Use of AI suddenly drops

 

From Futurism

After three years of unprecedented tech spending and nonstop hype, the demand for AI in the workplace seems to be drying up fast.

Referencing data from a recent US Census Bureau survey, The Economist estimated that the percentage of Americans using AI to “produce goods and services” at large companies rang in at a modest 11 percent in October, the latest available survey date. It’s not just that the figure is a bit soggy for the supposedly world-changing technology, but that it’s suddenly moving in the wrong direction: the financial publication notes that the percentage is actually down from 12 percent in the prior survey, conducted two weeks previously.

Looking at the big picture doesn’t make it any prettier. Back in March, the number of businesses with 100-249 employees that reported not using AI within the last two weeks stood at 74.1 percent. The survey results show a steady uptick in “no” results over the past few months, culminating in a dreadful 81.4 percent as of the latest poll.

For big corporations with over 250 employees, meanwhile, the “no” reports have crept up to 68.6 percent, up from the year’s low of 62.4 percent recorded in February.

The data is nothing if not a major red flag for an industry which is expected to spend $5 trillion on AI infrastructure between now and 2030. To do so will require a massive increase in revenue from both business and personal AI use — the latter of which has been lagging.

Unfortunately for the tech industry, enterprise AI customers aren’t picking up the slack. Though various non-government surveys cited by The Economist varied wildly in their numbers, they all seemed to spell out the same results: AI remains more of an experimental plaything in the workplace than a serious driver of productivity.

One economist at Stanford who tracks the use of generative AI at work found a major drop in usage month to month: though 46 percent of respondents reported using the tech in June, that number had fallen to 37 percent by September. Another estimate, by Fintech firm Ramp, found that AI use at American corporations went through the roof earlier in 2025 to around 40 percent, but has since plateaued.

The results follow a disappointing summer for AI advancements, with models like OpenAI’s GPT-5 falling short of expected performance gains. Still, the cracks in enterprise AI adoption had begun to show as early as December of 2024, when an EY pulse survey of 500 senior executives found over half felt they were “failing in their role” of supporting AI in their companies.

Instead, executives pointed to a creep of “AI fatigue” among the rank and file — which a year of AI horrors probably hasn’t helped.

With a $600 billion gulf between AI revenue and AI spending, an immense amount is riding on whether the tech can start bringing home the bacon.

My wild and woolly guess:  it's not going to bring home the bacon.    And that's bad news for the US economy, because most of the rise in spending recently has been on AI.

Whales suddenly go silent

Image by Getty/Futurism

 From Futurism

Researchers are alarmed after noticing a significant drop in the number of vocalizations from blue whales.

As National Geographic reports, scientists used specialized underwater hydrophones, meaning the aquatic version of microphones, to record and trace the sounds of marine life, allowing them to analyze the impact human activity is having on various species.

However, as detailed in a study published in the journal PLOS One, devastating heat waves have triggered worrying changes over the past decade, allowing toxic algae to bloom and undermine food sources for whales.

 "It caused the most widespread poisoning of marine mammals ever documented," coauthor and Monterey Bay Aquarium Research Institute biological oceanographer John Ryan told NatGeo. "These were hard times for whales."

As a result, blue whale vocalizations dropped by almost 40 percent, according to the study, with populations of krill and anchovy collapsing.

"When you really break it down, it’s like trying to sing while you're starving," Ryan explained. "They were spending all their time just trying to find food."

Scientists are still racing to understand the causes of devastating marine heatwaves. An ominous pool of warm water in the ocean, nicknamed "The Blob" following its discovery in early 2013, confounded scientists.

By 2016, it had grown to a region spanning over 2,000 miles in the Pacific Ocean, NatGeo reports.

The rising temperatures caused important sources of sustenance for blue whales to all but vanish.

Scientists are still racing to understand the effects of rising ocean temperatures. And as climate change continues to rear its ugly head, scientists are worried about future extreme weather events, including marine heatwaves.

