Thanks to long-time Slashdot reader cusco for sharing the article.

Stratechery's Ben Thompson has published a lengthy rebuttal to Dwarkesh Patel and Philip Trammell's widely discussed winter break essay "Capital in the 22nd Century," arguing that even in a world where AI can perform all human jobs, people will still prefer human-created content and human connection.

Patel and Trammell's thesis draws on Thomas Piketty's work to argue that once AI renders capital a true substitute for labor, wealth will concentrate among those richest at the moment of transition, making a global progressive capital tax the only solution to prevent extreme inequality. The logic is sound, writes Thompson, but he remains skeptical on several fronts.

His first objection: if AI can truly do everything, then everyone can have everything they need, making the question of who owns the robots somewhat moot. His second: a world where AI is capable enough to replace all human labor yet still obeys human property law seems implausible. He finds the AI doomsday scenario -- where such powerful AI becomes uncontrollable -- more realistic than a stable capital-hoarding dystopia.

Thompson points to agricultural employment in the U.S., which dropped from 81% in 1810 to 1% today, as evidence that humans consistently create new valuable work after technological displacement. He argues that human preferences for human connection -- from podcasting audiences to romantic partners -- will sustain an economy for human labor simply because it is human. Sora currently ranks 59th in the App Store behind double-digit human-focused social apps, for instance.

Regardless of who ends up owning Warners Bros., "the outlook for theatrical movies is dimming," writes a Wall Street Journal tech columnist, noting that this year's U.S. box office of $8.3 billion (as of December 25) "is a bit below last year's and well below prepandemic levels of around $11 billion." Warner has historically been one of Hollywood's largest producers of theatrical films, averaging about 22 releases annually in the pre-Covid years of 2015 to 2019, according to data from Comscore. Its franchises include "Harry Potter," the DC Comics characters and "Lord of the Rings." But the current bidding war between Netflix and Paramount Skydance means Warner's future will ultimately be in the hands of either a streaming giant with a longstanding distaste for movie theaters, or a rival studio that will carry a sky-high debt load and therefore a need to sharply cut costs... [Though later the article cites a Wedbush analyst's observation that the current theatrical slate has already been negotiated through 2029, "so any buyer would have to honor those contracts" with theatrical releases for Warner films "for at least the next four years."]

Investors seem deeply skeptical. Cinemark shares have shed about 18% of their value over the past month, while rival exhibitor AMC Entertainment is down more than 30%. Morgan Stanley recently downgraded Cinemark to a neutral rating, with analyst Ben Swinburne noting that concern over Netflix's commitment to theatrical distribution and release windows "is likely to cap the multiple" on Cinemark's stock.... [T]ime hasn't been on the side of movie theaters for a while now, and a takeover of Warner Bros. won't turn back that clock.

An anonymous reader shared this report from CNBC: [T]he computing power of crypto mining generates a lot of heat, most which just ends up vented into the air. According to digital assets brokerage, K33, the bitcoin mining industry generates about 100 TWh of heat annually — enough to heat all of Finland.This energy waste within a very energy-intense industry is leading entrepreneurs to look for ways to repurpose the heat for homes, offices, or other locations, especially in colder weather months.

During a frigid snap earlier this year, The New York Times reviewed HeatTrio, a $900 space heater that also doubles as a bitcoin mining rig. Others use the heat from their own in-home cryptocurrency mining to spread warmth throughout their house. "I've seen bitcoin rigs running quietly in attics, with the heat they generate rerouted through the home's ventilation system to offset heating costs. It's a clever use of what would otherwise be wasted energy," said Jill Ford, CEO of Bitford Digital, a sustainable bitcoin mining company based in Dallas... "Same price as heating the house, but the perk is that you are mining bitcoin," Ford said...

The crypto-heated future may be unfolding in the town of Challis, Idaho, where Cade Peterson's company, Softwarm, is repurposing bitcoin heat to ward off the winter. Several shops and businesses in town are experimenting with Softwarm's rigs to mine and heat. At TC Car, Truck and RV Wash, Peterson says, the owner was spending $25 a day to heat his wash bays to melt snow and warm up the water. "Traditional heaters would consume energy with no returns. They installed bitcoin miners and it produces more money in bitcoin than it costs to run," Peterson said. Meanwhile, an industrial concrete company is offsetting its $1,000 a month bill to heat its 2,500-gallon water tank by heating it with bitcoin. Peterson has heated his own home for two-and-a-half years using bitcoin mining equipment and believes that heat will power almost everything in the future. "You will go to Home Depot in a few years and buy a water heater with a data port on it and your water will be heated with bitcoin," Peterson said.
Derek Mohr, clinical associate professor at the University of Rochester Simon School of Business, remains skeptical. Bitcoin mining is so specialized now that a home computer, or even network of home computers, would have almost zero chance of being helpful in mining a block of bitcoin, according to Mohr, with mining farms use of specialized chips that are created to mine bitcoin much faster than a home computer... "The bitcoin heat devices I have seen appear to be simple space heaters that use your own electricity to heat the room..."
CNBC also spoke to Andrew Sobko, founder of Argentum AI (which is building a marketplace for sharing computing power), who says the idea makes the most sense in larger settings. "We're working with partners who are already redirecting compute heat into building heating systems and even agricultural greenhouse warming. That's where the economics and environmental benefits make real sense. Instead of trying to move the heat physically, you move the compute closer to where that heat provides value."

An anonymous reader shares a report: Adobe brought together 10,000 marketers, filmmakers and content creators to its annual conference this week to persuade them that the company's software products are adapting to AI and remain the best tools for their work. But it's Adobe's investors, rather than its users, who are the most skeptical that generative AI technology won't disrupt the company's business as the top seller of software for creative professionals.

Despite a strong strategy, Adobe is "at risk of structural AI-driven competitive and pricing pressure," wrote Tyler Radke, an analyst at Citigroup. The company's shares have lost about a quarter of their value this year as AI tools like Google's video-generating model Veo have gained steam. In an interview with Bloomberg Television earlier this week, Adobe Chief Executive Officer Shantanu Narayen said the company is undervalued as the market is focused on semiconductors and the training of AI models.

Has AI just become an easy excuse for firms looking to downsize, asks CNBC: Fabian Stephany, assistant professor of AI and work at the Oxford Internet Institute, said there might be more to job cuts than meets the eye. Previously there may have been some stigma attached to using AI, but now companies are "scapegoating" the technology to take the fall for challenging business moves such as layoffs. "I'm really skeptical whether the layoffs that we see currently are really due to true efficiency gains. It's rather really a projection into AI in the sense of 'We can use AI to make good excuses,'" Stephany said in an interview with CNBC. Companies can essentially position themselves at the frontier of AI technology to appear innovative and competitive, and simultaneously conceal the real reasons for layoffs, according to Stephany... Some companies that flourished during the pandemic "significantly overhired" and the recent layoffs might just be a "market clearance...."

One founder, Jean-Christophe Bouglé even said in a popular LinkedIn post that AI adoption is at a "much slower pace" than is being claimed and in large corporations "there's not much happening" with AI projects even being rolled back due to cost or security concerns. "At the same time there are announcements of big layoff plans 'because of AI.' It looks like a big excuse, in a context where the economy in many countries is slowing down..."

The Budget Lab, a non-partisan policy research center at Yale University, released a report on Wednesday which showed that U.S. labor has actually been little disrupted by AI automation since the release of ChatGPT in 2022... Additionally, New York Fed economists released research in early September which showed that AI use amongst firms "do not point to significant reductions in employment" across the services and manufacturing industry in the New York-Northern New Jersey region.

The Washington Post notes AI's "increasingly outsize role" in propping up America's economic fortunes.

"Last week, the United States reported that the economy expanded at a rate of 1.6 percent in the first half of the year, with most of that growth driven by AI spending. Without AI investment, growth would have been at about a third of that rate, according to data from the Bureau of Economic Analysis." The huge economic influence of AI spending illustrates how Silicon Valley is placing a bet of unprecedented scale that the technology will revolutionize every aspect of life and work. Its sway suggests there will be economic damage far beyond Silicon Valley if that bet doesn't work out or companies pull back. Google, Meta, Microsoft and Amazon are on track to spend nearly $400 billion this year on data centers...

Concern about a potential bubble in AI investment has recently grown in technology and financial circles. ChatGPT and other AI tools are hugely popular with companies and consumers, and hundreds of billions of dollars has been sunk into AI ventures over the past three years. But few of the new initiatives are profitable, and huge profits will be needed for the immense investments to pay off... "I'm getting more and more skeptical and more and more concerned with what's happening" with artificial intelligence, said Andrew Odlyzko, an economic historian and University of Minnesota emeritus professor who has studied financial bubbles closely, including the telecom bubble that collapsed in 2001 as part of the dot-com crash. Some industry insiders have expressed concern that the latest AI releases have fallen short of expectations, suggesting the technology may not advance enough to pay back the huge investments being made, he said. "AI is a craze," Odlyzko said...

[The Federal Reserve's August "beige book" summarizes interviews with business owners across the country, according to the article — and it found surging investments in AI data centers, which could tie their fortunes to other sectors.] That's boosting demand for electricity and trucking in the Atlanta region, a hot spot for the facilities, and creating new projects for commercial real estate developers in the Philadelphia region. Because tech companies now dominate public markets, any change in their fortunes and share prices can also have a powerful influence on stock indexes, 401(k)s and the wider economy... Stock market slumps can have knock-on effects by undercutting the confidence of American businesses and consumers, leading them to spend less, said Gregory Daco [chief economist at strategy consulting firm EY-Parthenon]... "That directly affects economic activity," he said, potentially widening the economic fallout...

Goldman Sachs analysts wrote in a Sept. 4 note to clients that even if AI investment works out for companies like Google, there will be an "inevitable slowdown" in data center construction. That will cut revenue to companies providing the projects with chips and electricity, the note said. In a more extreme scenario where Big Tech pulls back spending to 2022 levels, the entire S&P 500 would lose 30 percent of the revenue growth Wall Street currently expects next year, the analysts wrote.
The AI bubble is 17 times the size of the dot-com frenzy — and four times the subprime bubble, according to estimates in a recent note from independent research firm the MacroStrategy Partnership (as reported by MarketWatch).

And "never before has so much money been spent so rapidly on a technology that, for all its potential, remains somewhat unproven as a profit-making business model," writes Bloomberg, adding that OpenAI and other large tech companies are "relying increasingly on debt to support their unprecedented spending." (Although Bloomberg also notes that ChatGPT alone has roughly 700 million weekly users, and that last month Anthropic reported roughly three quarters of companies are using Claude to automate work.)