Scientists have found that the duration of these heatwaves has tripled since the 1940s.

 "There are whole ecosystem consequences of these marine heat waves," coauthor and Monterey Bay Aquarium marine biologist Kelly Benoit-Bird told NatGeo. "If they can't find food, and they can traverse the entire West Coast of North America, that is a really large-scale consequence."

Worse yet, "fewer feeding opportunities" mean that blue whales "put less effort into reproduction," as Oregon State University ecologist Dawn Barlow added.

"Where they are, and what they’re doing can tell you a lot about the health of the ecosystem," Barlow explained. "The Blob really highlighted how long-term these consequences can be."

Benoit-Bird voiced concerns of reaching a "tipping point," which could have devastating consequences on our climate, changing how the "ocean absorbs carbon."

"Science shows that climate change is impacting the oceans," Barlow told NatGeo. "Listening and learning from these places is essential to our future. Now more than ever, it’s important to listen."

[More on marine life: Using the Ocean to Suck Up CO2 Could Come With the Small, Unintended Side Effect of Wiping Out Marine Life ]

Our oceans are dying.  Yet still we refuse to take global heating seriously. 

AI models are starting to fall apart

From Futurism

 

As CEOs trip over themselves to invest in artificial intelligence, there's a massive and growing elephant in the room: that any models trained on web data from after the advent of ChatGPT in 2022 are ingesting AI-generated data — an act of low-key cannibalism that may well be causing increasing technical issues that could come to threaten the entire industry.

In a new essay for The Register, veteran tech columnist Steven Vaughn-Nichols warns that even attempts to head off so-called "model collapse" — which occurs when large language models (LLMs) are fed synthetic, AI-generated data and consequently go off the rails — are another kind of nightmare.

As Futurism and countless other outlets have reported over the last few years, the AI industry has continuously barreled toward the moment at which all available authentic training data — that is, information that was produced by humans and not AI — will be exhausted. Some pundits, including Elon Musk, believe we're already there.

To circumvent this "Garbage In/Garbage Out" conundrum, industry titans including Google, OpenAI, and Anthropic have engaged in what's known as retrieval-augmented generation (RAG), which essentially involves plugging LLMs up to the internet so they can look things up if they're presented with prompts that don't have answers in their training data.

That concept seems pretty intuitive on its face, especially when presented with the specter of rapidly-approaching model collapse. There's only one problem: the internet is now full of lazy content that uses AI to drum up answers to common questions, often with hilariously bad and inaccurate results.

In a recent study from the research arm of Michael Bloomberg's media empire that was presented at a computational linguistics conference in April, 11 of the latest LLMs, including OpenAI's GPT-4o, Anthropic's Claude-3.5-Sonnet, and Google's Gemma-7B, produced far more "unsafe" responses than their non-RAG counterparts. As the paper put it, those safety concerns can include "harmful, illegal, offensive, and unethical content, such as spreading misinformation and jeopardizing personal safety and privacy."

"This counterintuitive finding has far-reaching implications given how ubiquitously RAG is used in [generative AI] applications such as customer support agents and question-answering systems," explained Amanda Stent, Bloomberg's head of AI research and strategy, in another interview with Vaughn-Nichols published in ZDNet earlier this month. "The average internet user interacts with RAG-based systems daily. AI practitioners need to be thoughtful about how to use RAG responsibly."

So if AI is going to run out of training data — or it has already — and plugging it up to the internet doesn't work because the internet is now full of AI slop, where do we go from here? Vaughn-Nichols notes that some folks have suggested mixing authentic and synthetic to produce a heady cocktail of good AI training data — but that would require humans to keep creating real content for training data, and the AI industry is actively undermining the incentive structures for them to continue — while pilfering their work without permission, of course.

A third option, Vaughn-Nichols predicts, appears to already be in motion.

"We're going to invest more and more in AI, right up to the point that model collapse hits hard and AI answers are so bad even a brain-dead CEO can't ignore it," he wrote.